Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior

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

*この研究の対応する著者

研究成果: Conference contribution

48 被引用数 (Scopus)

抄録

In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. 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 histogram of the estimated direction of arrival (DOA) with a Gaussian mixture model (GMM) with a Dirichlet prior. Then we estimate the model parameters by using the maximum a posteriori 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. By using this prior, without any specific model selection process, our proposed method can estimate the number of sources and time-frequency masks simultaneously. Experimental results show the performance of our proposed method.

本文言語English
ホスト出版物のタイトル2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
出版社Institute of Electrical and Electronics Engineers Inc.
ページ33-36
ページ数4
ISBN(印刷版)9781424423545
DOI
出版ステータスPublished - 2009
外部発表はい
イベント2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
継続期間: 2009 4 192009 4 24

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
国/地域Taiwan, Province of China
CityTaipei
Period09/4/1909/4/24

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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