About: Listening and grouping: an online autoregressive approach for monaural speech separation

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #71467 Listening and grouping: an online autoregressive approach for monaural speech separation
sameAs	Listening and grouping: an online autoregressive approach for monaural speech separation
http://eprints.org/ontology/hasAccepted	Listening and grouping: an online autoregressive approach for monaural speech separation (PDF)
http://eprints.org/ontology/hasDocument	Listening and grouping: an online autoregressive approach for monaural speech separation (PDF) Listening and grouping: an online autoregressive approach for monaural speech separation (Other) Listening and grouping: an online autoregressive approach for monaural speech separation (Other) Listening and grouping: an online autoregressive approach for monaural speech separation (Other) Listening and grouping: an online autoregressive approach for monaural speech separation (Other) Listening and grouping: an online autoregressive approach for monaural speech separation (Other)
dc:hasVersion	Listening and grouping: an online autoregressive approach for monaural speech separation (PDF)
Title	Listening and grouping: an online autoregressive approach for monaural speech separation
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-01-10
Creator	Zheng-Xi Li Li-Rong Dai Yan Song I. McLoughlin
status	peer reviewed published
Publisher	IEEE
abstract	This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation. It exploits a causal temporal context in both mixture and past estimated separated signals and performs online separation that is compatible with real-time applications. The approach adopts a learned listening and grouping architecture motivated by computational auditory scene analysis, with a grouping stage that effectively addresses the label permutation problem at both frame and segment levels. Experimental results on the benchmark WSJ0-2mix dataset show that the new approach can outperform the majority of state-of-the-art methods in both closed-set and open-set conditions in terms of signal-to-distortion ratio (SDR) improvement and perceptual evaluation of speech quality (PESQ), even approaches that exploit whole-utterance statistics for separation, with relatively fewer model parameters.
Is Part Of	IEEE Transactions On Audio Speech And Language Processing https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/71467#authors
issue	4
volume	27
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #71467 Listening and grouping: an online autoregressive approach for monaural speech separation

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