A novel supervised learning algorithm for musical instrument classification

In this paper, a novel supervised learning algorithm for automatic classification of individual musical instrument sounds is addressed deriving from the idea of supervised non-negative matrix factorization (NMF) algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which supervised NMF algorithm is unable to do. Afterwards, each data is projected onto several training orthogonal basis matrices and three classifiers have been employed to compare the performance with different methods. In addition, feature selection is also applied in order to choose the most discriminative features for instrument classification. The results indicate that the classification accuracy of proposed method is 87.6%, which is comparable to the performance of supervised NMF algorithm for the same experiments.