Comparison of Features for Musical Instrument Recognition

Several features were compared with regard to recognition performance in a musical instrument recognition system. Both mel-frequency and linear prediction cepstral and delta cepstral coefficients were calculated. Linear prediction analysis was carried out both on a uniform and a warped frequency scale, and reflection coefficients were also used as features. The performance of earlier described features relating to the temporal development, modulation properties, brightness, and spectral synchronity of sounds was also analysed. The data base consisted of 5286 acoustic and synthetic solo tones from 29 different Western orchestral instruments , out of which 16 instruments were included in the test set. The best performance for solo tone recognition, 35% for individual instruments and 77% for families, was obtained with a feature set consisting of two sets of mel-frequency cepstral coefficients and a subset of the other analysed features. The confusions made by the system were analysed and compared to results reported in a human perception experiment.