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

Li, Zheng-Xi, Song, Yan, Dai, Li-Rong, McLoughlin, Ian (2019) Listening and grouping: an online autoregressive approach for monaural speech separation. IEEE Transactions On Audio Speech And Language Processing, 27 (4). pp. 692-703. ISSN 1558-7916. E-ISSN 2329-9304. (doi:10.1109/TASLP.2019.2892241) (KAR id:71467)


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.

Item Type: Article
DOI/Identification number: 10.1109/TASLP.2019.2892241
Uncontrolled keywords: Speech separation, deep learning, label permutation problem, computational auditory scene analysis
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 31 Dec 2018 03:36 UTC
Last Modified: 09 Dec 2022 05:19 UTC
Resource URI: (The current URI for this page, for reference purposes)

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