The automatic identification of musical instruments is a relatively unexplored and potentially very important field for its promise to free humans from time-consuming searches on the Internet and indexing of audio material. Speaker identification techniques have been used in this paper to determine the properties (features) which are most effective in identifying a statistically significant number of sounds representing four classes of musical instruments (oboe, sax, clarinet, flute) excerpted from actual performances. Features examined include cepstral coefficients, constant-Q coefficients, spectral centroid, autocorrelation coefficients, and moments of the time wave. The number of these coefficients was varied, and in the case of cepstral coefficients, ten coefficients were sufficient for identification. Correct identifications of 79%-84% were obtained with cepstral coefficients, bin-to-bin differences of the constant-Q coefficients, and autocorrelation coefficients; the latter have not been used previously in either speaker or instrument identification work. These results depended on the training sounds chosen and the number of clusters used in the calculation. Comparison to a human perception experiment with sounds produced by the same instruments indicates that, under these conditions, computers do as well as humans in identifying woodwind instruments.
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