Independent Component Analysis Using Spherical Microphone Arrays

نویسندگان

  • Nicolas Epain
  • Craig Jin
چکیده

Spherical microphone arrays provide a new and promising tool for the spatial analysis of complex sound fields. Considerable previous work has investigated the use of spherical microphone arrays for phase-mode beamforming, which relies on the Bessel-weighted spherical harmonic transform of the sound field. In this paper, we investigate the advantages that spherical microphone arrays provide for blind separation of convolutive mixtures using independent component analysis. We demonstrate that applying a standard, linear independent component analysis model in the phase-mode domain enables one to both localize the sources and resolve the permutation problem that plagues most implementations of independent component analysis. As well, we show that the standard linear independent component analysis model can be incorporated into beamforming approaches for source localization and source separation. Simulation results indicate that this approach works in realistic scenarios that include room reverberation.

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تاریخ انتشار 2011