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Automatic Classification of Large Musical Instrument Databases Using Hierarchical Classifiers with Inertia Ratio Maximization

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This paper addresses the problem of classifying large databases of musical instrument sounds. An efficient algorithm is proposed for selecting the most appropriate signal features for a given classification task. This algorithm, called IRMFSP, is based on the maximization of the ratio of the between-class inertia to the total inertia combined with a step-wise feature space orthogonalization. Several classifiers - flat gaussian, flat KNN, hierarchical gaussian, hierarchical KNN and decision tree classifiers - are compared for the task of large database classification. Especially considered is the application when our classification system is trained on a given database and used for the classification of another database possibly recorded in completely diffierent conditions. The highest recognition rates are obtained when the hierarchical gaussian and KNN classifiers are used. Organization of the instrument classes is studied through an MDS analysis derived from the acoustic features of the sounds.

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