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Open Access

Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

  • Lucas Cuadra1Email author,
  • Enrique Alexandre1,
  • Roberto Gil-Pita1,
  • Raúl Vicen-Bueno1 and
  • Lorena Álvarez1
EURASIP Journal on Advances in Signal Processing20092009:465189

Received: 1 December 2008

Accepted: 9 September 2009

Published: 29 October 2009


Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.


Learn StrategySuccess ProbabilityElderly UserReal PatientPublisher Note

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Authors’ Affiliations

Departamento de Teoria de la Señal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, Alcala de Henares, Spain


© Lucas Cuadra 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.