Performance Analysis of a Convolutional Transformer-Based Automatic Chord Recognition Model for Extended Chord Qualities - Melo, Daniel Antonio de Jesus; Medeiros, Pedro Augusto Gomes; Schmitt, Luigi Emanuel Martins; Silva Filho, Telmo M.; Barbosa, Yuri de Almeida Malheiros; Rêgo, Thaís Gaudencio do (Anais do Simpósio Brasileiro de Computação Musical; 2025: Anais do XIX Simpósio Brasileiro de Computação MusicalDO - 10.5753/sbcm.2025.13240, 2025)
Automatic Chord Recognition (ACR) has seen significant advancements with the advent of deep learning, particularly with convolutional transformer architectures. However, a detailed performance analysis, especially concerning their ability to recognize chord qualities, remains an area for investigation. This paper presents an analysis of a convolutional transformer-based automatic chord recognition model for extended chord qualities. We evaluate its accuracy on three benchmark datasets—McGill-Billboard, RWC-Pop, and JAAH—, focusing on root notes and specific chord qualities (maj, min, maj7, min7, 7, hdim7, and 7(b9)). Using annotations from the original ground truth files and the CHOCO (Chord Corpus) as a reference, with a duration-weighted evaluation methodology, we conduct an assessment of the model’s capabilities. Our results reveal that while the model achieves high accuracy on basic triads, its ability to correctly identify extended chords drops in comparison. We conclude that the model’s limitations stem from the underrepresentation of extended chords in common training corpora, underscoring the critical need for more balanced and harmonically diverse datasets to potentially mitigate the reported issues.