Double Compressed AMR Audio Detection Using Spectral Features With Temporal Segmentation

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dc.authorid0000-0002-6404-1499en_US
dc.authorid0000-0002-9174-0367en_US
dc.authorscopusid57215422993en_US
dc.authorscopusid35781455400en_US
dc.contributor.authorBüker, Aykut
dc.contributor.authorHanilçi, Cemal
dc.date.accessioned2022-05-16T07:44:27Z
dc.date.available2022-05-16T07:44:27Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDouble compressed (DC) AMR audio detection is an important audio forensic problem which is used to authenticate the originality of an auido recording. Majority of the existing studies use audio features extracted from the AMR encoder parameters such as linear prediction (LP) coefficients. Recently, we proposed to use the long-term average spectrum (LTAS) features for DC AMR audio detection and promising results were achieved. In this paper, we propose a novel feature extraction techniques which does not require any prior knowledge about the details of the encoding and decoding processes of the AMR codec. The proposed features are extracted from the temporal segmentation of the short-term Fourier transform (STFT) representation of the audio signal. The proposed features are then classified using deep neural network (DNN) classifier. Experimental results conducted on two different databases show that the proposed features considerably outperform the long-term average spectrum (LTAS) features. The average detection rate is improved from 92.44% to 96.48% on MDSVC dataset and from 80.95% to 83.67% on TIMIT database with the proposed features.en_US
dc.identifier.doi10.23919/ELECO54474.2021.9677718en_US
dc.identifier.isbn978-605011437-9
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1972
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBüker, Aykut
dc.institutionauthorHanilçi, Cemal
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof13th International Conference on Electrical and Electronics Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature extractionen_US
dc.subjectSignal encodingen_US
dc.subjectEncoding processen_US
dc.subjectTemporal segmentationsen_US
dc.titleDouble Compressed AMR Audio Detection Using Spectral Features With Temporal Segmentationen_US
dc.typeConference Objecten_US

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