Single and multi-microphone techniques of speech enhancement for reliable voice communications under adverse acoustic conditions

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Speech enhancement against various environmental acoustic noises is a fundamental component of many voice processing devices and systems. It finds wide applications ranging from mobile phones and PDAs for wireless access, to software-defined tools for low-cost internet-based access. Noise reduction and speech enhancement algorithms not only contribute to improve the quality of speech signals, but also serve as front-end processing to improve the performance of other applications such as speech coding and recognition. In spite of many advances in recent years, the enhancement of speech contaminated by moderate to high levels of non-stationary noise (e.g: cafeteria, busy street, sporting events) remains a challenging research avenue and a specific problem of concern to our sponsor. The main objective of this project is therefore to develop improved signal processing algorithms for this critical task. The proposed work consists of several sub-projects structured along two main avenues, namely: (1) development of new core techniques for single-channel speech enhancement and; (2) extension of these techniques for audio devices equipped with multi-microphones. The first topic aims to explore new codebook-based approaches that take advantage of available information about the speaker and background noise, and to investigate the use of vanguard compressive sensing principles for the reduction of non-stationary noise. The second topic focuses on the development of new beamforming techniques that incorporate compressive sensing to enable optimal coherent combination of the multiple microphone signals, and the investigation of new approaches for estimation of the noise field statistics, as required in these systems. The improved speech enhancement algorithms proposed in this project will provide our sponsor, Microsemi with practical and cost-effective solutions to the many challenges posed by non-stationary acoustic noise in its quest to develop and commercialize new lines of DSP products for voice applications, thereby helping the company to grow its customer base and annual sales. The project also includes a significant training component that will take place at both McGill and Concordia universities.