NDLI: Reducing the computational cost for sound classification in hearing aids by selecting features via genetic algorithms with restricted search

Content Provider	IEEE Xplore Digital Library
Author	Cuadra, L. Alexandre, E. Alvarez, L. Rosa-Zurera, M.
Copyright Year	2008
Description	Author affiliation: Dept. de Teor. de la Senal y Comun., Univ. de Alcala, Madrid (Cuadra, L.; Alexandre, E.; Alvarez, L.; Rosa-Zurera, M.)
Abstract	This paper centers on designing a feature-selection algorithm able to provide a ldquosmallrdquo number of adequate features that assist a sound classification system for hearing aids in reducing its computational load without degrading its performance. Because of the problem complexity, we have explored the use of genetic algorithms with restricted search for the mentioned feature selection. In an effort to evaluate its performance, the algorithm has been compared to a standard unconstrained genetic algorithm and with sequential methods. The restricted search driven by the proposed algorithm performs better than both the sequential methods and unconstrained genetic algorithms. The proposed algorithm selects a feature subset composed of only 21 features, much smaller than the 76 features of the complete, original set of available features. This low-cardinality subset of signal-describing features is the one implemented on the hearing aid, saving thus a great number of the scarce computational resources, and making possible to put into practice the concept at reasonable cost.
Starting Page	1320
Ending Page	1327
File Size	267646
Page Count	8
File Format	PDF
ISBN	9781424417230
DOI	10.1109/ICALIP.2008.4590248
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Support vector machine classification Auditory system Speech Genetics Classification algorithms Biological cells
Content Type	Text
Resource Type	Article

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