BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked
  Iterative Decoding on Pop and Classical Music
Authors: name: Mingyang Yao, name: Ke Chen, name: Shlomo Dubnov, name: Taylor Berg-Kirkpatrick
Organization: UCSD

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.