Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation

The gist The 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.

How it works

The authors define attribution for a generated output as identifying which audio sources were used as inputs and verifying whether their musical characteristics shaped it Once the evidence supports these claims and its scope is clear, it can ground recognition of the identified source’s contribution and inform rights-holder reporting and compensation Attribution is difficult because it requires separate evidence for two claims: a particular track was used in generation, and its musical characteristics shaped the output They resolve the first uncertainty by conditioning generation on audio with known provenance and recording each input This identifies which audio sources were used without inferring their use from the output They then evaluate whether those inputs shaped the generated music, grounding attribution in both the record and evidence of musical effect Even when conditioning is faithful, a generated output may still reproduce a training track that was not supplied as input The input record cannot account for the relationship between that track and the output, so they compare generated outputs with the training data separately To implement and evaluate this approach, they present MixAudio and musicDNA MixAudio produces music from audio with known provenance and records which inputs are used by stem and track section musicDNA, a version identification model, compares generated stems with training data to find potential reproductions of works not supplied as inputs Conditioning tests show that MixAudio’s generated stems reflect the intended musical characteristics of their stems specific inputs and the accompanying music An musicDNA-based audit detected rare reproductions outside the input records In a separate comparison on the stems flagged by any retriever, it identified confirmed reproductions more reliably than the other methods tested These findings support their approach to music attribution, which combines input records and output analysis as complementary evidence

Background and Related Work

Prior research offers three approaches to attribution Training data attribution (TDA) uses influence estimates to support claims about a training work’s contribution to an output Output-based attribution searches for training works that resemble a generated output Input-based attribution records which music was used in a particular generation

Audio Conditioning and Faithfulness

MixAudio draws on prior studies where a short audio reference is used as an audio prompt for each stem to guide timbre and playing style, while a mix of existing stems provides harmonic and rhythmic context A short reference recording, used as an audio prompt for each stem, is intended to guide timbre and playing style, while a mix of existing stems provides harmonic and rhythmic context They compare each generated stem with its own prompt and prompts from other tracks to test how each affects the output The analysis shows that replacing the prompt moves timbre and leaves harmony in place, and replacing the context moves harmony and leaves timbre in place

Memorization Audits

Detecting reproduction depends on what kind of match is sought between an output and a training work They compare generated stems with training data using several methods, including musicDNA, to find candidate reproductions and verify them by listening The audit finds that generations rarely reproduce training stems the attribution record does not name Unnamed memorization is rare: one case, in the training split, a reproduction of a recording (Type I) flagged by both musicDNA and CLEWS No Type II or D case appears

Conclusion

Attribution in music generation should identify which music was used and how its musical characteristics shaped the output MixAudio’s outputs reflected the intended musical characteristics of their inputs, and an musicDNA-based audit found that reproductions outside the input records were rare These complementary findings provide an evidentiary basis which rights-holder reporting and compensation can draw on

Impact Statement

This paper presents work whose goal is to advance the field of machine learning The attribution framework they propose can help direct revenue from generated music to its rightful rights holders

Acknowledgements

This work was supported by the Korea Creative Content Agency (KOCCA) and the Artificial Intelligence Industry Cluster Agency (AICA)

References

The paper references numerous prior works across various domains including music information retrieval and machine learning applications in audio generation Agostinelli et al. MusicLM: Generating music from text Barnett et al. Exploring musical roots: Applying audio embeddings to empower influence attribution for a generative music model Batlle-Roca et al. Towards assessing data replication in music generation with music similarity metrics on raw audio Choi et al. Large-scale training data attribution for music generative models via unlearning Deng et al. Computational copyright: Towards a royalty model for music generative AI Dhariwal et al. Jukebox: A generative model for music Evans et al. Stable Audio Open Gong et al. ACE-Step: A step towards music generation foundation model Hammoudeh and Lowd Training data influence analysis and estimation: a survey Han et al. ARIA: A diagnostic framework for music training data attribution Karchkhadze et al. Multi-track MusicLDM: Towards versatile music generation with latent diffusion model Kim and Nam All-in-one metrical and functional structure analysis with neighborhood attentions on demixed audio Koh and Liang Understanding black-box predictions via influence functions Li et al. MERT: Acoustic music understanding model with large-scale self-supervised training Mariani et al. Multi-source diffusion models for simultaneous music generation and separation Mlodozeniec et al. Influence functions for scalable data attribution in diffusion models Morreale et al. Attribution-by-design: Ensuring inference-time provenance in generative music systems<ref:2610.

1. Improve attribution by combining input records and output analysis to provide complementary evidence for attribution, as this approach is shown to be more reliable than using only one method alone.

2. Implement musicDNA-based auditing to detect rare reproductions outside the input records, which helps establish that input records and output analysis provide complementary evidence for attribution.

3. Refine generation fidelity by ensuring generated stems follow inputs, as demonstrated by observing that a generated stem resembles its own prompt more than another track’s in prompt adherence tests.

4. Enhance control over musical characteristics by using conditioning swaps to verify input influence, where replacing the prompt moves timbre and leaves harmony in place, and replacing the context moves harmony and leaves timbre in place.

5. Develop a system capable of distinguishing between memorization types, specifically identifying Type I reproductions (reproduced recording) from other forms of unintentional reproduction.

6. Improve model robustness by testing transformations, as seen when a moderate transformation of the prompt leaves its timbre essentially intact, allowing for better analysis of how musical elements are affected by input manipulation.