aceRevising research practices for singing data collection
Abstract As AI voice synthesis enables increasingly sophisticated vocal deepfakes and non-consensual voice cloning, the governance, licensing and access of singing datasets has become an urgent concern for data-contributors, who face significant harms from downstream and non-consensual usage of their singing data. Singing datasets are foundational to the development of high fidelity voice AI synthesis, yet current data collection practices pose challenges: data-contributors have an event-centric contribution to…
Singing datasets enable development of high-fidelity AI voice synthesis models.
Singers who contribute singing data face significant harms from downstream non-consensual use including vocal deepfakes and voice cloning.
Analysis is based on only three singing datasets, limiting generalizability of power-to-interest findings.
Evidence
- Peer-reviewedAI & SOCIETY2026-07-12
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Truvace Impact Record TRV-2026-0125, v2: “Revising research practices for singing data collection.” Truvace, 2026-07-19. /record/TRV-2026-0125 (accessed at citation time). sha256 ebe1763c8aae53c4…
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