Publications
443 results found
Naylor PA, 2010, PROVIDING A PLURALITY OF AUDIO FILES WITH CONSISTENT LOUDNESS LEVELS BUT DIFFERENT AUDIO CHARACTERISTICS
Gaubitch ND, Thomas MRP, Naylor PA, 2010, Dereverberation Using LPC-based Approaches, SPEECH DEREVERBERATION, Editors: Naylor, Gaubitch, Publisher: SPRINGER-VERLAG BERLIN, Pages: 95-128, ISBN: 978-1-84996-055-7
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- Citations: 4
Khong AWH, Naylor PA, 2010, Adaptive Blind Multichannel System Identification, SPEECH DEREVERBERATION, Editors: Naylor, Gaubitch, Publisher: SPRINGER-VERLAG BERLIN, Pages: 157-187, ISBN: 978-1-84996-055-7
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- Citations: 1
Gaubitch ND, Naylor PA, 2010, Subband Inversion of Multichannel Acoustic Systems, SPEECH DEREVERBERATION, Editors: Naylor, Gaubitch, Publisher: SPRINGER-VERLAG BERLIN, Pages: 189-217, ISBN: 978-1-84996-055-7
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- Citations: 1
Naylor PA, Habets EAP, Wen JY-C, et al., 2010, Models, Measurement and Evaluation, SPEECH DEREVERBERATION, Editors: Naylor, Gaubitch, Publisher: SPRINGER-VERLAG BERLIN, Pages: 21-56, ISBN: 978-1-84996-055-7
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- Citations: 11
Naylor PA, Gaubitch ND, 2010, Speech Dereverberation Introduction, SPEECH DEREVERBERATION, Editors: Naylor, Gaubitch, Publisher: SPRINGER-VERLAG BERLIN, Pages: 1-19, ISBN: 978-1-84996-055-7
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- Citations: 14
Nordholm S, Abhayapala T, Doclo S, et al., 2010, Microphone Array Speech Processing, EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, ISSN: 1687-6180
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- Citations: 1
Gudnason J, Thomas MRP, Naylor PA, et al., 2009, Voice source waveform analysis and synthesis using principal component analysis and Gaussian mixture modelling, Pages: 108-111
The paper presents a voice source waveform modeling techniques based on principal component analysis (PCA) and Gaussian mixture modeling (GMM). The voice source is obtained by inverse-filteirng speech with the estimated vocal tract filter. This decomposition is useful in speech analysis, synthesis, recognition and coding. Existing models of the voice source signal are based on function-fitting or physically motivated assumptions and although they are well defined, estimation of their parameters is not well understood and few are capable of reproducing the large variety of voice source waveforms. Here, a data-driven approach is presented for signal decomposition and classification based on the principal components of the voice source. The principal components are analyzed and the 'prototype' voice source signals corresponding to the Gaussian mixture means are examined. We show how an unknown signal can be decomposed into its components and/or prototypes and resynthesized. We show how the techniques are suited for both low bitrate or high quality analysis/synthesis schemes. Copyright © 2009 ISCA.
Loganathan P, Khong AWH, Naylor PA, 2009, A Class of Sparseness-Controlled Algorithms for Echo Cancellation, IEEE Trans. Audio Speech Language Proc., Vol: 17, Pages: 1591-1601-1591-1601
Habets EAP, Benesty J, Gannot S, et al., 2009, On the Application of the LCMV Beamformer to Speech Enhancement, Pages: 141-144-141-144
Gaubitch ND, Habets EAP, Naylor PA, 2009, Signal-based Performance Evaluation of Dereverberation Algorithms, Journal of Electrical and Computer Engineering
Gaubitch ND, Naylor PA, 2009, Equalization of Multichannel Acoustic Systems in Oversampled Subbands, IEEE Trans. Audio Speech Language Proc., Vol: 17, Pages: 1061 - 1070-1061 - 1070
Zhang W, Khong AWH, Naylor PA, 2009, Acoustic System Equalization using Channel Shortening Techniques for Speech Dereverberation, Pages: 1427-1431-1427-1431
Zhang W, Naylor PA, 2009, An Experimental Study of the Robustness of Multichannel Inverse Filtering Systems to Near-Common Zeros
Lin X, Khong AWH, Naylor PA, 2009, Blind system identification for speech dereverberation with Forced Spectral Diversity, Pages: 3737-3740-3737-3740
Tsakiris MC, Naylor PA, 2009, FAST EXACT AFFINE PROJECTION ALGORITHM USING DISPLACEMENT STRUCTURE THEORY, 16th International Conference on Digital Signal Processing, Publisher: IEEE, Pages: 69-74
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- Citations: 4
Gudnason J, Thomas MRP, Naylor PA, et al., 2009, Voice Source Waveform Analysis and Synthesis using Principal Component Analysis and Gaussian Mixture Modelling, 10th INTERSPEECH 2009 Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 120-+
Gaubitch ND, Brookes M, Naylor PA, 2009, Blind Channel Identification in Speech using the Long-term Average Speech Spectrum
Thomas MRP, Gudnason J, Naylor PA, 2009, Data-driven voice soruce waveform modelling, Pages: 3965-3968-3965-3968
Wen JYC, Sehr A, Naylor PA, et al., 2009, Blind Estimation of a Feature-Domain Reverberation Model in Non-diffuse Environments with Variance Adjustment
Tsakiris MC, Naylor PA, 2009, Fast exact Affine Projection Algorithm using displacement structure theory, Pages: 1-6-1-6
Thomas MRP, Gudnason J, Naylor PA, 2009, Detection of Glottal Closing and Opening Instants using an Improved DYPSA Framework
Sharma D, Naylor PA, 2009, Evaluation of Pitch Estimation in Noisy Speech for Application in Non-intrusive Speech Quality Assessment
Thomas MRP, Naylor PA, 2009, The SIGMA Algorithm: A Glottal Activity Detector for Electroglottographic Signals, IEEE Trans. Audio Speech and Language Processing, Vol: 17, Pages: 1557-1566-1557-1566
Loganathan P, Lin XS, Khong AWH, et al., 2009, Frequency-domain Adaptive Multidelay Algorithm with Sparseness Control for Acoustic Echo Cancellation
Manmontri U, Naylor PA, 2008, A Class of Frobenius Norm-Based Algorithms Using Penalty Term and Natural Gradient for Blind Signal Separation, IEEE Trans. Audio, Speech, and Language Processing, Vol: 16, Pages: 1181-1193-1181-1193
We consider the blind signal separation (BSS) problem of instantaneous mixtures using penalty term and natural gradient. A class of Frobenius norm-based algorithms consisting of the offline/block processing (BP), online processing (OP) algorithms, an.....
Gaubitch ND, Habets E, Naylor PA, 2008, Multimicrophone speech dereverberation using spatiotemporal and spectral processing, Pages: 3222-3225-3222-3225
Speech signals acquired in a reverberant room with microphones positioned at a distance from the talker are degraded in quality due to reverberation and measurement noise. Therefore, enhancement of reverberant speech is important in hands-free teleco.....
Naylor PA, Lin X, Khong AWH, 2008, Near-Common Zeros in Blind Identification of Simo Acoustic Systems, Pages: 21-24-21-24
The common zeros problem for Blind System Identification (BSI) has been well known to degrade the performance of classic BSI algorithms and therefore limits performance of subsequent speech dereverberation. Recently, we have shown that multichannel s.....
Wen Y-CJ, Habets E, Naylor PA, 2008, Blind estimation of reverberation time based on the distribution of signal decay rates, Pages: 329-332-329-332
The reverberation time is one of the most prominent acoustic characteristics of an enclosure. Its value can be used to predict speech intelligibility, and is used by speech enhancement techniques to suppress reverberation. The reverberation time is u.....
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