E. Alexandre, L. Álvarez, J. Amor, R. Gil-Pita, and E. Huerta, “Music-Inspired Harmony Search Algorithm Applied to Feature Selection for Sound Classification in Hearing Aids,” in Proc. AES Convention 124, May 2008, Paper 7419. [Online]. Available: https://aes.org/publications/elibrary-page/?id=14549
Alexandre E, Álvarez L, Amor J, Gil-Pita R, Huerta E. Music-Inspired Harmony Search Algorithm Applied to Feature Selection for Sound Classification in Hearing Aids. In: AES Convention 124. Audio Engineering Society; 2008. Paper 7419. Available from: https://aes.org/publications/elibrary-page/?id=14549
@inproceedings{Alexandre2008_14549,
author = {Alexandre, Enrique and Álvarez, Lorena and Amor, Javier and Gil-Pita, Roberto and Huerta, Ester},
title = {{Music-Inspired Harmony Search Algorithm Applied to Feature Selection for Sound Classification in Hearing Aids}},
booktitle = {AES Convention 124},
note = {Paper 7419},
year = {2008},
month = may,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=14549}
}
TY - CPAPER
TI - Music-Inspired Harmony Search Algorithm Applied to Feature Selection for Sound Classification in Hearing Aids
AU - Alexandre, Enrique
AU - Álvarez, Lorena
AU - Amor, Javier
AU - Gil-Pita, Roberto
AU - Huerta, Ester
T2 - AES Convention 124
M1 - Paper 7419
PY - 2008
DA - 2008/05/06
UR - https://aes.org/publications/elibrary-page/?id=14549
PB - Audio Engineering Society
LA - en
AB - This paper explores the application of the music-inspired Harmony-Search algorithm to the problem of feature selection for sound classification in digital hearing aids. The importance of this problem is given by the strong computational constraints inherent to the DSPs used in modern digital hearing aids. The goal of the feature selection algorithm is to select a subset of features in order to reduce the computational complexity of the system while maintaining a low probability of error. A set of experiments will be performed to test the performance of the proposed system, using a total of 74 different features. The results will be compared with those obtained using other widely-used algorithms, such as sequential search algorithms or random search.
ER -