Research · PAKDD 2024

DLVS4Music2Sheet: Deep Learning Vocal Separation for A Cappella Transcription to Sheet Music

Alexander Vincent Lewi et al.

Pacific-Asia Conference on Knowledge Discovery and Data Mining 2024

Separating the voices of an a cappella recording so each line can be written down as sheet music.

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 stateof-the-art models, methodologies, and techniques for converting choral audio into music sheets. Code and datasets are available at: https://github.com/DevGoliath/DLVS4Audio2Sheet

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