Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2024

Abstract

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly in generating vocal melodies and simulating singing voices. While NLP techniques have been successfully used to analyze vocal performance data, providing insights into performance quality and style, the automatic transcription of vocal performances into sheet music remains a complex challenge. Existing tools for manual transcription are often insufficient due to the intricate dynamics of vocal expression. This study addresses the challenge of automating the conversion of vocal performances into sheet music using a combination of novel techniques, including large language models (LLMs). We present a method that successfully translates vocal audio input into display-ready sheet music. Our findings highlight the strengths and limitations of various approaches, particularly in the transcription of a cappella performances into notes and lyrics. This research contributes to the expanding field of NLP-driven music analysis and showcases the potential of these models to revolutionize vocal transcription in the future.

Keywords

Natural Language Processing, Vocal Performance, Automatic Music Transcription (AMT), Large Language Models, Machine Learning, A Cappella, Lyric Transcription, Sheet Music

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2024 IEEE International Conference on Data Mining

ISBN

979-8-3315-0668-1

Identifier

10.1109/ICDMW65004.2024.00063

Publisher

IEEE

City or Country

Piscataway, NJ, USA

Additional URL

https://doi.org/10.1109/ICDMW65004.2024.00063

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