Application of Non-negative Matrix Factorization to Musical Instrument Classification

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 different musical instrument classes (piano, violin, cello, flute, bassoon, and soprano saxophone). In addition, MPEG-7 basic spectral and spectral basis descriptors were considered, providing an effective combination for accurately describing the spectral and timbral audio characteristics. The audio files were split using 70% of the available data for training and the remaining 30% for testing A classifier was developed based on non-negative matrix factorization (NMF) techniques, thus introducing a novel application of NMF. The standard NMF method was examined, as well as its modifications: the local NMF, the sparse NMF, and the discriminant NMF. Experimental results are presented to compare MPEG-7 spectral basis representations with MPEG-7 basic spectral features in conjunction with the various NMF algorithms. The results indicate that the use of the spectrum projection coefficients for feature extraction and the standard NMF classifier yields an accuracy exceeding 95%.