Representation Matters: An Evaluation of MEL, CQT, VQT, and MCQT for Beat Tracking - Cavalcante, Joaquim B.; Hsu, Julio; Batista, Carlos; M. Filho, Telmo; Gaudêncio, Thaís; Malheiros, Yuri (Anais do Simpósio Brasileiro de Computação Musical, 2025)
Neural beat-tracking models predominantly rely on Mel spectrograms. These representations often fail to capture harmonic nuances in complex genres (e.g., jazz, classical), leading to rhythmically ambiguous signal patterns and suboptimal performance. We challenge the architectural-centric paradigm in music analysis by (1) evaluating alternative time-frequency representations, and (2) proposing a dual-input architecture to synergize complementary spectrogram properties. Through controlled experiments, we compare Mel, Constant-Q Transform (CQT), and Variable-Q Transform (VQT) inputs. We then introduce MCQT: a dual-branch model that fuses Mel and CQT, validated using 8-fold cross-validation and ensemble inference. Replacing Mel with CQT reduced subharmonic errors by 22% in harmony-rich contexts. MCQT further outperformed single-input baselines, achieving state-of-the-art performance in annotation coverage (0.912 vs. Mel’s 0.910) and reducing subharmonic errors by 26% compared to Mel. It excelled in complex genres (e.g., 6.4% higher downbeat F1-score in classical). Strategic representation engineering, not just architectural complexity, is pivotal for robust beat tracking. MCQT’s fusion of complementary spectrograms establishes a new paradigm for MIR tasks, particularly in harmonically complex music.