Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures

Michael Michelashvili; Sagie Benaim; Lior Wolf

doi:10.1109/ICASSP.2019.8682443

Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures

Michael Michelashvili, Sagie Benaim, Lior Wolf

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

13 Scopus citations

Abstract

We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music. Our solution employs a single mapping function g, which, applied to a mixed sample, recovers the underlying instrumental music, and, applied to an instrumental sample, returns the same sample. The network g is trained using purely instrumental samples, as well as on synthetic mixed samples that are created by mixing reconstructed singing voices with random instrumental samples. Our results indicate that we are on a par with or better than fully supervised methods, which are also provided with training samples of unmixed singing voices, and are better than other recent semi-supervised methods.

Original language	English
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	291-295
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682443
State	Published - May 2019
Externally published	Yes
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Adversarial training
Semi-supervised learning
Singing voice separation

Access to Document

10.1109/ICASSP.2019.8682443

Cite this

Michelashvili, M., Benaim, S., & Wolf, L. (2019). Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 291-295). Article 8682443 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682443

Michelashvili, Michael ; Benaim, Sagie ; Wolf, Lior. / Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 291-295 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music. Our solution employs a single mapping function g, which, applied to a mixed sample, recovers the underlying instrumental music, and, applied to an instrumental sample, returns the same sample. The network g is trained using purely instrumental samples, as well as on synthetic mixed samples that are created by mixing reconstructed singing voices with random instrumental samples. Our results indicate that we are on a par with or better than fully supervised methods, which are also provided with training samples of unmixed singing voices, and are better than other recent semi-supervised methods.",

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Michelashvili, M, Benaim, S & Wolf, L 2019, Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8682443, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 291-295, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8682443

Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures. / Michelashvili, Michael; Benaim, Sagie; Wolf, Lior.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 291-295 8682443 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

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

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Michelashvili M, Benaim S, Wolf L. Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 291-295. 8682443. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2019.8682443

Semi-supervised Monaural Singing Voice Separation with a Masking Network Trained on Synthetic Mixtures

Abstract

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Bibliographical note

Keywords

Access to Document

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