Complex extension of infinite sparse factor analysis for blind speech separation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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).

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages388-396
Number of pages9
DOIs
Publication statusPublished - 2012 Feb 27
Externally publishedYes
Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
Duration: 2012 Mar 122012 Mar 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7191 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

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

Keywords

  • Blind source separation
  • Infinite sparse factor analysis
  • Non-parametric Bayes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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