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

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• 作者：Caio Cesar Enside de Abreu^a ; ^b ; ^{caioenside@unemat.br} ; Marco Aparecido Queiroz Duarte^c ; ^{marco@uems.br} ; Francisco Villarreal^d ; ^{villa@mat.feis.unesp.br}
• 关键词：Noise classification ; Speech enhancement ; Artificial immune systems ; Negative selection algorithm ; Dual-tree complex wavelet transform
• 刊名：AEU - International Journal of Electronics and Communications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：72
• 期：Complete
• 页码：125-133
• 全文大小：549 K
• 卷排序：72

文摘

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.