An Automatic Sound Classification Framework with Non-volatile Memory

Environmental sounds form part of our daily life. With the advancement of deep learning models and the abundance of training data, the performance of automatic sound classification (ASC) systems has improved significantly in recent years. However, the high computational cost, hence high power consumption, remains a major hurdle for large-scale implementation of ASC systems on mobile and wearable devices. Motivated by the observations that humans are highly effective and consume little power whilst analyzing complex audio scenes, a biologically plausible ASC framework is introduced, namely SOM-SNN. The emerging dense crossbar array of non-volatile memory (NVM) devices have been recognized as a promising approach to emulate such distributed, massively-parallel and densely connected neuromorphic computing systems. This chapter presents the general structure of this framework for sound event and speech recognition, demonstrating attractive computational benefits and suitableness with an NVM implementation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.