Leveraging LLMs for Automated MIDI Generation

This paper explores the application of large language models (LLMs)for music generation, specifically focusing on generating MIDI files,JSON representations, and music-related code. We aim to leverageLLM capabilities to automate and enhance the music compositionprocess. We systematically examine methodologies and tools forintegrating LLMs in music generation with direct music creationand code-based techniques. The challenge of automating musiccomposition using AI remains significant due to the complexityand creativity required in the process. Our findings demonstrate thepotential of LLMs to innovate and streamline music composition,offering new tools and approaches for musicians and developers.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-01
DOI
https://doi.org/10.5281/zenodo.14279479
Primary Topic
Music Technology and Sound Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Leveraging LLMs for Automated MIDI Generation

Zenodo (CERN European Organization for Nuclear Research)
Music Technology and Sound Studies
article

Leveraging LLMs for Automated MIDI Generation

article en

Abstract

This paper explores the application of large language models (LLMs)for music generation, specifically focusing on generating MIDI files,JSON representations, and music-related code. We aim to leverageLLM capabilities to automate and enhance the music compositionprocess. We systematically examine methodologies and tools forintegrating LLMs in music generation with direct music creationand code-based techniques. The challenge of automating musiccomposition using AI remains significant due to the complexityand creativity required in the process. Our findings demonstrate thepotential of LLMs to innovate and streamline music composition,offering new tools and approaches for musicians and developers.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 65%
Music Technology and Sound Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Leveraging LLMs for Automated MIDI Generation · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS