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Selection of Approximated Activation Functions in Neural Network-Based Sound Classifiers for Digital Hearing Aids

The feasible implementation of signal processing techniques on hearing aids is constrained to the limited number of instructions per second to implement the algorithms on the digital signal processor the hearing aid is based on. This adversely limits the design of a neural network-based classifier embedded in the hearing aid. Aiming at helping the processor achieve accurate enough results, and in the effort of reducing the number of instructions per second, this paper focuses on exploring the most adequate approximations for the activation function. The experimental work proves that the approximated neural network-based classifier achieves the same efficiency as that reached by exact networks (without these approximations), but, this is the crucial point, with the added advantage of extremely reducing the computational cost on digital signal processor.

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