Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation

TL;DR

This paper introduces a deep CASA method for talker-independent monaural speaker separation, decomposing the task into simultaneous and sequential grouping stages, achieving state-of-the-art results on WSJ0-2mix.

Contribution

It proposes a novel deep CASA framework that combines neural network-based spectral separation with clustering for speaker tracking, advancing monaural separation performance.

Findings

Achieves state-of-the-art results on WSJ0-2mix.

Uses a permutation-invariant neural network for spectral separation.

Employs clustering for effective speaker tracking.

Abstract

We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach optimizes frame-level separation and speaker tracking in turn, and produces excellent results for both objectives. Experimental results on the benchmark WSJ0-2mix database show that the new approach achieves the state-of-the-art results with a modest model size.