MUSICAL STYLE RECOGNITION — A QUANTITATIVE APPROACH

In this chapter a study is described in which the possibilities of statistical pattern recognition for musical style recognition are explored. A dataset with compositions of Bach, Handel, Telemann, Mozart and Haydn is investigated. The used featureset consists mainly of features that describe the different sonorities in the compositions. It is shown that with these features it is possible to separate the styles of the various composers. For this a k-nearest neighbor classifier is used which is trained in a featurespace that is spanned by the fisher-discriminants. In the untransformed featurespace, clusters are found with the k-means algorithm. It appears that these clusters reflect the styles of the various composers. The pattern recognition tools are also used to learn something about the characteristics of the styles. For this, decisiontrees are built using the C4.5 algorithm. It turns out that each of the styles represented in the dataset can be described with a few features.