dc.contributor.author | Liu, L | en_US |
dc.contributor.author | KONG, Q | en_US |
dc.contributor.author | Morfi, G-V | en_US |
dc.contributor.author | Benetos, E | en_US |
dc.contributor.author | 23rd International Society for Music Information Retrieval Conference (ISMIR) | en_US |
dc.date.accessioned | 2022-09-23T14:25:10Z | |
dc.date.available | 2022-07-14 | en_US |
dc.date.issued | 2022-12-18 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/80694 | |
dc.description | Best paper award | en_US |
dc.description.abstract | Rhythm quantisation is an essential part of converting performance MIDI recordings into musical scores. Previous works on rhythm quantisation are limited to the use of probabilistic or statistical methods. In this paper, we propose a MIDI-to-score quantisation method using a convolutional-recurrent neural network (CRNN) trained on MIDI note sequences to predict whether notes are on beats. Then, we expand the CRNN model to predict the quantised times for all beat and non-beat notes. Furthermore, we enable the model to predict the key signatures, time signatures, and hand parts of all notes. Our proposed performance MIDI-to-score system achieves significantly better performance compared to commercial software evaluated on the MV2H metric. We release the toolbox for converting performance MIDI into MIDI scores at: https://github.com/cheriell/PM2S | en_US |
dc.rights | This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.title | Performance MIDI-to-score conversion by neural beat tracking | en_US |
dc.type | Conference Proceeding | |
dc.rights.holder | © 2022, The Author(s) | |
pubs.author-url | https://cheriell.github.io/ | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2022-07-14 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | UKRI Centre for Doctoral Training in Artificial Intelligence and Music::Engineering and Physical Sciences Research Council | en_US |