Adaptive processing and learning for audio source separation

Jen Tzung Chien, Hiroshi Sawada, Shoji Makino

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper overviews a series of recent advances in adaptive processing and learning for audio source separation. In real world, speech and audio signal mixtures are observed in reverberant environments. Sources are usually more than mixtures. The mixing condition is occasionally changed due to the moving sources or when the sources are changed or abruptly present or absent. In this survey article, we investigate different issues in audio source separation including overdetermined/underdetermined problems, permutation alignment, convolutive mixtures, contrast functions, nonstationary conditions and system robustness. We provide a systematic and comprehensive view for these issues and address new approaches to overdetermined/underdetermined convolutive separation, sparse learning, nonnegative matrix factorization, information-theoretic learning, online learning and Bayesian approaches.

本文言語English
ホスト出版物のタイトル2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOI
出版ステータスPublished - 2013
外部発表はい
イベント2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan, Province of China
継続期間: 2013 10 292013 11 1

出版物シリーズ

名前2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

Conference

Conference2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
国/地域Taiwan, Province of China
CityKaohsiung
Period13/10/2913/11/1

ASJC Scopus subject areas

  • 情報システム
  • 信号処理

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