Musical Instrument Classification using Non-Negative Matrix Factorization Algorithms and Subset Feature Selection

In this paper, a class, of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in sound classification applications as well as MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrument classes. Subsets of the feature set are selected using branch-and-bound search, obtaining the most suitable features for classification, A class of classifiers is developed based on the non-negative matrix factorization (NMF). The standard NMF method is examined as well as its modifications: the local, the sparse, and the discriminant NMF. The experimental results compare feature subsets of varying sizes alongside the various NMF algorithms. It has been found that a subset containing the mean and die variance of the first mel-frequency cepstral coefficient and the audiospectrumflatness descriptor along with the means of the audiospectrumenvelope and the audiospectrumspread descriptors when is fed to a standard NMF classifier yields an accuracy exceeding 95%