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Blind Separation of Acoustic Signals Combining SIMO-Model-Based Independent Component Analysis and Binary Masking

  • Yoshimitsu Mori1Email author,
  • Hiroshi Saruwatari1,
  • Tomoya Takatani1,
  • Satoshi Ukai1,
  • Kiyohiro Shikano1,
  • Takashi Hiekata2,
  • Youhei Ikeda2,
  • Hiroshi Hashimoto2 and
  • Takashi Morita2
EURASIP Journal on Advances in Signal Processing20062006:034970

https://doi.org/10.1155/ASP/2006/34970

Received: 1 January 2006

Accepted: 22 June 2006

Published: 12 September 2006

Abstract

A new two-stage blind source separation (BSS) method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, our novel SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by the proposed method compared with that achieved by conventional BSS methods. In addition, the real-time implementation of the proposed BSS is illustrated.

Keywords

Independent Component AnalysisAcoustic SignalSound SourceIndependent Component AnalysisAttractive Property

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Authors’ Affiliations

(1)
Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
(2)
Kobe Steel, Ltd., Kobe, Japan

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Copyright

© Yoshimitsu Mori et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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