Voice Separation with an Unknown Number of Multiple Speakers

Eliya Nachmani; Yossi Adi; Lior Wolf

Voice Separation with an Unknown Number of Multiple Speakers

Eliya Nachmani^*
, Yossi Adi^*
, Lior Wolf^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

36 Scopus citations

Abstract

We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

Original language	English
Pages (from-to)	7164-7175
Number of pages	12
Journal	Proceedings of Machine Learning Research
Volume	119
State	Published - 2020
Externally published	Yes
Event	37th International Conference on Machine Learning, ICML 2020 - Virtual, Online Duration: 13 Jul 2020 → 18 Jul 2020

Bibliographical note

Publisher Copyright:
© 2020 by the author(s).

Cite this

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AB - We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

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Voice Separation with an Unknown Number of Multiple Speakers

Abstract

Bibliographical note

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