Abstract

Frequency-domain blind source separation (BSS) performs poorly in high reverberation because the independence assumption collapses at each frequency bins when the number of bins increases. To improve the separation result, this paper proposes a method which combines two techniques by using beamforming as a preprocessor of blind source separation. With the sound source locations supposed to be known, the mixed signals are dereverberated and enhanced by beamforming; then the beamformed signals are further separated by blind source separation. To implement the proposed method, a superdirective fixed beamformer is designed for beamforming, and an interfrequency dependence-based permutation alignment scheme is presented for frequency-domain blind source separation. With beamforming shortening mixing filters and reducing noise before blind source separation, the combined method works better in reverberation. The performance of the proposed method is investigated by separating up to 4 sources in different environments with reverberation time from 100 ms to 700 ms. Simulation results verify the outperformance of the proposed method over using beamforming or blind source separation alone. Analysis demonstrates that the proposed method is computationally efficient and appropriate for real-time processing.

Highlights

  • The objective of acoustic source separation is to estimate original sound sources from the mixed signals

  • In [33] we have proposed a permutation alignment approach with good results, which is based on an interfrequency dependence measure: the powers of separated signals

  • The Tukey window is used in short-time Fourier transform (STFT), with a shift size of 1/4 window length

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Summary

Introduction

The objective of acoustic source separation is to estimate original sound sources from the mixed signals. This technique has found a lot of applications in noise-robust speech recognition and high-quality hands-free telecommunication systems. The weights of a fixed beamformer do not depend on array data and are chosen to present a specified response for all scenarios. The most conventional fixed beamformer is a delay-and-sum one, which requires a large number of microphones to achieve high performance. Another filter-and-sum beamformer has superdirectivity response with optimized weights. Beamforming has limited performance in highly reverberant conditions because it can not suppress the interfering reverberation coming from the desired direction

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