On Microphone-Array Beamforming From a MIMO Acoustic Signal Processing Perspective

• Published in: IEEE transactions on audio, speech, and language processing Vol. 15; no. 3; pp. 1053 - 1065
• Main Authors: Benesty, J., Jingdong Chen, Yiteng Huang, Dmochowski, J.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.03.2007; Institute of Electrical and Electronics Engineers
• Subjects: Acoustic signal processing; Acoustics; Algorithm design and analysis; Algorithms; Applied sciences; Array signal processing; Arrays; Beamforming; Channels; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filters; Frost; Information, signal and communications theory; Interference suppression; Inverse; Joints; linearly constrained minimum variance (LCMV); Microphone arrays; MIMO; minimum variance distortionless response (MVDR); Miscellaneous; multiple-input/output inverse theorem (MINT); Performance analysis; Signal and communications theory; Signal processing; Signal processing algorithms; Signal, noise; Speech; Telecommunications and information theory; Performance evaluation; MIMO system; Microphone; Noise reduction; microphone arrays; multiple-input/ output inverse theorem (MINT); minimum variance distortionless response (MVDR); Acoustic signal processing; Frost; linearly constrained minimum variance (LCMV); Beam forming; Interference suppression; Minimal variance; Beamforming; Algorithm performance; Pulse response; Signal processing; Fast algorithm; Sensor array
• ISSN: 1558-7916; 1558-7924
• DOI: 10.1109/TASL.2006.885251

Although many microphone-array beamforming algorithms have been developed over the past few decades, most such algorithms so far can only offer limited performance in practical acoustic environments. The reason behind this has not been fully understood and further research on this matter is indispensable. In this paper, we treat a microphone array as a multiple-input multiple-output (MIMO) system and study its signal-enhancement performance. Our major contribution is fourfold. First, we develop a general framework for analyzing performance of beamforming algorithms based on the acoustic MIMO channel impulse responses. Second, we study the bounds for the length of the beamforming filter, which in turn shows the performance bounds of beamforming in terms of speech dereverberation and interference suppression. Third, we address the connection between beamforming and the multiple-input/output inverse theorem (MINT). Finally, we discuss the intrinsic relationships among different classical beamforming techniques and explain, from the channel condition perspective, what the prerequisites are for those techniques to work