ARIA: A Diagnostic Framework for Music Training Data Attribution
Abstract
Training data attribution (TDA) for music generation must answer two questions that copyright analysis requires, namely which training songs influence a generated output and along which musical aspects the influence operates. Existing methods reduce influence to a single scalar, without revealing which musical aspects are dominant in that influence. We propose ARIA, a framework that decomposes attribution along musical aspects (five for symbolic music, three for audio) and pairs the decomposition with reliability diagnostics computed from the segment-level score matrix. It measures within-group similarity among the top-K attributed tracks against random reference groups drawn from the training pool, and diagnoses the score matrix through its singular value decomposition and column statistics. On a symbolic-music model where attribution ground truth is available through counterfactual retraining, the reliability diagnostics rank four attribution methods identically to that ground truth. On an audio music generation model, ARIA reveals attribution behaviors that vary substantially across TDA methods, flags score matrices whose retrieved tracks are nearly identical across queries rather than reflecting per-query attribution, and characterizes embedding-similarity retrieval baselines by the musical aspect each encoder surfaces. Together, ARIA produces per-aspect attribution evidence aligned with the musical aspects considered under the idea-expression distinction in copyright analysis.
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Research summary
The paper presents ARIA, a novel framework for music training data attribution that effectively decomposes influence along musical aspects and provides reliability diagnostics, addressing a critical need in the intersection of machine learning and copyright law.
Methodology
The proposed ARIA framework innovatively decomposes training data attribution (TDA) along multiple musical aspects, addressing a significant gap in existing methods that reduce influence to a single scalar. The methodology includes reliability diagnostics based on segment-level score matrices and singular value decomposition, which are crucial for understanding the attribution behavior of different methods. This multi-faceted approach is particularly relevant in the context of music generation and copyright analysis, as it aligns with the legal framework of idea-expression distinction.
Results & evaluation
The experiments conducted on both symbolic and audio music generation models are well-structured, utilizing a benchmark with ground truth for validation and exploring the performance of various attribution methods. The results demonstrate the effectiveness of ARIA in revealing the influence of training songs on generated outputs and highlight the variability of attribution behaviors across different methods. The use of statistical measures to assess within-group similarity adds robustness to the findings.
Reproducibility
The paper provides comprehensive details on the experimental setup, including model architectures, datasets, and evaluation metrics, which enhances reproducibility. However, the absence of publicly available code or a demo limits the practical reproducibility of the results.
Limitations
One limitation is the reliance on existing benchmarks and the challenges associated with creating ground truth for audio attribution, which may affect the generalizability of the findings. Additionally, the framework's performance may vary with different types of music or genres, which is not fully explored in the experiments.
Broader impact
The implications of this research extend to the legal domain, particularly in copyright analysis, as it provides a framework for understanding the influence of training data on generative models. This could aid in developing fair compensation mechanisms for artists and inform future regulations regarding AI-generated content. The framework also sets a foundation for further research in music generation and attribution, potentially influencing how generative models are evaluated and utilized in practice. The paper presents ARIA, a novel framework for music training data attribution that effectively decomposes influence along musical aspects and provides reliability diagnostics, addressing a critical need in the intersection of machine learning and copyright law.
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