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

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

doi:10.21437/Interspeech.2016-216

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 proceeding › Conference contribution › peer-review

23 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 language	English
Title of host publication	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Pages	3339-3343
Number of pages	5
DOIs	https://doi.org/10.21437/Interspeech.2016-216
Publication status	Published - Sept 2016
Externally published	Yes
Event	17th Annual Conference of the International Speech Communication Association - Hyatt Regency, San Francisco, United States Duration: 8 Sept 2016 → 12 Sept 2016

Publication series

Name
ISSN (Electronic)	1990-9772

Conference

Conference	17th Annual Conference of the International Speech Communication Association
Abbreviated title	INTERSPEECH 2016
Country/Territory	United States
City	San Francisco
Period	8/09/16 → 12/09/16

Access to Document

10.21437/Interspeech.2016-216Licence: Unspecified

Cite this

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title = "Combining mask estimates for single channel audio source separation using deep neural networks",

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 {\textcopyright} 2016 ISCA.",

keywords = "Combining estimates, Deep learning, Deep neural networks, Neural network ensembles, Single channel source separation",

author = "Grais, {Emad M} and Gerard Roma and Simpson, {Andrew JR} and Plumbley, {Mark D}",

year = "2016",

month = sep,

doi = "10.21437/Interspeech.2016-216",

language = "English",

pages = "3339--3343",

booktitle = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

note = "17th Annual Conference of the International Speech Communication Association , INTERSPEECH 2016 ; Conference date: 08-09-2016 Through 12-09-2016",

}

Grais, EM, Roma, G, Simpson, AJR & Plumbley, MD 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, 17th Annual Conference of the International Speech Communication Association , San Francisco, California, United States, 8/09/16. https://doi.org/10.21437/Interspeech.2016-216

Combining mask estimates for single channel audio source separation using deep neural networks. / Grais, Emad M; Roma, Gerard; Simpson, Andrew JR et al.
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2016. p. 3339-3343.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combining mask estimates for single channel audio source separation using deep neural networks

AU - Grais, Emad M

AU - Roma, Gerard

AU - Simpson, Andrew JR

AU - Plumbley, Mark D

PY - 2016/9

Y1 - 2016/9

N2 - 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.

AB - 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.

KW - Combining estimates

KW - Deep learning

KW - Deep neural networks

KW - Neural network ensembles

KW - Single channel source separation

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DO - 10.21437/Interspeech.2016-216

M3 - Conference contribution

SP - 3339

EP - 3343

BT - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - 17th Annual Conference of the International Speech Communication Association

Y2 - 8 September 2016 through 12 September 2016

ER -

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

Abstract

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Conference

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