Combining mask estimates for single channel audio source separation using deep neural networks

Emad M Grais, Gerard Roma, Andrew JR Simpson, Mark D Plumbley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Deep neural networks (DNNs) are usually used for single channel source separation to predict either soft or binary time frequency masks. The masks are used to separate the sources from the mixed signal. Binary masks produce separated sources with more distortion and less interference than soft masks. In this paper, we propose to use another DNN to combine the estimates of binary and soft masks to achieve the advantages and avoid the disadvantages of using each mask individually. We aim to achieve separated sources with low distortion and low interference between each other. Our experimental results show that combining the estimates of binary and soft masks using DNN achieves lower distortion than using each estimate individually and achieves as low interference as the binary mask. Copyright © 2016 ISCA.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Pages3339-3343
Number of pages5
DOIs
Publication statusPublished - Sep 2016
Externally publishedYes
Event17th Annual Conference of the International Speech Communication Association - Hyatt Regency, San Francisco, United States
Duration: 8 Sep 201612 Sep 2016

Publication series

Name
ISSN (Electronic)1990-9772

Conference

Conference17th Annual Conference of the International Speech Communication Association
Abbreviated titleINTERSPEECH 2016
CountryUnited States
CitySan Francisco
Period8/09/1612/09/16

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  • Cite this

    Grais, E. M., Roma, G., Simpson, A. JR., & Plumbley, M. D. (2016). Combining mask estimates for single channel audio source separation using deep neural networks. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 3339-3343) https://doi.org/10.21437/Interspeech.2016-216