BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked
  Iterative Decoding on Pop and Classical Music
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.
68e701c57ae125f9582e6845
Mingyang Yao
68e701c57ae125f9582e6846
Ke Chen
68e701c57ae125f9582e6847
Shlomo Dubnov
68e701c57ae125f9582e6848
Taylor Berg-Kirkpatrick
BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked
  Iterative Decoding on Pop and Classical Music
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.
BACHI, a symbolic chord recognition model, achieves state-of-the-art performance by decomposing the task into boundary detection and iterative ranking, using an enhanced POP909-CL dataset.
automatic chord recognition deep learning models symbolic music POP909-CL dataset BACHI model boundary detection iterative ranking chord root chord quality bass inversion human ear-training practices