Music performer recognition using an ensemble of simple classifiers

Preliminary experiments show that the resulting ensemble is able to efficiently cope with this difficult musical task, displaying a level of accuracy unlikely to be matched by human listeners (under similar conditions).

Abstract

Abstract. This. paper addresses the problem of identifying the most likely music performer, given a set of performances of the same piece by a number of skilled candidate pianists. We propose a set of features for representing the stylistic characteristics of a music performer. A database of piano performances of 22 pianists playing two pieces by F. Chopin is used in the presented experiments. Due to the limitations of the training set size and the characteristics of the input features we propose an ensemble of simple classifiers derived by both subsampling the training set and subsampling the input features. Preliminary experiments show that the resulting ensemble is able to efficiently cope with this difficult musical task, displaying a level of accuracy unlikely to be matched by human listeners (under similar conditions). 1