Binaural Speech Separation Using Recurrent Timing Neural Networks for Joint F0-Localisation Estimation

  • Stuart N. Wrigley
  • Guy J. Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4892)

Abstract

A speech separation system is described in which sources are represented in a joint interaural time difference-fundamental frequency (ITD-F0) cue space. Traditionally, recurrent timing neural networks (RTNNs) have been used only to extract periodicity information; in this study, this type of network is extended in two ways. Firstly, a coincidence detector layer is introduced, each node of which is tuned to a particular ITD; secondly, the RTNN is extended to become two-dimensional to allow periodicity analysis to be performed at each best-ITD. Thus, one axis of the RTNN represents F0 and the other ITD allowing sources to be segregated on the basis of their separation in ITD-F0 space. Source segregation is performed within individual frequency channels without recourse to across-channel estimates of F0 or ITD that are commonly used in auditory scene analysis approaches. The system is evaluated on spatialised speech signals using energy-based metrics and automatic speech recognition.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Stuart N. Wrigley
    • 1
  • Guy J. Brown
    • 1
  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUnited Kingdom

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