A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

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 pre-processing 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 article, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: a) the acoustic impulse response model, b) the spatial filter design criterion, c) the parameter estimation algorithm, and d) 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.

Acoustic Impulse Responses for Wearable Audio Devices

“…Thanks to advances in transducer technology, such as tiny digital MEMS microphones [1], multiple audio sensors can be embedded in wearable devices such as watches, headphones, eyeglasses, and other accessories. These microphones could be combined to perform array processing such as beamforming, localization, and source separation [2][3][4]. A wearable array with many microphones spread over a wide area would offer greater spatial resolution than the small arrays embedded in most hearing aids, headsets, and mobile phones today.…”

Keyword Based Speaker Localization: Localizing a Target Speaker in a Multi-speaker Environment

“…This is useful for various applications such as speech enhancement and separation [2][3][4][5][6][7] or robotic sensing [8]. DOA estimation is usually done in the short time Fourier transform (STFT) domain [9].…”

Sidelobe suppression by desired directivity pattern optimization for a small circular loudspeaker array

“…In addition, directivity control, also known as beamforming in the field of microphone array processing, is widely studied for speech enhancement, sound source separation, and direction of arrival estimation [11][12][13][14][15]. Filter design methods that allow filter coefficients to be obtained are important to control the directivity or sound field in these applications.…”