# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 3 Related Works  
To address limitations, CNNs and TCNs were introduced to capture local rhythmic features more efficiently. [PERSON] and [PERSON]'s explorations of TCNs [1; 5; 6; 7] offered a scalable alternative to RNNs, as TCNs provided faster training and required fewer parameters, making them highly suitable for large music datasets. By using dilated convolutions, TCNs could capture temporal dependencies without the need for sequential processing, thus reducing computational demands and improving model robustness. The advent of Transformer models brought a new level of precision to beat tracking, as self-attention mechanisms could model both short- and long-term dependencies within music sequences [10; 11; 27]. [PERSON] et al. [10] proposed a variant of Transformer that models the spectrogram along both time and frequency axes, marking the first successful attempt to apply Transformer-based models to beat and downbeat tracking. Their model uses a spectral Transformer and a temporal Transformer, where the spectral Transformer extracts features for each frame, focusing on harmonic components, while the temporal Transformer exchanges local information along the time axis to identify beats and downbeats. Building on this, the SpecTNT model further improves on this approach by stacking two levels of Transformer encoders, with the spectral encoder focusing on harmonic changes and the temporal encoder aggregating spectral information for precise beat and downbeat tracking. Additionally, combining SpecTNT with TCN further enhances performance, significantly improving downbeat tracking while maintaining comparable results in beat tracking. Models like [PERSON] et al. [28] present a versatile all-in-one model that combines beat tracking with downbeat and structural segmentation. This model leverages source-separated spectrograms as inputs and uses dilated neighborhood attentions to capture both short- and long-term temporal dependencies, along with non-dilated attentions for local instrumental dependencies. Building on this, it introduces a unified model that jointly performs beat and downbeat tracking, as well as functional structure segmentation and labeling. The model achieves state-of-the-art performance on the Harmonix Set, with fewer parameters than recent models. An ablation study demonstrates that the concurrent learning of beats, downbeats, and segments enhances overall performance, with each task benefiting from the others. [PERSON] et al. [8] introduced demixing, where audio is separated into individual instrument channels, aligning with human perception of metrical structures from richer musical contexts. While this approach is reasonable from a music perception standpoint, it reduces the generality of the system, as it assumes that input pieces will contain such instruments to some extent. Building on this, Beat Transformer is a novel Transformer encoder model for joint beat and downbeat tracking that uses demixed spectrograms. The model employs timewise and instrument-wise attention to capture metrical cues and a dilated self-attention mechanism for efficient hierarchical modeling. Experiments show significant improvements in demixed beat tracking and a 4% boost in downbeat tracking accuracy over TCN architectures, along with an interpretable attention pattern reflecting hierarchical metrical structures. Another recent advancement, proposed by [PERSON] et al. [12], sought to replace DBN-based post-processing with a minimal, transformer-only model. By using rotary transformers, the model achieved high accuracy without relying on complex post-processing,marking a significant step forward in both efficiency and accuracy. Extending this, a new system addresses challenges in beat and downbeat tracking, emphasizing generality across diverse genres and improved accuracy through a novel, shift-tolerant weighted BCE loss function that uses max-pooling (e.g., over seven frames) to focus on the largest prediction near each label while ignoring negative examples within \(\pm\)six frames.