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  • Conference paper
    Castro B, Gaubitch ND, Habets EAP, Gannot S, Naylor PA, Grant Set al., 2010,

    Subband Scale Factor Ambiguity Correction Using Multiple Filterbanks

  • Conference paper
    Naylor PA, Evers C, Eman, 2010,

    Speech Dereverberation

  • Conference paper
    Jarrett DP, Habets EAP, Naylor PA, 2010,

    Eigenbeam-based acoustic source tracking in noisy reverberant environments

  • Conference paper
    Thomas MRP, Gudnason J, Naylor PA, Geiser B, Vary Pet al., 2010,

    Voice Source Estimation for Artificial Bandwidth Extension of Telephone Speech

  • Conference paper
    Habets E, Naylor PA, 2010,

    An Online Quasi-Newton Algorithm for Blind SIMO Identification

  • Journal article
    Nordholm S, Abhayapala T, Doclo S, Gannot S, Naylor P, Tashev Iet al., 2010,

    Microphone Array Speech Processing

  • Conference paper
    Loganathan P, Habets E, Naylor PA, 2010,

    Performance Analysis of IPNLMS for Identification of Time-varying System

  • Conference paper
    Zhang W, Habets EAP, Naylor PA, 2010,

    A System Identification-error-robust method for equalization of multichannel acoustic systems

  • Conference paper
    Gudnason J, Thomas MRP, Naylor PA, Ellis DPWet 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.

  • Journal article
    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
  • Journal article
    Gaubitch ND, Habets EAP, Naylor PA, 2009,

    Signal-based Performance Evaluation of Dereverberation Algorithms

    , Journal of Electrical and Computer Engineering
  • Conference paper
    Habets EAP, Benesty J, Gannot S, Naylor PA, Cohen Iet al., 2009,

    On the Application of the LCMV Beamformer to Speech Enhancement

    , Pages: 141-144-141-144
  • Journal article
    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
  • Conference paper
    Loganathan P, Lin XS, Khong AWH, Naylor PAet al., 2009,

    Frequency-domain Adaptive Multidelay Algorithm with Sparseness Control for Acoustic Echo Cancellation

  • Conference paper
    Sharma D, Naylor PA, 2009,

    Evaluation of Pitch Estimation in Noisy Speech for Application in Non-intrusive Speech Quality Assessment

  • Conference paper
    Thomas MRP, Gudnason J, Naylor PA, 2009,

    Detection of Glottal Closing and Opening Instants using an Improved DYPSA Framework

  • Conference paper
    Tsakiris MC, Naylor PA, 2009,

    Fast exact Affine Projection Algorithm using displacement structure theory

    , Pages: 1-6-1-6
  • Conference paper
    Wen JYC, Sehr A, Naylor PA, Kellermann Wet al., 2009,

    Blind Estimation of a Feature-Domain Reverberation Model in Non-diffuse Environments with Variance Adjustment

  • Conference paper
    Zhang W, Khong AWH, Naylor PA, 2009,

    Acoustic System Equalization using Channel Shortening Techniques for Speech Dereverberation

    , Pages: 1427-1431-1427-1431
  • Conference paper
    Zhang W, Naylor PA, 2009,

    An Experimental Study of the Robustness of Multichannel Inverse Filtering Systems to Near-Common Zeros

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