发表机构
Shenzhen University（深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比合成和弦损坏与ACR重放对MIDI-SAG的影响，发现局部损坏测试机制、完整重放评估和弦条件传播，匹配重放支持可减少两者不匹配。
AI 中文摘要
合成和弦损坏为歌唱伴奏生成（SAG）提供了可控的压力测试，而完整的自动和弦识别（ACR）重放则衡量部署系统所接收的条件。我们通过固定的MIDI-SAG生成器重放CNN-CRF和DeepChroma+CRF的预测结果，保持音轨、随机种子、上下文和评分窗口不变。在30条配对音轨和3个随机种子中，4秒的中心三全音在STFT、CQT和CENS上产生的目标变更和输出内部效应大于CNN-CRF重放；29/30条音轨的CENS目标差距为正（均值0.462）。匹配重放支持与关系组合可减少这种不匹配，联合匹配在全30条音轨分析中给出最低的重放距离。在剂量相近时，相对根替换产生的CENS全窗口输出变化是同根质量翻转的2.88倍。匹配代理对于两种识别器路径都更接近重放。我们得出结论：局部损坏测试机制，而完整重放评估部署的和弦条件传播。
英文摘要
Synthetic chord substitutions offer controlled tests of music generation, but their effects can differ from those of a complete recognized chord sequence. We propose structure-matched calibration, which constructs synthetic chord sequences that match the changed positions and harmonic-relation composition of recognizer replay. Paired generation compares both target-response magnitude and output-chord agreement with replay. On 29 of 30 MUSDB18-HQ songs, central four-second tritone corruption produces a larger target response than complete replay in MIDI-SAG. On 24 held-out MoisesDB songs, structure matching reduces target-response distance to replay by 81% for MIDI-SAG and 77% for MusicGen-Chord. Distance decreases on every song in both models with CNN--CRF. Joint matching also reduces output-chord mismatch with replay by 8--17 percentage points relative to temporal or relational matching alone.
Comments5 pages, 4 figures, 2 tables. Code and derived results: https://github.com/Viwennnnnn/local-chord-corruption-replay
