HIERARCHICAL GAUSSIAN TREE WITH INERTIA RATIO MAXIMIZATION FOR THE CLASSIFICATION OF LARGE MUSICAL INSTRUMENT DATABASES

This paper addresses the problem of classifying large databases of musical instrument sounds. We propose an efficient algorithm for selecting the most appropriate 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. The IRMFSP algorithm is then compared successfully to the widely used feature selection algorithm CFS. We then show the limits of usual flat (all classes considered on a same level) classifiers for large database classification and propose the use of hierarchical classifiers. Finally, we show the applicability of our system for 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 different conditions.