Machine recognition of timbre using steady-state tone of acoustic musical instruments
Ichiro Fujinaga · 1998
Introduction A timbre recognition experiment to classify 39 different orchestral instrument timbres was conducted using an exemplar-based learning system. The data consisted of the steady-state spectrum of each of the instruments played at different pitches (Sandell 1994). It has been shown that the attack portion of a musical instrument is important for identification tasks. Yet other studies show that steady-state portion is also significant (Grey 1978; Kendall and Carterette 1986). In addition to the spectral data, the moments of the spectrum, including the centroid, were considered as potential features for the identification process. The implementation of the identification task is based on a combination of a k-nearest neighbor (k-NN) classifier and a genetic algorithm, which is used for feature selection and feature weighting. This paradigm, also known as the exemplar-based learning model (Aha 1997), is attractive because training is not necessary, learning is extremely