Go To Program 62 A Multi-Microphone Speech Enhancement Algorithm Tested Using Acoustic Vector Sensors Ping-Kun Tony Wu 1 Craig Jin 1 Alan Kan 1 Andre Van Schaik 1 1 School of Electrical and Information Engineering, University of Sydney, Sydney, New South Wales, Australia In this paper, we present a speech enhancement algorithm for multi-microphone systems that enhances a target signal in noisy multi-talker situations. We apply the general multichannel Wiener filtering framework, for which we have developed a new technique to directly estimate the auto-correlation of the target signal assuming its direction is known. The advantage of our approach compared to traditional multichannel Wiener filtering is that it effectively works with both non-stationary, speech-like noise interference and also stationary background noise. The estimation of the auto-correlation of the target signal is derived by minimizing the target signal power across pairs of microphones. For this reason, we refer to our algorithm as MWF-minPESP (Multichannel Wiener Filtering based on minimum Pair-wise Estimated Signal Power). In a sense, MWF-minPESP is a beamforming algorithm that minimizes the output signal power. We present an experiment that compares the performance of MWF-minPESP with traditional multichannel Wiener filtering and frequency-domain minimum variance distortionless response (FMV) beamforming using two acoustic vector sensors (AVS), comprising six microphone signals, in an office space. The results indicate that MWFminPESP outperforms the other two state-of-the-art speech enhancement techniques. View pdf