ChordFormer: A Conformer-Based Architecture for Large-Vocabulary Audio Chord Recognition

Chord recognition serves as a critical task in music information retrieval due to the abstract and descriptive nature of chords in music analysis. While audio chord recognition systems have achieved significant accuracy for small vocabularies (e.g., major/minor chords), large-vocabulary chord recognition remains a challenging problem. This complexity also arises from the inherent long-tail distribution of chords, where rare chord types are underrepresented in most datasets, leading to insufficient training samples. Effective chord recognition requires leveraging contextual information from audio sequences, yet existing models, such as combinations of convolutional neural networks, bidirectional long short-term memory networks, and bidirectional transformers, face limitations in capturing long-term dependencies and exhibit suboptimal performance on large-vocabulary chord recognition tasks. This work proposes ChordFormer, a novel conformer-based architecture designed to tackle structural chord recognition (e.g., triads, bass, sevenths) for large vocabularies. ChordFormer leverages conformer blocks that integrate convolutional neural networks with transformers, thus enabling the model to capture both local patterns and global dependencies effectively. Experimental results show that ChordFormer achieves a 2.3% improvement in frame-wise accuracy and a 6% increase in class-wise accuracy on large-vocabulary chord datasets. Moreover, our evaluation demonstrates that ChordFormer handles class imbalance more effectively than existing models, achieving balanced recognition across a wide range of chord types. We further assess the ChordFormer effectiveness through detailed ablation studies and additional metrics, including ARWCSR and chord inversion matrices. This approach bridges the gap between theoretical music knowledge and practical applications, advancing the field of large-vocabulary chord recognition.