Name
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 catego- rization of note durations. Although quantization of a pure mechanical performance is rather straightforward, the task becomes increasingly difficult i n presence of musical expression, i.e. systematic variations in timing of notes and in tempo. For transcription of natural per- formances, 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 distri- butions. The derived quantizer operates on short groups of o nsets and is thus flexible both in capturing the structure of timing deviations and in contr olling 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. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1162/014892600559218 | Computer Music Journal |
Keywords | Field | DocType |
rhythm quantization,bayesian statistics,bayes theorem | Categorization,Notation,Algorithm,Rounding,Vector quantization,Probabilistic logic,Bayesian statistics,Statistics,Quantization (signal processing),Mathematics,Bayes' theorem | Journal |
Volume | Issue | ISSN |
24 | 2 | 0148-9267 |
Citations | PageRank | References |
17 | 5.33 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Taylan Cemgil | 1 | 535 | 54.39 |
| Hilbert J. Kappen | 2 | 834 | 103.74 |
| peter desain | 3 | 165 | 31.76 |