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Two-Stage Monaural Source Separation in Reverberant Room Environments using Deep Neural Networks

Lookup NU author(s): Yang Sun, Professor Jonathon Chambers, Dr Mohsen Naqvi

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2019.

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Abstract

Deep neural networks (DNNs) have been used for dereverberation and separation in the monaural source sepa-ration problem. However, the performance of current state-of-the-art methods is limited, particularly when applied in highly reverberant room environments. In this paper, we propose a two-stage approach with two DNN-based methods to address this problem. In the first stage, the dereverberation of the speech mixture is achieved with the proposed dereverberation mask (DM). In the second stage, the dereverberant speech mixture is separated with the ideal ratio mask (IRM). To realize this two-stage approach, in the first DNN-based method, the DM is integrated with the IRM to generate the enhanced time-frequency (T-F) mask, namely the ideal enhanced mask (IEM), as the training target for the single DNN. In the second DNN-based method, the DM and the IRM are predicted with two individual DNNs. The IEEE and the TIMIT corpora with real room impulse responses (RIRs) and noise from the NOISEX dataset are used to generate speech mixtures for evaluations. The proposed methods outperform the state-of-the-art specifically in highly reverberant room environments.


Publication metadata

Author(s): Sun Y, Wang W, Chambers J, Naqvi MN

Publication type: Article

Publication status: Published

Journal: IEEE/ACM Transactions on Audio Speech and Language Processing

Year: 2019

Volume: 27

Issue: 1

Pages: 125-139

Print publication date: 01/01/2019

Online publication date: 17/10/2018

Acceptance date: 01/10/2018

Date deposited: 03/10/2018

ISSN (print): 2329-9290

ISSN (electronic): 2329-9304

Publisher: IEEE

URL: https://doi.org/10.1109/TASLP.2018.2874708

DOI: 10.1109/TASLP.2018.2874708


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