Classification of the Radif of Mirza Abdollah a canonic repertoire of Persian music using SVM method

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Automatic music classification is very useful to music indexing, content-based retrieval and on-line music distribution, but it is a challenge to extract the most common and salient themes from unstructured raw music data. In this paper, a novel approach is proposed to automatically classify the Radif of Mirza Abdollah a canonic repertoire of Persian music. The Radif is made up essentially of non-measured pieces or free rhythm which provide a generative model or pattern for the creation of new composition. Music Segments are decomposed according to time segments obtained from the beginning parts of the original music signal into segments of 3 sec . In order to better classify pure and vocal music, a number of features including inharmonicity, mel-frequency cepstral coefficient, pitch, mean and standard deviation of spectral centroid are extracted to characterize the music content which are mainly related to frequency domain. Experimental results are carried out on a novel database, which contains 250 gushe of the repertoire played by the four most famous Iranian masters and performed on two stringed instruments the Tar & The Setar. Classical machine learning algorithms such as MLP neural networks, KNN and SVM are employed. Finally, SVM shows a better performance in music classification than the others.

Key words: EEG, Migraine, Spectral Analysis, Welch, Burg, Covariance, Modified Covariance.


Repertoire, inharmonicity,Mel-Frequency Cepstral Coefficient, pitch, gushe, K- nearest neighbors, support Vector Machines

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Seibt P., “Algorithmic Information Theory”, Mathematics of Digital Information Processing, pp. 23-30. Springer, New York (2006)

Meana H.B., “Advances in Audio and Speech Signal Applications Idea Group, Mexico (2007) and

Li T., Ogihara M. “Toward Intelligent Music Information Retrieval”, IEEE Trans. Multimedia, vol. 13, no. 3. pp. 564-574 (2005)

Changsheng X., “Automatic Music Classification and Summarization”, IEEE transactions on speech and audio processing. Vol. 13. No. 3. pp. 441-450 (2005)

Pikarkis A., Theoridis S., Kamarotos D. “Classification of Musical Patterns Using Variables Duration Hidden Markov Models” IEEE Transactions on Audio Speech and Language Processing, Vol.14. No.5. p.p. 1795- 1807 (2006)

Simms R., Koushkani A., “Mohammad Reza Shajarian's avaz in Iran and beyond 1979 – 2010” Lexington Books, United States of America (2012)

Scaringella N., Zoia G., Mlynek D., “Automatic Genre Classification of Music Content” IEEE signal processing, 23(2): 133-141 (2006)

Yu Q., Miche1 Y., Sorjamaa1 A., Guillen A., Lendasse A., Severin E., “OP-KNN Method and Applications” Hindawi (2010)

Zhu J., Xue X., Lu L., “Musical Genre Classification By Instruental Features”, Paper presented on the International Computer Music Conference, Shanghai, China, ICMC 2004

Cunningham P., Delany S., “k-Nearest Neighbour Classifiers”, Technical Report UCD-CSI-2007. March 27,(2007)

Steve R.G., “Support Vector Machines for Classification and Regression”, University Of Southampton, Technical Report, 10 May (1998)

Carlos N., Silla J., Alessandro L., Kaestner A., “A Machine Learning Approach to Automatic Music Genre Society, Brazil (2008) Brazilian Computer

Hao Zhang Alexander C. Berg Michael Maire Jitendra Malik, SVM-KNN,

“Discriminative Nearest Neighbor”, Paper presented on the Computer Vision and Pattern Recognition, Rhode Island, 17-22 June (2006)

Changsheng X., Maddage N.C., Shao X., Cao F., Tian Q., “Musical Genre Classification Using Support Vector Machines”, Paper presented on the 2003 IEEE International Conference. Singapore, 6- 10 April 2003

Saeed Khan M.K., “Automatic Classification of Speech & Music in Digitized Audio”, Master degree thesis, University of Dhahran (2005) [16] Galembo A., Askenfelt inharmonicity through pitch extraction”, Quarterly Progress and Status Report, STL-QPSR. vol. 35, pp. 135-144 (1994) A., “Measuring

Järveläinen H., Välimäki V., Karjalainen M., Laboratory of Acoustics and Audio Signal Processing, Doi: 10.1121/1.1374756

Zhou R., “Feature Extraction of Musical Content for Automatic Music Transcription”, Master Thesis, Chinese Academy of Science (2006)

Herman G.L., “Fundamental Frequency Tracking In Music With Multiple Voices”, Thesis for the degree of Master of Science., University of Illinois at Urbana-Champaign (2007)

Jiajun Z., Xiangyang X., Hong L., “Musical Genre Classification By Instrumental Features”, paper presented on International Computer Music Conference, Fudan University, Shanghai. China (2004)

Aliaksandr V., Paradzinets., “Variable Resolution Transform-based Music Feature Extraction and their Retrieval”, Dissertation, University of Lyon (2007) Music Information

Tzanetakis G., Essl G., Cook P., “Automatic Musical Genre Classification Of Audio Signals”, IEEE Transactions On Speech And Audio Processing, Vol. 10. No. 5. 293-302 (2002).