Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2024
Abstract
While manual transcription tools exist, music enthusiasts, including amateur singers, still encounter challenges when transcribing performances into sheet music. This paper addresses the complex task of translating music audio into music sheets, particularly challenging in the intricate field of choral arrangements where multiple voices intertwine. We propose DLVS4Audio2Sheet, a novel method leveraging advanced deep learning models, Open-Unmix and Band-Split Recurrent Neural Networks (BSRNN), for vocal separation. DLVS4Audio2Sheet segments choral audio into individual vocal sections and selects the optimal model for further processing, aiming towards audio into music sheet conversion. We evaluate DLVS4Audio2Sheet’s performance using these deep learning algorithms and assess its effectiveness in producing isolated vocals suitable for notated scoring music conversion. By ensuring superior vocal separation quality through model selection, DLVS4Audio2Sheet enhances audio into music sheet conversion. This research contributes to the advancement of music technology by thoroughly exploring state-of-the-art models, methodologies, and techniques for converting choral audio into music sheets. Code and datasets are available at: https://github.com/DevGoliath/DLVS4Audio2Sheet.
Keywords
Music, Choral audio, Music sheet, Vocal separation, Audio-to-Sheet, Deep learning, Open-Unmix, Band-Split Recurrent Neural Networks (BSRNN)
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Areas of Excellence
Digital transformation
Publication
Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2024 Workshops, RAFDA and IWTA, Taipei, May 7-10: Proceedings
Volume
14658
First Page
95
Last Page
107
ISBN
9789819726509
Identifier
10.1007/978-981-97-2650-9_8
Publisher
Springer
City or Country
Cham
Citation
TEO, Nicole; WANG, Zhaoxia; GHE, Ezekiel; TAN, Yee Sen; OKTAVIO, Kevan; LEWI, Alexander Vincent; ZHANG, Allyne; and HO, Seng-Beng.
DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion. (2024). Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2024 Workshops, RAFDA and IWTA, Taipei, May 7-10: Proceedings. 14658, 95-107.
Available at: https://ink.library.smu.edu.sg/sis_research/9160
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-981-97-2650-9_8