Artículos de revistas
An immunological approach based on the negative selection algorithm for real noise classification in speech signals
AEU - International Journal of Electronics and Communications, v. 72, p. 125-133.
State University of Mato Grosso - UNEMAT
Universidade Estadual Paulista (UNESP)
Universidade Estadual de Mato Grosso do Sul (UEMS)
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.