Mitigation of noise and reverberation for hearing aids, voice-enabled devices, and speech transmission

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This proposal is for the development of improved de-noising algorithms to reduce the impairments caused by additive noise and reverberation in the following three applications, which are all of significant importance for the Canadian economy. 1) Hearing aids: they are becoming increasingly important as the proportion of Canadians over 65 is getting higher. 2)Voice enabled devices: typically using hands-free speech technology, they are quickly emerging. 3)Speech transmission: still a hot topic (launch of Voice over IP services, etc.), and there is still a need for developing more efficient pre-processors to remove additive noise or reverberation from speech before it is encoded. In our group we have recently developed an approach for speech enhancement in additive noise, which integrates human hearing characteristics into powerful statistical modeling and tracking algorithms. In the proposed program we seek to adapt these speech enhancement algorithms to the problem of de-noising for hearing aids. We also want to investigate the efficiency of our speech enhancement algorithms in the context of voice enabled devices. We have recently developed some promising Blind Source Separation (BSS) algorithms, and we intend to apply and test them for removing additive noise in speech. Since the problem of removing a reverberation effect from a speech signal can be formulated as a problem of Blind Deconvolution, we also intend to extend the promising BSS algorithms developed in our group to the case of convolutive mixtures, providing a powerful method to perform de-reverberation. We also seek to include auditory modeling aspects into the resulting blind deconvolution algorithms. Moreover, the use of the powerful and emerging particle filter methods for the mitigation of additive noise in speech will be investigated. Particle filters have recently been used for blind deconvolution, so we will also investigate their use in the context of speech de-reverberation. The investigation of different ways of including auditory models or perceptual aspects into the training of particle filters is another objective of the program. Finally, we will also seek to find ways to reduce the complexity of particle filters in the context of speech de-noising.