Open Access

Analysis of the Effects of Finite Precision in Neural Network-Based Sound Classifiers for Digital Hearing Aids

  • Roberto Gil-Pita (EURASIP Member)1Email author,
  • Enrique Alexandre1,
  • Lucas Cuadra (EURASIP Member)1,
  • Raúl Vicen1 and
  • Manuel Rosa-Zurera (EURASIP Member)1
EURASIP Journal on Advances in Signal Processing20092009:456945

Received: 1 December 2008

Accepted: 9 September 2009

Published: 28 October 2009


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.

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Authors’ Affiliations

Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá


© Roberto Gil-Pita et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.