Dr Wenwu Wang

Research Interests

Research Grants

I appreciate the financial support for my research from the following bodies (since 2008): Engineering and Physical Science Research Council (EPSRC), Ministry of Defence (MOD), Defence Science and Technology Laboratory (DSTL), Home Office (HO), Royal Academy of Engineering (RAENG), European Commission (EC), Samsung Electronics Research Institute UK (SAMSUNG), National Natural Science Foundation of China (NSFC), the University Research Support Fund (URSF), and the Ohio State University (OSU). [Total award to Surrey where I am a PI/CI: approximately £5.4M (as PI £1.2M, as CI £4.2M). As PI/CI, on a total grant award portfolio: approximately £15M]

  1. 01/2015-01/2019, "MacSeNet: machine sensing training network", EC(Horizon 2020, Marie Curie Actions - Innovative Training Network). [jointly with INRIA (France), University of Edinburgh (UK), Technical University of Muenchen (Germany), EPFL (Switzerland), Computer Technology Institute (Greece), Institute of Telecommunications (Portugal), Tampere University of Technology (Finland), Fraunhofer IDMT (Germany), Cedar Audio Ltd (Cambridge, UK), Audio Analytic (Cambridge, UK), VisioSafe SA (Switzerland), and Noiseless Imaging Oy (Finland)]
  2. 02/2015-09/2015, "Array processing exploiting sparsity for submarine hull mounted arrays", Atlas Electronik&MOD (MarCE scheme)
  3. 03/2015-09/2015, "Speech enhancement based on lip tracking",EPSRC(impact acceleration account). [jointly with SAMSUNG (UK)]
  4. 10/2014-10/2018, "SpaRTaN: Sparse representation and compressed sensing training network", EC (FP7, Marie Curie Actions - Initial Training Network). [jointly with University of Edinburgh (UK), EPFL (Switzerland), Institute of Telecommunications (Portugal), INRIA (France), VisioSafe SA (Switzerland), Noiseless Imaging Oy (Finland), Tampere University of Technology (Finland), Cedar Audio Ltd (Cambridge, UK), and Fraunhofer IDMT (Germany)] (project website)
  5. 01/2014-01/2019, "S3A: future spatial audio for an immersive listener experience at home", EPSRC (programme grant). [jointly with University of Southampton, University of Salford, and BBC.] (project website)
  6. 04/2013-04/2018, "Signal processing solutions for a networked battlespace", EPSRC >and DSTL signal processing call). [jointly with Loughborough University, University of Strathclyde, and Cardiff University.] (project website)
  7. 10/2013-03/2014, "Enhancing speech quality using lip tracking", SAMSUNG industrial grant).
  8. 12/2012-12/2013, "Audio-visual cues based attention switching for machine listening", MILES and EPSRC (feasibility study). [jointly with School of Psychology and Department of Computing.]
  9. 11/2012-07/2013, "Audio-visual blind source separation", NSFC (international collaboration scheme). [jointly with Nanchang University, China.]
  10. 12/2011-03/2012, "Enhancement of audio using video", HO (pathway to impact). [jointly with University of East Anglia.]
  11. 10/2010-10/2013, "Audio and video based speech separation for multiple moving sources within a room environment", EPSRC responsive mode). [jointly with Loughborough University.]
  12. 10/2009-10/2012, "Multimodal blind source separation for robot audition", EPSRC and DSTL signal processing call). (project website)
  13. 05/2008-06/2008, "Convolutive non-negative sparse coding", RAENG (international travel grant).
  14. 02/2008-06/2008, "Convolutive non-negative matrix factorization", URSF (small grant).
  15. 02/2008-03/2008, "Computational audition", OSU (visiting scholarship).

Research Team

Postdoc Research Fellows

  • Dr Mark Barnard (09/2014 - ): Visual tracking for future spatial audio (Co-supervisor. Co-supervised with Prof Adrian Hilton and Dr Philip Jackson)
  • Dr Qingju Liu (04/2014 - ): Source separation and objectification for future spatial audio (Primary supervisor. Co-supervised with Dr Philip Jackson and Prof Adrian Hilton)
  • Dr Cemre Zor 04/2013 - ): Statistical anomaly detection (Primary supervisor. Co-supervised with Prof Josef Kittler)
  • Dr Swati Chandna (05/2013 - ): Bootstrapping for robust source separation (Primary supervisor. Co-supervised with Dr Philip Jackson)

  • Dr Mark Barnard (10/2010 - 12/2013): Audio-visual speech separation of multiple moving sources (Primary supervisor. Co-supervised with Prof Josef Kittler. External Collaborators: Prof Jonathon Chambers, Loughborough University; Dr Sangarapillai Lambotharan, Loughborough University; Prof Christian Jutten, Grenoble, France, and Dr Rivet Bertrand, Grenoble, France)
  • Dr Qingju Liu (01/2013 - 03/2014): Words spotting from noisy mixtures & Lip-tracking for voice enhancement

PhD Students

  • Pengming Feng: Multi-target tracking (Co-supervisor. Co-supervised with Prof Jonathon Chambers and Dr Syed Mohsen Naqvi)
  • Waqas Rafique: Acoustic source separation (Co-supervisor. Co-supervised with Prof Jonathon Chambers and Dr Syed Mohsen Naqvi)
  • Luca Remaggi: Informed acoustic source separation (Co-supervisor. Co-supervised with Dr Philip Jackson)
  • Jing Dong: Analysis model based sparse representations for denoising (Primary supervisor. Co-supervised with Dr Philip Jackson; External Collaborator: Dr Wei Dai, Imperial College London)
  • Volkan Kilic: Robust audio visual tracking of multiple moving sources for robot audition (Primary supervisor. Co-supervised with Prof Josef Kittler and Dr Mark Barnard)
  • Shahrzad Shapoori: Tensor factorization in EEG signal processing (Co-supervisor. Co-supervised with Dr Saeid Sanei, Department of Computing)
  • Amran Abdul Hadi: Audio-visual fusion for convolutive source separation (Co-supervisor. Co-supervised with Dr Saeid Sanei, Department of Computing)
  • Atiyeh Alinaghi: Joint sound source localisation and separation (Co-supervisor. Co-supervised with Dr Philip Jackson)

  • Dr Marek Olik (PhD defended in December 2014): Personal sound zone reproducation with room reflections (Co-supervisor. Co-supervised with Dr Philip Jackson)
  • Dr Syed Zubair (PhD awarded in June 2014): Dictionary learning for signal classification (Primary supervisor. Co-supervised with Dr Philip Jackson; Internal collaborator: Dr Fei Yan; External collaborator: Dr Wei Dai, Imperial College London)
  • Dr Philip Coleman (PhD awarded in May 2014): Loudspeaker array processing for personal sound zone reproduction (Co-supervisor. Co-supervised with Dr Philip Jackson)
  • Dr Qingju Liu (PhD awarded in October 2013): Multimodal blind source separation for robot audition (Primary supervisor. Co-supervised with Dr Philip Jackson, Prof Josef Kittler; External collaborator: Prof Jonathon Chambers, Loughborough University) [Qingju Liu was the winner of the Best Solution Award on the DSTL Challenge Workshop for the signal processing challenge "Undersampled Signal Recognition", announced on the SSPD 2012 conference, London, September 25-27, 2012.]
  • Dr Tao Xu (PhD awarded in June 2013): Dictionary learning for sparse representations with applications to blind source separation (Primary supervisor. Co-supervised with Dr Philip Jackson; External collaborator: Dr Wei Dai, Imperial College London)
  • Dr Rakkrit Duangsoithong (PhD awarded in Oct 2012): Feature selection and causal discovery for ensemble classifiers (Co-supervisor; Co-supervised with Dr Terry Windeatt)
  • Dr Tariqullah Jan (PhD awarded in Feb 2012): Blind convolutive speech separation and dereverberation (Primary Supervisor; Co-Supervised with Prof Josef Kittler; External collaborator: Prof DeLiang Wang, The Ohio State University)

Academic Visitors

  • Dr Xiaorong Shen (02/2015 - ): Associate Professor, Beihang University, Beijing, China. Topic: Audio-visual source detection, localization and tracking.
  • Mr Hatem Deif (02/2015 - ): PhD student, Brunel University, London, UK. Topic: Single channel audio source separation.
  • Mr Jian Guan (10/2014 - ): PhD student, Harbin Institute of Techonology, Shenzhen Graduate School, Shenzhen, China. Topic: Approximate message passing and belief propagation.
  • Dr Yang Yu (04/2014 - ): Associate Professor, Northwestern Polytechnical University, Xi'an, China. Topic: Underwater acoustic source localisation and tracking with sparse array and deep learning.

  • Mr Jamie Corr (10/2014 - 10/2014): PhD student, Strathclyde University, Glasgow, UK. Topic: Underwater acoustic data processing with polynomial matrix decomposition.
  • Dr Xionghu Zhong (07/2014 - 07/2014): Independent Research Fellow, Nanyang Technological University, Singapore. Topic: Acoustic source tracking.
  • Xiaoyi Chen (10/2012 - 09/2013 ): PhD student, Northwestern Polytechnical University, Xi'an, China. Topic: Convolutive blind source separation of underwater acoustic mixtures.
  • Dr Ye Zhang (12/2012 - 08/2013): Associate Professor, Nanchang University, Nanchang, China. Topic: Analysis dictionary learning and source separation.
  • Victor Popa (04/2013 - 07/2013), PhD student, University Politehnica of Bucharest, Bucharest, Romania. Topic: Audio source separation.
  • Dr Stefan Soltuz (10/2008 -07/2009), Research Scientist, Tiberiu Popoviciu Institute of Numerical Analysis, Romania. Topic: Non-negative matrix factorization for music audio separation (Primary supervisor. Co-supervised with Dr Philip Jackson)
  • Yanfeng Liang (MSc, 05/2009), MSc Student: Harbin Engineering University, Harbin, China. Topic: Adaptive signal processing for clutter removal in radar images (Co-supervisor. Co-supervised with Prof Jonathon Chambers, Loughborough University)

MSc Students

  • Denise Chew (MSc, 2014, awarded Distinction); Project: Audio impainting
  • Yan Yin (MSc, 2014); Project: Audio super-resolution
  • Dalton Whyte (MSc, 2014); Project: Audio retrieval using deep learning
  • Dan Hua (MSc, 2013, awarded Distinction) Project: Super-resolution audio based on sparse signal processing
  • Dichao Lu (MSc, 2013) Project: Polyphonic pitch tracking of music
  • Xiao Han (MSc, 2012, awarded Distinction); Project: Underdetermined reverberant speech separation
  • Jian Han (MSc, 2012, awarded Distinction); Project: Microphone array based acoustic tracking of multiple moving speakers (co-supervised with Dr Mark Barnard)
  • Tianyu Feng (MSc, 2012); Project: Multi-pitch estimation and tracking
  • Yuli Ling (MSc, 2012); Project: Audio event detection from sound mixtures
  • Danyang Shen (MSc, 2012); Project: Audio-visual tracking of multiple moving speakers (co-supervised with Dr Mark Barnard)
  • Kai Song (MSc, 2012); Project: Environment recognition from sound scenes (co-supervised with Dr Fei Yan)
  • Xinpu Han (MSc, 2012); Project: Compressed sensing for natural image coding
  • Steven Grima (MSc, 2011, awarded Distinction); Project: Multimodal tracking of multiple moving sources (co-supervised with Dr Mark Barnard)
  • Anil Lal (MSc, 2011, awarded Distinction); Project: Monaural music sound separation using spectral envelop template and isolated note information
  • Xi Luo (MSc, 2011, awarded Distinction); Project: Reverberant speech enhancement
  • Yunyi Wang (MSc, 2011); Project: Compressed sensing for image coding
  • Ritesh Agarwal (MSc, 2011); Project: Multiple pitch tracking
  • Yichen Li (MSc, 2011); Project: Environmental sound recognition (co-supervised with Dr Fei Yan)
  • Tengxu Yang (MSc, 2011); Project: Ideal binary mask estimation in computational auditory scene analysis
  • Jin Ke (MSc, 2011); Project: Audio-visual tracking and localisation of moving speakers (co-supervised with Dr Mark Barnard)
  • Zijian Zhang (MSc, 2011); Project: Convolutive blind source separation of speech mixtures
  • Hafiz Mustafa (MSc, 2010); Project: Single channel music sound separation

BSc Students

  • Xiao Cao (BSc, 2014); Project: Real-time speech separation demonstration

Research Collaborations

Academic:

  • Loughborough University (UK)
  • RIKEN (Japan)
  • Ohio State University (USA)
  • Imperial College London (UK)
  • Cardiff University (UK)
  • Strathclyde University (UK)
  • University of East Anglia (UK)
  • Nanchang University (China)
  • Northwestern Polytechnical University (China)
  • RMIT University (Australia)
  • Gipsa-lab (France)
  • Nanyang Technological University (Singapore)

Industrial:

  • Dstl
  • BBC
  • Thales
  • Qinetiq
  • Texas Instruments
  • Stellar
  • Digital Barriers
  • Selex Galileo
  • PrismTech
  • Steepest Ascent

Teaching

2012/2013

  • EEM.ivc - Image and Video Compression (Spring 2013)
  • EEM.sap - Speech and Audio Processing & Coding (Autumn 2012)
  • EE2.mpr - Media (Audio-Visual) Processing (Spring 2013)
  • EE1.pro - Programming: Labs & Marking (Spring 2013)

2011/2012

  • EEM.ivc - Image and Video Compression (Spring 2012)
  • EEM.sap - Speech and Audio Processing & Coding (Autumn 2011)
  • EE2.mpr - Media (Audio-Visual) Processing (Autumn 2011)
  • EE1.pro - Programming: Labs & Marking (Autumn 2011 & Spring 2012)
  • EE1.eps - EDPS: Basic Computing Skills (Autumn 2011)

2010/2011

  • EEM.ivc - Image and Video Compression (Spring 2011)
  • EEM.sap - Speech and Audio Processing & Coding (Autumn 2010)
  • EE1.pro - Programming: Labs & Marking (Autumn 2010 & Spring 2011)
  • EE1.eps - EDPS: Basic Computing Skills (Autumn 2010)

2009/2010

  • EEM.ivc - Image and Video Compression (Spring 2010)
  • EEM.sap - Speech and Audio Processing & Coding (Autumn 2009)
  • EE1.pro - Programming: Labs & Marking (Autumn 2009 & Spring 2010)
  • EE1.eps - EDPS: Basic Computing Skills (Autumn 2009)

2008/2009

  • EEM.ivc - Image and Video Compression (Spring 2009)
  • EEM.sap - Speech and Audio Processing & Coding (Autumn 2008)
  • EE1.pro - Introduction to Programming: Labs & Marking (Autumn 2008 & Spring 2009)

2007/2008

  • EEM.sap - Speech and Audio Processing & Coding (Autumn 2007)
  • EE1.pca - C Programming Labs (Autumn 2007 & Spring 2008)

Note: EEM - Master students module; EE1 - First-year undergraduate students module.

Departmental Duties

Selected Recent Activities

  • Organising Committee Member, CISP 2013, London, December, 2-3, 2013.
  • Program Committee Member, BMVC 2013, Bristol, UK, Sept 9-13, 2013.
  • Program Committee Member, SIP 2013, Banff, Canada, July 17-19, 2013.
  • Special Session Co-Chair (with Jonathon Chambers and Zoran Cvetkovic), DSP 2013, Santorini, Greece, July, 1-3, 2013.
  • Program Committee Member, ICICIP 2013, Beijing, China, June, 09-11, 2013.
  • Program Committee Member, ICASSP 2013, Vancouver, Canada, May, 26-31, 2013.
  • Tutorial Speaker (with Wei Dai and Boris Mailhe), ICASSP 2013, "Dictionary Learning for Sparse Representations: Algorithms and Applications", Vancovar, Canada, May, 26-31, 2013.
  • Program Committee Member, SENSORNETS 2013, Barcelona, Spain, February 19-21, 2013.
  • External PhD Examiner, PhD Thesis: "Sparse Approximation and Dictionary Learning with Applications to Audio Signals", Queen Mary University of London, December 2012.
  • Independent Expert, European Commission, grant evaluation, November 2012.
  • Session Chair, SSPD 2012, "Sensor Arrays", London, UK, 25-27 September, 2012.
  • Program Committee Member, ISCSLP 2012, Hong Kong, China, December 5-8, 2012.
  • Program Committee Member, SSPD 2012, London, UK, 25-27 September, 2012.
  • Session Chair, EUSIPCO 2012, "P-ML-1: Machine Learning", Bucharest, Romania, 27 - 31 August, 2012.
  • Session Co-Chair (with Ali Taylan Cemgil), ICASSP 2012, "MLSP-L3: Applications in Audio, Speech, and Image Processing", Kyoto, Japan, 25-30 March, 2012.
  • Program Committee Member, S+SSPR 2012, Hiroshima, Japan, 7 - 9 November, 2012.
  • Program Committee Member, CISP 2012, Chongqing, China, 16-18 October, 2012.
  • Program Committee Member, UKCI 2012, Edinburgh, UK, 5-11 September, 2012.
  • Area Chair, EUSIPCO 2012, Bucharest, Romania, 27 - 31 August, 2012.
  • Program Committee Member, BMVC 2012, Guildford, UK, 3 - 7 September, 2012.
  • Program Committee Member, EUSIPCO 2012, Bucharest, Romania, 27 - 31 August, 2012.
  • Program Committee Member, SIP 2012, Honolulu, USA, 20 - 22 August, 2012.
  • Program Committee Member, ISNN 2012, Shenyang, China, 11-14 July, 2012.
  • Program Committee Member, ICSAI 2012, Yantai, China, 19-21 May, 2012.
  • Program Committee Member, ICASSP 2012, Kyoto, Japan, 25-30 March, 2012.
  • Program Committee Member, ICIST 2012, Wuhan, China, 23-25 March, 2012.
  • Program Committee Member, SENSORNETS 2012, Rome, Italy, 24-26 February, 2012.
  • Internal PhD Examiner, PhD Thesis: "Novel Tensor Factorization Based Approaches for Blind Source Separation", Department of Computing, University of Surrey, December 2011.
  • Session Chair, EUSIPCO 2011, "Multichannel Acoustic Processing I", Barcelona, Spain, 29 August -2 Sept, 2011.
  • Program Committee Member, SIP 2011, Dallas, USA, 14-16 December, 2011.
  • Program Committee Member, CISP 2011, Shanghai, China, 15-17 October, 2011.
  • Technical Committee Member, SSPD 2011, London, UK, 28-29 September, 2011.
  • Program Committee Member, UKCI 2011, Manchester, UK, September, 2011.
  • Special Session Co-Chair (with Jonathon Chambers and Bertrand Rivet), EUSIPCO 2011, "Multimodal (Audio-Visual) Speech Separation", Barcelona, Spain, 29 August -2 Sept, 2011.
  • Program Committee Member, BMVC 2011, Dundee, UK, 29 August -2 Sept, 2011.
  • Headstart Project Leader, School Outreach, Guildford, 17-20 July, 2011.
  • Program Committee Member, SIPA 2011, Crete, Greece, 22-24 June, 2011.
  • Program Committee Member, CSIE 2011, Changchun, China, 17-19 June, 2011.
  • Program Committee Member, ISNN 2011, Guilin, China, 29 May- 1 June, 2011.
  • Program Committee Member, ICIST 2011, Nanjing, China, 26-28 March, 2011.
  • Grant Reviewer, EPSRC, first grant proposal, March, 2011.
  • External PhD Examiner, School of Engineering, University of Edinburgh, 2010.
  • Grant Reviewer, PASCAL2, internal visiting proposal, August, 2010.
  • Headstart Project Leader, School Outreach, Guildford, 19-22 July, 2010.
  • Technical Committee Member, Conference on Sensor Signal Processing for Defence (SSPD 2010), London, UK, 29-30 September, 2010.
  • Program Committee Member, BMVC 2010, Aberystwyth, UK, 31 August - 3 September, 2010.
  • Program Committee Member, IEEE WCSE 2010, Wuhan, China, December 19-20, 2010.
  • Program Committee Member, IWACI 2010, Suzhou, China, August 25-27, 2010.
  • Program Committee Member, SIP 2010, Maui, Hawaii, USA, August 23-25, 2010.
  • Program Committee Member, SSSPR 2010, Cesme, Turkey, August 18-20, 2010.
  • Program Committee Member, ISNN 2010, Shanghai, China, June 6-9, 2010.
  • Publicity chair, IEEE International Workshop on Statistical Signal Processing (SSP 2009), Cardiff, UK, Aug. 31- Sept. 3, 2009.
  • Program Co-chair, IEEE Global Congress on Intelligent Systems (GCIS 2009), Xiamen, China, May 19-21, 2009.
  • Program Committee Member, SIP 2009, Honolulu, Hawaii, USA, August 17-19, 2009.
  • Program Committee Member, IEEE WCSE 2009, Xiamen, China, May 19-21, 2009.
  • Session Chair, ICA Research Network International Workshop (ICARN 2008), Liverpool, UK, September 25-26, 2008.
  • Chair, oral session "Unsupervised learning III", IEEE WCCI 2008, HongKong, China, June 1-6, 2008.
  • Guest editor, special issue "Advances in Nonnegative Matrix and Tensor Factorization", Computational Intelligence and Neuroscience(Hindawi), edited by A. Cichocki, M. Morup, P. Smaragdis, W. Wang, and R. Zdunek, May 2008.
  • Program Committee Member, SIP 2008, Kailua-Kona, Hawaii, USA, August 18-20, 2008.
  • Technical Committee Member, IEEE WCCI 2008, HongKong, China, June 1-6, 2008.

Invited Talks

  • W. Wang, "Machine Audition at CVSSP", in UK & IE Speech Conference, Birmingham, UK, December 17-18, 2012.
  • W. Wang, "Dictionary Learning Algorithms in Sparse Representations and Signal Processing," (Organizer: Dr Wei Liu),Department of Eletronic and Electrical Engineering , Sheffield University, October 24, 2012.
  • W. Wang, "Dictionary Learning Algorithms in Signal Processing," (Organizer: Dr Lu Gan), School of Engineering and Design, Brunel University, August 1, 2012.
  • W. Wang, "Adaptive Dictionary Learning Algorithms for Image Denoising, Source Separation, and Visual Tracking," (Organizer: Dr Andrew Aubrey), Cardiff School of Computer Science and Informatics, Cardiff University, May 24, 2012.
  • W. Wang, "Dictionary Learning Algorithms and Their Applications in Source separation, Speaker Tracking, and Image Denoising," (Organizer: Prof Mark Plumbley), School of Electronic Engineering and Computer Science, Queen Mary University of London, April 25, 2012.
  • W. Wang, "Audio and Audio-Visual Source Separation," (Organizer: Dr Xiaorong Shen), School of Automation Science and Electrical Engineering, Beihang University, Beijing, September 20, 2011.
  • T. Xu and W. Wang, "Compressive Sensing," (Organizer: Prof. Anthony Ho), Department of Computer Science, University of Surrey, Guildford, January 11, 2010.
  • W. Wang, "Multimodal Blind Source Separation for Robot Audition," (Organizer: Dr. Tania Stathaki), MOD University Defence Research Centre Launch & Theme Meeting, Imperial College London, London, November 5, 2009.
  • W. Wang, "Two-microphone Speech Separation Based on Convolutive ICA and Ideal Binary Mask Coupled with Cepstral Smoothing," (Organizer: Prof. Francis Rumsey), Institute of Sound Recording (IoSR), University of Surrey, Guildford, October 21, 2008.
  • W. Wang, "Convolutive ICA and NMF for Audio Source Separation and Perception," (Organizers: Prof. Vladimir M. Sloutsky & Prof. DeLiang Wang), Center for Cognitive Science, Ohio State University, Columbus, April 11, 2008.
  • W. Wang, "Audio Source Separation and Perception," (Organizer: Prof. DeLiang Wang), Perception and Neurodynamics Laboratory (PNL), Department of Computer Science and Engineering, Ohio State University, Columbus, March 07, 2008.
  • W. Wang, "Intelligent Data Fusion Based Blind Source Separation," (Organizer: Dr Nathan Wood), Royal Academy of Engineering, London, April 11, 2005.
  • W. Wang and J.A. Chambers, "Frequency Domain Blind Source Separation," IEE Seminar on Blind Source Separation in Biomedicine (Organizer: Dr. Christopher J. James), British Institute of Radiology, London, 1 Dec. 2004.
  • W. Wang, "Frequency Domain BSS and its Associated Permutation Problem," Contract Researchers Conference at Cardiff School of Engineering (Organizer: Dr. Adrian Porch), Cardiff University, Cardiff, July 16, 2004.
  • W. Wang, "Blind Signal Processing and Speech Enhancement," Series Forum for Celebration of the 50th Anniversary of Harbin Engineering University (Organizer: Prof. Yanling Hao), Harbin, Apr. 11, 2003.
  • W. Wang, S. Sanei, and J.A. Chambers, "Has the Permutation Problem in Transform Domain BSS Been Solved?," IEE Workshop on Independent Component Analysis: Generalizations, Algorithms and Applications (Organizer: Dr. Mike Davies), Queen Mary University of London, London, Dec. 20, 2002

Tutorial Speech 

  • W. Dai, W. Wang, and B. Mailhe, ICASSP 2013, "Dictionary Learning for Sparse Representations: Algorithms and Applications", Vancovar, Canada, May, 26-31, 2013.

Contact Me

E-mail:
Phone: 01483 68 6039

Find me on campus
Room: 04 BB 01

Publications

Highlights

  • Wang W. (2011) Preface of Machine Audition: Principles, Algorithms and Systems. in Wang W (ed.) Machine Audition: Principles, Algorithms and Systems Information Science Reference , pp. xv-xxi.

    Abstract

    "This book covers advances in algorithmic developments, theoretical frameworks, andexperimental research findings to assist professionals who want an improved ...

  • Jan T, Wang W, Wang D. (2011) 'A Multistage Approach to Blind Separation of Convolutive Speech Mixtures'. Speech Communication, 53 (4), pp. 524-539.
  • Wang W. (2010) Machine Audition: Principles, Algorithms and Systems. New York, USA : Information Science Reference
  • Wang W. (2010) Instantaneous versus Convolutive Non-negative Matrix Factorization: Models, Algorithms and Applications to Audio Pattern Separation. in Wang W (ed.) Machine Audition: Principles, Algorithms and Systems Information Science Reference Article number 15 , pp. 353-370.
  • Jan T, Wang W. (2010) Cocktail Party Problem: Source Separation Issues and Computational Methods. in Wang W (ed.) Machine Audition: Principles, Algorithms and Systems New York, USA : Information Science Reference Article number 3 , pp. 61-79.
  • Wang W, Cichocki A, Chambers JA. (2009) 'A multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance'. IEEE Transactions on Signal Processing, 57 (7), pp. 2858-2864.
  • Zhou S, Wang W. (2009) IEEE/WRI Global Congress on Intelligent Systems Proceedings. USA : IEEE Computer Society
  • Wang W, Cichocki A, Mørup M, Smaragdis P, Zdunek R. (2008) 'Advances in nonnegative matrix and tensor factorization'. Hindawi Publishing Corporation Computational Intelligence and Neuroscience, 2008 Article number 852187
  • Wang W, Luo Y, Chambers JA, Sanei S. (2008) 'Note onset detection via nonnegative factorization of magnitude spectrum'. HINDAWI PUBLISHING CORPORATION EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, Article number ARTN 231367

Journal articles

  • Wang W, Feng P, Dlay S, Naqvi SM, Chambers J. (2017) 'Social Force Model based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking'. IEEE Transactions on Multimedia,
    [ Status: Accepted ]
  • Barnard M, Wang W. (2016) 'Audio Head Pose Estimation using the Direct to Reverberant Speech Ratio'. Speech Communication,

    Abstract

    Head pose is an important cue in many applications such as, speech recognition and face recognition. Most approaches to head pose estimation to date have focussed on the use of visual information of a subject’s head. These visual approaches have a number of limitations such as, an inability to cope with occlusions, changes in the appearance of the head, and low resolution images. We present here a novel method for determining coarse head pose orientation purely from audio information, exploiting the direct to reverberant speech energy ratio (DRR) within a reverberant room environment. Our hypothesis is that a speaker facing towards a microphone will have a higher DRR and a speaker facing away from the microphone will have a lower DRR. This method has the advantage of actually exploiting the reverberations within a room rather than trying to suppress them. This also has the practical advantage that most enclosed living spaces, such as meeting rooms or offices are highly reverberant environments. In order to test this hypothesis we also present a new data set featuring 56 subjects recorded in three different rooms, with different acoustic properties, adopting 8 different head poses in 4 different room positions captured with a 16 element microphone array. As far as the authors are aware this data set is unique and will make a significant contribution to further work in the area of audio head pose estimation. Using this data set we demonstrate that our proposed method of using the DRR for audio head pose estimation provides a significant improvement over previous methods.

  • Feng P, Wang W, Naqvi SM, Chambers J. (2016) 'Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking'. IEEE Signal Processing Letters,
  • Dong J, Wang W, Dai W, Plumbley MD, Han Z-F, Chambers J. (2016) 'Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON SIGNAL PROCESSING, 64 (2), pp. 417-431.
  • Kilic V, Barnard M, Wang W, Hilton A, Kittler J. (2016) 'Mean-Shift and Sparse Sampling Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking'. IEEE Transactions on Multimedia,
    [ Status: Accepted ]
  • Zhao L, Hu Q, Wang W. (2015) 'Heterogeneous Feature Selection with Multi-Modal Deep Neural Networks and Sparse Group Lasso'. IEEE Transactions on Multimedia, 17 (11), pp. 1936-1948.
    [ Status: Accepted ]
  • Chen X, Wang W, Wang Y, Zhong X, Alinaghi A. (2015) 'Reverberant speech separation with probabilistic time-frequency masking for B-format recordings'. Speech Communication, 68, pp. 41-54.
  • Kiliç V, Barnard M, Wang W, Kittler J. (2015) 'Audio assisted robust visual tracking with adaptive particle filtering'. IEEE Transactions on Multimedia, 17 (2), pp. 186-200.

    Abstract

    © 1999-2012 IEEE.The problem of tracking multiple moving speakers in indoor environments has received much attention. Earlier techniques were based purely on a single modality, e.g., vision. Recently, the fusion of multi-modal information has been shown to be instrumental in improving tracking performance, as well as robustness in the case of challenging situations like occlusions (by the limited field of view of cameras or by other speakers). However, data fusion algorithms often suffer from noise corrupting the sensor measurements which cause non-negligible detection errors. Here, a novel approach to combining audio and visual data is proposed. We employ the direction of arrival angles of the audio sources to reshape the typical Gaussian noise distribution of particles in the propagation step and to weight the observation model in the measurement step. This approach is further improved by solving a typical problem associated with the PF, whose efficiency and accuracy usually depend on the number of particles and noise variance used in state estimation and particle propagation. Both parameters are specified beforehand and kept fixed in the regular PF implementation which makes the tracker unstable in practice. To address these problems, we design an algorithm which adapts both the number of particles and noise variance based on tracking error and the area occupied by the particles in the image. Experiments on the AV16.3 dataset show the advantage of our proposed methods over the baseline PF method and an existing adaptive PF algorithm for tracking occluded speakers with a significantly reduced number of particles.

  • Gu F, Li W, Wang W. (2014) 'Fourth-order cumulant based sources number estimation from mixtures of unknown number of sources'. 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014,
  • Zhong X, Wang W, Naqvi M, Chng ES. (2014) 'A Bayesian performance bound for time-delay of arrival based acoustic source tracking in a reverberant environment'. FUSION 2014 - 17th International Conference on Information Fusion,
  • Kiliç V, Zhong X, Barnard M, Wang W, Kittler J. (2014) 'Audio-visual tracking of a variable number of speakers with a random finite set approach'. FUSION 2014 - 17th International Conference on Information Fusion,
  • Liu Q, Aubrey AJ, Wang W. (2014) 'Interference reduction in reverberant speech separation with visual voice activity detection'. IEEE Transactions on Multimedia, 16 (6), pp. 1610-1623.
  • Alinaghi A, Jackson PJB, Liu Q, Wang W. (2014) 'Joint Mixing Vector and Binaural Model Based Stereo Source Separation'. IEEE Transactions on Audio, Speech, & Language Processing, 22 Article number 9 , pp. 1434-1448.
  • Barnard M, Wang W, Kittler J, Koniusz P, Naqvi SM, Chambers J. (2014) 'Robust multi-speaker tracking via dictionary learning and identity modeling'. IEEE Transactions on Multimedia, 16 (3), pp. 864-880.
  • Chandna S, Wang W. (2014) 'Improving model-based convolutive blind source separation techniques via bootstrap'. IEEE Workshop on Statistical Signal Processing Proceedings, , pp. 424-427.
  • Liu Q, Wang W, Jackson PJB, Barnard M, Kittler J, Chambers J. (2013) 'Source separation of convolutive and noisy mixtures using audio-visual dictionary learning and probabilistic time-frequency masking'. IEEE Transactions on Signal Processing, 61 (22) Article number 99 , pp. 5520-5535.
  • Zubair S, Yan F, Wang W. (2013) 'Dictionary learning based sparse coefficients for audio classification with max and average pooling'. ACADEMIC PRESS INC ELSEVIER SCIENCE DIGITAL SIGNAL PROCESSING, 23 (3), pp. 960-970.
  • Xu T, Wang W, Dai W. (2013) 'Sparse coding with adaptive dictionary learning for underdetermined blind speech separation'. Speech Communication, 55 (3), pp. 432-450.
  • Zubair S, Yan F, Wang W. (2013) 'Dictionary learning based sparse coefficients for audio classification with max and average pooling'. Digital Signal Processing: A Review Journal,
  • Naik GR, Wang W. (2012) 'Audio analysis of statistically instantaneous signals with mixed Gaussian probability distributions'. International Journal of Electronics, 99 (10), pp. 1333-1350.
  • Liu Q, Wang W, Jackson PJB. (2012) 'Use of bimodal coherence to resolve the permutation problem in convolutive BSS'. Elsevier Signal Processing, 92 (8), pp. 1916-1927.
  • Liu Q, Wang W, Jackson P. (2012) 'Use of bimodal coherence to resolve the permutation problem in convolutive BSS'. Signal Processing, 92 (8), pp. 1916-1927.

    Abstract

    Recent studies show that facial information contained in visual speech can be helpful for the performance enhancement of audio-only blind source separation (BSS) algorithms. Such information is exploited through the statistical characterization of the coherence between the audio and visual speech using, e.g., a Gaussian mixture model (GMM). In this paper, we present three contributions. With the synchronized features, we propose an adapted expectation maximization (AEM) algorithm to model the audiovisual coherence in the off-line training process. To improve the accuracy of this coherence model, we use a frame selection scheme to discard nonstationary features. Then with the coherence maximization technique, we develop a new sorting method to solve the permutation problem in the frequency domain. We test our algorithm on a multimodal speech database composed of different combinations of vowels and consonants. The experimental results show that our proposed algorithm outperforms traditional audio-only BSS, which confirms the benefit of using visual speech to assist in separation of the audio. © 2011 Elsevier B.V. All rights reserved.

  • Mohsen Naqvi S, Wang W, Khan MS, Barnard M, Chambers JA. (2012) 'Multimodal (audio-visual) source separation exploiting multi-speaker tracking, robust beamforming and time-frequency masking'. IET Signal Processing, 6 (5), pp. 466-477.
  • Dai W, Xu T, Wang W. (2012) 'Simultaneous codeword optimization (SimCO) for dictionary update and learning'. IEEE Transactions on Signal Processing, 60 (12), pp. 6340-6353.
  • Jan T, Wang W, Wang D. (2011) 'A Multistage Approach to Blind Separation of Convolutive Speech Mixtures'. Speech Communication, 53 (4), pp. 524-539.
  • Liu Q, Wang W. (2011) 'Blind source separation and visual voice activity detection for target speech extraction'. Proceedings of 2011 3rd International Conference on Awareness Science and Technology, iCAST 2011, , pp. 457-460.
  • Jan T, Wang W, Wang D. (2011) 'A multistage approach to blind separation of convolutive speech mixtures'. Speech Communication, 53 (4), pp. 524-539.
  • Wang W, Cichocki A, Chambers JA. (2009) 'A multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance'. IEEE Transactions on Signal Processing, 57 (7), pp. 2858-2864.
  • Wang W, Luo Y, Chambers JA, Sanei S. (2008) 'Note onset detection via nonnegative factorization of magnitude spectrum'. HINDAWI PUBLISHING CORPORATION EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, Article number ARTN 231367
  • Wang W, Cichocki A, Mørup M, Smaragdis P, Zdunek R. (2008) 'Advances in nonnegative matrix and tensor factorization'. Hindawi Publishing Corporation Computational Intelligence and Neuroscience, 2008 Article number 852187
  • Luo Y, Wang W, Chambers JA, Lambotharan S, Proudler I. (2006) 'Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques'. IEEE Transactions on Signal Processing, 54 (6 I), pp. 2198-2212.
  • Jafari MG, Wang W, Chambers JA, Hoya T, Cichocki A, Cichocki A. (2006) 'Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals'. IEEE Transactions on Signal Processing, 54 (3), pp. 1028-1040.
  • Yuan L, Wang W, Chambers JA, Yuan L, Wang W. (2005) 'Variable step-size sign natural gradient algorithm for sequential blind source separation'. IEEE Signal Processing Letters, 12 (8), pp. 589-592.
  • Wang W, Sanei S, Chambers JA. (2005) 'Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources'. IEEE Transactions on Signal Processing, 53 (5), pp. 1654-1669.
  • Shoker L, Sanei S, Wang W, Chambers JA. (2005) 'Removal of eye blinking artifact from the electro-encephalogram, incorporating a new constrained blind source separation algorithm.'. Med Biol Eng Comput, England: 43 (2), pp. 290-295.
  • Wang W, Jafari M, Sanei S, Chambers J. (2004) 'Blind Separation of Convolutive Mixtures of Cyclostationary Signals'. International Journal of Adaptive Control and Signal Processing, 18 (3), pp. 279-298.

Conference papers

  • Barnard M, Wang W, Kittler J, Naqvi SM, Chambers JA. 'A dictionary learning approach to tracking'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 981-984.
  • Xu Y, Huang Q, Wang W, Jackson PJB, Plumbley MD. (2016) 'Fully DNN-based Multi-label regression for audio tagging'. Tampere University of Technology Proceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016), Budapest, Hungary: DCASE2016 Workshop (Workshop on Detection and Classification of Acoustic Scenes and Events), pp. 110-114.

    Abstract

    Acoustic event detection for content analysis in most cases relies on lots of labeled data. However, manually annotating data is a time-consuming task, which thus makes few annotated resources available so far. Unlike audio event detection, automatic audio tagging, a multi-label acoustic event classification task, only relies on weakly labeled data. This is highly desirable to some practical applications using audio analysis. In this paper we propose to use a fully deep neural network (DNN) framework to handle the multi-label classification task in a regression way. Considering that only chunk-level rather than frame-level labels are available, the whole or almost whole frames of the chunk were fed into the DNN to perform a multi-label regression for the expected tags. The fully DNN, which is regarded as an encoding function, can well map the audio features sequence to a multi-tag vector. A deep pyramid structure was also designed to extract more robust high-level features related to the target tags. Further improved methods were adopted, such as the Dropout and background noise aware training, to enhance its generalization capability for new audio recordings in mismatched environments. Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method could well utilize the long-term temporal information with the whole chunk as the input. The results show that our approach obtained a 15% relative improvement compared with the official GMM-based method of DCASE 2016 challenge.

  • Xu Y, Huang Q, Wang W, Plumbley MD. (2016) 'Hierarchical Learning for DNN-Based Acoustic Scene Classification'. Tampere University of Technology Proceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016), Budapest, Hungary: DCASE2016 Workshop (Workshop on Detection and Classification of Acoustic Scenes and Events), pp. 105-109.

    Abstract

    In this paper, we present a deep neural network (DNN)-based acoustic scene classification framework. Two hierarchical learning methods are proposed to improve the DNN baseline performance by incorporating the hierarchical taxonomy information of environmental sounds. Firstly, the parameters of the DNN are initialized by the proposed hierarchical pre-training. Multi-level objective function is then adopted to add more constraint on the cross-entropy based loss function. A series of experiments were conducted on the Task1 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge. The final DNN-based system achieved a 22.9% relative improvement on average scene classification error as compared with the Gaussian Mixture Model (GMM)-based benchmark system across four standard folds.

  • Font F, Brookes TS, Fazekas G, Guerber M, La Burthe A, Plans D, Plumbley M, Shaashua M, Wang W, Serra X. (2016) 'Audio Commons: bringing Creative Commons audio content to the creative industries'. London, UK: 61st International Conference: Audio for Games

    Abstract

    Significant amounts of user-generated audio content, such as sound effects, musical samples and music pieces, are uploaded to online repositories and made available under open licenses. Moreover, a constantly increasing amount of multimedia content, originally released with traditional licenses, is becoming public domain as its license expires. Nevertheless, the creative industries are not yet using much of all this content in their media productions. There is still a lack of familiarity and understanding of the legal context of all this open content, but there are also problems related with its accessibility. A big percentage of this content remains unreachable either because it is not published online or because it is not well organised and annotated. In this paper we present the Audio Commons Initiative, which is aimed at promoting the use of open audio content and at developing technologies with which to support the ecosystem composed by content repositories, production tools and users. These technologies should enable the reuse of this audio material, facilitating its integration in the production workflows used by the creative industries. This is a position paper in which we describe the core ideas behind this initiative and outline the ways in which we plan to address the challenges it poses.

  • Remaggi L, Jackson PJB, Coleman P, Wang W. (2014) 'Room boundary estimation from acoustic room impulse responses'. Edinburgh, UK : IEEE Proc. Sensor Signal Processing for Defence (SSPD 2014), Edinburgh: Sensor Signal Processing for Defence (SSPD 2014), pp. 1-5.
  • Zubair S, Wang W, Chambers JA. (2014) 'Discriminativetensor dictionaries and sparsity for speaker identification'. 2014 4th Joint Workshop on Hands-Free Speech Communication and Microphone Arrays, HSCMA 2014, , pp. 37-41.
  • Dong J, Wang W, Dai W. (2014) 'Analysis SimCO: A new algorithm for analysis dictionary learning'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 7193-7197.
  • Dong J, Wang W. (2014) 'Analysis dictionary learning based on Nesterov's gradient with application to SAR image despeckling'. ISCCSP 2014 - 2014 6th International Symposium on Communications, Control and Signal Processing, Proceedings, , pp. 501-504.
  • Popa V, Wang W, Alinaghi A. (2013) 'Underdetermined model-based blind source separation of reverberant speech mixtures using spatial cues in a variational bayesian framework'. IET IET Conference Publications, London: IET Intelligent Signal Processing Conference 2013 (619 CP), pp. 1-6.

    Abstract

    In this paper, we propose a new method for underdetermined blind source separation of reverberant speech mixtures by classifying each time-frequency (T-F) point of the mixtures according to a combined variational Bayesian model of spatial cues, under sparse signal representation assumption. We model the T-F observations by a variational mixture of circularly-symmetric complex-Gaussians. The spatial cues, e.g. interaural level difference (ILD), interaural phase difference (IPD) and mixing vector cues, are modelled by a variational mixture of Gaussians. We then establish appropriate conjugate prior distributions for the parameters of all the mixtures to create a variational Bayesian framework. Using the Bayesian approach we then iteratively estimate the hyper-parameters for the prior distributions by optimizing the variational posterior distribution. The main advantage of this approach is that no prior knowledge of the number of sources is needed, and it will be automatically determined by the algorithm. The proposed approach does not suffer from overfitting problem, as opposed to the Expectation-Maximization (EM) algorithm, therefore it is not sensitive to initializations.

  • Alinaghi A, Jackson PJB, Wang W. (2013) 'Comparison between the statistical cues in BSS techniques and Binaural cues in CASA approaches for reverberant speech separation'. IET Conference Publications, 2013 (619 CP)

    Abstract

    Reverberant speech source separation has been of great interest for over a decade, leading to two major approaches. One of them is based on statistical properties of the signals and mixing process known as blind source separation (BSS). The other approach named as computational auditory scene analysis (CASA) is inspired by human auditory system and exploits monaural and binaural cues. In this paper these two approaches are studied and compared in more depth.

  • Kilic V, Barnard M, Wang W, Kittler J. (2013) 'Audio constrained particle filter based visual tracking'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 3627-3631.
  • Barnard M, Wang W, Kittler J. (2013) 'Audio head pose estimation using the direct to reverberant speech ratio'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 8056-8060.
  • Alinaghi A, Wang W, Jackson PJB. (2013) 'Spatial and coherence cues based time-frequency masking for binaural reverberant speech separation'. Vancouver, Canada : IEEE Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), , pp. 4-4.

    Abstract

    Most of the binaural source separation algorithms only consider the dissimilarities between the recorded mixtures such as interaural phase and level differences (IPD, ILD) to classify and assign the time-frequency (T-F) regions of the mixture spectrograms to each source. However, in this paper we show that the coherence between the left and right recordings can provide extra information to label the T-F units from the sources. This also reduces the effect of reverberation which contains random reflections from different directions showing low correlation between the sensors. Our algorithm assigns the T-F regions into original sources based on weighted combination of IPD, ILD, the observation vectors models and the estimated interaural coherence (IC) between the left and right recordings. The binaural room impulse responses measured in four rooms with various acoustic conditions have been used to evaluate the performance of the proposed method which shows an improvement of more than 1:4 dB in signal-to-distortion ratio (SDR) in room D with T60 = 0:89 s over the state-of-the-art algorithms.

  • Zhao X, Zhou G, Dai W, Xu T, Wang W. (2013) 'Joint image separation and dictionary learning'. 2013 18th International Conference on Digital Signal Processing, DSP 2013,
  • Barnard M, Wang W, Kittler J, Naqvi SM, Chambers J. (2013) 'Audio-visual face detection for tracking in a meeting room environment'. Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, , pp. 1222-1227.
  • Kilic V, Barnard M, Wang W, Kittler J. (2013) 'Adaptive particle filtering approach to audio-visual tracking'. IEEE European Signal Processing Conference, Marrakech: 21st European Signal Processing Conference, pp. 1-5.

    Abstract

    Particle filtering has emerged as a useful tool for tracking problems. However, the efficiency and accuracy of the filter usually depend on the number of particles and noise variance used in the estimation and propagation functions for re-allocating these particles at each iteration. Both of these parameters are specified beforehand and are kept fixed in the regular implementation of the filter which makes the tracker unstable in practice. In this paper we are interested in the design of a particle filtering algorithm which is able to adapt the number of particles and noise variance. The new filter, which is based on audio-visual (AV) tracking, uses information from the tracking errors to modify the number of particles and noise variance used. Its performance is compared with a previously proposed audio-visual particle filtering algorithm with a fixed number of particles and an existing adaptive particle filtering algorithm, using the AV 16.3 dataset with single and multi-speaker sequences. Our proposed approach demonstrates good tracking performance with a significantly reduced number of particles. © 2013 EURASIP.

  • Ye Z, Wang H, Yu T, Wang W. (2013) 'Subset pursuit for analysis dictionary learning'. European Signal Processing Conference,
  • Shapoori S, Wang W, Sanei S. (2013) 'A constrained approach for extraction of pre-ictal discharges from scalp EEG'. IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Southampton: International Workshop on Machine Learning for Signal Processing (MLSP)
  • Zhong X, Premkumar AB, Chen X, Wang W, Alinaghi A. (2013) 'Acoustic vector sensor based reverberant speech separation with probabilistic time-frequency masking'. IEEE European Signal Processing Conference, Marrakech: 21st European Signal Processing Conference, pp. 1-5.

    Abstract

    Most existing speech source separation algorithms have been developed for separating sound mixtures acquired by using a conventional microphone array. In contrast, little attention has been paid to the problem of source separation using an acoustic vector sensor (AVS). We propose a new method for the separation of convolutive mixtures by incorporating the intensity vector of the acoustic field, obtained using spatially co-located microphones which carry the direction of arrival (DOA) information. The DOA cues from the intensity vector, together with the frequency bin-wise mixing vector cues, are then used to determine the probability of each time-frequency (T-F) point of the mixture being dominated by a specific source, based on the Gaussian mixture models (GMM), whose parameters are evaluated and refined iteratively using an expectation-maximization (EM) algorithm. Finally, the probability is used to derive the T-F masks for recovering the sources. The proposed method is evaluated in simulated reverberant environments in terms of signal-to-distortion ratio (SDR), giving an average improvement of approximately 1:5 dB as compared with a related T-F mask approach based on a conventional microphone setting. © 2013 EURASIP.

  • Chen X, Alinaghi A, Wang W, Zhong X. (2013) 'Acoustic vector sensor based speech source separation with mixed Gaussian-laplacian distributions'. IEEE 2013 18th International Conference on Digital Signal Processing, DSP 2013, Fira: 18th International Conference on Digital Signal Processing, pp. 1-5.

    Abstract

    Acoustic vector sensor (AVS) based convolutive blind source separation problem has been recently addressed under the framework of probabilistic time-frequency (T-F) masking, where both the DOA and the mixing vector cues are modelled by Gaussian distributions. In this paper, we show that the distributions of these cues vary with room acoustics, such as reverberation. Motivated by this observation, we propose a mixed model of Laplacian and Gaussian distributions to provide a better fit for these cues. The parameters of the mixed model are estimated and refined iteratively by an expectation-maximization (EM) algorithm. Experiments performed on the speech mixtures in simulated room environments show that the mixed model offers an average of about 0.68 dB and 1.18 dB improvements in signal-to-distotion (SDR) over the Gaussian and Laplacian model, respectively. © 2013 IEEE.

  • Zhong X, Premkumar AB, Wang W. (2013) 'Direction of arrival tracking of an underwater acoustic source using particle filtering: Real data experiments'. IEEE IEEE 2013 Tencon - Spring, TENCONSpring 2013 - Conference Proceedings, Sydney, Australia: TENCON Spring Conference 2013, pp. 420-424.
  • Zubair S, Wang W. (2013) 'Tensor dictionary learning with sparse tucker decomposition'. IEEE 2013 18th International Conference on Digital Signal Processing, DSP 2013, Fira: 18th International Conference on Digital Signal Processing, pp. 1-6.

    Abstract

    Dictionary learning algorithms are typically derived for dealing with one or two dimensional signals using vector-matrix operations. Little attention has been paid to the problem of dictionary learning over high dimensional tensor data. We propose a new algorithm for dictionary learning based on tensor factorization using a TUCKER model. In this algorithm, sparseness constraints are applied to the core tensor, of which the n-mode factors are learned from the input data in an alternate minimization manner using gradient descent. Simulations are provided to show the convergence and the reconstruction performance of the proposed algorithm. We also apply our algorithm to the speaker identification problem and compare the discriminative ability of the dictionaries learned with those of TUCKER and K-SVD algorithms. The results show that the classification performance of the dictionaries learned by our proposed algorithm is considerably better as compared to the two state of the art algorithms. © 2013 IEEE.

  • Zhong X, Premkumar AB, Mohammadi A, Asif A, Wang W. (2013) 'Acoustic source tracking in a reverberant environment using a pairwise synchronous microphone network'. IEEE Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, Istanbul: 16th International Conference on Information Fusion, pp. 953-960.
  • Zhang Y, Wang H, Wang W, Sanei S. (2013) 'K-plane clustering algorithm for analysis dictionary learning'. IEEE International Workshop on Machine Learning for Signal Processing, MLSP,
  • Liu Q, Wang W. (2013) 'Show-through removal for scanned images using non-linear NMF with adaptive smoothing'. 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings, , pp. 650-654.
  • Liu Q, Wang W, Jackson PJB, Barnard M. (2012) 'Reverberant Speech Separation Based on Audio-visual Dictionary Learning and Binaural Cues'. IEEE Proc. of IEEE Statistical Signal Processing Workshop (SSP), Ann Abor, USA: IEEE Statistical Signal Processing Workshop (SSP), pp. 664-667.

    Abstract

    Probabilistic models of binaural cues, such as the interaural phase difference (IPD) and the interaural level difference (ILD), can be used to obtain the audio mask in the time-frequency (TF) domain, for source separation of binaural mixtures. Those models are, however, often degraded by acoustic noise. In contrast, the video stream contains relevant information about the synchronous audio stream that is not affected by acoustic noise. In this paper, we present a novel method for modeling the audio-visual (AV) coherence based on dictionary learning. A visual mask is constructed from the video signal based on the learnt AV dictionary, and incorporated with the audio mask to obtain a noise-robust audio-visual mask, which is then applied to the binaural signal for source separation. We tested our algorithm on the XM2VTS database, and observed considerable performance improvement for noise corrupted signals.

  • Dai W, Xu T, Wang W. (2012) 'Dictionary learning and update based on simultaneous codeword optimization (SimCO)'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 2037-2040.
  • Jan T, Wang W. (2012) 'Frequency dependent statistical model for the suppression of late reverberations'. IET Seminar Digest, London, UK: Sensor Signal Processing for Defence (SSPD 2012) 2012 (3)
  • Zhao X, Zhou G, Dai W, Wang W. (2012) 'Weighted SimCO: A novel algorithm for dictionary update'. IET Seminar Digest, London: Sensor Signal Processing for Defence (SSPD 2012) 2012 (3), pp. 1-5.
  • Jan T, Wang W. (2012) 'Joint blind dereverberation and separation of speech mixtures'. 2012 EUSIPCO European Signal Processing Conference Proceedings, 20th European Signal Processing Conference, pp. 2343-2347.
  • Jan T, Wang W. (2012) 'Blind reverberation time estimation based on Laplace distribution'. European Signal Processing Conference, Bucharest: 20th European Signalling Processing Conference, pp. 2050-2054.
  • Xu T, Wang W. (2011) 'Methods for learning adaptive dictionary in underdetermined speech separation'. IEEE Proceedings of MLSP2011, Beijing, China: 2011 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1-6.
  • Jan T, Wang W. (2011) 'Empirical mode decomposition for joint denoising and dereverberation'. European Signal Processing Conference, , pp. 206-210.

    Abstract

    We propose a novel algorithm for the enhancement of noisy reverberant speech using empirical-mode-decomposition (EMD) based subband processing. The proposed algorithm is a one-microphone multistage algorithm. In the first step, noisy reverberant speech is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) via an EMD algorithm. Denoising is then applied to selected high frequency IMFs using EMD-based minimum mean-squared error (MMSE) filter, followed by spectral subtraction of the resulting denoised high-frequency IMFs and low-frequency IMFs. Finally, the enhanced speech signal is reconstructed from the processed IMFs. The method was motivated by our observation that the noise and reverberations are disproportionally distributed across the IMF components. Therefore, different levels of suppression can be applied to the additive noise and reverberation in each IMF. This leads to an improved enhancement performance as shown in comparison to a related recent approach, based on the measurements by the signal-to-noise ratio (SNR). © 2011 EURASIP.

  • Wang W, Mustafa H. (2011) 'Single channel music sound separation based on spectrogram decomposition and note classification'. Springer Lecture Notes in Computer Science: Exploring Music Contents, Malaga, Spain: CMMR 2010: 7th International Symposium 6684, pp. 84-101.

    Abstract

    Separating multiple music sources from a single channel mixture is a challenging problem. We present a new approach to this problem based on non-negative matrix factorization (NMF) and note classification, assuming that the instruments used to play the sound signals are known a priori. The spectrogram of the mixture signal is first decomposed into building components (musical notes) using an NMF algorithm. The Mel frequency cepstrum coefficients (MFCCs) of both the decomposed components and the signals in the training dataset are extracted. The mean squared errors (MSEs) between the MFCC feature space of the decomposed music component and those of the training signals are used as the similarity measures for the decomposed music notes. The notes are then labelled to the corresponding type of instruments by the K nearest neighbors (K-NN) classification algorithm based on the MSEs. Finally, the source signals are reconstructed from the classified notes and the weighting matrices obtained from the NMF algorithm. Simulations are provided to show the performance of the proposed system. © 2011 Springer-Verlag Berlin Heidelberg.

  • Naqvi SM, Khan MS, Chambers JA, Liu Q, Wang W. (2011) 'Multimodal blind source separation with a circular microphone array and robust beamforming'. European Signal Processing Conference, , pp. 1050-1054.
  • Zubair S, Wang W. (2011) 'Audio classification based on sparse coefficients'. IET Seminar Digest, London, UK: Sensor Signal Processing for Defence (SSPD 2011) 2011 (4)
  • Liu Q, Naqvi SM, Wang W, Jackson PJB, Chambers J. (2011) 'Robust feature selection for scaling ambiguity reduction in audio-visual convolutive BSS'. European Signal Processing Conference, Barcelona, Spain: 19th European Signal Processing Conference 2011 (EUSIPCO 2011), pp. 1060-1064.

    Abstract

    Information from video has been used recently to address the issue of scaling ambiguity in convolutive blind source separation (BSS) in the frequency domain, based on statistical modeling of the audio-visual coherence with Gaussian mixture models (GMMs) in the feature space. However, outliers in the feature space may greatly degrade the system performance in both training and separation stages. In this paper, a new feature selection scheme is proposed to discard non-stationary features, which improves the robustness of the coherence model and reduces its computational complexity. The scaling parameters obtained by coherence maximization and non-linear interpolation from the selected features are applied to the separated frequency components to mitigate the scaling ambiguity. A multimodal database composed of different combinations of vowels and consonants was used to test our algorithm. Experimental results show the performance improvement with our proposed algorithm.

  • Liu Q, Wang W. (2011) 'Blind source separation and visual voice activity detection for target speech extraction'. IEEE Proceedings of 2011 3rd International Conference on Awareness Science and Technology, Dalian, China: iCAST 2011, pp. 457-460.

    Abstract

    Despite being studied extensively, the performance of blind source separation (BSS) is still limited especially for the sensor data collected in adverse environments. Recent studies show that such an issue can be mitigated by incorporating multimodal information into the BSS process. In this paper, we propose a method for the enhancement of the target speech separated by a BSS algorithm from sound mixtures, using visual voice activity detection (VAD) and spectral subtraction. First, a classifier for visual VAD is formed in the off-line training stage, using labelled features extracted from the visual stimuli. Then we use this visual VAD classifier to detect the voice activity of the target speech. Finally we apply a multi-band spectral subtraction algorithm to enhance the BSS-separated speech signal based on the detected voice activity. We have tested our algorithm on the mixtures generated artificially by the mixing filters with different reverberation times, and the results show that our algorithm improves the quality of the separated target signal. © 2011 IEEE.

  • Liu Q, Wang W, Jackson PJB. (2011) 'A visual voice activity detection method with adaboosting'. IET IET Seminar Digest, London, UK: Sensor Signal Processing for Defence (SSPD 2011) 2011 (4)

    Abstract

    Spontaneous speech in videos capturing the speaker's mouth provides bimodal information. Exploiting the relationship between the audio and visual streams, we propose a new visual voice activity detection (VAD) algorithm, to overcome the vulnerability of conventional audio VAD techniques in the presence of background interference. First, a novel lip extraction algorithm combining rotational templates and prior shape constraints with active contours is introduced. The visual features are then obtained from the extracted lip region. Second, with the audio voice activity vector used in training, adaboosting is applied to the visual features, to generate a strong final voice activity classifier by boosting a set of weak classifiers. We have tested our lip extraction algorithm on the XM2VTS database (with higher resolution) and some video clips from YouTube (with lower resolution). The visual VAD was shown to offer low error rates.

  • Alinaghi A, Wang W, Jackson PJB. (2011) 'Integrating binaural cues and blind source separation method for separating reverberant speech mixtures'. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, , pp. 209-212.

    Abstract

    This paper presents a new method for reverberant speech separation, based on the combination of binaural cues and blind source separation (BSS) for the automatic classification of the time-frequency (T-F) units of the speech mixture spectrogram. The main idea is to model interaural phase difference, interaural level difference and frequency bin-wise mixing vectors by Gaussian mixture models for each source and then evaluate that model at each T-F point and assign the units with high probability to that source. The model parameters and the assigned regions are refined iteratively using the Expectation-Maximization (EM) algorithm. The proposed method also addresses the permutation problem of the frequency domain BSS by initializing the mixing vectors for each frequency channel. The EM algorithm starts with binaural cues and after a few iterations the estimated probabilistic mask is used to initialize and re-estimate the mixing vector model parameters. We performed experiments on speech mixtures, and showed an average of about 0.8 dB improvement in signal-to-distortion (SDR) over the binaural-only baseline. © 2011 IEEE.

  • Liu Q, Wang W, Jackson P. (2010) 'Audio-visual Convolutive Blind Source Separation'. London : IEEE Proc. Sensor Signal Processing for Defence (SSPD 2010), London, UK: Sensor Signal Processing for Defence

    Abstract

    We present a novel method for speech separation from their audio mixtures using the audio-visual coherence. It consists of two stages: in the off-line training process, we use the Gaussian mixture model to characterise statistically the audio-visual coherence with features obtained from the training set; at the separation stage, likelihood maximization is performed on the independent component analysis (ICA)-separated spectral components. To address the permutation and scaling indeterminacies of the frequency-domain blind source separation (BSS), a new sorting and rescaling scheme using the bimodal coherence is proposed.We tested our algorithm on the XM2VTS database, and the results show that our algorithm can address the permutation problem with high accuracy, and mitigate the scaling problem effectively.

  • Liu Q, Wang W, Jackson PJB. (2010) 'Use of Bimodal Coherence to Resolve Spectral Indeterminacy in Convolutive BSS'. Springer Lecture Notes in Computer Science (LNCS 6365), St. Malo, France: 9th International Conference on Latent Variable Analysis and Signal Separation (formerly the International Conference on Independent Component Analysis and Signal Separation) 6365/2010, pp. 131-139.

    Abstract

    Recent studies show that visual information contained in visual speech can be helpful for the performance enhancement of audio-only blind source separation (BSS) algorithms. Such information is exploited through the statistical characterisation of the coherence between the audio and visual speech using, e.g. a Gaussian mixture model (GMM). In this paper, we present two new contributions. An adapted expectation maximization (AEM) algorithm is proposed in the training process to model the audio-visual coherence upon the extracted features. The coherence is exploited to solve the permutation problem in the frequency domain using a new sorting scheme. We test our algorithm on the XM2VTS multimodal database. The experimental results show that our proposed algorithm outperforms traditional audio-only BSS.

  • Liu Q, Wang W, Jackson PJB. (2010) 'Bimodal Coherence based Scale Ambiguity Cancellation for Target Speech Extraction and Enhancement'. ISCA-International Speech Communication Association Proceedings of 11th Annual Conference of the International Speech Communication Association 2010, Makuhari, Japan: 11th Annual Conference of the International Speech Communication Association 2010, pp. 438-441.

    Abstract

    We present a novel method for extracting target speech from auditory mixtures using bimodal coherence, which is statistically characterised by a Gaussian mixture modal (GMM) in the offline training process, using the robust features obtained from the audio-visual speech. We then adjust the ICA-separated spectral components using the bimodal coherence in the time-frequency domain, to mitigate the scale ambiguities in different frequency bins. We tested our algorithm on the XM2VTS database, and the results show the performance improvement with our proposed algorithm in terms of SIR measurements.

  • Xu T, Wang W. (2010) 'Learning Dictionary for Underdetermined Blind Speech Separation Based on Compressed Sensing Method'. Proc. INSPIRE Conference on Information Representation and Estimation, London, UK: INSPIRE 2010
  • Xu T, Wang W. (2010) 'A block-based compressed sensing method for underdetermined blind speech separation incorporating binary mask'. IEEE Proceedings of 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, Dallas, USA: ICASSP 2010, pp. 2022-2025.
  • Mustafa H, Wang W. (2010) 'Single Channel Music Sound Separation Based on Spectrogram Decomposition and Note Classification'. Proc. 7th International Symposium on Computer Music Modeling and Retrieval, Malaga, Spain: CMMR 2010
  • Xu T, Wang W. (2009) 'A compressed sensing approach for underdetermined blind audio source separation with sparse representation'. IEEE IEEE Workshop on Statistical Signal Processing Proceedings, Cardiff, UK: SSP '09, pp. 493-496.
  • Jan T, Wang W, Wang D. (2009) 'A multistage approach for blind separation of convolutive speech mixtures'. IEEE IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Taipei, Taiwan: ICASSP'09, pp. 1713-1716.
  • Soltuz SM, Wang W, Jackson PJB. (2009) 'A HYBRID ITERATIVE ALGORITHM FOR NONNEGATIVE MATRIX FACTORIZATION'. IEEE 2009 IEEE/SP 15TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING, VOLS 1 AND 2, Cardiff, WALES: 15th IEEE/SP Workshop on Statistical Signal Processing, pp. 409-412.
  • Liang Y, Wang W, Chambers J. (2009) 'Adaptive signal processing techniques for clutter removal in radar-based navigation systems'. IEEE Conference Record of the 43rd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA: Asilomar 2009, pp. 1855-1858.
  • Wang W. (2008) 'One Microphone Audio Source Separation Using Convolutive Non-negative Matrix Factorization with Sparseness Constraints'. Proc. 8th IMA International Conference on Mathematics in Signal Processing, Cirencester, UK: IMA 2008
  • Jan T, Wang W, Wang D. (2008) 'Binaural Speech Separation Based on Convolutive ICA and Ideal Binary Mask Coupled with Cepstral Smoothing'. Proc. 8th IMA International Conference on Mathematics in Signal Processing, Cirencester, UK: IMA 2008
  • Zou X, Wang W, Kittler J. (2008) 'Non-negative Matrix Factorization for Face Illumination Analysis'. Proc. ICA Research Network International Workshop, Liverpool, UK: ICARN 2008, pp. 52-55.
  • Wang W, Zou X. (2008) 'Non-Negative Matrix Factorization based on Projected Nonlinear Conjugate Gradient Algorithm'. Proc. ICA Research Network International Workshop, Liverpool, UK: ICARN 2008, pp. 5-8.
  • Wang W. (2008) 'Convolutive non-negative sparse coding'. IEEE Proceedings of the International Joint Conference on Neural Networks, Hong Kong: IJCNN 2008, pp. 3681-3684.
  • Zhang Y, Chambers JA, Wang W, Kendrick P, Cox TJ. (2007) 'A new variable step-size LMS algorithm with robustness to nonstationary noise'. IEEE IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Hawaii, USA: ICASSP'07 3, pp. III-1349-III-1352.
  • Wenwu W, Yuhui L, Chambers JA, Saeid S. (2007) 'Non-negative matrix factorization for note onset detection of audio signals'. IEEE Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, Arlington, USA: MSLP 2006, pp. 447-452.
  • Wang W. (2007) 'Squared Euclidean distance based convolutive non-negative matrix factorization with multiplicative learning rules for audio pattern separation'. IEEE IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt: ISSPIT 2007, pp. 347-352.
  • Wang W, Hicks Y, Sanei S, Chambers J, Cosker D. (2005) 'Video assisted speech source separation'. IEEE IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Philadelphia, USA: ICASSP'05 V, pp. 425-428.
  • Yuan L, Sang E, Wang W, Chambers JA. (2005) 'An effective method to improve convergence for sequential blind source separation'. SPRINGER-VERLAG BERLIN ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS, Changsha, PEOPLES R CHINA: 1st International Conference on Natural Computation (ICNC 2005) 3610, pp. 199-208.
  • Wang W, Chambers J, Sanei S. (2004) 'Subband Decomposition for Blind Speech Separation Using a Cochlear Filterbank'. Proc. IMA 6th International Conference on Mathematics in Signal Processing, Cirencester, UK: IMA 2004, pp. 207-210.
  • Wang W, Chambers J, Sanei S. (2004) 'Penalty Function Based Joint Diagonalization Approach for Convolutive Constrained BSS of Nonstationary Signals'. Technische Universität Wien Proc. 12th European Signal Processing Conference, Vienna, Austria: EUSIPCO 2004
  • Sanei S, Wang W, Chambers J. (2004) 'A Coupled HMM for Solving the Permutation Problem in Frequency Domain BSS'. IEEE Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Montreal, Canada: ICASSP 2004, pp. 565-568.
  • Wang W, Chambers JA, Sanei S. (2004) 'Penalty function approach for constrained convolutive blind source separation'. Springer Lecture Notes in Computer Science: Independent Component Analysis and Blind Signal Separation, Granada, Spain: ICA 2004: 5th International Conference 3195, pp. 661-668.
  • Wang W, Sanei S, Chambers JA. (2004) 'A novel hybrid approach to the permutation problem of frequency domain blind source separation'. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, pp. 532-539.
  • Chambers J, Wang W. (2004) 'Frequency domain blind source separation'. IET Seminar Digest, 2004 (10774)
  • Sanei S, Spyrou L, Wang W, Chambers JA. (2004) 'Localization of P300 sources in schizophrenia patients using constrained BSS'. Springer Lecture Notes in Computer Science: Independent Component Analysis and Blind Signal Separation, Malaga, Spain: ICA 2004: 5th International Conference 3195, pp. 177-184.
  • Wang W, Sanei S, Chambers J. (2003) 'Hybrid Scheme of Convolutive BSS and Beamforming for Speech Signal Separation Using Psychoacousitcs Filtering'. Proc. International Conference on Control Science and Engineering, Harbin, China: ICCSE 2003
  • Wang W, Jafari M, Sanei S, Chambers J. (2003) 'Blind Separation of Convolutive Mixtures of Cyclostationary Sources Using an Extended Natural Gradient Method'. IEEE Proc. IEEE 7th International Symposium on Signal Processing and its Applications, Paris, France: ISSPA 2003 2, pp. 93-96.
  • Wang W, Sanei S, Chambers J. (2003) 'A Joint Diagonalization Method for Convolutive Blind Separation of Nonstationary Sources in the Frequency Domain'. Proc. 4th International Symposium on Independent Component Analysis and Blind Signal Separation, Nara, Japan: ICA 2003, pp. 939-944.

Books

  • Wang W. (2010) Machine Audition: Principles, Algorithms and Systems. New York, USA : Information Science Reference
  • Zhou S, Wang W. (2009) IEEE/WRI Global Congress on Intelligent Systems Proceedings. USA : IEEE Computer Society

Book chapters

  • Wang W. (2011) 'Preface of Machine Audition: Principles, Algorithms and Systems'. in Wang W (ed.) Machine Audition: Principles, Algorithms and Systems Information Science Reference , pp. xv-xxi.

    Abstract

    "This book covers advances in algorithmic developments, theoretical frameworks, andexperimental research findings to assist professionals who want an improved ...

  • Wang W. (2010) 'Instantaneous versus Convolutive Non-negative Matrix Factorization: Models, Algorithms and Applications to Audio Pattern Separation'. in Wang W (ed.) Machine Audition: Principles, Algorithms and Systems Information Science Reference Article number 15 , pp. 353-370.
  • Jan T, Wang W. (2010) 'Cocktail Party Problem: Source Separation Issues and Computational Methods'. in Wang W (ed.) Machine Audition: Principles, Algorithms and Systems New York, USA : Information Science Reference Article number 3 , pp. 61-79.

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