Benetos, EmmanouilEmmanouilBenetosKotti, MargMargKottiKotropoulos, ConstantineConstantineKotropoulos2013-08-142006-052013-08-142006-05IEEE International Conference on Acoustics, Speech and Signal Processing, 2006, pp.221-224142440469Xhttp://hdl.handle.net/10044/1/1172114.08.13 KB. Ok to add accepted version to Spiral. IEEEIn 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 the 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%. © 2006 IEEE.© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Conference Paperhttps://www.dx.doi.org/10.1109/ICASSP.2006.1661252