Rhythm quantization for transcription
InProceedings of the AISB '99 Symposium on Musical Creativity, pp. 140-146
Article in monograph or in proceedings
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SW OZ DCC KI
Proceedings of the AISB '99 Symposium on Musical Creativity
Automatic Music Transcription is the extraction of an acceptable notation from performed music. One important task in this problem is rhythm quantization which refers to categorization of note durations. Although quantization of a pure mechanical performance is rather straightforward, the task becomes increasingly difficult in presence of musical expression, i.e. systematic variations in timing of notes and tempo changes. For quantization of natural performances, we employ a framework based on Bayesian statistics. Expressive deviations are modelled by a probabilistic performance model from which the corresponding optimal quantizer can be derived by Bayes theorem. We demonstrate that some simple quantization schemata can be derived in this framework by simple assumptions about timing deviations. A general quantization method, which can be derived in this framework, is vector quantization (VQ). The algorithm operates on short groups of onsets and is thus flexible in capturing the structure of timing deviations between neighbouring onsets and thus performs better than simple rounding methods. Finally, we present some results on simple examples.
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