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

  • Stuart N. Wrigley
  • Guy J. Brown
Conference paper
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.

Keywords

Automatic Speech Recognition Binary Mask Coincidence Detector Automatic Speech Recognition System Pitch Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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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