Voice Separation with an Unknown Number of Multiple Speakers

02/29/2020
by   Eliya Nachmani, et al.
0

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 a 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.

READ FULL TEXT
research
11/04/2020

Single channel voice separation for unknown number of speakers under reverberant and noisy settings

We present a unified network for voice separation of an unknown number o...
research
06/02/2020

Neural Speaker Diarization with Speaker-Wise Chain Rule

Speaker diarization is an essential step for processing multi-speaker au...
research
05/09/2019

Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion

We present a method for converting the voices between a set of speakers....
research
06/17/2022

Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios(V1)

Recently, the target speech separation or extraction techniques under th...
research
03/06/2020

Lightweight Speaker Verification for Online Identification of New Speakers with Short Segments

Verifying if two audio segments belong to the same speaker has been rece...
research
11/30/2018

Neural separation of observed and unobserved distributions

Separating mixed distributions is a long standing challenge for machine ...
research
10/26/2020

Speaker Anonymization with Distribution-Preserving X-Vector Generation for the VoicePrivacy Challenge 2020

In this paper, we present a Distribution-Preserving Voice Anonymization ...

Please sign up or login with your details

Forgot password? Click here to reset