Abstract
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.
Abstract (translated)
通过深度学习模型进行自动和弦识别（ACR）已经逐渐实现了有前景的识别准确性，但仍存在两个关键挑战。首先，先前的研究主要集中在音频域的ACR上，而符号音乐（如乐谱）的ACR由于数据稀缺性问题，受到的关注较少。其次，现有的方法仍然忽视了与人类音乐分析实践相一致的战略。 为了解决这些挑战，我们做出了两项贡献：(1) 我们引入了POP909-CL，这是POP909数据集的一个增强版本，包含了节奏对齐的内容以及和弦、节拍、调性和时间签名的人工校正标签；(2) 我们提出了BACHI，这是一种符号和弦识别模型，将任务分解为不同的决策步骤，即边界检测与和弦根音、质量及低音（反向）的迭代排序。这种机制反映了人类耳朵训练的方法。 实验表明，BACHI在古典音乐和流行音乐基准测试中均实现了最先进的和弦识别性能，并且消融研究验证了每个模块的有效性。
URL
https://arxiv.org/abs/2510.06528
