dc.contributor.author | EDWARDS, D | |
dc.contributor.author | Riley, J | |
dc.contributor.author | Sarmento, P | |
dc.contributor.author | DIXON, S | |
dc.contributor.author | ISMIR | |
dc.date.accessioned | 2024-07-09T11:20:08Z | |
dc.date.available | 2024-06-28 | |
dc.date.available | 2024-07-09T11:20:08Z | |
dc.date.issued | 2024-11-10 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/97939 | |
dc.description.abstract | Guitar tablatures enrich the structure of traditional music notation by assigning each note to a string and fret of a guitar in a particular tuning, indicating precisely where to play the note on the instrument. The problem of generating tablature from a symbolic music representation involves inferring this string and fret assignment per note across an entire composition or performance. On the guitar, multiple string-fret assignments are possible for each pitch, which leads to a large combinatorial space that prevents exhaustive search approaches. Most modern methods use constraint-based dynamic programming approaches to minimize some cost function (e.g.\ hand position movement). In this work, we introduce a novel deep learning solution to symbolic guitar tablature estimation. We train an encoder-decoder Transformer model in a masked language modeling paradigm to assign notes to strings. The model is first pre-trained on DadaGP, a dataset of over 25K tablatures, and then fine-tuned on a curated set of professionally transcribed guitar performances. Given the subjective nature of assessing tablature quality, we conduct a user study amongst guitarists, wherein we ask participants to rate the playability of multiple versions of tablature for the same four-bar excerpt. The results indicate our system significantly outperforms competing algorithms. | en_US |
dc.subject | music information retrieval | en_US |
dc.title | MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling | en_US |
dc.type | Conference Proceeding | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2024-06-28 | |
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 |
rioxxterms.funder.project | b215eee3-195d-4c4f-a85d-169a4331c138 | en_US |