MULTI-DECODER DPRNN: HIGH ACCURACY SOURCE COUNTING AND SEPARATION

Zhu, Junzhe

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https://hdl.handle.net/2142/125114 Copy

Description

Title

MULTI-DECODER DPRNN: HIGH ACCURACY SOURCE COUNTING AND SEPARATION

Author(s)

Zhu, Junzhe

Issue Date

2020-12-01

Keyword(s)

Source separation

Abstract

We propose an end-to-end trainable approach to single-channel speech separation with unknown number of speakers. Our approach extends the MulCat source separation backbone with additional output heads: a count-head to infer the number of speakers, and decoder-heads for reconstructing the original signals. Beyond the model, we also propose a metric on how to evaluate source separation with variable number of speakers. Specifically, we cleared up the issue on how to evaluate the quality when the ground-truth has more or less speakers than the ones predicted by the model. We evaluate our approach on the WSJ0-mix datasets, with mixtures up to five speakers. We demonstrate that our approach outperforms stateof-the-art in counting the number of speakers and remains competitive in quality of reconstructed signals.

Type of Resource

text

Language

eng

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