<%BANNER%>

Advanced Signal Processing Techniques for Speaker Recognition and Communications

Permanent Link: http://ufdc.ufl.edu/UFE0044022/00001

Material Information

Title: Advanced Signal Processing Techniques for Speaker Recognition and Communications
Physical Description: 1 online resource (207 p.)
Language: english
Creator: Hu, Yakun
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: beamforming -- clustering -- communications -- cyclostationary -- decision -- fading -- fresh -- fuzzy -- gender -- gmm -- hierarchical -- identification -- interference -- jamming -- mfcc -- ofdm -- pitch -- processing -- recognition -- signal -- speaker -- stbc -- tree
Electrical and Computer Engineering -- Dissertations, Academic -- UF
Genre: Electrical and Computer Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Advanced signal processing techniques can help us well analyze signals of interests and perform proper operations on signals of interests for many useful applications. In this dissertation, we aim at developing signal processing techniques for speaker recognition (e.g. feature extraction, classifier design) and for communications (e.g. filtering, modulation, beamforming). In the first part, we focus on speaker recognition. For gender identification, we proposed a pitch-based system with a two-stage classifier to ensure accurate identification and low complexity. Our pitch extraction algorithm is able to produce robust pitch estimations. The proposed system is speech language/content independent, microphone independent, and robust against noisy recording conditions. For large population speaker identification under noisy conditions, we proposed a fuzzy-clustering-based decision tree approach. Our approach aims at partitioning the whole population into subgroups in a hierarchical way. We only apply mel-frequency cepstral coefficients (MFCC) + Gaussian mixture model (GMM) to the leaf node which has a very small population size and hence MFCC+GMM is effective. To achieve a low probability of classification error, we adopted fuzzy clustering in constructing the decision tree, i.e., a speaker may belong to multiple nodes at each level of the tree. We derived six features (including pitch and five vocal source characteristics) and constructed a six-level decision tree. Compared to MFCC+GMM, our proposed approach achieves much higher accuracy with much less complexity. In the second part, we study signal processing for communications. To address limitations of orthogonal frequency-division multiplexing (OFDM), we proposed a multi-carrier transceiver based on frequency-shift filter. Compared with OFDM, the proposed transceiver is much less sensitive to carrier frequency offset and has a lower peak-to-average ratio; moreover, the proposed transceiver has the advantage of being able to mitigate strong co-channel interference and strong narrowband interference. To improve the anti-jamming capability of a space-time block coding system over fading channels, we proposed to use Capon's beamforming to extract the intended signal while suppressing jamming signals coming from directions different from the intended signal. We evaluate the anti-jamming performance and the system cost with different numbers of array elements under different types of jamming signals.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Yakun Hu.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
Local: Adviser: Wu, Dapeng.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044022:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044022/00001

Material Information

Title: Advanced Signal Processing Techniques for Speaker Recognition and Communications
Physical Description: 1 online resource (207 p.)
Language: english
Creator: Hu, Yakun
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: beamforming -- clustering -- communications -- cyclostationary -- decision -- fading -- fresh -- fuzzy -- gender -- gmm -- hierarchical -- identification -- interference -- jamming -- mfcc -- ofdm -- pitch -- processing -- recognition -- signal -- speaker -- stbc -- tree
Electrical and Computer Engineering -- Dissertations, Academic -- UF
Genre: Electrical and Computer Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Advanced signal processing techniques can help us well analyze signals of interests and perform proper operations on signals of interests for many useful applications. In this dissertation, we aim at developing signal processing techniques for speaker recognition (e.g. feature extraction, classifier design) and for communications (e.g. filtering, modulation, beamforming). In the first part, we focus on speaker recognition. For gender identification, we proposed a pitch-based system with a two-stage classifier to ensure accurate identification and low complexity. Our pitch extraction algorithm is able to produce robust pitch estimations. The proposed system is speech language/content independent, microphone independent, and robust against noisy recording conditions. For large population speaker identification under noisy conditions, we proposed a fuzzy-clustering-based decision tree approach. Our approach aims at partitioning the whole population into subgroups in a hierarchical way. We only apply mel-frequency cepstral coefficients (MFCC) + Gaussian mixture model (GMM) to the leaf node which has a very small population size and hence MFCC+GMM is effective. To achieve a low probability of classification error, we adopted fuzzy clustering in constructing the decision tree, i.e., a speaker may belong to multiple nodes at each level of the tree. We derived six features (including pitch and five vocal source characteristics) and constructed a six-level decision tree. Compared to MFCC+GMM, our proposed approach achieves much higher accuracy with much less complexity. In the second part, we study signal processing for communications. To address limitations of orthogonal frequency-division multiplexing (OFDM), we proposed a multi-carrier transceiver based on frequency-shift filter. Compared with OFDM, the proposed transceiver is much less sensitive to carrier frequency offset and has a lower peak-to-average ratio; moreover, the proposed transceiver has the advantage of being able to mitigate strong co-channel interference and strong narrowband interference. To improve the anti-jamming capability of a space-time block coding system over fading channels, we proposed to use Capon's beamforming to extract the intended signal while suppressing jamming signals coming from directions different from the intended signal. We evaluate the anti-jamming performance and the system cost with different numbers of array elements under different types of jamming signals.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Yakun Hu.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
Local: Adviser: Wu, Dapeng.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044022:00001


This item has the following downloads:


Full Text