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This paper explores the application of multilayer perceptrons (MLP) to the problem of speech/non-speech classification in digital hearing aids. When properly designed and trained, MLPs are able to generate an arbitrary classification frontier with a relatively low computational complexity. The paper will focus on studying the key influence of the training process on the performance of the system. An appropriate election of the training algorithm will help to provide better classification with a lower number of neurons in the network, which leads to a lower computational complexity. The results obtained will be compared with those obtained from two reference algorithms (the Fisher linear discriminant and the k-Nearest Neighbour), along with some comments regarding the computational complexity.
Author (s): Alexandre, Enrique; Álvarez, Lorena; Cuadra, Lucas; Rosa-Zurera, Manuel
Affiliation:
Universidad de Alcalá
(See document for exact affiliation information.)
AES Convention: 122
Paper Number:7136
Publication Date:
2007-05-06
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Session subject:
Analysis and Synthesis of Sound
Permalink: https://aes2.org/publications/elibrary-page/?id=14121
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Alexandre, Enrique; Álvarez, Lorena; Cuadra, Lucas; Rosa-Zurera, Manuel; 2007; On the Training of Multilayer Perceptrons for Speech/Non-Speech Classification in Hearing Aids [PDF]; Universidad de Alcalá; Paper 7136; Available from: https://aes2.org/publications/elibrary-page/?id=14121
Alexandre, Enrique; Álvarez, Lorena; Cuadra, Lucas; Rosa-Zurera, Manuel; On the Training of Multilayer Perceptrons for Speech/Non-Speech Classification in Hearing Aids [PDF]; Universidad de Alcalá; Paper 7136; 2007 Available: https://aes2.org/publications/elibrary-page/?id=14121
@article{alexandre2007on,
author={alexandre enrique and álvarez lorena and cuadra lucas and rosa-zurera manuel},
journal={journal of the audio engineering society},
title={on the training of multilayer perceptrons for speech/non-speech classification in hearing aids},
year={2007},
number={7136},
month={may},}