# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 3 Related Works
This ensures the network learns sharp, localized activations. Combined with an architecture alternating convolutions and transformers over time and frequency, the model achieves state-of-the-art F1 scores. However, removing the DBN post-processing comes at a cost, particularly for continuity metrics like CMLt and AMLt. To mitigate this, training continues even after the validation loss begins to increase, leveraging overconfident predictions that improve the validation F1 score. The lack of DBN post-processing disrupts the mechanism of relying on high-confidence predictions to infer beats in low-confidence areas, thereby reducing its effectiveness in these metrics. TCNs leverage dilated causal convolutions, as introduced in WaveNet [29], to effectively capture temporal patterns without information leakage from future to past data points. TCNs have proven particularly adept in handling sequential data, such as time series, and are highly effective at extracting local features. The Transformer model uses a self-attention mechanism that scans the entire dataset, capturing global interactions, which enables it to learn long-range dependencies. This has led to significant improvements in tasks like machine translation. Transformer variants have also shown success across various domains, including language processing, vision, and time series analysis. Some studies [30; 31; 32] combine TCN and Transformer models sequentially or in parallel. In sequential designs, a Transformer is placed either before or after a TCN layer, but this introduces dependencies between the models. In contrast, a parallel architecture allows each branch to learn independently, capturing local and global features without interference and facilitating effective feature fusion. [PERSON] et al. [10] combined Transformers with TCNs, enabling accurate beat detection by using TCNs to capture rapid changes and Transformers to recognize broader patterns. In their SpectTNT-TCN model, the Transformer encoders modeled spectral and temporal features, with inputs divided into 6 s chunks to manage long sequences effectively. Our approach enhances this by using a parallel architecture where both TCN and Transformer branches operate independently on the same continuous input sequence. The TCN branch captures local temporal patterns through dilated causal convolutions, while the Transformer branch learns global dependencies with self-attention. This ensures that both local and global features are extracted without interference. Unlike previous models that average the outputs of each branch, our approach employs a more sophisticated fusion strategy, preserving the unique features learned by each branch for better performance in beat and downbeat tracking. We process the entire input sequence for both branches, avoiding the discontinuities caused by chunking in previous models. While [PERSON] et al. used a ResNet50 front-end for feature extraction, our model uses a lightweight convolution design, reducing computational complexity without sacrificing performance. Additionally, our model optimizes both beat and downbeat tracking simultaneously, eliminating the need for separate loss functions for each task. This joint optimization makes the training process more efficient and ensures consistent performance across both tasks. Our approach offers a more flexible, efficient, and robust solution for beat tracking, providing a significant advancement over existing hybrid models with comparatively fewer trainable parameters.