EAGER: Automatic Indexing of Polyphonic Music by Cascade Classifiers

University Of North Carolina At Charlotte, Charlotte NC

Investigators

Abstract

IIS - 0968647 Automatic Indexing of Polyphonic Music by Cascade Classifiers Ras, Zbigniew W. University of North Carolina at Charlotte Abstract The goal of this EAGER project is to support exploratory work on a new class of cascade classifiers and hybrid classifiers for automatic indexing of polyphonic music according to instruments and types of instruments. Testing new classifiers for automatic indexing of polyphonic music, specifically those for the automatic classification of instrumental sound from recordings of orchestral music is difficult and involves a high degree of risk and uncertainty as to the outcome. If successful, the results may prove to be transformative and have significant impact on music information analysis. The work will employ resources in the MIRAI database developed in an earlier NSF supported project. The main MIRAI database contains about 1,000,000 musical instrument sounds, each represented as a vector of approximately 1,000 features. Each instrument sound is identified and matched to a corresponding instrument.