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.

翻译：音乐生成的训练数据归因必须回答版权分析所需的两类问题，即哪些训练曲目影响了生成输出，以及影响沿哪些音乐维度展开。现有方法将影响简化为单一标量，无法揭示哪些音乐维度在该影响中占主导地位。我们提出ARIA框架，该框架沿音乐维度（符号音乐五个维度、音频三个维度）分解归因，并将分解结果与基于片段级得分矩阵计算出的可靠性诊断指标配对。它测量排名前K的归因曲目组与从训练池中抽取的随机参考组之间的组内相似度，并通过奇异值分解和列统计量诊断得分矩阵。在可通过对反事实重训练获得归因真值的符号音乐模型上，可靠性诊断指标对四种归因方法的排序与真值一致。在音频音乐生成模型上，ARIA揭示了不同TDA方法显著不同的归因行为，标记了那些检索曲目在各查询间几乎相同而非反映逐查询归因的得分矩阵，并通过每个编码器呈现的音乐维度来表征嵌入相似性检索基线。综合而言，ARIA产生的逐维度归因证据与版权分析中思想表达二分法所考虑的音乐维度保持一致。