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
How should this claim be treated?
Truvace Impact Record TRV-2026-0125, v1: “Revising research practices for singing data collection.” Truvace, 2026-07-13. /record/TRV-2026-0125 (accessed at citation time). sha256 c5a11b6dd09cdfea…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0125 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.