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dc.creatorGutiérrez Muñoz, Michelle
dc.creatorCoto Jiménez, Marvin
dc.date.accessioned2022-07-05T18:01:02Z
dc.date.available2022-07-05T18:01:02Z
dc.date.issued2022-06-20
dc.identifier.citationhttps://www.mdpi.com/2079-3197/10/6/102?type=check_update&version=1es_ES
dc.identifier.issn2079-3197
dc.identifier.urihttps://hdl.handle.net/10669/86896
dc.description.abstractThe purpose of speech enhancement is to improve the quality of speech signals degraded by noise, reverberation, or other artifacts that can affect the intelligibility, automatic recognition, or other attributes involved in speech technologies and telecommunications, among others. In such applications, it is essential to provide methods to enhance the signals to allow the understanding of the messages or adequate processing of the speech. For this purpose, during the past few decades, several techniques have been proposed and implemented for the abundance of possible conditions and applications. Recently, those methods based on deep learning seem to outperform previous proposals even on real-time processing. Among the new explorations found in the literature, the hybrid approaches have been presented as a possibility to extend the capacity of individual methods, and therefore increase their capacity for the applications. In this paper, we evaluate a hybrid approach that combines both deep learning and wavelet transformation. The extensive experimentation performed to select the proper wavelets and the training of neural networks allowed us to assess whether the hybrid approach is of benefit or not for the speech enhancement task under several types and levels of noise, providing relevant information for future implementations.es_ES
dc.language.isoenges_ES
dc.sourceComputation; Vol. 10 Núm. 102: 2022es_ES
dc.subjectspeech enhancementes_ES
dc.subjectdenoisinges_ES
dc.subjectSignal processinges_ES
dc.subjectDeep learninges_ES
dc.subjectwaveletses_ES
dc.titleAn Experimental Study on Speech Enhancement Based on a Combination of Wavelets and Deep Learninges_ES
dc.typeartículo originales_ES
dc.identifier.doi10.3390/ computation10060102
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Eléctricaes_ES


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