Learning to Play Like the Great Pianists
نویسندگان
چکیده
An application of relational instance-based learning to the complex task of expressive music performance is presented. We investigate to what extent a machine can automatically build ‘expressive profiles’ of famous pianists using only minimal performance information extracted from audio CD recordings by pianists and the printed score of the played music. It turns out that the machine-generated expressive performances on unseen pieces are substantially closer to the real performances of the ‘trainer’ pianist than those of all others. Two other interesting applications of the work are discussed: recognizing pianists from their style of playing, and automatic style replication.
منابع مشابه
ÖEFAI Technical Report OEFAI-TR-2003-01 RECOGNITION OF FAMOUS PIANISTS USING MACHINE LEARNING ALGORITHMS: FIRST EXPERIMENTAL RESULTS
The paper addresses the question whether a machine can learn to identify famous performers (pianists) based on their style of playing. A preliminary study is presented where different machine learning algorithms are applied to performance data derived from Mozart sonata recordings by several famous pianists. It is shown that the algorithms learn to recognize pianists at a level better than chan...
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