Spatial dispersion constrained NMF for monaural source separation

Viet Hang Duong; Yuan Shan Lee; Bach Tung Pham; Seksan Mathulaprangsan; Pham The Bao; Jia Ching Wang

doi:10.1109/ISCSLP.2016.7918452

Spatial dispersion constrained NMF for monaural source separation

Viet Hang Duong, Yuan Shan Lee, Bach Tung Pham, Seksan Mathulaprangsan, Pham The Bao, Jia Ching Wang

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

2 Scopus citations

Abstract

Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound source separation, intra-sample structure constraints that are learnt from the input data are utilized. Based on the hypothesis that adjacent spectrogram points should not be dispersed in basis spectra, this framework is provided for supervised source separation. To improve the separation performance, group sparse penalties are simultaneously constructed. A multiple-update-rule optimization scheme was used to solve the objective function of the proposed SpaSNMF. Experiments on single-channel source separation reveal that the proposed method provides more robust basis factors and achieves better results than standard NMF and its extensions.

Original language	English
Title of host publication	Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
Editors	Hsin-Min Wang, Qingzhi Hou, Yuan Wei, Tan Lee, Jianguo Wei, Lei Xie, Hui Feng, Jianwu Dang, Jianwu Dang
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509042937
DOIs	https://doi.org/10.1109/ISCSLP.2016.7918452
State	Published - 2 May 2017
Event	10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 - Tianjin, China Duration: 17 Oct 2016 → 20 Oct 2016

Publication series

Name	Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

Conference

Conference	10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
Country/Territory	China
City	Tianjin
Period	17/10/16 → 20/10/16

Keywords

Graph regularization
Multiple update rule
Non-negative matrix factorization
Source separation

Access to Document

10.1109/ISCSLP.2016.7918452

Cite this

Duong, V. H., Lee, Y. S., Pham, B. T., Mathulaprangsan, S., Bao, P. T., & Wang, J. C. (2017). Spatial dispersion constrained NMF for monaural source separation. In H.-M. Wang, Q. Hou, Y. Wei, T. Lee, J. Wei, L. Xie, H. Feng, J. Dang, & J. Dang (Eds.), Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 Article 7918452 (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCSLP.2016.7918452

Duong, Viet Hang ; Lee, Yuan Shan ; Pham, Bach Tung et al. / Spatial dispersion constrained NMF for monaural source separation. Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. editor / Hsin-Min Wang ; Qingzhi Hou ; Yuan Wei ; Tan Lee ; Jianguo Wei ; Lei Xie ; Hui Feng ; Jianwu Dang ; Jianwu Dang. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016).

@inproceedings{e8968fc10fa242c68ec1ecfb72aa3a44,

title = "Spatial dispersion constrained NMF for monaural source separation",

abstract = "Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound source separation, intra-sample structure constraints that are learnt from the input data are utilized. Based on the hypothesis that adjacent spectrogram points should not be dispersed in basis spectra, this framework is provided for supervised source separation. To improve the separation performance, group sparse penalties are simultaneously constructed. A multiple-update-rule optimization scheme was used to solve the objective function of the proposed SpaSNMF. Experiments on single-channel source separation reveal that the proposed method provides more robust basis factors and achieves better results than standard NMF and its extensions.",

keywords = "Graph regularization, Multiple update rule, Non-negative matrix factorization, Source separation",

author = "Duong, {Viet Hang} and Lee, {Yuan Shan} and Pham, {Bach Tung} and Seksan Mathulaprangsan and Bao, {Pham The} and Wang, {Jia Ching}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 ; Conference date: 17-10-2016 Through 20-10-2016",

year = "2017",

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day = "2",

doi = "10.1109/ISCSLP.2016.7918452",

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series = "Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Hsin-Min Wang and Qingzhi Hou and Yuan Wei and Tan Lee and Jianguo Wei and Lei Xie and Hui Feng and Jianwu Dang and Jianwu Dang",

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Duong, VH, Lee, YS, Pham, BT, Mathulaprangsan, S, Bao, PT & Wang, JC 2017, Spatial dispersion constrained NMF for monaural source separation. in H-M Wang, Q Hou, Y Wei, T Lee, J Wei, L Xie, H Feng, J Dang & J Dang (eds), Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016., 7918452, Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016, Institute of Electrical and Electronics Engineers Inc., 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016, Tianjin, China, 17/10/16. https://doi.org/10.1109/ISCSLP.2016.7918452

Spatial dispersion constrained NMF for monaural source separation. / Duong, Viet Hang; Lee, Yuan Shan; Pham, Bach Tung et al.
Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. ed. / Hsin-Min Wang; Qingzhi Hou; Yuan Wei; Tan Lee; Jianguo Wei; Lei Xie; Hui Feng; Jianwu Dang; Jianwu Dang. Institute of Electrical and Electronics Engineers Inc., 2017. 7918452 (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016).

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

TY - GEN

T1 - Spatial dispersion constrained NMF for monaural source separation

AU - Duong, Viet Hang

AU - Lee, Yuan Shan

AU - Pham, Bach Tung

AU - Mathulaprangsan, Seksan

AU - Bao, Pham The

AU - Wang, Jia Ching

PY - 2017/5/2

Y1 - 2017/5/2

N2 - Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound source separation, intra-sample structure constraints that are learnt from the input data are utilized. Based on the hypothesis that adjacent spectrogram points should not be dispersed in basis spectra, this framework is provided for supervised source separation. To improve the separation performance, group sparse penalties are simultaneously constructed. A multiple-update-rule optimization scheme was used to solve the objective function of the proposed SpaSNMF. Experiments on single-channel source separation reveal that the proposed method provides more robust basis factors and achieves better results than standard NMF and its extensions.

AB - Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound source separation, intra-sample structure constraints that are learnt from the input data are utilized. Based on the hypothesis that adjacent spectrogram points should not be dispersed in basis spectra, this framework is provided for supervised source separation. To improve the separation performance, group sparse penalties are simultaneously constructed. A multiple-update-rule optimization scheme was used to solve the objective function of the proposed SpaSNMF. Experiments on single-channel source separation reveal that the proposed method provides more robust basis factors and achieves better results than standard NMF and its extensions.

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KW - Multiple update rule

KW - Non-negative matrix factorization

KW - Source separation

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T3 - Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

BT - Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

A2 - Wang, Hsin-Min

A2 - Hou, Qingzhi

A2 - Wei, Yuan

A2 - Lee, Tan

A2 - Wei, Jianguo

A2 - Xie, Lei

A2 - Feng, Hui

A2 - Dang, Jianwu

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

Y2 - 17 October 2016 through 20 October 2016

ER -

Duong VH, Lee YS, Pham BT, Mathulaprangsan S, Bao PT, Wang JC. Spatial dispersion constrained NMF for monaural source separation. In Wang HM, Hou Q, Wei Y, Lee T, Wei J, Xie L, Feng H, Dang J, Dang J, editors, Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7918452. (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016). doi: 10.1109/ISCSLP.2016.7918452

Spatial dispersion constrained NMF for monaural source separation

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