Cemgil, T., Desain, P., and Kappen, B. (2000) Rhythm Quantization for Transcription. Computer Music Journal, 60-76.
Abstract
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 in tempo. For transcription 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 is derived by Bayes theorem. We demonstrate that many different quantization schemata can be derived in this framework by proposing suitable prior and likelihood distributions. The derived quantizer operates on short groups of onsets and is thus flexible both in capturing the structure of timing deviations and in controlling the complexity of resulting notations. The model is trained on data resulting from a psychoacoustical experiment and thus can mimic the behaviour of a human transcriber on this task.
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