Content-Based Classification of Musical Instrument Timbres

A set of features extracted from audio sources is investigated for content-based classification of musical instrument timbres. The adopted features describe spectral characteristics of monophonic sounds and rely on the previous segmentation of the signal and the estimation of pitch. The dataset is composed by 1007 tones from 27 musical instruments ranging from orchestral sounds (strings, woodwinds, brass) to pop/electronic instruments (bass, electric and distorted guitar). The extracted features are then classified by widely used pattern recognition techniques. A thorough evaluation of the resulting performances and comparative analysis with previous works is presented. Quadratic Discriminant Analysis shows an error rate of 7.19% for the individual instruments and 3.23% for instrument families. These results are by far superior to the performances of other classification methods (Canonical Discriminant Analysis, Support Vector Machines, Nearest Neighbours). The use of a machine-built decision hierarchy did not improve the results.