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

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

*この研究の対応する著者

研究成果: Conference article査読

32 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)742-750
ページ数9
ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5441
DOI
出版ステータスPublished - 2009
外部発表はい
イベント8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil
継続期間: 2009 3月 152009 3月 18

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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