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Musical-Inspired Features for Automatic Sound Classification in Digital Hearing Aids

This paper proposes the use of some musical-inspired features for the automatic classification of sounds in digital hearing aids. This kind of application is characterized by very strong constraints in terms of computational complexity. The proposed features are based on fundamental frequency detection and exhibit a low computational complexity while providing good results in terms of probability of correct classification. The performance of the system will be tested using an 1-NN classifier being the goal to distinguish among speech, noise and music. For the experiments, a sound database, obtained using a hearing aid simulator, will be used.

Authors: Alexandre, Enrique; Canadas-Quesada, Francisco Jesus; Rosa-Zurera, Manuel; Ruiz-Reyes, Nicolas; Vera-Candeas, Pedro
Affiliations: University of Jaen;University of Alcala(See document for exact affiliation information.)
AES Convention:124 (May 2008) Paper Number:7435 Import into BibTeX
Session Subject:Room and Architectural Acoustics; Sound Reinforcement

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