Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Music and Science

ISSN

20592043

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85181706077