Automatic sound classification for improving speech intelligibility in hearing aids using a layered structure

Authors:
Enrique Alexandre;Lucas Cuadra;Lorena Álvarez;Manuel Rosa-Zurera;Francisco López-Ferreras
Affiliations:
Dept. de Teoría de la Señal y Comunicaciones. Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Spain;Dept. de Teoría de la Señal y Comunicaciones. Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Spain;Dept. de Teoría de la Señal y Comunicaciones. Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Spain;Dept. de Teoría de la Señal y Comunicaciones. Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Spain;Dept. de Teoría de la Señal y Comunicaciones. Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

This paper presents some of our first results in the development of an automatic sound classification algorithm for hearing aids. The goal is to classify the input audio signal into four different categories: speech in quiet, speech in noise, stationary noise and non-stationary noise. In order to make the system more robust, a divide and conquer strategy is proposed, resulting thus in a layered structure. The considered classification algorithms will be based on the Fisher linear discriminant and neural networks. Some results will be given demonstrating the good behavior of the system compared with a classical approach with a four-classes classifier based on neural networks.