Complex extension of infinite sparse factor analysis for blind speech separation

Kohei Nagira*, Toru Takahashi, Tetsuya Ogata, Hiroshi G. Okuno

*この研究の対応する著者

研究成果

3 被引用数 (Scopus)

抄録

We present a method of blind source separation (BSS) for speech signals using a complex extension of infinite sparse factor analysis (ISFA) in the frequency domain. Our method is robust against delayed signals that usually occur in real environments, such as reflections, short-time reverberations, and time lags of signals arriving at microphones. ISFA is a conventional non-parametric Bayesian method of BSS, which has only been applied to time domain signals because it can only deal with real signals. Our method uses complex normal distributions to estimate source signals and mixing matrix. Experimental results indicate that our method outperforms the conventional ISFA in the average signal-to-distortion ratio (SDR).

本文言語English
ホスト出版物のタイトルLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
ページ388-396
ページ数9
DOI
出版ステータスPublished - 2012
外部発表はい
イベント10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
継続期間: 2012 3 122012 3 15

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7191 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
国/地域Israel
CityTel Aviv
Period12/3/1212/3/15

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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