DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion

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 cho...

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Main Authors: TEO, Nicole, WANG, Zhaoxia, GHE, Ezekiel, TAN, Yee Sen, OKTAVIO, Kevan, LEWI, Alexander Vincent, ZHANG, Allyne, HO, Seng-Beng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9160
https://ink.library.smu.edu.sg/context/sis_research/article/10163/viewcontent/4._DLVS4Audio2Sheet_Deep_Learning_based_Vocal_Separation_for_Audio_into_Music_Sheet_Conversion.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-101632024-08-01T08:38:13Z DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion TEO, Nicole WANG, Zhaoxia GHE, Ezekiel TAN, Yee Sen OKTAVIO, Kevan LEWI, Alexander Vincent ZHANG, Allyne HO, Seng-Beng 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. 2024-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9160 info:doi/10.1007/978-981-97-2650-9_8 https://ink.library.smu.edu.sg/context/sis_research/article/10163/viewcontent/4._DLVS4Audio2Sheet_Deep_Learning_based_Vocal_Separation_for_Audio_into_Music_Sheet_Conversion.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Music Choral audio Music sheet Vocal separation Audio-to-Sheet Deep learning Open-Unmix Band-Split Recurrent Neural Networks (BSRNN) Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Music
Choral audio
Music sheet
Vocal separation
Audio-to-Sheet
Deep learning
Open-Unmix
Band-Split Recurrent Neural Networks (BSRNN)
Databases and Information Systems
spellingShingle Music
Choral audio
Music sheet
Vocal separation
Audio-to-Sheet
Deep learning
Open-Unmix
Band-Split Recurrent Neural Networks (BSRNN)
Databases and Information Systems
TEO, Nicole
WANG, Zhaoxia
GHE, Ezekiel
TAN, Yee Sen
OKTAVIO, Kevan
LEWI, Alexander Vincent
ZHANG, Allyne
HO, Seng-Beng
DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion
description 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.
format text
author TEO, Nicole
WANG, Zhaoxia
GHE, Ezekiel
TAN, Yee Sen
OKTAVIO, Kevan
LEWI, Alexander Vincent
ZHANG, Allyne
HO, Seng-Beng
author_facet TEO, Nicole
WANG, Zhaoxia
GHE, Ezekiel
TAN, Yee Sen
OKTAVIO, Kevan
LEWI, Alexander Vincent
ZHANG, Allyne
HO, Seng-Beng
author_sort TEO, Nicole
title DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion
title_short DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion
title_full DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion
title_fullStr DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion
title_full_unstemmed DLVS4Audio2Sheet: Deep learning-based vocal separation for audio into music sheet conversion
title_sort dlvs4audio2sheet: deep learning-based vocal separation for audio into music sheet conversion
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9160
https://ink.library.smu.edu.sg/context/sis_research/article/10163/viewcontent/4._DLVS4Audio2Sheet_Deep_Learning_based_Vocal_Separation_for_Audio_into_Music_Sheet_Conversion.pdf
_version_ 1814047759128330240