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

Autor(es)

Enside de Abreu, Caio Cesar ; Queiroz Duarte, Marco Aparecido ; Villarreal, Francisco

• Tipo de documento: Artigo Científico
• Fonte: AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS; v. 72, p. 125-133, 2017.
• Citações Web of Science: 6

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. (C) 2016 Elsevier GmbH. All rights reserved. (AU)

• Processo FAPESP: 11/17610-0 - Monitoramento e controle de sistemas dinâmicos sujeitos a falhas
• Beneficiário: Roberto Kawakami Harrop Galvão
• Modalidade de apoio: Auxílio à Pesquisa - Temático