A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

TLDR

Speech enhancement and separation are core problems in audio signal processing, crucial for devices such as mobile phones, conference call systems, hands‑free systems, and hearing aids, and serve as preprocessing steps for noise‑robust automatic speech and speaker recognition; with many devices now equipped with two to eight microphones, multichannel interfaces offer greater capabilities than single‑channel ones, and research has converged from microphone array processing and blind source separation, yet a comprehensive overview of their common foundations and differences remains lacking. The paper aims to fill this gap by analyzing a large number of established and recent techniques. The analysis is performed along four axes: acoustic impulse response model, spatial filter design criterion, parameter estimation algorithm, and optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.

Abstract

Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.