Title: Discrimination of changes in complex auditory stimuli
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DISCRIMINATION OF CHANGES IN
COMPLEX AUDITORY STIMULI















By

JILL JOHNSON RANEY


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY


UNIVERSITY OF FLORIDA


1990















ACKNOWLEDGMENTS

I would like to thank the members of my committee,

Dave Green, Ira Fischler, Keith White, Wilse Webb, and

Craig Formby, for their help and guidance with this

dissertation and throughout my doctoral program. A

special note of thanks goes to my advisor, Dave Green, who

has taught me a tremendous amount about how to conduct

research, and as importantly, how to convey my ideas in

writing. My appreciation goes to Ginny Richards for her

many contributions to this project, Zekiye Onsan for help

in constructing the figures, to the staff of the

Psychoacoustics laboratory for their support and

encouragement, and to the many listeners who

enthusiastically participated in these studies.

I would like to acknowledge my many friends whose

support helped me to persevere. I wish to thank my

parents who provided me with the opportunity to pursue my

academic dreams. I would also like to thank D. J. and

Chuck Raney for their love and support. And to my

husband, Gary, I give my love and thanks for all that he

has done to help me reach my goal.

I dedicate this dissertation to my teachers... past,

present, and future.


















TABLE OF CONTENTS



page

ACKNOWLEDGMENTS.................................i

ABSTRACT.........................................v

CHAPTERS

1 INTRODUCTION..................................1


Historical Perspective.............................1
Broadband Auditory Processing......................3
Profile Analysis................................4
Comodulation Masking Release....................5
Recent Signal-in-Noise Study....................6
Auditory Processing Strategy.......................7
Overview of Dissertation Studies................9


2 THE EFFECT OF SIGNAL FREQUENCY UNCERTAINTY
IN PROFILE AND NOISE TASKS......................18

Introduction............................... 1
General Procedure................................2
Method...................................... 2
Profile Task...................................22
Noise Task.....................................2
General Discussion of Profile and Noise Tasks.....30
Summary.................................. 3

3 MASKER UNCERTAINTY AND THE DETECTION OF
AMPLITUDE MODULATION ................... .........35

Introduction................................. 3
Previous Experiments...........................37
General Procedure. ...............................4
Method....................................... 4
Results and Discussion............................46
Discussion of Modulation Rates.................60
Control Experiment...............................6
Summary...................................... 6








iii












4 ACROSS-FREQUENCY INTERFERENCE PRODUCED BY
TWO-TONE WAVEFORMS..............................67

Introduction.................................6
Previous Study.................................68
Waveform Stimuli...............................69
General Procedure. ...............................7
Results and Discussion............................74
Across-Frequency Interference..................74
Increase in Modulation Rate for Both
Signal and Masker... ........................80
Frequency Relation Between Signal and Masker...82
Increase in Masker Modulation Rate.............83
Summary and Conclusions...........................86

5 THE DETECTION OF CHANGES IN AMPLITUDE-MODULATION
RATE ................... ................... ......88

Introduction................................. 8
General Procedure. ...............................9
Method....................................... 9
Carrier Frequency..............................93
Spectral Cue...................................95
Depth of Modulation...........................103
Summary and Conclusions..........................107

6 SUMMARY AND CONCLUSIONS..........................109

REFERENCES.. ...............................17

BIOGRAPHICAL SKETCH. ..................................12















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy



DISCRIMINATION OF CHANGES IN COMPLEX AUDITORY STIMULI

By

Jill Johnson Raney

May, 1990


Chairman: Dr. David M. Green
Major Department: Psychology

Four studies explored how listeners process complex

auditory information. In Experiment 1, the effect of

signal frequency uncertainty on the detectability of a

tone was investigated. Listeners' thresholds for the

detection of a signal in a broadband noise masker and in a

multitone (profile) masker were measured. The results

indicated that, for both masker conditions, uncertainty

about signal frequency hindered detection. In Experiment

2, the effect of masker uncertainty on the detection of

amplitude modulation was explored. Detection for two

rates of amplitude modulation, 10 and 100 Hz, was

compared. The 100-Hz modulation rate was more susceptible

to masker uncertainty than the 10-Hz modulation rate. In

Experiment 3, the ability to detect signal envelope

fluctuations was measured when a masker with a fluctuating









envelope was presented simultaneously. Signal envelope

detection was poorer with the masker present, despite the

fact that the signal and masker were separated in

frequency by 2 octaves. This interference occurred for

signal and masker envelope fluctuation rates up to 160 Hz,

and for makers with envelope fluctuation rates up to

twice the rate of the signal. In Experiment 4,

discrimination of changes in amplitude-modulation rate was

explored. Four carrier frequencies were compared. The

frequency of the carrier had little or no effect on rate

discrimination. The spectral cue available to the

listener was also varied. From those conditions, it

appeared that the most likely discrimination cue was a

comparison between the absolute frequencies of the

sidebands. Under certain conditions, pitch, too, could be

used as a cue. The effect of modulation depth on rate

discrimination ability was also measured and performance

was found to deteriorate as the depth of modulation was

reduced.

These studies provide support for the more recent

view of signal detection that describes it in terms of

simultaneous, across-frequency comparisons. Results

indicate that listeners are able to monitor frequency

information across the spectrum, and that additional,

irrelevant frequency information can be detrimental to

signal detection. The results are also considered in

terms of spectral and temporal modes of processing.















CHAPTER 1

INTRODUCTION



Historical Perspective

G. von Bekesy (1960), in his classic experiments,

showed that each point along the basilar membrane vibrates

with a frequency equal to the frequency of the input

sinusoid. He also showed that the amplitude of the

vibration at each point varies with the frequency of the

input, and at some place along the basilar membrane a

maximum vibration occurs. Thus the point of maximum

vibration also varies with the frequency of the input

tone. For higher frequencies the maximum vibration occurs

near the base of the basilar membrane and moves nearer the

apex for lower frequencies. Thus, one can view the

cochlear excitation process as a distributed (spatial)

filter.

Egan and Hake (1950), Fletcher (1940), and Wegel and

Lane (1924) have interpreted pure-tone masking data as

evidence that the peripheral auditory system can be

modeled as a set of filters or a series of resonant

systems. Each of the filters appears to respond

selectively to a narrow range of frequencies, while

attenuating those frequencies outside this critical range.









Fletcher (1940) referred to this region as a "critical

band. "

Fletcher (1940) and a later replication (Swets, Green

and Tanner, 1962) determined the effect of bandwidth on

the detection of a sinusoidal signal located in the center

of a noise-band masker. In all bandwidth conditions, the

noise-power density, N was held constant. The data

suggested that for large bandwidths of a noise the

detectability of the signal was essentially constant and

independent of bandwidth. For example, the amount of

masking produced by two noises, having bandwidths of

10,000 Hz and 90 Hz and differing in power by

approximately 20 dB, was about the same. It was as if the

energy at frequencies outside the critical band were

ignored by the detection process. These results

demonstrated that noise energy outside the critical band

had little effect on the detectability of the signal

within the critical band.

Fletcher also found that as the noise band was

reduced to within a certain width, the signal became

easier to detect. The bandwidth of the noise where the

detectability of the signal changed was referred to as the

critical band. Fletcher measured critical bands for

several signal frequencies. He found that the width of

the critical band varied as a function of the signal

frequency. Subsequent to Fletcher's work, a variety of

techniques have been used to estimate the width of the









critical band region. Critical band width estimations

have varied across techniques. All estimates agree,

though, that the width of the critical band is

approximately a constant proportion of the frequency

centered within the band. The exact proportion value

varies from 0.07 to 0.15, depending on the experimental

technique. The approximate width of the critical band is

about 0.1 times the center frequency. Thus, the estimated

width of the critical band at 1000 Hz is 100 Hz.


Broadband Auditory Processing

The energy within the critical band has been the

focus of theory in the traditional masking experiments.

Specifically, the ratio of signal energy to noise energy

was considered to be the critical quantity. One idea

proposed that the signal is just detectable when the

signal power is equal to the noise power in a critical

band (see Eq. 1.1). Zwicker, Flottorp and Stevens (1957)

called this hypothesis the critical ratio hypothesis.

According to the critical ratio hypothesis,


SP = NOW, (1.1)

where Sp is the power of a signal that is just

detectable in the noise (i.e., masked threshold), No is

the noise-power density, and W is the bandwidth of the

noise. The quantity No times W is the total noise power

in the critical band. Therefore, when a signal is added









to the noise the power in the critical band is doubled.

In the typical two-alternative, forced-choice task the

signal interval can be identified by the elevated power in

that critical band containing the signal. Recently,

several lines of research have shown that energy located

outside the critical band of a signal may contribute to

its detection.


Profile Analysis

Green and his colleagues have proposed a detection

strategy (Green, 1988) referred to as "profile analysis."

Results from a series of studies have shown that spectral

information outside a signal's critical band may

facilitate its detection. The basis of this type of

detection is a simultaneous comparison across two or more

critical bands within a single-trial interval.

In a typical experiment, listeners are asked to

detect an increase in the intensity of one component of a

multicomponent waveform. This multicomponent waveform

generally consists of 21 sinusoids that range in frequency

from 200 to 5000 Hz and are equally spaced on a

logarithmic frequency scale. The spacing of these

components is such that there is no more than one

component per critical band.

A two-alternative, forced-choice (2AFC) task is used.

On each stimulus presentation, the overall level of the

stimulus is randomly chosen from a 20-dB range. The










median level of the intensity range is typically 60-dB.

Randomizing the overall level reduces the possibility of

listeners using the absolute level in one critical band as

a cue for detection of the signal.

The profile studies have shown that the detection of

an intensity increment in a single component is improved

as the number of components of a multicomponent waveform

is increased (Green, Kidd, and Picardi, 1983; Green,

Mason, and Kidd, 1984). Listeners' thresholds are

approximately 10 dB better in the 11-component condition

than in the 3-component condition. These results suggest

that energy in frequency regions remote from the signal

frequency may facilitate the detection of the signal.

Green (1988) has suggested that when remote frequency

information is available, the crucial comparison is a

simultaneous one across different critical bands.


Comodulation Masking Release

A different line of research has also demonstrated

that simultaneous, across-frequency comparisons may be

advantageous in signal detections tasks. Hall, Haggard,

and Fernandes (1984) compared listeners' abilities to

detect a 1000-Hz tone in two different noise conditions.

In one condition, random noise was used. The envelope

fluctuations of the random noise in different critical

bands were essentially independent. Coherent noise was

used in the other condition. It was generated by









multiplying a wideband noise by a 50-Hz low-pass noise.

The fluctuations of the noise in this condition were

similar across the various frequency bands, that is, the

different noise envelopes were correlated or coherent.

Hall et al. (1984) determined signal thresholds as a

function of bandwidth for both the random and coherent

noises. For both conditions, signal thresholds increased

as the noise bandwidths were increased up to a critical

band. In the random noise condition, thresholds remained

constant when the bandwidth of the noise exceeded a

critical band as it did in Fletcher's original experiment.

For the coherent noise condition, when the noise bandwidth

exceeded a critical band, signal thresholds decreased.

This result suggested that the across-frequency envelope

coherence reduced the amount of masking due to the noise.

This finding has been termed comodulation masking release

or CMR. Additional studies of the CMR effect have also

been published by Buus (1985), Cohen and Schubert (1987a,

1987b), Hall (1986), McFadden (1986, 1987).


Recent Signal-in-Noise Study

Gilkey (1987) extended the previous work on the

detection of a signal in noise using a stimulus

presentation level that was randomized over a 30 dB range.

With the presentation level randomized over such a large

range, a comparison of the energy level outputs of a

critical band across trial intervals would not be a









reliable cue for the detection of the signal. Signal

thresholds, for both fixed and random stimulus

presentation levels, revealed that the randomization of

presentation level had a greater effect on the narrowband

condition than on the wideband condition. The additional

spectral information in the wideband condition enabled

listeners to detect the signal almost equally well whether

the level was fixed or random.

In addition, Gilkey looked separately at the effect

of randomizing the stimulus level as a function of masker

bandwidth and as a function of masker duration. As either

bandwidth or duration was increased, a reduction in

masking was observed. The results are consistent with the

findings of Green and Hall that listeners are able to use

frequency or temporal information located outside a

signal's critical band in a simultaneous detection

process.


Auditory Processing Strategy

Two views of how listeners detect the presence of a

signal within a complex waveform have been presented. The

important aspect of signal detection, according to the

traditional view, was the energy within the signal's

critical band. It was believed that energy outside this

frequency region did not influence detection and was

essentially ignored. Signal detection in a two AFC task

would be carried out as follows. In interval one, the









acoustic energy within the narrow region around the signal

frequency would be computed and stored in a short-term

memory. In interval two, a second energy computation

would be made regarding the same frequency region. These

two computations would then be compared and the one with

the greater amount of energy would be selected as the

signal. The important comparison, then, is the

successive, across-interval comparison of the energy

levels within a single critical band centered at the

signal frequency.

A more recent view of signal detection in complex

waveforms has been proposed and is based on the findings

of the CMR and profile analysis experiments. One

important difference between the more recent view and the

traditional view is the role of energy outside the

signal's critical band. Energy that is remote from the

signal frequency is believed to influence the decision

about whether a signal is present or absent, according to

the more recent view. Signal detection in a two AFC task

would be carried out as follows. In interval one, a

simultaneous comparison across critical bands would be

made. A decision would then be made, based on this

across-frequency comparison, as to whether the signal was

present or absent. In interval two, a second simultaneous

comparison and decision would be made. Finally, the two

decisions from interval one and two would be compared

successively. This successive comparison does not provide









much information about the signal's occurrence in terms of

absolute energy because the overall level of the sounds is

randomly determined for each interval. The important

comparison is the simultaneous, within-interval comparison

of energy levels across critical bands.


Overview of Dissertation Studies

The purpose of the studies presented in this

dissertation was to further the investigation of auditory

processing of complex stimuli. The first study, which

will be discussed in Chapter 2, was undertaken with these

two views of auditory processing in mind. Listeners in

the experiment were asked to detect a signal whose

frequency was randomly determined on each trial.

The two views might make different predictions

regarding the effects of signal frequency uncertainty. If

the listener was relying on a computation of the energy

within the signal's critical band, frequency uncertainty

would make signal detection more difficult. The listener

would always be uncertain as to which frequency region

would contain the signal. If the listener was relying on

simultaneous comparisons across critical bands, frequency

uncertainty might have a different effect. If an across-

frequency comparison strategy was a more effective way to

locate a signal of random frequency, then frequency









uncertainty may be less likely to interfere with signal

detection in this case.

Previous work on signal frequency uncertainty has

shown the effect to be small (Buus, Schorer, Florentine,

and Zwicker, 1986; Creelman, 1960; Green, 1961; Tanner,

Swets, and Green, 1956; Veniar, 1958a; Veniar, 1958b).

These findings have been difficult to interpret within the

framework of the traditional view of signal detection and

seem to provide support for the more recent view. In the

study reported in Chapter 2, there were two primary goals.

The first goal was to compare the same listeners'

performance on signal detection tasks thought to rely on

different types of auditory processing. The second goal

was to include a task in which listeners are thought to

rely on simultaneous, across-frequency comparisons.

Listeners' performance was compared across two tasks.

The first task was referred to as the profile task.

Listeners were asked to detect a change in the intensity

of one component of a multicomponent waveform. Studies

have shown that profile analysis relies on across-

frequency comparisons (Green, 1983, 1988).

The second task was referred to as the noise task.

Listeners were to detect a signal in wideband noise.

Traditionally, the detection of a tone in noise has been

thought to rely on successive comparisons of energy levels

within a critical band (Fletcher, 1940; Weber, 1978).

More recently, it has been thought that the process may be









based on some type of across-frequency comparison (Gilkey,

1987; Gilkey and Robinson, 1986; Green, 1988; Kidd, Mason,

Brantley, and Owen, 1989).

Experimental evidence that supports the more recent

across-frequency comparison view of auditory processing is

growing. A second source of evidence for across-frequency

auditory processing is studies that have shown that

additional frequency information can be detrimental to

signal detection (Neff and Green, 1987; Spiegel, Picardi,

and Green, 1981;). In these studies, listeners were asked

to detect the presence or absence of a signal presented

simultaneously with additional frequency information

(i.e., nonsignal tones). The results showed that signal

detection thresholds were poorer when the frequencies of

the nonsignal tones were selected randomly than when the

nonsignal tone frequencies were fixed across a block of

trials. These findings indicate that when the frequency

composition of additional information was uncertain

listeners were unable to ignore that information, and

signal detection was impaired.

In Chapter 3, we summarize a study that extends

previous work on the adverse effects of additional

frequency information. The primary goal of the experiment

was to determine if the detection of an attribute of a

signal, amplitude modulation, was susceptible to these

detrimental effects. In each experimental condition, a

1000-Hz, amplitude-modulated tone was presented in one









interval and a 1000-Hz, unmodulated tone was presented in

the other interval of a 2AFC task. Listeners were asked

to indicate in which interval the tone was amplitude

modulated. Thus, the listener was not asked to detect the

presence or absence of a signal as in previous studies,

but rather a change in an ongoing signal.

On each trial interval, the modulated or unmodulated

signal tone was presented simultaneously with nonsignal

tones that we will refer to as masker tones. Listeners'

performance was compared across two masker conditions,

fixed and random. In the fixed masker condition, the

frequencies of the masker tones were the same across a

block of trials. In the random masker condition, the

frequencies of the masker tones were selected randomly for

each trial interval. The effect of masker uncertainty was

determined by comparing the two masker conditions. It was

expected that the detection of a change in an ongoing

signal would be less susceptible to the deleterious

effects of masker uncertainty than previous studies had

shown.

A second issue of interest in this study was the

across-frequency interaction between different types of

auditory information. In this experiment, we will make

the distinction between temporal and spectral information.

Temporal information refers to the change in the amplitude

of the stimulus over time. We will be referring to

temporal information in terms of the waveform's envelope









fluctuation rate. Spectral information refers to the

frequency composition of a waveform.

Two rates of amplitude modulation, 10 and 100 Hz,

were used in the primary study. These rates of modulation

were chosen because each is probably mediated by a

different process for detection. Detection of 10 and 100

Hz modulation is best described in terms of a temporal and

spectral process, respectively. Differences in listeners'

thresholds for the two rates of modulation will be

discussed in terms of temporal and spectral processes.

Another source of evidence for across-frequency

auditory processing is a study conducted by Yost and Sheft

(1989). They showed that across-frequency interference

resulted when an amplitude-modulated masker tone was

presented simultaneously with an amplitude-modulated

signal tone. In Yost and Sheft's study, the signal and

masker were modulated at 10 Hz and were separated in

frequency by 2 octaves. Thus it is unlikely that the

interference resulted from peripheral masking. Rather,

the data suggest that the interference was between the

envelope of the masker waveform and the envelope of the

signal waveform (i.e., temporal interference).

In Chapter 4, we summarize a study in which we

investigated several aspects of across-frequency

interference. Our primary interest was in determining

whether such interference occurred for another type of

signal and masker waveform, two-tone complexes. We used









two-tone complexes because they produce a sinusoidal

envelope similar to that produced by amplitude modulation.

In each experimental condition, listeners were asked to

detect the presence of envelope modulation in a 2AFC task.

On both the signal and nonsignal trial intervals, a masker

with a sinusoidal envelope was presented simultaneously.

We also explored the effect of increasing the

envelope modulation rate of the signal and masker. At

faster modulation rates, listeners are unable to follow

the envelope fluctuations (temporal information) and have

to rely on spectral information. We wanted to determine

if across-frequency interference would occur when

listeners had to rely on spectral information. We

compared listeners' thresholds at three rates of

modulation, 10, 40, and 160 Hz. We selected a rate of 160

Hz because detection is best described in terms of a

spectral process. We selected a rate of 40 Hz because it

is near the temporal/spectral breakpoint. The 10 Hz

modulation rate provided a comparison with detection based

on a temporal process.

Finally, we wanted to determine if across-frequency

interference occurred when the signal and masker were

modulated at different rates. We explored how the amount

of interference changed with variation in the masker

modulation rate. In this condition, the modulation rate

of the signal was held constant at 10 Hz and the









modulation rate of the masker was varied. Masker

modulation rates of 20, 40 and 80 Hz were used.

Two themes, across-frequency auditory processing and

temporal vs spectral information, have been the major

focus the work discussed thus far. Evidence from the

studies presented indicates that a large portion of

listeners' information was obtained from within-interval,

simultaneous comparisons of different frequency regions.

In Chapter 5, we will describe a study that explored

a related question. We wanted to determine listeners'

abilities to discriminate an across-interval change in a

signal. In contrast to the previous studies, only minimal

information regarding the signal could be obtained from an

within-interval, across-frequency comparison. The

important comparison was between the two trial intervals.

As in Chapter 3, we used an attribute of a signal,

amplitude modulation. A carrier that was amplitude

modulated by a slower modulation rate (standard) and a

carrier that was amplitude modulated by a faster

modulation rate (comparison) were presented sequentially

in 2AFC trials. Listeners were asked to discriminate

which of the two amplitude-modulated carriers had the

lower envelope frequency.

The first question we explored was what effect does

carrier frequency have on rate discrimination. In this

condition the carrier frequency was fixed across a block

of trials. The task, then, was to compare across the two









trial intervals either temporal (slower modulation rates)

or spectral (faster modulation rates) features of the

stimulus. We compared listeners' thresholds at four

carrier frequencies.

The second question we explored was what effect does

varying the type of spectral cues available to the

listeners have on rate discrimination. We compared

listeners' thresholds for three spectral cue conditions.

It was expected that thresholds would be similar at the

slower modulation rates where discrimination is based on

temporal cues, but would differ at the faster modulation

rates where discrimination is based on spectral cues.

The third and final question we explored was what

effect does the depth of modulation have on rate

discrimination. Reduction in the depth of modulation

reduces the amplitude of the carrier's sidebands. This

reduction would presumably hinder spectral processing of

amplitude modulation more than temporal processing. Thus

it was expected that the greatest decrement in listeners'

performance would be at the faster modulation rates where

spectral cues are important.

We have briefly described the focus of the studies of

this dissertation which is how listeners process complex

auditory information. Each individual study will be

presented in a separate chapter with an introduction that

will summarize the specific issues related to that

investigation. The experimental methods, results and






17


conclusions for each study will be also be presented

within each chapter. Finally in Chapter 6, we will

present a summary of the results of these investigations

and the conclusions that can be drawn from this work.















CHAPTER 2

THE EFFECT OF SIGNAL FREQUENCY UNCERTAINTY
IN PROFILE AND NOISE TASKS



Introduction

The detection of a change in spectral shape,

designated as profile analysis, has been shown to depend

on the intensity levels of components distributed over a

wide spectral region and not simply on the intensity at

one part of the frequency spectrum (Green, 1986; Green et

al., 1984). In most previous studies of profile analysis,

the observers knew where in frequency the spectrum was to

be altered, that is, there was no uncertainty about the

frequency locus of the signal. Because some results

obtained in profile analysis suggest global processing of

the auditory spectrum, it was unclear how uncertainty

about the frequency locus of a potential spectral

alteration would affect the detectability of such a

change.

Spiegel et al. (1981) investigated signal uncertainty

with tones and makers that were randomly selected from

200 possible equal amplitude components. The frequencies

of those components ranged between 300 and 3000 Hz, and

were equally spaced on a logarithmic frequency scale.









In this experiment, we wanted to investigate signal

frequency uncertainty when the masker energy was

uniformly distributed on a log-frequency scale. Because

of the log spacing and the fact that critical band width

is proportional to center frequency, this uniform

distribution results in approximately a constant number of

tones per critical band.

Our interest was in comparing the effects of signal

uncertainty with a profile masker and with a noise masker.

Profile analysis appears to rely on across-frequency,

within-interval comparisons (Green, 1983, 1988).

Historically, in noise-masking experiments, it has been

thought that the analysis process relied on within

critical-band, across-interval energetic comparisons

(Fletcher, 1940; Weber, 1978). However, more recently it

has been suggested that the detection of a tone in noise

may be based on some type of within-interval, across-

frequency comparison (Gilkey, 1987; Gilkey and Robinson,

1986; Green, 1988; Kidd et al., 1987). We were interested

in comparing the detection of changes in spectral shape

using both multicomponent (profile) and noise makers.

The current experiment was aimed at one aspect of

detection, comparing the effect of signal frequency

uncertainty on the detectability of a tone in the profile

and noise paradigms.









General Procedure

Ten, normal-hearing listeners participated in this

experiment. All listeners were college students recruited

through advertisements placed in the student newspaper.

They were paid at an hourly rate for their participation.

The listeners that participated in this experiment had

previous experience in tone-detection tasks, and received

1 to 2 hours of practice prior to data collection.

The observers were seated in individual, sound-

treated rooms. The stimuli were presented diotically over

Sennheiser HD 414 SL earphones, and both phones were

driven in-phase. All the stimuli were generated

digitally, played over digital-to-analog converters

(D/A's) at a sampling rate of 25,000 Hz, and low-pass

filtered at 10,000 Hz. The duration of the stimulus was

100 msec, including 5-msec cos2 rise/decay ramps.

On each stimulus presentation, the overall level of

the stimulus was chosen, at random, from a 20-dB range in

1-dB steps. This random level procedure was used to

reduce the possibility of listeners using either the

overall stimulus level or the absolute level in one

critical band as a cue for detection of the signal.

Two-alternative, forced-choice trials (AFC) were

used. In one interval, the standard alone was presented.

In the other interval, a signal was added to the standard.

The signal occurred with equal a priori probability in the

first or second interval.









Method

Two conditions were completed for both the profile

and noise tasks in this experiment. In the first,

referred to as the "uncertain" condition, listeners were

presented with the multicomponent waveform or noise

(standard) in one interval. In the other interval, one of

21 possible signal frequencies was selected randomly on

each trial and added to the standard. On each trial of

this condition, the subject was to report which interval

contained the signal.

The second condition will be referred to as the

"certain" condition. The procedure for this condition was

identical to that for the uncertain condition, except for

one important difference. Prior to each trial, a tone of

the same frequency as the signal to be detected was

presented for a duration of 100 msec. The first interval

of a trial began 400 msec after the termination of this

tone. The tone was presented at a level of 60 dB which

was clearly audible to the listener. Thus, the subject

heard the frequency of the signal prior to each trial.

The difference in signal threshold measured in these two

conditions was used as a measure of the effect of signal

frequency uncertainty.

Each listener first completed the certain condition

and then completed the uncertain condition, for both the

profile and noise tasks. These two conditions were run at

the end of a series of conditions, each utilizing the same









certain and uncertain paradigms. Given the extensive

experience of the listeners, it is unlikely that there was

any effect of presentation order on these data. In

addition, no practice effect was evident in the data.

The amplitude of the signal was varied adaptively to

estimate the level that produced 79% correct detection

(Levitt, 1971). The amplitude was decreased by 4 dB

following three correct responses and increased by 4 dB

following one incorrect response. After 4 "reversals,"

the step size was reduced to 2 dB. Fifty trials were run

per block and each block produced approximately 14

reversals. Thresholds were determined by averaging the

signal level across the last even number of reversals,

excluding the first four reversals. For each condition,

the reported threshold is the average of twenty-four such

estimates for each subject.


Profile Task

In this task, the listeners detected a change in the

intensity of one component of a multicomponent waveform.

The "standard" waveform consisted of 21 equal-amplitude

sinusoid components. The components ranged in frequency

from 200 to 5000 Hz and were spaced logarithmically with a

ratio of 1.175. The phase of each component was chosen

randomly. One "standard" waveform was used for all

presentations in both the certain and uncertain

conditions. The median presentation level was 60 dB SPL.









The "signal" waveform was a single sinusoid added in-

phase to one component of the standard. Thus there were

21 potential signals that had the same frequencies as the

components of the standard. The possible signals ranged

in frequency from 200 to 5000 Hz and were equally spaced

on a logarithmic frequency scale.

Previous work has shown that the detectability of an

increment to a single component in a multicomponent

waveform varies as a function of frequency (Green, Onsan

and Forrest, 1987). For that reason, equal-amplitude

tones were not employed. The sound pressure level (SPL)

values for the signals were generated as a function of

frequency in accordance with Bernstein and Green's (1988)

equation for equal detectability,


EDLp = 20 [log(f/1148)]2 0.07, (1)

where EDLP is the equal-detectability level for profiles

in dB, f is the frequency of the signal in Hz, and 0.07 dB

adjusts the equation to equal 0-dB correction at 1000 Hz.

EDLp, then, is the detectability relative to detectability
at 1000 HZ. Thus at 1000 Hz the EDLp is 0 dB, at 4000 Hz
it is 5.8 dB, and at 250 Hz it is 8.7 dB. We will report

as "signal threshold" the signal-to-standard level, in dB,

minus the EDLp value for each frequency. This makes the

threshold value essentially independent of signal

frequency.






























the Detection of an Increment in
Profile, dB Relative to EDL,


Results and discussion

Listed in Table 2-1 are the subjects' thresholds for

the certain and uncertain signal frequency conditions in

the profile task. In Table 2-1 and elsewhere S. E. refers

to the standard error of the mean, computed across all

listeners and replications.


Table 2-1

Listeners' Thresholds for
a Single Comoonent of the


Subject

1

2

3

4

5

6

7

8

9

10


AVERAGE

S. E.


Certain

-21.8

-20.5

-20.1

-19.9

-19.1

-19.0

-17.0

-16.8

-14.3

-11.5


-18.0

0.27


Uncertain

-17.7

-16.4

-14.3

-14.9

-16.3

-16.6

-16.9

-16.4

-13.2

-9.3


Difference

4.1

4.1

5.8

5.0

2.8

2.4

0.1

0.4

1.1

2.2


-15.2

0.22


2.8

0.61









All listeners performed better in the certain

condition than in the uncertain condition. As a measure

of the effect of signal frequency uncertainty, we

calculated the difference in listeners' thresholds for the

certain and uncertain conditions. The differences between

the two conditions ranged from 0.1 to 5.8 dB, depending on

the listener. The average difference between the two

conditions was 2.8 dB. For listeners 7 and 8, however,

the difference was less than 1 dB. The average standard

deviation of the threshold estimates, for all subjects and

for both conditions combined, was about 2.7 dB (range =

2.01 to 4.00 dB). The relatively small uncertainty effect

is consistent with a previous study of frequency

uncertainty using a multicomponent complex (Spiegel et

al., 1981).

The data indicate that those listeners with lower

thresholds (better sensitivity) in the certain condition

showed larger effects of signal uncertainty. That is, the

better listeners showed more effect of signal frequency

uncertainty. Figure 2-1 presents the effect of

uncertainty as a function of the signal threshold in the

certain condition. The correlation obtained for the ten

observers indicated a moderate negative correlation (r=

-0.62). We will postpone a discussion of these results

until after presenting the noise task.

Randomizing the overall stimulus level reduces the

possibility of listeners using the absolute intensity



















6 -






C, *


o2-



CC



-22-1-4 0
Certain Condition Threshold (dB rel. to EDL )




Figure 2-1. The magnitude of the uncertainty effect in
the profile task is plotted as a function of listeners'
thresholds when a tone having the same frequency as the
signal is heard prior to each trial (certain condition).
The correlation obtained was -0.62.









level in one critical band as a cue for the detection of

the signal. The effect of level randomization is that a

larger signal-to-standard ratio is needed for signal

detection to be based solely on intensity. This ratio

varies as a function of the range of level variation and

the adaptive rule being used (see Green, 1988, Appendix

A). If absolute intensity level were the only cue

available to listeners in the profile task, then the

expected signal threshold would have been 2.0 dB (Green,

1988, p. 20).

As can be seen from the data that were presented in

Table 2-1, listeners' thresholds are much better than the

prediction based solely on an intensity cue. From these

data, it is concluded that listeners did not rely on a

comparison of a single critical band across trial

intervals. Rather, it is likely that detection was based

on a simultaneous comparison of two or more critical bands

within a single trial interval.


Noise Task

In this task, we investigated listeners' abilities to

detect a signal of random frequency in a wideband noise.

The noise was generated digitally and was Gaussian

distributed. The noise was low-pass filtered at 10,000 Hz

and had a median spectrum level of 30 dB SPL. Different

noise samples were obtained for each trial by choosing

randomly the starting point in a 32-K buffer that was









filled with noise samples. The same 21 potential signals

that were used in the profile task were also used in this

task. To make the tones nearly equally detectable in

noise, we adjusted the amplitudes of the tones as a

function of frequency (Green, McKey and Licklider, 1964)

so that the energy in decibels was,


EDLN = 2(f/1000) 2, (2)


where EDLN is the correction in decibels and f is the

frequency of the tone in Hz. The constant, 2 dB, was

chosen so that the correction at 1000 Hz is 0 dB. The

threshold reported will be the signal energy to noise

power density in dB, 10'log(E/No), minus EDLN.

Results and discussion

Listed in Table 2-2 are subjects' thresholds for the

certain and uncertain signal frequency conditions in the

noise task. Performance in the uncertain condition was on

average 2.6 dB poorer than performance in the certain

condition.

Green et al. (1964) established that the expected

threshold for the detection of a 1000 Hz tone in noise is

10 dB. The average threshold in the certain condition of

10.5 dB, which was presented in Table 2-2, is consistent

with this expectation. With the exception of subject 10,

all of the individual thresholds in the certain condition

were within +/- 2 dB of this value.










Table 2-2

Listeners' Thresholds for the Detection of a Sinusoid
Signal in Noise, dB Relative to EDL


Subject

1

2

3

4

5

6

7

8

9

10


AVERAGE

S. E.


Certain

10.1

11.4

9.3

8.9

8.9

10.3

10.6

10.4

11.8

13.5


Uncertain

12.2

13.6

13.2

12.4

13.1

12.3

13.7

12.9

14.0

14.2


Difference

2.1

2.2

3.9

3.5

4.2

2.0

3.1

2.5

2.2

0.7


10.5

0.15


13.2

0.10


2.6

0.33


Previous studies have shown (Buus et al., 1986;

Creelman, 1960; Green, 1961; Tanner et al., 1956; Veniar,

1958a; Veniar, 1958b), the effect of signal frequency

uncertainty is small. The average difference between the

two conditions, 2.6 dB, is about the same as that found in

the profile task. The range of threshold differences

(uncertain certain) is not the same as that found in the

profile task. The differences between the two conditions










ranged from 0.7 to 4.2 dB. This range of 3.5 dB is

smaller than that found in the profile task (range = 5.7).

The average standard deviation of the threshold estimates

for all subjects and for both conditions combined was 1.6

dB (range = 1.0 to 2.63 dB).

As in the profile task, we compared listeners'

thresholds in the certain condition with their difference

between the two conditions. Figure 2-2 presents the

effect of uncertainty as a function of the certain

condition threshold. The correlation obtained for these

data indicated a strong negative correlation (r = -0.88).

Those subjects who performed better in the certain

condition showed larger effects of uncertainty.


General Discussion of Profile and Noise Tasks

Comparing the results from the previous two tasks,

two trends are apparent. First, regardless of the task

(profile or noise), uncertainty about the signal's

frequency hinders its detection. Listeners were better

able to detect the signal if they heard a tone of the same

frequency prior to the trials. In both the profile and

noise tasks the effect of uncertainty was small, between

two and three dB for both tasks. Even though the effect

of uncertainty was small in terms of dB, it should be

noted that the difference in the means (i.e., the change

in threshold due to uncertainty) for both tasks was

greater than 5 times the standard error of the mean and




















*
4
*


*
*



*


5-


4





2-


0 t


10 11


12 13 14


rel. to EDL


Certain Condition Threshold (dB


Figure 2-2. Same as Figure 2-1, except that the masker
was wideband noise. The correlation obtained was -0.88.










statistically significant (:p < 0.05). Although the effect

of signal frequency uncertainty was approximately the same

for the profile and noise tasks in terms of dB, it is

difficult to generalize about the uncertainty effects

because the psychometric functions for the two tasks are

different (Raney, Richards, Onsan, and Green, 1989).

A second trend was also noted. In both profile and

noise tasks, a comparison between listeners' thresholds in

the certain condition and their difference between the two

conditions (certain minus uncertain) yielded at least

moderate correlations (profile, r = -0.62; noise, r =

-0.88). Those listeners who performed better in the

certain conditions showed larger effects of uncertainty.

There is, however, a potential problem in

interpreting the significance of these correlations. This

problem is due to the dependence of the uncertainty effect

values (certain threshold minus uncertain threshold) on

the certain thresholds. Because of this relation, a

negative correlation may occur in the absence of a real

effect. If the thresholds in the certain and uncertain

conditions were completely independent, we could expect an

r value of -0.69 (s.d. = 0.2) due to chance.

The two conditions in this experiment, however, were

not completely independent. The correlation between the

certain and uncertain thresholds was 0.79 for the profile

task and 0.71 for the noise task. Simulations were

conducted to determine the likelihood that the obtained










correlations between the certain conditions and the

uncertainty effects would have been due to chance. For

the profile task, the obtained correlation of -0.62 would

have been expected at a level of about 0.15 (s.d. = 0.02).

For the noise task, the obtained correlation of -0.88

would have been expected at a level of about 0.003 (s.d. =

0.002). Thus the profile correlation is approaching

significance and the noise task is significant at the

0.003 level.

Because the same listeners completed both the profile

and noise tasks, we can compare their performance across

tasks. The correlation between thresholds obtained for

the certain or uncertain conditions were at least moderate

(r = 0.77--certain conditions; r = 0.64--uncertain

conditions). Those listeners that performed better in the

profile task also performed better in the noise task.

There is clearly some similarity in listeners'

performance across the two tasks. However, there is less

agreement among observers as to the effects of

uncertainty. The correlation of uncertainty scores between

the two types of makers is only 0.31.


Summary

In this study, we were interested in comparing the

effects of signal frequency uncertainty on the detection

of a tone presented with a profile masker and with a noise

masker. The results indicated that uncertainty about a






34


signal's frequency elevates detection thresholds,

regardless of the masker type. This effect of uncertainty

was approximately 3 dB, in both the profile and noise

tasks. Comparing the results across tasks, we found that

those listeners that performed better in the profile task

also performed better in the noise task.















CHAPTER 3

MASKER UNCERTAINTY AND THE DETECTION
OF AMPLITUDE MODULATION



Introduction

Spiegel et al. (1981) and Neff and Green (1987)

established the detrimental effect of masker uncertainty

on the detection of sinusoidal signals. In this study, we

explored whether the same set of principles applied to the

detection of an attribute of a signal, namely amplitude

modulation. In this case, the detection of the signal was

not at issue. Rather, we wanted to determine if the

listener could detect changes in an ongoing signal,

specifically, dynamic changes in the amplitude of the

signal.

We argue that masking can be described as the sum of

two processes. The first process we call peripheral

masking. Historically, this term has been used to refer

to conditions in which the threshold for a signal is

increased (i.e., signal is more difficult to detect) due

to the presence of nonsignal energy in the signal

frequency channel. Nonsignal energy could be, for

example, noise or tones that produce energy near the

signal frequency. The basis of peripheral masking is a

lowered signal-to-noise ratio in the signal channel.









The second process we refer to as central masking.

We will be using this term as it was used by Watson and

Kelly (1981). They used the term central masking to

describe limits on the discrimination of complex auditory

sounds that can be manipulated by stimulus uncertainty or

by overtraining. The focus of our discussion in this

chapter will be on the effects of stimulus uncertainty.

We should note that we do not use central masking as

Zwislocki (1970) did to refer to the increase in the

threshold of audibility in one ear due to the presence of

a sound in the other ear.

Stimulus uncertainty has been varied most often by

manipulating the probability of the stimulus, either

signal or masker or both. In this experiment, we will

manipulate the probability of the masker. A common

approach used to change the masker probability has been to

vary the total number of potential tones from which the

specific tones composing a masker can be selected. As the

number of possible masker tones is increased, the

predictability of the next occurring masker decreases.

Increases in the total number of masker tones, however,

may also increase the amount of peripheral masking. The

basic problem is how to separate central and peripheral

masking, since in most experiments increases in one are

accompanied by increases in the other.

In this investigation, we wanted to determine the

effects of masker uncertainty on the detection of









amplitude modulation. In the experiments that follow, we

present an experimental approach that attempts to estimate

the relative contributions of peripheral and central

masking. Before beginning a description of these

experiments, we briefly review the work of Spiegel,

Picardi, and Green (1981), Spiegel and Green (1982) and

Neff and Green (1987). They studied how masker

uncertainty affects the detectability of a signal.


Previous Experiments

Spiegel et al. (1981) investigated listeners'

abilities to detect an increment in one component of a

multicomponent stimulus. They compared listeners'

thresholds in conditions of signal frequency uncertainty

to conditions of masker uncertainty. In the uncertain

signal conditions, the frequency of the component to be

incremented was selected randomly for each trial. In the

uncertain masker conditions, the frequencies of the

nonsignal components of the masker were chosen randomly on

each trial. Results indicated that listeners'

discrimination thresholds were poorer in the conditions of

masker uncertainty than in the conditions of signal

uncertainty. This result was independent of the number of

components composing the masker waveform.

Spiegel and Green (1982) compared the effects of

signal frequency and masker uncertainty using a detection

task. In their study, listeners were asked to detect a









sinusoidal signal in noise. As in the previous study,

signal uncertainty was manipulated by selecting randomly

the signal frequency on each trial. In the uncertain

masker conditions, a different noise waveform was selected

for each trial. For both uncertainty conditions,

listeners' abilities to detect the signal were poorer by

2-5 dB.

Neff and Green (1987) provided additional evidence

that masker uncertainty may result in a sizeable decrement

in performance. They asked listeners to detect a

sinusoidal signal of fixed frequency presented

simultaneously with a multicomponent masker. In each

condition, the frequencies of the masker components were

selected randomly Thus in each condition, listeners

were uncertain as to the spectral composition of the

makers. The number of masker components was held

constant across a block of trials and ranged from 2 to

100. Neff and Green used a different masker for each

interval of a trial. Spiegel and his associates had used

the same masker for both intervals of a given trial.

Neff and Green compared thresholds from the

conditions of masker (spectral) uncertainty with the same

listeners' thresholds measured for known signals (250,

1000, and 4000 Hz) in wideband noise. A surprising

finding was that 3 or 4 component makers produced as much

masking as wideband noise, and that 10 component makers

produced 10 to 20 dB more masking than the noise. Neff









and Green concluded that the listeners were unable to

ignore the information that was spectrally uncertain,

although it would have been advantageous for them to do

so. Even in conditions where there was little or no

masker energy near the signal frequency (i.e., few

components composing the masker), performance appeared to

be hindered.

A second experimental manipulation indicated that

when masker uncertainty was reduced, listeners' abilities

to detect the signal improved. Conditions were run in

which the same masker was used for both intervals of a

given trial. In all cases, fixing the masker across

intervals resulted in an improvement in performance.

Although the magnitude of these results would be

difficult to explain based on a traditional view of

masking (i.e., peripheral masking), Neff and Callaghan

(1987) investigated this possibility. One could argue

that the masking found in the previous study was due to

peripheral masking (i.e., the frequency of the masker

components) was near the signal frequency), rather than

central masking (i.e., uncertainty about the frequency of

the masker componentss).

To minimize peripheral masking, Neff and Callaghan

excluded masker components from a 160-Hz region around the

1000-Hz signal. For makers with 2 and 4 components, this

procedure resulted in no decrease in the amount of

masking. For makers with 6, 8, or 10 components there









was a small, but significant decrease in masking (5 dB).

This reduction in masking suggests that peripheral masking

contributed in part to the findings of the previous

experiment. However, for all the makers with 10 or fewer

components, 37-40 dB of masking still resulted. These

large amounts of masking are difficult to explain in terms

of peripheral masking. It seems likely that central

masking also played a role in the elevated detection

thresholds.

These experiments have provided evidence that

detection of a known signal may be hindered when the

frequency composition of a masker is randomly selected on

each trial or each presentation. One common feature

across the previous conditions of masker uncertainty was

that the signal was unchanged within a trial interval.

Signal frequency was always constant and listeners were

asked to detect either the presence of a signal or a

change in its intensity from one interval to the next. It

is clear from these studies that central masking makes the

detection of a signal more difficult.

In this study, we investigated whether the ability to

discriminate an attribute of a signal is also susceptible

to the deleterious effects of central masking. The

purpose of this work was to extend the previous research

by examining whether masker uncertainty would influence

the detection of amplitude modulation. Listeners were

asked to discriminate whether a signal was steady in










amplitude or sinusoidally modulated. The signal was

always clearly audible; at issue was whether the amplitude

of the signal was constant or varying over time. We will

describe two methods used to estimate the relative

contributions of peripheral and central masking on the

detection of amplitude modulation.


General Procedure

A total of five, normal-hearing listeners

participated in the study. All the listeners were college

students recruited through advertisements placed in the

student newspaper. They were paid at an hourly rate for

their participation. Each of the listeners received

several hours of practice prior to data collection.

The observers were seated in individual, sound-

treated rooms. The stimuli were presented diotically over

Sennheiser HD 414 SL earphones, and both phones were

driven in-phase. All the stimuli were generated

digitally, played over D/A's at a sampling rate of 25,000

Hz, and low-pass filtered at 10,000 Hz. The duration of

the stimulus was 100 msec in the preliminary work and 400

msec in the main experiment. Five-msec cos2 rise/decay

ramps were used for all stimulus presentations.

The same task was used throughout the study.

Listeners were asked to detect an amplitude-modulated

signal in 2AFC trials. The signal waveform may be

described as follows:










s(t) = [1 + m cos(2xfmt)] [cos(2%f t)], (3.1)


where m is the depth of modulation, fm is the rate of

modulation in Hertz, and f, is the frequency of the

carrier.

In the signal interval, listeners were presented with

a 1000-Hz carrier that was amplitude modulated. In the

nonsignal interval, an unmodulated, 1000-Hz tone was

presented. Three rates of amplitude modulation were

employed. In the preliminary work, the signal was

modulated at a rate of 40 Hz. In the main experiment,

modulation rates of 10 and 100 Hz were used.

Listeners' thresholds were determined varying

adaptively the depth of modulation (reported as dB, 20 log

m) of the signal. A 2-down, 1-up procedure (Levitt, 1971)

was used to estimate a threshold that corresponded to

70.7% correct detection. The depth of modulation was

decreased by 4 dB following two correct responses and

increased by 4 dB following one incorrect response. After

4 "reversals," the step size was reduced to 2 dB. Fifty

trials were run per block and each block produced

approximately 14 reversals. Thresholds were determined

by averaging the signal modulation depth in dB across the

last even number of reversals, excluding the first four

reversals.

On each stimulus presentation, the overall level of

the stimulus was chosen at random from a 20-dB range in










1-dB steps. This random level procedure was used to

reduce the possibility of listeners using either the

overall stimulus level or the absolute level in one

critical band as a cue for detection of the signal.

The number of components composing the masker was

varied, and the frequencies of the masker components were

either fixed or selected randomly on each presentation.

In the preliminary study, the number of sinusoids ranged

from 0 (no masker) to 20. In the main experiment, makers

were composed of 0, 1, and 10 tones. A given masker was

generated by selecting randomly and without replacement

the appropriate number of sinusoids from a pool. Twenty

equal-amplitude tones, ranging in frequency from 200 to

5000 Hz, made up the pool. The frequencies of the 20

possible masker tones were determined using logarithmic

spacing with a ratio of 1.175. This spacing resulted in

approximately one tone per critical band. The 1000-HZ

masker component was omitted since that was always the

frequency of the signal.


Method

In the preliminary work, the number of tones

composing a masker varied from 0 to 20, and was held

constant across a block of trials. For each tone number

condition, the tones composing the masker were selected

randomly for each trial interval.










By varying the number of tones used as a masker we

could vary masker uncertainty and, hence, the amount of

central masking. In fact, as the number of masker tones

varies from 0 to 20, the amount of masker uncertainty will

first increase and then decrease. This can be confirmed

by noting that the selection of 2 tones out of 20 produces

the same uncertainty as the selection of 18 tones out of

20. Maximal uncertainty would occur when 10 tones are

selected out of the 20 possible tones. Thus, we expect

the amount of central masking to follow an inverted U

shape function, being maximum when 10 tones are selected

from the pool of 20 tones.

Peripheral masking, on the other hand, would only

increase with increases in the number of masking tones.

For example, there would be no peripheral masking when no

makers were present and it would reach a maximum when all

20 masker tones present. The total masking observed for

the other numbers of masker tones will be some combination

of peripheral and central masking, as the data will

clearly show.

The problem with this preliminary study was that we

had no firm way of assessing the relative amounts of

peripheral and central masking. This problem led us to

the main experiment, where we used a subtractive method.

Suppose that we fixed the frequencies of the masking tones

across a block of trials. If the frequencies of tones

were fixed on each trial, then there would be no










uncertainty and hence no central masking. The threshold

for amplitude modulation would then reflect only

peripheral masking. If the same number of masker tones

were used, but their frequencies selected at random on

each trial, the same average amount of peripheral masking

would be present, but in addition, some central masking

would be created. In the main experiment, we determined

listeners' thresholds for both the fixed and random

conditions. We then used the difference in thresholds

between the random and fixed conditions as our estimate of

the amount of central masking.

In the main experiment, we used 1 or 10 masking

tones. Ten masking tones will produce the highest value

of masker uncertainty and one masking tone will produce a

value near the minimum. We also compared two rates of

amplitude modulation. We used a slow rate, 10 Hz, where

the discriminable cue is probably based on some temporal

process and a much faster rate, 100 Hz, where a spectral

cue is the probable basis of the discrimination. In the

preliminary work, we used a modulation rate of 40 Hz. In

retrospect this was a poor choice because at that rate of

modulation it is not clear whether the important detection

cue is temporal or spectral.

It should be noted that the frequency ranges from

which the masker tones could be selected differed across

the modulation rates. When the signal was modulated at 10

Hz or 40 Hz the masker tones were selected from 200-851 Hz










and 1175-5000 Hz. When the signal was modulated at 100 Hz

the masker tones were selected from 200-725 Hz and 1380-

5000 Hz. These ranges were selected so that the frequency

spacing between the sidebands of the modulated signal and

the closest possible masker tones was similar regardless

of the modulation rate.


Results and Discussion

Results for the preliminary study are shown in

Figure 3-1. The number of masking tones used for the

threshold estimates was varied from 0 to 20. The results

show the expected inverse U function that we described

earlier. In the no masker condition (NM) there was

neither peripheral nor central masking and the average

threshold for discriminating amplitude modulation is -23.4

dB. With a 20-tone masker present there was no central

masking, therefore all the masking must have been

peripheral. The threshold in this condition is -10.6 dB

thus the amount of peripheral masking must be -12.8 dB.

This result is consistent with Neff and Green's (1987)

comparison between 20-tone fixed and random makers that

also found 13 dB of peripheral masking.

A comparison between the 20-tone condition and the 5,

10, 15, and 18 tone conditions suggests that a lower bound

on the estimate of central masking would be 4 dB. This

estimate is calculated by subtracting 13 dB from the total

amount of masking in each of the conditions, and averaging









1111111 I
ML


.0 0 ..


0'


-10

-15 -

-20 -


O


JD


D 0 ..0.


0


0


-10

-15-

-20 -


AVG


-10

-15-

-20 -


Peripheral Masking


1 1111111 1 I
NM 2 5 10 15 18 20
# Tones In Masker




Figure 3-1. Two listeners' thresholds (20 log m) are
plotted as a function of the number of tones composing the
masker and for the no masker (NM) condition. The average
thresholds across listeners are also plotted.









across conditions. The estimate is a lower bound because

less than 13 dB of peripheral masking would be expected in

those conditions with fewer masker tones. The relative

masking contributions in the 2-tone condition cannot be

obtained by this method.

The analysis of these preliminary data provides a

general conclusion that both peripheral and central

masking contributed to a decrease in listeners' abilities

to detect a 40-Hz amplitude-modulated tone. The problem

is that we can only estimate roughly the relative

contributions of peripheral and central masking for each

masker condition. To remedy this problem, we used a

second procedure a subtraction method.

This procedure allowed us to quantify more accurately

the relative contributions of peripheral and central

masking. We determined the amount of masking produced by

a fixed set of masker tones. This gave us a measure of

peripheral masking. We determined the amount of masking

produced by choosing randomly a different set of masker

tones on each trial interval (i.e., peripheral plus

central masking). Finally, we took the difference of the

two masking amounts to get a measure of central masking.

We also examined how the amounts of peripheral and central

masking changed as a function of the modulation rate of

the signal.

Listeners' thresholds for the 10-Hz modulated signal

in the 1 and 10 tone masker conditions are shown in










Figures 3-2 and 3-3, respectively. Listeners' thresholds

for the 100-Hz modulated signal in the 1- and 10-tone

masker conditions are shown in Figures 3-4 and 3-5,

respectively.

The dependent variable on all four graphs is the

threshold for amplitude modulation, 20 log m. Data for

one listener for the no masker (NM) and the random (RND)

conditions is shown in each panel. For the fixed

conditions, the amount of masking produced by single

frequencies (in the 1-tone masker conditions) and for sets

of masker tones (in the 10-tone masker conditions) is

presented. The average amount of masking in the fixed

conditions is shown by the horizontal dashed line. To

simplify the discussion of the data, we next present

summary tables of the results.

In Tables 3-1 and 3-2, we summarize the results for

each masker condition when the signal was modulated at 10

and 100 Hz, respectively. In each table, we present the

estimates of the amounts of central and peripheral masking

for each listener and the averages across listeners.

When the signal was modulated at 10 Hz (see Table

3-1), the average amounts of peripheral masking in the 1-

and 10-tone masker conditions were small, 2.2 and 4.1 dB,

respectively. The average amounts of central masking were

also small, 1.1 dB in the 1-tone condition and 4.3 dB in

the 10-tone condition. Because all of the masking amounts

are small, we conclude from these data that the 10-Hz





























Figure 3-2. Four listeners' thresholds (20 log m) are
plotted as a function of the single tone masker frequency
for the fixed condition. The signal was amplitude
modulated at a rate of 10 Hz. Thresholds are also plotted
for the no masker (NM) condition and for the listeners'
obtained thresholds in the single tone random masker
condition (RND). The dotted horizontal line represents
listeners' expected thresholds for the single tone random
masker condition.









SI I I "r


-- -
D I I I I 1 1 1 1 I I I


I JI


1 1 I l l I I I


| l i l i : a : r


I I


I II


* I
LP


-20



-25


P.-O-- 0


I I


I I


I


** *


I |


ill


ML


-20-



-25-


ep

P


I I


I


I


I I


I I i I i I i I


JL


-20-



-25-


I I


I


I


II I


I I





I i


MM


-------~---------~~~-----~--o
L
P


-20



-25


I~ I


I~ I


NM RND 200


1000


5000


Single Tone Masker Frequency (Hz)


,------~--P-------------- -----:
P





























Figure 3-3. Four listeners' thresholds (20 log m) are
presented for the 3, 10-tone makers selected for the
fixed condition. The signal was amplitude modulated at 10
Hz. Listeners' thresholds are also presented for the no
masker (NM) condition and for the 10-tone random masker
condition (RND) The dotted horizontal line represents
listeners' expected thresholds for the random masker
condition.









__


- I I I I I


__


-20 ~


e-


,,I


-20 E-


,I


............ ..........-


-20 I-


-20 I-


RND


M I


M 2


M 3


Masker Condition





























Figure 3-4. Same as Figure 3-2, except the signal was
amplitude modulated at 100 Hz.























I I I


I 1 1 1 l i l I 1 I


,& I I I I 1 1 1 1 I I I


7/


// I


I I I I I I I I


LP


-20-



-25-


P "


I I


I


H


O O
O
O O


-25


I I II


I


I


77 I


I I I I I I I I


I I I


-20 t


O


t~-


1 1 I IIII


I I It
MM


~--------~--------------------------P-i Y~~~~P~~g


-20-



-25-


I I


i I


I


NM RND 200


1000


5000


Single Tone Masker Frequency (Hz)





























Figure 3-5. Same as Figure 3-4, except the signal was
amplitude modulated at 100 Hz and thresholds (20 log m)
are presented for 2 rather than 3, 10-tone makers.









i I I I


I I I I


SI I I I
MM


-20 -


O
O


II


-20-


o-----------------~


P
o


-20-


-20-


P
P~-------~-~----~~


RND M 1

Masker Condition


M 2











Table 3-1

Amounts of Peripheral and Central Masking for Each Masker
Condition When the Signal Was Modulated at 10 Hz


SIGNAL
MOD. RATE


MASKER
CONDITION

No Masker


LISTENERS'
THRESHOLDS


AMOUNT OF MASKING
Peripheral Central


10 Hz


-24.5
-24.2
-25.3
-23.1


AVG -24.3


1-Tone Fixed


-23.0
-22.7
-22.4
-20.4


1.5
1.5
2.9
2.7

2.2


AVG -22.1


1-Tone Random


-23.2
-20.9
-22.2
-17.6


-0.2
1.8
0.2
2.8

1.1


AVG -21.0


LP -21.5
ML -20.8
JL -20.4
MM -18.1

AVG -20.2


LP -20.1
ML -19.3
JL -12.3
MM -11.8

AVG -15.9


10-Tone Fixed


3.0
3.4
4.9
5.0

4.1


10-Tone Random


1.4
1.5
8.1
6.3

4.3











Table 3-2

Amounts of Peripheral and Central Masking for Each Masker
Condition When the Signal Was Modulated at 100 Hz


SIGNAL
MOD. RATE


MASKER
CONDITION

No Masker


LISTENERS'
THRESHOLDS

LP -28.0
ML -24.8
JL -25.7
MM -23.2

AVG -25.4


LP -24.4
ML -21.6
JL -23.9
MM -20.7

AVG -22.6


LP -23.9
ML -16.6
JL -19.3
MM -20.0

AVG -20.0


LP -18.0
ML -18.6
JL -9.0
MM -13.4

AVG -14.8


AMOUNT OF MASKING
Peripheral Central


100 Hz


1-Tone Fixed


3.6
3.2
1.8
2.5

2.8


1-Tone Random


0.5
5.0
4.6
0.7

2.6


10-Tone Fixed


10.0
6.2
16.7
9.8

10.6


10-Tone Random


LP
ML
JL
MM


-7.7
-5.3
-3.6
-3.9


10.3
13.3
5.4
9.5

9.7


AVG -5.1










signal is fairly resistant to both peripheral and central

masking.

In addition, it should be noted that there were

individual differences in the amounts of central masking

in the 10-tone condition. Listeners LP and ML had an

average of 1.5 dB of masking, whereas listeners JL and MM

had an average of 7.2 dB. Thus JL and MM were more

affected by masker uncertainty than LP and ML.

When the signal was modulated at 100 Hz (see Table

3-2), a different picture emerges. The average amounts of

peripheral masking in the 1- and 10-tone masker conditions

were 2.8 and 10.6 dB, respectively. The average amounts

of central masking were 2.6 dB in the 1-tone condition and

9.7 dB in the 10-tone condition. All of these masking

amounts are larger than what was found when the signal was

modulated at 10 Hz. The 100-Hz modulation, therefore,

appears to be more susceptible to both peripheral and

central masking than the 10-Hz modulation, particularly

when the masker is composed of 10 tones.


Discussion of Modulation Rates

The rates of amplitude modulation used in our main

experiment were 10 and 100 Hz. These rates were selected

because they are probably mediated by different detection

processes (Viemeister, 1979). In the case of 10 Hz,

detection is best thought of in terms of a temporal

process. That is, listeners detect amplitude modulation










by noting that the envelope of the waveform increases and

decreases in intensity over time. In the case of 100 Hz,

detection can be described in terms of a spectral process.

That is, listeners detect the presence of the sidebands to

identify an amplitude modulated signal. In the present

study, listeners were always better able to detect a

slower rate of amplitude modulation in conditions of both

peripheral and central masking, particularly for 10-tone

makers.

One explanation for this difference in masking

effects on the two rates of modulation is based on the two

hypothetical detection processes. There could have been

some type of interference between the process by which a

modulated signal was detected (i.e., temporal or spectral)

and the process invoked by the masker. The masker tones

in this study could be spaced no closer than a critical

band apart. Thus, the analysis of the makers presumably

would have been in terms of the frequencies of the tones

composing them. The makers, therefore, would most likely

activate a spectral process.

If the modulation rate were fast enough for signal

detection to be based on a spectral process, then

interference by the masker could have resulted. However,

if the modulation rate were slow enough for signal

detection to be based on a temporal process, then

interference by the masker may have been reduced or

avoided. In such a case, the signal could have activated









a temporal process and the masker could have activated a

spectral process. If these two processes were essentially

independent, the result could be reduced interference or

masking.


Control Experiment

As mentioned earlier, the masker tones were selected

from a wider frequency range for the 10-Hz modulated

signal than for the 100-Hz modulated signal. This was

done so that the frequency spacing between the sidebands

of the signal and the closest possible masker tones would

be similar at both modulation rates. A question could be

raised, however, regarding what would have happened if we

had used the wider frequency range with the 100-Hz

modulated signal. To answer this question, we determined

two previous listeners' (LP and ML) thresholds for the 10-

tone fixed and random conditions using the wider frequency

range.

Listeners' thresholds (20 log m) for the five, fixed,

10-tone makers and the random, 10-tone condition are

shown in Figure 3-6. In addition, LP and ML's previous

thresholds from the no masker condition are presented for

comparison. For the fixed, 10-tone condition, the average

threshold for these two listeners was -13.7 dB. Thus,

when the masker tones are selected from a wider frequency

region approximately 12.8 dB of peripheral masking

results. For the random, 10-tone condition, the average






63







-5 --
LP

-10 --






-20 -


-25-

E rm
CD -30--

-5-
O
CV ML




-15-


-20 --


-25 ~- m-


-30-

NM RND M 1 M 2 M 3 M 4 M 5

Masker Condition


Figure 3-6. Two listeners' thresholds (20 log m) are
presented for the 5, 10-tone makers selected for the
fixed condition. Listeners' thresholds are also presented
for the no masker (NM) condition and for the 10-tone
random masker condition (RND). The dotted horizontal line
represents listeners' expected thresholds for the random
masker condition.









threshold for these two listeners was -9.1 dB. The

difference in thresholds for the fixed and random

conditions indicates that there is about 4.6 dB of masking

due to masker uncertainty.

In Table 3-3, the present 100-Hz modulation data for

the wider frequency range is compared with the 100-Hz

modulation data where the frequency range of the masker

tones was narrower. Comparing the wider frequency range

data (i.e., present data) with the narrower frequency

range data (i.e., previous data), the wider frequency

range produces approximately 5 dB more peripheral masking

than the narrower range. This result is expected because

the masker tone energy could be present nearer the signal

channel.

The effects of central masking for the two frequency

ranges are also different. For the wider frequency range,

there was an average of 4.6 dB of masking due to masker

uncertainty. For the narrower range, the average amount

of masking was 11.8 dB. Although this difference in

central masking looks fairly large, it is somewhat

deceptive. If we compare the average thresholds for the

two random masker conditions (wider and narrower frequency

ranges), then the difference is only 2.6 dB. Thus,

listeners' performances in the 10-tone random masker

conditions were quite similar for both frequency ranges.

The difference in central masking seems to be primarily

due to the 5 dB difference in the fixed masker conditions.










Table 3-3

Amounts of Peripheral and Central Masking for Each 10-Tone
Masker Condition When the Masking Tones Were Selected from
the Narrower and Wider Frequency Ranges


SIGNAL
MOD. RATE

100 Hz


MASKER
CONDITION

No Masker


LISTENERS'
THRESHOLDS

LP -28.0
ML -24.8

AVG -26.4


LP -18.0
ML -18.6

AVG -18.3

LP -13.5
ML -13.8

AVG -13.7


LP -7.7
ML -5.3

AVG -6.5


LP -9.4
ML -8.7

AVG -9.1


AMOUNT OF MASKING
Peripheral Central


10-Tone Fixed
Narrower Range



10-Tone Fixed
Wider Range




10-Tone Random
Narrower Range




10-Tone Random
Wider Range


10.0
6.2

8.1

14.5
11.0

12.8


10.3
13.3

11.8


4.1
5.1

4.6


Summary

The primary purpose of this study was to investigate

the effects of central masking on listeners' abilities to

discriminate amplitude modulation. We described two

methods to estimate the relative contributions of

peripheral and central masking. In preliminary work, we






66


were unable to quantify accurately the masking amounts so

we then turned to the subtractive method. We could assess

the relative contributions of both peripheral and central

masking with the subtractive method. The results

indicated that the 100-Hz modulation rate was more

susceptible to both peripheral and central masking,

particularly for 10-tone makers.















CHAPTER 4

ACROSS-FREQUENCY INTERFERENCE
PRODUCED BY TWO-TONE WAVEFORMS



Introduction

Yost and Sheft (1989) showed that the threshold for

amplitude modulation detection was increased (poorer

performance) when an amplitude-modulated masker tone was

presented simultaneously with the modulated signal. The

data were particularly surprising because the signal and

masker carrier frequencies were separated by approximately

2 octaves. Yost and Sheft concluded that listeners were

unable to completely ignore modulation at a distant

frequency and attend only to the modulation at the signal

frequency. Because the carrier tones were 2 octaves

apart, it is unlikely that peripheral masking was

occurring. Thus, these data suggest that there was

interference between the envelope of the masker waveform

and the envelope of the signal waveform.

We considered several questions related to these

findings in our study. Our primary question focused on

the type of waveform stimuli used for the signal and

masker. First, we attempted to replicate Yost and Sheft's

findings using the same amplitude-modulated carriers they

employed. Second, we considered a different type of










waveform. We used a two-tone complex that produces a

sinusoidal envelope similar to that produced by amplitude

modulation. We will discuss the two-tone waveform in more

detail later.

The second question of interest was whether or not

this interference occurred for different modulation rates.

We chose three different modulation rates in this

condition.

Our final question concerned the frequency relation

between the signal and masker. In Yost and Sheft's work,

the signal and masker were multiples of each other. Using

the two-tone waveform stimuli, we determined the amount of

interference that occurred when the signal and masker

frequencies were not multiples of each other. In

addition, we explored the effect of a difference in

modulation rate between the signal and masker. We held

the modulation rate of the signal constant and determined

how the amount of interference changed with variation in

the masker modulation rate.


Previous Study

In Yost and Sheft's study, the signal and masker were

always carrier tones that were amplitude modulated at a

rate of 10 Hz. The listener's task was to detect the

presence of the amplitude-modulated signal in a 2AFC

procedure. In one interval, the modulated signal, a

carrier frequency (CF) plus the two sidebands located at










CF +/- 10 Hz, was presented. In the other interval, an

unmodulated tone of the same carrier frequency was

presented. In both intervals, the same modulated masker

was presented simultaneously. Thresholds were determined

as the depth of modulation (20 log m) necessary to

estimate the 70.7% point on the psychometric function.

Yost and Sheft compared two frequency conditions. In

one condition, the signal was at 1000 Hz and the masker

was at 4000 Hz. In the other condition, the signal and

masker frequencies were reversed. For both conditions,

the modulated signal was always more difficult to detect

in the presence of the modulated masker. The amount of

interference due to the masker was approximately 15 dB for

the 1000 Hz signal, and 9 dB for the 4000 Hz signal.


Waveform Stimuli

Since the rate of modulation (10 Hz) is relatively

slow, it is best understood in terms of a temporal

process. Thus, the important cue for detection is the

fluctuations of the waveform's envelope over time. Yost

and Sheft's work suggests, then, that processing temporal

information at one frequency location can interfere with

the processing of similar temporal information at a

distant frequency location.

We wanted to determine if temporal interference

occurs with a different type of waveform. In our study,

we used two-tone complexes, where the frequency separation










between the two tones was small. Two tones close in

frequency produce "beats" at a rate equal to their

frequency separation. That is, the envelope of the two-

tone combination fluctuates over time like the envelope of

an amplitude-modulated tone. Throughout the remainder of

this chapter, we will often refer to the two-tone waveform

as the "beating" waveform.

Figure 4.1 illustrates the similarity in the envelope

fluctuations for a beating waveform and amplitude-

modulated waveform. The top waveform is two tones that

are separated in frequency by 10 Hz (i.e., beating

waveform), thus their envelope fluctuates at a rate of 10

Hz. The bottom waveform is a carrier tone that is

amplitude-modulated at a rate of 10 Hz, thus its envelope

fluctuates at a rate of 10 Hz. Although there are some

differences in detail between the two waveforms, they both

provide the same temporal information (i.e., their

envelopes are exactly sinusoidal (AM) and nearly

sinusoidal (two-tone), and their rate of fluctuation is 10

Hz).


General Procedure

Two, normal-hearing listeners participated in this

study. Both of the listeners were college students

recruited through advertisements placed in the student

newspaper. They were paid at an hourly rate for their


















+





































Figure 4-1. The top waveform illustrates two tones that
are separated in frequency by 10 Hz (i.e., beating
waveform). The bottom waveform illustrates a carrier tone
that is amplitude modulated at a rate of 10 Hz.









participation. Each of the listeners received several

hours of practice prior to data collection.

The observers were seated in individual, sound-

treated rooms. The stimuli were presented diotically over

Sennheiser HD 414 SL earphones, and both phones were

driven in-phase. All the stimuli were generated

digitally, played over D/A's at a sampling rate of 14,286

Hz, and low-pass filtered at 6,000 Hz. The duration of

the stimulus was 440 msec, including 20-msec cos2

rise/decay ramps.

In each condition of this study, listeners were asked

to detect the presence of envelope modulation in 2AFC

trials. Two types of waveforms were used as signals. For

some conditions, the signal was a beating waveform and for

other conditions, the signal was an amplitude-modulated

waveform. As was described previously, both types of

signals have sinusoidal envelopes that fluctuate at a

particular rate. We will refer to the frequency of

fluctuation as the envelope modulation rate.

In addition, in both the signal and nonsignal trial

intervals, a masker was presented simultaneously. The

same masker was used for a block of trials. Two types of

makers were also used, either a beating or an amplitude-

modulated waveform. By pairing each signal type with each

masker type, a total of four conditions were possible.

Each condition of the study, therefore, will be described

in terms of the signal type and masker type (i.e.,










amplitude-modulated signal (SAMI) and amplitude-modulated

masker (MAMl), beating signal (SB) and beating masker (MB),

beating signal (SB) and amplitude-modulated masker (MAMI)'

amplitude-modulated signal (SA~M) and beating masker (]MB))

For each block of trials, the depth of envelope

modulation was varied using an adaptive procedure (Levitt,

1971). A 2-down, 1-up procedure was used to estimate

listeners' thresholds that corresponded to 70.7% correct

detection. Following two correct responses, the level of

the signal was decreased. Following one incorrect

response, the level of the signal was increased. The

initial stepsize was 4 dB, and after 4 "reversals" the

stepsize was reduced to 2 dB. Trials were run in blocks

of 50 and each run produced approximately 14 reversals.

Thresholds were determined by averaging the signal level

across the last even number of reversals, excluding the

first four reversals.

The signal level was varied differently depending on

the type of signal. If the signal was an amplitude-

modulated tone, then the depth of modulation (20 log m)

was varied. If the signal was beating tones, then the

amplitude of one of the tones was varied relative to the

amplitude of the second tone. Although the signal level

was varied differently for each signal, the variation

preserved the peak-to-valley or max/min ratio of the

envelope. That is, for a given signal level the max/min









ratio of the envelopes was the same for both the

amplitude-modulated tone and beating tone waveforms.

On each stimulus presentation, the overall level of

the stimulus was chosen, at random, from a 20-dB range in

1-dB steps. The median stimulus level was 70 dB SPL.

This random level procedure was used to reduce the

possibility that listeners used changes in overall

stimulus level as a cue for the detection of envelope

modulation.

In the next section of this chapter, we will focus on

the specific questions related to across-frequency

interference. First, what effect does the type of

waveform have on interference? We will describe our

attempt to replicate the findings of Yost and Sheft

(1989). Then we will determine whether across-frequency

interference occurs with a beating waveform. Second, we

will focus on the question of whether interference occurs

for different rates of modulation. Third, we will

consider the frequency relation between the signal and

masker locations. Finally, we will determine how

interference depends on the difference in modulation rate

of the signal and masker.


Results and Discussion


Across-Frequency Interference

First, we attempted to replicate Yost and Sheft's

findings. In this condition, both the signal and masker









were amplitude-modulated tones. We compared two frequency

conditions, as Yost and Sheft did. In one condition, the

signal was at 1000 Hz and the masker was at 4000 Hz. In

the other condition, the signal and masker frequencies

were reversed. In both conditions, the envelope

modulation rate was 10 Hz for both the signal and masker.

Listeners' average thresholds for the detection of

the amplitude-modulated signal (SAMI) alone and when the

signal was presented with an amplitude-modulated masker

(:MAM) are shown in Table 4-1. The difference in the two
thresholds (i.e., signal alone minus signal plus masker)

represents the amount of interference due to the presence

of the modulated masker.

For these conditions, we found 13.6 and 8.0 dB of

interference when a modulated masker was presented with a

1000 and 4000 Hz signal, respectively. These data are

similar to the results reported by Yost and Sheft (1989).

They found approximately 13 and 9 dB of interference in

the comparable conditions. From these data, we too

conclude that the detection of a modulated signal is more

difficult when a modulated masker is present at a distant

frequency.

After replicating the finding that across-frequency

interference occurs between two amplitude-modulated tones,

we turned our attention to the next question. Does this

type of temporal interference occur with beating tones?

In this condition both the signal and masker were beating










Table 4-1

Listeners' Average Thresholds (20 log m) for the Detection
of an Amplitude-Modulated Signal (SAM) When It Was
Presented Alone, and With an Amplitude-Modulated Masker
(MAM). The Envelope Modulation Rate Was 10 Hz for Both
the Signal and Masker.



LISTENERS' AMOUNT OF
SIGNAL (SAM) MASKER (MAM) THRESHOLDS INTERFERENCE

1000 Hz None LP -26.1
CB -23.4

AVG -24.8


1000 HZ 4000 Hz LP -12.4 13.7 dB
CB -9.9 13.5 dB

AVG -11.2 13.6 dB


4000 Hz None LP -26.9
CB -24.3

AVG ~25.6


4000 Hz 1000 Hz LP -19.5 7.5 dB
CB -15.7 8.5 dB

AVG -17.6 8.0 dB




waveforms. Again we ran the two frequency conditions

described in the replication of Yost and Sheft's work. In

this condition, one tone of the two-tone complex was

located at either 1000 or 4000 Hz. The second tone of the

complex was always 10 Hz above that frequency and hence

produced an envelope fluctuation of 10 Hz.










Listeners' average thresholds for detecting the

presence of a beating signal (SB) alone and when the

signal was presented with a beating masker (]MB) are

presented in Table 4-2. The difference in the two

thresholds represents the amount of interference due to

the presence of the masker.



Table 4-2

Listeners' Average Thresholds for the Detection of a
Beating Signal (SB) When It Is Presented Alone, and With a
Beating Masker (]MB). The Envelope Modulation Rate Was
10 Hz for Both the Signal and Masker.



LISTENERS' AMOUNT OF
SIGNAL (SB) MASKER (MB) THRESHOLDS INTERFERENCE

1000 Hz None LP -25.7
CB -24.8

AVG -25.2


1000 Hz 4000 Hz LP -20.2 5.5 dB
CB -16.9 7.9 dB

AVG -18.9 6.3 dB


4000 Hz None LP -24.9
CB -21.6

AVG -23.2


4000 Hz 1000 Hz LP -14.5 10.4 dB
CB -16.2 5.4 dB

AVG -15.4 7.8 dB









For this condition, we found 6.3 and 7.8 dB of

interference when a beating masker was presented with a

1000 and 4000 Hz signal, respectively. The amount of

interference when the signal is 1000 Hz is approximately

one-half what is was in the amplitude-modulation condition

(shown in Table 4-1). The amount of interference when the

signal is 4000 Hz is about the same for both the beating

and amplitude-modulation conditions.

We conclude from these data that the across-frequency

interference does occur with a beating waveform. The

decrease in the amount of interference from the amplitude-

modulation condition is not completely understood.

Listeners first completed amplitude-modulation condition

and then the beating condition. One possibility is that

the improvement in performance is due to practice.

However, the similar amounts of interference for both

signal frequencies found in the beating condition do seem

more sensible. It is unclear why such asymmetries would

occur in the amplitude-modulation condition.

In addition to using the same type of waveform for

both the signal and masker, we compared conditions where

the waveform type was not the same for the signal and

masker. That is, in one condition the signal was a

beating waveform and the masker was an amplitude-modulated

waveform and in the other the condition the waveform types

were reversed. Listeners' average thresholds are

presented in Table 4-3. Note that the signal was always










located at 1000 Hz and the masker was always located at

4000 Hz. The envelope modulation rate was always 10 Hz

for both the signal and masker.

We found that an amplitude-modulated masker produced

9.8 dB of interference when listeners were trying to

detect a beating signal. In contrast, a beating masker

produced 12.5 dB of interference when listeners were



Table 4-3

Listeners' Average Thresholds for the Signal Alone
Conditions and the Signal Plus Masker Conditions. The
Signal Frequency Was 1000 Hz and the Masker Frequency Was
4000 Hz. The Envelope Modulation Rate Was 10 Hz for Both
the Signal and Masker.


LISTENERS'
THRESHOLDS

LP -25.7
CB -24.8

AVG -25.2


LP -15.5
CB -15.3

AVG -15.4


LP -26.1
CB -23.4

AVG -24.8


LP -13.6
CB -10.9

AVG -12.2


AMOUNT OF
INTERFERENCE








10.3 dB
9.6 dB

9.8 dB


SIGNAL

SB


MASKER

None


SAM


None


SAM


12.5 dB
12.5 dB

12.5 dB









trying to detect an amplitude-modulated signal. These

waveform combinations for the signal and masker produced

more interference than when the signal and masker were

both beating waveforms (6.3 dB of interference), and

similar amounts as when both were amplitude-modulated

waveforms (13.6 dB of interference).


Increase in Modulation Rate for Both Signal and Masker

In this section, we will address the question of

whether or not across-frequency interference occurs when

the rate of envelope modulation is increased for both the

signal and masker. As previously discussed, the detection

of envelope modulation is based on a temporal process for

slow rates and on a spectral process for faster rates. If

the interference is the result of a conflict in temporal

information, then we would expect the amount of

interference to decrease with an increase in modulation

rate. This would be the expected outcome because the

spectral process results in frequency information being

coded in separate channels and interference between

distant frequency channels would be unlikely.

We completed the modulation rate condition for the

four possible combinations of signal and masker waveform

type. For each combination, the signal and masker both

had the same envelope modulation rate. Previously we

determined listeners' thresholds for a slow envelope

modulation rate of 10 Hz. In this section, we determined









listeners' thresholds for a fast modulation rate of 160 Hz

(detection based on a spectral cues) and an intermediate

rate of 40 Hz. In all conditions, the signal was located

at 1000 Hz and the masker was located at 4000 Hz.

In each condition, thresholds were similar across

listeners. Thus, to simplify the data presentation, a

summary of the results will be presented rather than

individual listeners' thresholds. The interference

amounts for each waveform condition and envelope

modulation rate are listed in Table 4-4. The interference

amounts were determined as in previous tables (i.e.,

signal alone minus signal plus masker). For ease of

comparison, the 10-Hz data previously discussed are also

included.



Table 4-4

Average Amounts of Interference (dB) for Each Waveform
Condition and Envelope Modulation Rate. The Signal
Frequency Was 1000 Hz and the Masker Frequency Was
4000 Hz.



SIGNAL/MASKER ENVELOPE MODULATION RATES
CONDITION 10 Hz 40 Hz 160 Hz

AM/AM 13.6 7.0 11.7

BEAT/BEAT 6.7 8.2 7.5

BEAT/AM 9.3 9.3 8.8

AM/ BEAT 12 .5 6.0 8.3

AVG 10.5 dB 7.6 dB 9.1 dB










It appears from the data that our expectation of a

decrease in the interference amount with an increase in

modulation rate was not fulfilled. One possible problem

may be that we did not increase the envelope modulation to

a high enough rate. As the results discussed in Chapter 5

will indicate, it is possible that a rate of 320 Hz may be

necessary for detection to be based primarily on spectral

cues.


Frequency Relation Between Signal and Masker

As we mentioned, Yost and Sheft (1989) used

frequencies for their signal and masker that were

multiples of each other. In this condition we used

approximately the same frequency separation they used, but

the frequencies of the signal and masker were not

multiples. The signal, in this condition, was located at

790 Hz and the masker was located at 3127 Hz. We looked

at two conditions, one where both the signal and masker

were beating waveforms, and a second where the signal was

a beating waveform and the masker was an amplitude-

modulated waveform. In both conditions, the envelope

modulation rate of the signal and masker was 10 Hz.

Again, we will present a summary of the results. The

average interference amounts for each waveform condition

are indicated in Table 4-5.

When the signal was a beating waveform, an average of

6.4 and 6.8 dB of interference resulted from the presence









Table 4-5

Average Interference Amounts for the Beating Signal and
Masker Condition, and the Beating Signal and Amplitude-
Modulated Masker Condition. The Signal Frequency Was
790 Hz and the Masker Frequency Was 3127 Hz.



SIGNAL/MASKER AMOUNT OF
CONDITION INTERFERENCE

BEAT/BEAT 6.4 dB

BEAT/AM 6.8 dB




of a beating masker and an amplitude-modulated masker,

respectively. Previously, when the signal was at 1000 Hz

and the masker was at 4000 Hz, we found 6.3 and 9.3 dB of

interference for the same two conditions. These data

suggest that masker interference is not limited to

conditions where the signal and masker frequencies are

multiples of each other.


Increase in Masker Modulation Rate

In an earlier experimental condition, we found no

systematic decrease in the amount of interference as the

envelope modulation of both the signal and masker was

increased. Now we turn our attention to a slightly

different question regarding envelope modulation rate. We

explored how the amount of interference changed with

variation in the masker modulation rate, while holding the

signal modulation rate constant.










In this condition, the signal was always a beating

waveform with an envelope modulation rate of 10 Hz.

Modulation rates of 20, 40, and 80 Hz were completed for

both a beating and an amplitude-modulated masker. The

signal frequency was 790 HZ and the masker frequency was

3127 Hz. The averaged (across listeners) amounts of

interference at each modulation rate for the beating and

amplitude-modulated masker conditions are shown in Figure

4-2.

We would expect that as the envelope modulation rate

of the makers was increased the amount of interference

would decrease. From the data, this expectation appears

to be supported for modulation rates greater than 20 Hz.

Comparing the 10 and 20 Hz modulation rates, the makers

produce the same average amount of interference. This

suggests that detection process of envelope modulation is

not sharply tuned. These results agree with interference

data reported for an amplitude-modulated tone probe and

masker (Yost, Sheft, and Opie, 1989) and an amplitude-

modulated, broadband noise signal and masker (Bacon and

Grantham, 1989). Both studies indicated there was a

decrease in the amount of interference when the

signal/probe and masker were modulated at different rates,

but for a given masker frequency there appeared to be

"spread of masking."







































Masker Envelope Modulation Rate (Hz)







Figure 4-2. The averaged amounts of interference (dB) are
presented for each masker envelope modulation rate. Two
masker conditions are shown: beating (open squares) and
amplitude-modulated (solid squares) .


I 1


I I


7.0-


6.0-


5.0


4.0-


3.0


2.0


1.0


II










Summary and Conclusions

In this study, we investigated several aspects of the

across-frequency interference demonstrated by Yost and

Sheft (1989). For amplitude-modulated carriers that were

2 octaves apart, Yost and Sheft found that a signal was

more difficult to detect when presented simultaneously

with a masker. We first replicated these results and then

considered a different type of stimulus, a beating

waveform. We, too, found interference between a beating

signal and a beating masker that were quite distant in

frequency. It is unlikely that the basis of this

interference is peripheral masking given the frequency

separation between the signal and masker. Thus, the data

for modulated and beating stimuli suggest that there is

interference between the envelopes of the signal and

masker.

The envelope modulation rate of the signal and masker

in all of this work was 10 Hz. The detection of a slow

rate of modulation, such as 10 Hz, is based on temporal

cues. One possible basis of the interference between the

signal and masker is a conflict in the temporal

information provided by each waveform. To test this

possibility, we compared the amount of interference that

occurred for several modulation rates. The results

indicated that interference was not limited to a slow rate

of modulation. We found that it occurred with a fast

modulation rate of 160 Hz.









A second possible basis of the interference was the

multiplicative relation between the signal and masker

carrier frequencies. In separate conditions, we used

carrier frequencies that had approximately the same

frequency separation as previous conditions, but that were

not multiples of each other. Again, we found interference

between the signal and masker.

Finally, we were interested in determining how the

amount of interference changed as a function of the masker

envelope modulation rate. We held the envelope modulation

rate of the signal constant and varied the rate of the

masker. In this condition, we expected that as the rate

of the masker was increased, the amount of interference

would decrease. Once the masker rate was greater than

twice the signal rate, the results supported this

expectation. This finding suggests that the detection of

envelope modulation is not a sharply tuned process.















CHAPTER 5

THE DETECTION OF CHANGES
IN AMPLITUDE-MODULATION RATE



Introduction

The purpose of this study was to investigate

listeners' abilities to discriminate a change in

amplitude-modulation rate. Specifically, we presented two

amplitude-modulated carriers sequentially and asked

listeners to indicate which one had the slower modulation

rate. We were interested in answering three questions.

First, what effect does carrier frequency have on

rate discrimination? Zwicker (1952) investigated

listeners' abilities to detect the presence of amplitude

modulation, for modulation rates ranging from 1 to 6000

Hz, with 250-, 1000- and 4000-Hz carriers. On each trial,

listeners had to compare two intervals, one containing a

modulated carrier and one containing an unmodulated

carrier of the same frequency. He found that for the

slower modulation rates, thresholds for detecting the

presence or absence of modulation were similar across

carrier frequency.

Buus (1983) used two-tone complexes to investigate

detection of a change in modulation rate (i.e., envelope

frequency). Thresholds were defined as the just









noticeable increase in frequency separation and were

measured for frequency separations ranging from 25 to 2560

Hz, depending on the center frequency. He compared four

center frequencies: 500, 1000, 2000, and 4000 Hz. Results

indicated that, for each center frequency, thresholds fell

into two groups. The thresholds were significantly

smaller at the narrower frequency separations (slower

modulation rates) than at the wider frequency separations

(faster modulation rates).

Second, what is the effect of varying the type of

spectral cues that are available to listeners? Previous

studies using amplitude-modulated noise (Viemeister, 1979)

and two-tone complexes (Buus, 1983) have shown a decrease

in listeners' performances as modulation rates increase

beyond 60 Hz. In both studies, spectral cues were

minimized by the choice of waveforms. The long-term power

spectrum of sinusoidally, amplitude-modulated, broad-band

noise is uniform and invariant across modulation

frequency. Therefore, listeners presumably cannot base

modulation detection on spectral cues (Viemeister, 1979).

In Buus' (1983) two-tone experiment, spectral cues were

minimized by changing the frequencies of both tones to

produce a desired change in modulation rate (envelope

frequency). Spectral cues were minimized because the

change in the frequency of each tone was small relative to

their pure-tone just noticeable differences. Therefore,









in both studies, performance was poorer at the faster

modulation rates.

Schodder and David (1960), using a two-tone complex,

asked listeners to indicate whether the pitch increased or

decreased when the frequency of the lower tone was

decreased. If listeners were attending to the lower tone,

then their response to the question of pitch would have

always been lower. However, if listeners were attending

to the envelope frequency of the tone complex, their

response to the question of pitch would have always been

higher. Schodder and David found that when there was a

small frequency separation between the two tones,

listeners attended to the envelope rate. In contrast

however, when the frequency separation was larger,

listeners attended to the lower frequency tone of the two-

tone complex.

In this study, we used amplitude-modulated tonal

carriers. At faster modulation rates, we expected that

listeners could no longer use a temporal cue (i.e.

envelope fluctuation rate) and would have to rely on

spectral cues (i.e., resolution of the sidebands of the

carrier). We used three spectral cue conditions and they

will be explained later.

Third, and finally, what is the effect of the depth

of modulation on modulation rate discrimination? We used

two depths of modulation. Reduction of the depth of

modulation reduces the amplitude of the sidebands and,










presumably, should make it more difficult to detect a

change in sideband frequency. Therefore, we expected to

find more differences across modulation depth for the

faster modulation rates than for the slower rates.


General Procedure

A total of three, normal-hearing listeners

participated in the experiment. All the listeners were

college students recruited through advertisements placed

in the student newspaper. They were paid at an hourly

rate for their participation. Each of the listeners

received several hours of practice prior to data

collection.

Observers were seated in individual, sound-treated

rooms. The stimuli were presented diotically over

Sennheiser HD 414 SL earphones, and both phones were

driven in-phase. All the stimuli were generated

digitally, played over D/A's at a sampling rate of 10,000

Hz, and low-pass filtered at 5,000 Hz. The duration of

the stimulus was 800 msec, including 10-msec cos2

rise/decay ramps.

The same task was used in each condition of the

experiment. Listeners were asked to discriminate which of

two amplitude-modulated carriers had the lower envelope

frequency. A carrier that was amplitude modulated by a

slower, standard modulation rate and a carrier that was

amplitude modulated by a faster, comparison modulation









rate were presented in 2AFC trials. The standard

modulation rate was held constant across a block of

trials. Six standard modulation rates were used, they

were 10, 20, 40, 80, 160, and 320 Hz.

On each trial the comparison modulation rate was

determined as follows:


20 log fm = 20 log fs + 20 log fm s',

20 log fm s, = a, (5.1)


where fm was the modulation rate of the comparison, fs was

the modulation rate of the standard, and a was a constant.

On this logarithmic scale of frequency, we increased or

decreased the comparison rate (20 log fm) by an equal

amount, a. Initially a was 4, then after 4 reversals, we

set a to 2. For each block of trials, the comparison

modulation rate was varied using an adaptive procedure

(Levitt, 1971). A 2-down, 1-up procedure was used to

estimate listeners' thresholds that corresponded to 70.7%

correct performance. The frequency of the comparison

modulation was decreased after two correct responses. The

frequency of the comparison modulation rate was increased

after one incorrect response. Trials were run in blocks

of 50 and each block produced approximately 14 reversals.

Thresholds were determined by averaging the last even

number of reversals, excluding the first four reversals.

Thresholds will be presented as the difference between the

standard and comparison modulation rates (Af), or the










difference between the standard and comparison modulation

rates divided by the standard modulation rate (Af/f).

The median level of the stimulus was 70 dB SPL. On

each presentation, the overall level of the stimulus was

chosen, at random, from a 10-dB range in 1-dB steps. This

random level procedure was used to reduce the possibility

of listeners using the stimulus level as a cue in any of

their decisions.


Method


Carrier Frequency

Four carrier frequencies, 516, 1006, 2025, and 4008

Hz, were used in this condition. The depth of modulation

was 100% (20 log m = 0). On each trial, the same carrier

frequency was used for both the standard and comparison

modulation rates. We will later refer to this condition

as the fixed carrier condition.

Results and discussion

Individual listeners' thresholds for each carrier

frequency are shown in Figure 5-1 as a function of the

standard modulation rate. For each carrier frequency,

listeners TM (squares) and UE (circles) appear to have

very similar thresholds across standard modulation rates.

Although listener CB (triangles) has the same pattern of

results, performance is consistently poorer than that of

the other two listeners.













r r 1 ~r


I " " I 'I

1006 Hz Carrier







...-

O


516 Hz Carrier


0.1






0.01








LU 1
















0.01






0.001


2025 Hz Carrier










-~"---

LY~~~ .O-..
~-----O


0-


r


4008 Hz Carrie


.O

f~;)(~2~,T~~4s'~~~ ~


..O
'0''


..













-


Cr~


Il II
100 320


100 320 10


Standard Modulation Frequency (Hz)









Figure 5-1. Thresholds for three listeners (TM, squares;
UE, circles; and CB, triangles) are presented for each
carrier frequency as a function of the standard modulation

frequency.




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