Adaptive processing and learning for audio source separation

Jen-Tzung Chien, Hiroshi Sawada, Shoji Makino

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
StatePublished - 1 Dec 2013
Event2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan
Duration: 29 Oct 20131 Nov 2013

Publication series

Name2013 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
CountryTaiwan
CityKaohsiung
Period29/10/131/11/13

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