An immunological approach based on the negative selection algorithm for real noise classification in speech signals

Nenhuma Miniatura disponível

Data

2017-02-01

Título da Revista

ISSN da Revista

Título de Volume

Editor

Resumo

This paper presents a new approach to detect and classify background noise in speech sentences based on the negative selection algorithm and dual-tree complex wavelet transform. The energy of the complex wavelet coefficients across five wavelet scales are used as input features. Afterward, the proposed algorithm identifies whether the speech sentence is, or is not, corrupted by noise. In the affirmative case, the system returns the type of the background noise amongst the real noise types considered. Comparisons with classical supervised learning methods are carried out. Simulation results show that the artificial immune system proposed overcomes classical classifiers in accuracy and capacity of generalization. Future applications of this tool will help in the development of new speech enhancement or automatic speech recognition systems based on noise classification.

Descrição

Palavras-chave

Artificial immune systems, Dual-tree complex wavelet transform, Negative selection algorithm, Noise classification, Speech enhancement

Como citar

AEU - International Journal of Electronics and Communications, v. 72, p. 125-133.