Effect of Spectrogram Parameters and Noise Types on The Performance of Spectro-temporal Peaks Based Audio Search Method

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dc.authoridYeraliyeva, Bakhyt/0000-0002-8680-7694
dc.authoridKoseoglu, Murat/0000-0003-3774-1083
dc.authorwosidYeraliyeva, Bakhyt/ABJ-7322-2022
dc.authorwosidKoseoglu, Murat/ABG-8975-2020
dc.contributor.authorKoseoglu, Murat
dc.contributor.authorUyanik, Hakan
dc.date.accessioned2024-08-04T20:53:44Z
dc.date.available2024-08-04T20:53:44Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAudio search algorithms are used to detect the queried file in large databases, especially in multimedia applications. These algorithms are expected to perform the detection in a reliable and robust way within the shortest time. In this study, based on spectral peaks method, an audio fingerprint algorithm with a few minor modifications was developed to detect the matching audio file in target database. This method has two stages as the audio fingerprint extraction and matching. In the first stage, fingerprint features are extracted from spectral peaks on the spectrograms of audio files by hash functions. This state-of-art technique reduces the processing load and time considerably compared to traditional methods. In the second stage, fingerprint data of the queried file are compared with the data created in the first stage in the database. The algorithm was demonstrated, and the effect of spectrogram parameters (window size, overlap, number of FFT) was investigated by considering reliability and robustness under different noise sources. Also, it was aimed to contribute to new audio retrieval studies based on spectral peaks method. It was observed that the variation in the spectrogram parameters significantly affected the number of matchings, reliability and robustness. Under high noise conditions, the optimal spectrogram parameters were determined as 512 (window size), 50% (overlap), 512 (number of FFT). It was seen in general that the algorithm successfully detected the queried file in the database even in high noise conditions for these parameters. No significant effect of music genre was observed.en_US
dc.identifier.doi10.35378/gujs.1000594
dc.identifier.endpage643en_US
dc.identifier.issn2147-1762
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85160455502en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage624en_US
dc.identifier.urihttps://doi.org/10.35378/gujs.1000594
dc.identifier.urihttps://hdl.handle.net/11616/101376
dc.identifier.volume36en_US
dc.identifier.wosWOS:001011280200010en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofGazi University Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAudio recognitionen_US
dc.subjectReliabilityen_US
dc.subjectNoise effecten_US
dc.subjectSpectrogram parametersen_US
dc.titleEffect of Spectrogram Parameters and Noise Types on The Performance of Spectro-temporal Peaks Based Audio Search Methoden_US
dc.typeArticleen_US

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