| Περίληψη: | 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, introducing norm-based features that are relevant to the average performance. 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. The presented experiments show that the proposed features are able to quantify the differences between music performers. The proposed ensemble can efficiently cope with multi-class music performer recognition under inter-piece conditions, a difficult musical task, displaying a level of accuracy unlikely to be matched by human listeners (under similar conditions). Moreover, it is empirically demonstrated that the average performance is at least as effective as the best of the constituent individual performances while ‘extreme’ performances have the lowest discriminatory potential when used as norm. |
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English