About Multichannel Speech Signal Extraction and Separation Techniques

TL;DR

This paper reviews multichannel speech extraction techniques, classifies them into beamforming, ICA, and T-F masking, discusses their limitations, and suggests combining methods for improved performance.

Contribution

It provides a comprehensive classification and analysis of existing multichannel speech separation techniques and proposes combining them to enhance effectiveness.

Findings

Existing techniques have limitations in efficiency.

Combining techniques may yield better results.

Further research is needed for improved methods.

Abstract

The extraction of a desired speech signal from a noisy environment has become a challenging issue. In the recent years, the scientific community has particularly focused on multichannel techniques which are dealt with in this review. In fact, this study tries to classify these multichannel techniques into three main ones: Beamforming, Independent Com-ponent Analysis (ICA) and Time Frequency (T-F) masking. This paper also highlights their advantages and drawbacks. However these previously mentioned techniques could not afford satisfactory results. This fact leads to the idea that a combination of those techniques, which is depicted along this study, may probably provide more efficient results. In-deed, giving the fact that those approaches are still be considered as being not totally efficient, has led us to review these mentioned above in the hope that further researches will provide this domain with suitable innovations.