arXiv (All)
open access
Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation
How can we verify whose music contributed to an AI-generated output? This paper demonstrates how input-based attribution can provide verifiable evidence of which audio sources were used in a generation and whether they shaped the output. To do so, we condition the generation solely on audio without
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Explainable Detection of Machine Generated Music and Early Systematic Evaluation#179103
- Explainable detection of machine generated music and early systematic evaluation.#148150
- Ghost in the Encoder: Decodable Artist Identity Representations in Lyrics-to-Song Generation#1144519
- Towards AI-Generated Music Plagiarism Detection as a Version Identification Problem#1233114
- SSIMuse: Assessing Data Replication in Symbolic Music via an Adapted Structural Similarity Index Measure#1117246
Record · ID 1234005
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive