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This paper tackles the problem of automatic chord recognition (ACR), particularly for non-triad chords, by reformulating it as a segment-level sequence-to-sequence prediction task. They introduce novel token representations and an encoder pre-training method tailored for time-aligned chord modeling to address oversegmentation and data scarcity. Experimental results demonstrate improved performance in both chord recognition and segmentation, especially for complex chords.
Segmenting music into meaningful chunks and predicting chords sequence-to-sequence boosts recognition accuracy, especially for those pesky, rare non-triad chords that plague existing systems.
Automatic chord recognition (ACR) extracts time-aligned chord labels from music audio recordings. Despite recent advances, ACR still struggles with oversegmentation, data scarcity, and imbalance, especially in recognizing complex chords such as non-triads, which are unpopular in existing datasets. To address these challenges, we reformulate ACR as a segment-level sequence-to-sequence prediction task, where chord sequences are predicted auto-regressively rather than frame by frame. This design mitigates excessive segmentation by detecting chord changes only at segment boundaries. We further introduce two types of token representations and an encoder pre-training method, both specifically designed for time-aligned chord modeling. Experimental results show that our model improves performance in both chord recognition and segmentation, with notable gains for complex and infrequent chord types. These findings demonstrate the effectiveness of segment-level sequence modeling, structured tokenization, and representation learning for advancing chord recognition systems.
