Generative AI-Driven Optimization of Digital Value Chains for Intangible Heritage Music

Xiaoyang Yu

doi:10.54254/2755-2721/2025.26013

Applied and Computational EngineeringOpen access

Generative AI-Driven Optimization of Digital Value Chains for Intangible Heritage Music

Research Article

Open Access

Generative AI-Driven Optimization of Digital Value Chains for Intangible Heritage Music

Xiaoyang Yu ^1*

¹ Tianjin Conservatory of Music, Tianjin, China

^*Corresponding author: 13336224967@163.com

Published on 13 August 2025

ACE Vol.176

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-239-3

ISBN (Online): 978-1-80590-240-9

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Abstract

Safeguarding intangible heritage music increasingly depends on endtoend digital value chains that can faithfully model, generate, track, and remunerate culturally embedded musical knowledge. This paper proposes a generative AI framework that jointly (i) learns a multilayer, ethnomusicologyaware representation across raw audio, symbolic structure, and contextual metadata, (ii) constrains a controllable transformer–diffusion pipeline to preserve modality, microtonal tuning, ornamentation practice, and rhythmic grammar, and (iii) embeds blockchainanchored provenance and smartcontract royalty logic inside the creative pipeline. Using 1,842 recordings (126.3 hours) spanning Southeast Asian gamelan, Chinese Buddhist chant, and Andean panpipe repertoires, we compare our system against an archiveonly baseline and an unconditioned generative baseline. Conditioned generation reduces average modal deviation from canonical tunings from 12.3 ± 4.1 cents to 4.9 ± 1.8 cents, decreases rhythmic dynamictimewarping distance by 44.5%, and raises expert authenticity ratings from 3.12 ± 0.61 to 4.47 ± 0.28 (ICC(2,k)=0.82). On the valuechain layer, median royalty settlement time drops from 23.7 days to 1.92 days, the Theil inequality index falls from 0.218 to 0.071, and Jain’s fairness index rises from 0.63 to 0.89. Listenerside evaluation shows higher longtail coverage (+31.4%), improved NDCG@20 (0.382→0.497), and a 26% reduction in the hazard of early session abandonment. The findings demonstrate that culturally bounded, rightssensitive generative pipelines can simultaneously enhance preservation fidelity, creative reuse, and equitable community remuneration.

Keywords:

Intangible cultural heritage, generative AI, music informatics, digital value chain, blockchain provenance

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References

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References

[2]. Mitra, R., & Zualkernan, I. (2025). Music generation using deep learning and generative AI: a systematic review. IEEE Access.

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Cite this article

Yu,X. (2025). Generative AI-Driven Optimization of Digital Value Chains for Intangible Heritage Music. Applied and Computational Engineering,176,50-55.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Machine Learning and Automation

ISBN: 978-1-80590-239-3(Print) / 978-1-80590-240-9(Online)

Editor: Hisham AbouGrad

Conference website: 978-1-80590-240-9

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.176

ISSN: 2755-2721(Print) / 2755-273X(Online)

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