Single-Channel Speech Quality Enhancement in Mobile Networks Based on Generative Adversarial Networks

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dc.contributor.authorWu, Guifencs
dc.contributor.authorHerencsár, Norbertcs
dc.coverage.issueneuvedenocs
dc.coverage.volume2024cs
dc.date.accessioned2024-08-22T08:02:32Z
dc.date.available2024-08-22T08:02:32Z
dc.date.issued2024-04-02cs
dc.description.abstractA large amount of randomly generated noise in mobile networks leads to a lack of targeting and gaming processes in the speech enhancement process, and the enhancement process from the perspective of acoustic features alone suffers from major drawbacks. Propose a single-channel speech quality enhancement method based on generative adversarial networks in mobile networks. Explain the principle of generative adversarial network to realize single-channel speech quality enhancement in mobile networks and clarify its shortcomings. Design an improved Mel frequency cepstral coefficient extraction method to extract 12 base features as the enhancement basis. Use the relative average least squares loss instead of the traditional loss function to enhance the training efficiency, use the hybrid penalty term to enhance the generator's ability to generate single-channel speech, and optimize the discriminator through the multi-layer convolution and the addition of fully connected layers to enhance the speech quality enhancement ability of adversarial generative networks in various aspects, forming a relative average generative adversarial network (RaGAN) based on hybrid penalty term to realize single-channel speech quality enhancement processing. Through the experiment, when the discriminator is applied with the size of a 3*3 convolutional kernel, the best effect of speech quality enhancement is achieved in the mobile network. This method can complete the enhancement of single-channel speech quality in the mobile network, and the effect is significant, which can effectively reduce the noise in the original single-channel speech.en
dc.description.embargo2025-04-03cs
dc.formattextcs
dc.format.extent1-15cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMobile Networks and Applications. 2024, vol. 2024, issue neuvedeno, p. 1-15.en
dc.identifier.doi10.1007/s11036-024-02300-4cs
dc.identifier.issn1572-8153cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other188428cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttps://hdl.handle.net/11012/249386
dc.language.isoencs
dc.publisherSPRINGERcs
dc.relation.ispartofMobile Networks and Applicationscs
dc.relation.urihttps://link.springer.com/article/10.1007/s11036-024-02300-4cs
dc.rights(C) SPRINGERcs
dc.rights.accessembargoedAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1572-8153/cs
dc.subjectGenerative adversarial networksen
dc.subjectRaGANen
dc.subjectHybrid penalty termen
dc.subjectSingle-channelen
dc.subjectSpeech qualityen
dc.subjectDiscriminatoren
dc.subjectMobile networksen
dc.titleSingle-Channel Speech Quality Enhancement in Mobile Networks Based on Generative Adversarial Networksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-188428en
sync.item.dbtypeVAVen
sync.item.insts2024.08.22 10:02:32en
sync.item.modts2024.08.20 14:33:32en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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