Design and implementation of spiking neural network based speech enhancement algorithms

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Mobile or wearable multimedia devices such as tablets, smartphones, smartwatches or smart glasses are used daily by millions of Canadians. All of these devices include different speech processing modules such as speech coders or automatic speech recognition systems, e.g. Apple's Siri or Amazon's Alexa. Due to the increasing mobility of multimedia devices, these modules are more than ever subjected to diverse noisy environments but, yet, their performance is seriously affected by the presence of noise. In order to limit the decrease in performance of these modules in the presence of noise, speech enhancement (SE) algorithms are used to reduce the noise without affecting the speech quality. Despite decades of research, the performance of these SE algorithms are largely sub-optimal, especially when using only one microphone. In sharp contrast, the auditory system deals very well with noise. In fact, it is somewhat easy for humans to follow a conversation in a relatively noisy environment. One approach to model neural processing in the auditory system is to use spiking neural networks (SNN). SNN mimic biological neurons more closely than classic neural networks. Moreover, SNN have been found to be more computationally powerful than classic approaches and have hardware implementations that are much faster than a CPU while consuming much less energy. In this research, I propose to design computationally efficient SNN-based SE algorithms that will model the processing performed by the auditory system. To do so, I will first develop SNN that will model the processing of noise in the auditory system. I will then design single-channel SNN-based SE algorithms. Finally, these SNN-based SE algorithms will be implemented on low-consumption neuromorphic chips. The proposed research will contribute to several fields of research such as neuroscience, machine learning and speech processing. More specifically, the development of new SNN models of neural activity in the presence of a noisy external stimulus will contribute to improving our understanding of the neural processing in the brain in the presence of noise. Moreover, the SNN will allow the development of novel SE estimators that will integrate such models and that will be further implemented in neuromorphic chips. The SE algorithms developed under this research program will eventually be integrated in commercial applications and they will improve the performance of the numerous devices that use them, e.g. tablets, smart phones, glasses or watches. Canada has always been an important player in speech technology and this research program will thus contribute to maintaining the high quality of this important Canadian industry.