Abreu, Caio Cesar Enside de [UNESP]Duarte, Marco Aparecido QueirozVillarreal, Francisco [UNESP]2018-12-112018-12-112017-02-01AEU - International Journal of Electronics and Communications, v. 72, p. 125-133.1618-03991434-8411http://hdl.handle.net/11449/174078This 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.125-133engArtificial immune systemsDual-tree complex wavelet transformNegative selection algorithmNoise classificationSpeech enhancementArtigo10.1016/j.aeue.2016.12.004Acesso restrito2-s2.0-85009388627