Instrument Identification in Polyphonic Music: Feature Weighting with Mixed Sounds, Pitch-Dependent Timbre Modeling, and Use of Musical Context

This paper addresses the problem of identifying musical instruments in polyphonic music. Musical instrument identification (MII) is an improtant task in music information retrieval because MII results make it possible to automatically retrieving certain types of music (e.g., piano sonata, string quartet). Only a few studies, however, have dealt with MII in polyphonic music. In MII in polyphonic music, there are three issues: feature variations caused by sound mixtures, the pitch dependency of timbres, and the use of musical context. For the first issue, templates of feature vectors representing timbres are extracted from not only isolated sounds but also sound mixtures. Because some features are not robust in the mixtures, features are weighted according to their robustness by using linear discriminant analysis. For the second issue, we use an F0-dependent multivariate normal distribution, which approximates the pitch dependency as a function of fundamental frequency. For the third issue, when the instrument of each note is identified, the a priori probablity of the note is calculated from the a posteriori probabilities of temporally neighboring notes. Experimental results showed that recognition rates were improved from 60.8% to 85.8% for trio music and from 65.5% to 91.1% for duo music.