• DocumentCode
    1653037
  • Title

    Speech dereverberation based on Linear Prediction: An Acoustic Vector Sensor approach

  • Author

    Shujau, Muawiyath ; Ritz, C.H. ; Burnett, Ian S.

  • Author_Institution
    Sch. of Electr., Comput., & Telecommun. Eng., Univ. of Wollongong, Wollongong, NSW, Australia
  • fYear
    2013
  • Firstpage
    639
  • Lastpage
    643
  • Abstract
    This paper introduces a dereverberation algorithm based on Linear Prediction (LP) applied to the outputs of an Acoustic Vector Sensor (AVS). The approach applies adaptive beamforming to take advantage of the directional outputs of the AVS array to obtain a more accurate LP spectrum than can be obtained with a single channel or Uniform Linear Array (ULA) with a comparable number of channels. This is then used within a modified version of the Spatiotemporal Averaging Method for Enhancement of Reverberant Speech (SMERSH) algorithm derived for the AVS to enhance the LP residual signal. In a highly reverberant environment, the approach demonstrates a significant improvement compared to a ULA as measured by both the Signal to Reverberant Ratio (SRR) and Speech to Reverberation Modulation Energy Ratio (SRMR) for sources ranging from at 1m to 5m from the array.
  • Keywords
    array signal processing; sensors; speech enhancement; vectors; AVS array; LP residual signal enhancement; SMERSH algorithm; SRMR; SRR; ULA; acoustic vector sensor approach; adaptive beamforming; linear prediction; signal to reverberant ratio; single channel; spatiotemporal averaging method for enhancement of reverberant speech algorithm; speech dereverberation; speech to reverberation modulation energy ratio; uniform linear array; Arrays; Microphones; Reverberation; Speech; Speech enhancement; Acoustic Vector Sensors; Dereverberation; Speech Enhancement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2013.6637726
  • Filename
    6637726