Analysis of the effects of finite precision in neural network-based sound classifiers for digital hearing aids

Authors:
Roberto Gil-Pita;Enrique Alexandre;Lucas Cuadra;Raúl Vicen;Manuel Rosa-Zurera
Affiliations:
Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcala de Henares, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcala de Henares, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcala de Henares, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcala de Henares, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcala de Henares, Spain
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
Year:
2009

Citing 6
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Abstract

The feasible implementation of signal processing techniques on hearing aids is constrained by the finite precision required to represent numbers and by 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 (1) the most appropriate quantization scheme and (2) the most adequate approximations for the activation function. The experimental work proves that the quantized, 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 the digital signal processor.