Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AGBQ5BWEV" target="_blank" >RIV/00216208:11320/25:GBQ5BWEV - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181706077&doi=10.1177%2f20592043231216254&partnerID=40&md5=a566dc80ac89da114c61dc9a6107bea3" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181706077&doi=10.1177%2f20592043231216254&partnerID=40&md5=a566dc80ac89da114c61dc9a6107bea3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/20592043231216254" target="_blank" >10.1177/20592043231216254</a>
Alternative languages
Result language
angličtina
Original language name
Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis
Original language description
The purpose of this research is to present a natural language processing-based approach to symbolic music analysis. We propose Mel2Word, a text-based representation including pitch and rhythm information, and a new natural language processing-based melody segmentation algorithm. We first show how to create a melody dictionary using Byte Pair Encoding (BPE), which finds and merges the most frequent pairs that appear in a collection of melodies in a data-driven manner. The dictionary is then used to tokenize or segment a given melody. Utilizing various symbolic melody datasets, we conduct an exploratory analysis and evaluate the classification performance of melody representation models on the MTC-ANN dataset. A comparison with existing segmentation algorithms is also carried out. The result shows that the proposed model significantly improves classification performance in comparison to various melodic features and several existing segmentation algorithms. © The Author(s) 2024.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Music and Science
ISSN
20592043
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
2024
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85181706077