| Paper: | AE-P4.2 | ||
| Session: | Applications to Music | ||
| Time: | Thursday, May 18, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Audio and Electroacoustics: Applications to Music | ||
| Title: | Musical instrument classification using non-negative matrix factorization algorithms and subset feature selection | ||
| Authors: | Emmanouil Benetos, Margarita Kotti, Constantine Kotropoulos, Aristotle University of Thessaloniki, Greece | ||
| Abstract: | 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, in order to obtain 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. Experimental results are presented to compare feature subsets of varying sizes alongside the various NMF algorithms. It has been found that a feature subset containing the mean and variance of the first Mel-Frequency Cepstral coefficient and the AudioSpectrumFlatness descriptor along with the mean of the AudioSpectrumEnvelope and the AudioSpectrumSpread descriptors when is fed to a standard NMF classifier yields an accuracy exceeding 95%. | ||
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