Search or filter publications

Filter by type:

Filter by publication type

Filter by year:



  • Showing results for:
  • Reset all filters

Search results

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

    Eigenbeam-based acoustic source tracking in noisy reverberant environments

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

    Speech Dereverberation

  • Conference paper
    Thomas MRP, Gaubitch ND, Habets EAP, Naylor PAet al., 2010,

    Supervised Identification and Removal of Common Filter Components in Adaptive Blind SIMO System Identification

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

    On the Use of Channel Shortening in Multichannel Acoustic System Equalization

  • Conference paper
    Filos J, Habets EAP, Naylor PA, 2010,

    A Two-Step Approach to Blindly Infer Room Geometries

  • Conference paper
    Habets E, Naylor PA, 2010,

    An Online Quasi-Newton Algorithm for Blind SIMO Identification

  • 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
  • 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, Habets EAP, Naylor PA, 2009,

    Signal-based Performance Evaluation of Dereverberation Algorithms

    , Journal of Electrical and Computer Engineering
  • 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
    Gudnason J, Thomas MRP, Naylor PA, Ellis DPWet 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-+
  • Conference paper
    Tsakiris MC, Naylor PA, 2009,


    , 16th International Conference on Digital Signal Processing, Publisher: IEEE, Pages: 69-74
  • Conference paper
    Zhang W, Naylor PA, 2009,

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

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

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

  • Conference paper
    Lin X, Khong AWH, Naylor PA, 2009,

    Blind system identification for speech dereverberation with Forced Spectral Diversity

    , Pages: 3737-3740-3737-3740
  • Journal article
    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
  • 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
    Gaubitch ND, Brookes M, Naylor PA, 2009,

    Blind Channel Identification in Speech using the Long-term Average Speech Spectrum

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=1226&limit=20&page=12&respub-action=search.html Current Millis: 1642693843828 Current Time: Thu Jan 20 15:50:43 GMT 2022