dc.contributor.author | Liu, L | |
dc.contributor.author | Morfi, G-V | |
dc.contributor.author | Benetos, E | |
dc.contributor.author | IEEE International Conference on Acoustics, Speech and Signal Processing | |
dc.date.accessioned | 2021-02-19T16:00:07Z | |
dc.date.available | 2021-01-30 | |
dc.date.available | 2021-02-19T16:00:07Z | |
dc.date.issued | 2021-06-06 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/70432 | |
dc.description.abstract | Research on automatic music transcription has largely focused on multi-pitch detection; there is limited discussion on how to obtain a machine- or human-readable score transcription. In this paper, we propose a method for joint multi-pitch detection and score transcription for polyphonic piano music. The outputs of our system include both a piano-roll representation (a descriptive transcription) and a symbolic musical notation (a prescriptive transcription). Unlike traditional methods that further convert MIDI transcriptions into musical scores, we use a multitask model combined with a Convolutional Recurrent Neural Network and Sequence-to-sequence models with attention mechanisms. We propose a Reshaped score representation that outperforms a LilyPond representation in terms of both prediction accuracy and time/memory resources, and compare different input audio spectrograms. We also create a new synthesized dataset for score transcription research. Experimental results show that the joint model outperforms a single-task model in score transcription. | en_US |
dc.format.extent | ? - ? (5) | |
dc.publisher | IEEE | en_US |
dc.subject | automatic music transcription | en_US |
dc.subject | sequence-to-sequence models | en_US |
dc.subject | score transcription | en_US |
dc.title | Joint multi-pitch detection and score transcription for polyphonic piano music | en_US |
dc.type | Conference Proceeding | en_US |
dc.rights.holder | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
pubs.author-url | https://cheriell.github.io/ | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
pubs.publisher-url | https://2021.ieeeicassp.org/ | en_US |
dcterms.dateAccepted | 2021-01-30 | |
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 |