A Machine Learning Approach to Musical Style Recognition

Abstract

Much of the work on perception and understanding of music by computers has focused on low-level perceptual features such as pitch and tempo. Our work demonstrates that machine learning can be used to build effective style classifiers for interactive performance systems. We also present an analysis explaining why these techniques work so well when hand-coded approaches have consistently failed. We also describe a reliable real-time performance style classifier.