Music Performers Classification by Using Multifractal Features : A Case Study

• Autorzy: Reljin N. , Pokrajac D.

Identyfikatory

DOI

10.1515/aoa-2017-0025

• Języki publikacji: EN

Abstrakty

EN

In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%.

Słowa kluczowe

EN

music classification; multifractal analysis; support vector machine; cross-validation; mel-frequency cepstral coefficients

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2017
• Tom: Vol. 42, No. 2
• Strony: 223--233
• Opis fizyczny: Bibliogr. 69 poz., rys., tab., wykr.

Twórcy

autor

Reljin N.

• University of Connecticut, 260 Glenbrook Road, Unit 3247, Storrs, CT 06269, USA

autor

Pokrajac D.

• Delaware State University, 1200 North DuPont Hwy, Dover, DE 19901, USA

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).