Separation matrix optimization using associative memory model for blind source separation

Motoi Omachi, Tetsuji Ogawa, Tetsunori Kobayashi, Masaru Fujieda, Kazuhiro Katagiri

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

A source signal is estimated using an associative memory model (AMM) and used for separation matrix optimization in linear blind source separation (BSS) to yield high quality and less distorted speech. Linear-filtering-based BSS, such as independent vector analysis (IVA), has been shown to be effective in sound source separation while avoiding non-linear signal distortion. This technique, however, requires several assumptions of sound sources being independent and generated from non-Gaussian distribution. We propose a method for estimating a linear separation matrix without any assumptions about the sources by repeating the following two steps: estimating non-distorted reference signals by using an AMM and optimizing the separation matrix to minimize an error between the estimated signal and reference signal. Experimental comparisons carried out in simultaneous speech separation suggest that the proposed method can reduce the residual distortion caused by IVA.

本文言語English
ホスト出版物のタイトル2015 23rd European Signal Processing Conference, EUSIPCO 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1098-1102
ページ数5
ISBN(電子版)9780992862633
DOI
出版ステータスPublished - 2015 12 22
イベント23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
継続期間: 2015 8 312015 9 4

出版物シリーズ

名前2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other23rd European Signal Processing Conference, EUSIPCO 2015
国/地域France
CityNice
Period15/8/3115/9/4

ASJC Scopus subject areas

  • メディア記述
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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