SS-ESC: a spectral subtraction denoising based deep network model on environmental sound classification

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dc.authorid0000-0002-6315-5750
dc.contributor.authorKorkmaz, Yunus
dc.date.accessioned2025-02-22T14:09:05Z
dc.date.available2025-02-22T14:09:05Z
dc.date.issued2025
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractEnvironmental Sound Classification (ESC), also referred as Sound Event Classification, is an essential part of many speech processing applications in terms of of separating background audio from original signal. By the recent developments in deep learning area, studies related to the ESC area have also been improved significiantly by the researchers. Because the nature of digital sound signals, the ESC was mostly developed using manually extracted one dimensional (1D) so far. In this paper, a novel ESC pipeline which uses spectral subtraction denoising as a preliminary stage was proposed based on deep learning architectures. The well-known deep learning architectures which are GoogLeNet, AlexNet, ShuffleNet, SqueezeNet and ResNet-18 were run over ESC problem by using ESC-10 benchmark dataset. Log-mel spectrogram images were preferred as feature matrices for mentioned networks. The results showed that the proposed SS-ESC model achieved the best results and outperformed many state-of-the-art methods with a test accuracy of 99.17% for the ESC-10 by the help of the AlexNet. These findings significiantly proved that the spectral subtraction denoising can contribute to the environmental sound classification problem in leveraging classification accuracy when it is used as a preliminary stage.en_US
dc.identifier.citationKorkmaz, Y. (2025). SS-ESC: a spectral subtraction denoising based deep network model on environmental sound classification. Signal, Image and Video Processing, 19(1), 1-13.
dc.identifier.doi10.1007/s11760-024-03649-5
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85211376616en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-024-03649-5
dc.identifier.urihttps://hdl.handle.net/11468/29787
dc.identifier.volume19en_US
dc.identifier.wosWOS:001369355200010
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKorkmaz, Yunus
dc.institutionauthorid0000-0002-6315-5750
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250222
dc.subjectSound classificationen_US
dc.subjectSpectral subtractionen_US
dc.subjectDeep learningen_US
dc.subjectLog-mel spectrogramen_US
dc.titleSS-ESC: a spectral subtraction denoising based deep network model on environmental sound classificationen_US
dc.typeArticleen_US

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