Examining EEG Signals with Spectral Analyses Methods in Migrain Patients during Pregnancy
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.
Keywords: Repertoire, inharmonicity,Mel-Frequency Cepstral Coefficient, pitch, gushe, K- nearest neighbors, support Vector Machines
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