Microphone array processing techniques for the enhancement, dereverberation and separation of speech

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Consumer products for voice and multimedia communications over wireless and internet access, e.g. cell phones, handheld tablets, VoIP phones, etc., make extensive use of digital speech processing (DSP) for the transmission, storage and playback of information. Speech enhancement, as a core technology and front-end to speech coding or automatic speech recognition, plays a key role in these devices. Indeed, as their usage becomes widespread, the voice signals received by the microphones are degraded by ever more adverse acoustic disturbances, including reverberation and non-stationary noise, which degrade the quality and intelligibility of the desired speech. Hence, there is a strong demand for improved enhancement techniques that can suppress these acoustic disturbances and produce a cleaner speech signal for transmission. In this project, we will investigate new DSP techniques for the enhancement of reverberant speech and its separation from a non-stationary acoustic background. Our work aims to achieve the following objectives: (1) Extension of multi-channel short time spectral amplitude (STSA) speech estimators to reverberant environments; (2) Development of joint dereverberation and noise reduction techniques based on subband decomposition; (3) Investigation of non-negative matrix factorization (NMF) algorithms for speech separation; and (4) System integration and validation with microphone array. This proposed research is an extension of a successfully completed NSERC CRD project under the sponsorship of industrial partner, Microsemi. In spite of the growing popularity of the company's broadband voice processing platforms, the reduction of non-stationary noise, the dereverberation of speech and its separation from acoustic backgrounds, remain challenging problems that hinder further technological progress. The proposed research aims to push back such limitations and provide Microsemi with innovative, cost-effective solutions to these challenges. In addition to technology transfer, the project will contribute to advance the engineering discipline of speech and audio processing and as to the training of highly qualified personal in this area at McGill and Concordia Universities.