BACHI通过分步解析，实现音乐乐谱的高精度和弦识别。
BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music
- 将和弦识别拆解为边界检测与逐轮排序根音、音程、低音三步，模拟人类听觉训练。
- 在古典与流行乐谱数据集上达到当前最优性能，准确率显著领先现有方法。
- 适用于音乐分析、自动配器等需要精准符号化理解的场景。
基于深度学习的自动和弦识别（ACR）已取得良好进展，但仍有两大挑战：一是现有研究主要集中在音频域，符号音乐（如乐谱）的和弦识别因数据稀缺而关注不足；二是现有方法未充分借鉴人类音乐分析习惯。为此，本文提出两项贡献：(1) 构建了增强版数据集POP909-CL，包含对齐速度的乐谱内容及人工校正的和弦、节拍、调性与拍号标签；(2) 提出BACHI模型，将符号和弦识别任务分解为边界检测与根音、音程、低音（转位）的迭代排序三个步骤，其机制契合人类耳训逻辑。实验表明，BACHI在古典与流行音乐基准上均达到当前最佳性能，消融实验验证了各模块的有效性。
原文摘要 · Abstract (English)
Automatic chord recognition (ACR) via deep learning models has gradually achieved promising recognition accuracy, yet two key challenges remain. First, prior work has primarily focused on audio-domain ACR, while symbolic music (e.g., score) ACR has received limited attention due to data scarcity. Second, existing methods still overlook strategies that are aligned with human music analytical practices. To address these challenges, we make two contributions: (1) we introduce POP909-CL, an enhanced version of POP909 dataset with tempo-aligned content and human-corrected labels of chords, beats, keys, and time signatures; and (2) We propose BACHI, a symbolic chord recognition model that decomposes the task into different decision steps, namely boundary detection and iterative ranking of chord root, quality, and bass (inversion). This mechanism mirrors the human ear-training practices. Experiments demonstrate that BACHI achieves state-of-the-art chord recognition performance on both classical and pop music benchmarks, with ablation studies validating the effectiveness of each module.
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