Stereo source separation and source counting with MAP estimation with dirichlet prior considering spatial aliasing problem

Shoko Araki*, Tomohiro Nakatani, Hiroshi Sawada, Shoji Makino

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

30 Citations (Scopus)

Abstract

In this paper, we propose a novel sparse source separation method that can estimate the number of sources and time-frequency masks simultaneously, even when the spatial aliasing problem exists. Recently, many sparse source separation approaches with time-frequency masks have been proposed. However, most of these approaches require information on the number of sources in advance. In our proposed method, we model the phase difference of arrival (PDOA) between microphones with a Gaussian mixture model (GMM) with a Dirichlet prior. Then we estimate the model parameters by using the maximum a posteriori (MAP) estimation based on the EM algorithm. In order to avoid one cluster being modeled by two or more Gaussians, we utilize a sparse distribution modeled by the Dirichlet distributions as the prior of the GMM mixture weight. Moreover, to handle wide microphone spacing cases where the spatial aliasing problem occurs, the indeterminacy of modulus 27rfc in the phase is also included in our model. Experimental results show good performance of our proposed method.

Original languageEnglish
Pages (from-to)742-750
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5441
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil
Duration: 2009 Mar 152009 Mar 18

Keywords

  • Blind source separation
  • Dirichlet distribution
  • Number of sources
  • Prior
  • Sparse
  • Spatial aliasing problem

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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