Musical instrument timbres classification with spectral features

A set of features is evaluated for musical instrument recognition out of monophonic musical signals. Aiming to achieve a compact representation, the adopted features regard only spectral characteristics of sound and are limited in number. On top of these descriptors, various classification methods are implemented and tested. Over a dataset of 1007 tones from 27 musical instruments and without employing any hierarchical structure, quadratic discriminant analysis shows the lowest error rate (7.19% for the individual instrument and 3.13% for instrument families), outperforming all the other classification methods (canonical discriminant analysis, support vector machines, nearest neighbours). The most relevant features are demonstrated to be the inharmonicity, the spectral centroid and the energy contained in the first partial