# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 5 Results and Discussions
Considering that Transformer is notoriously data-inefficient to train, it is remarkable that our model is well-trained with data without any augmentation or special modifications to the loss function. The number of trainable parameters is also lower compared with other transformer models, as mentioned in Table 3.  
To provide a more comprehensive discussion on the challenges of comparing results across studies, it is essential to consider how differences in training methods, loss functions, and data augmentation strategies affect model performance. The combined dataset and eight-fold cross-validation approach in our study likely result in different training dynamics compared with studies that use single datasets or different validation schemes. Variations in dataset composition, such as genre diversity, instrument types, or dataset size, can lead to differing model performances. For instance, a model trained on a larger, more diverse dataset might generalize better but also suffer from more complex decision boundaries due to the increased variance in the data. This could influence the performance metrics reported in previous studies, especially when evaluating models across datasets with different characteristics. The use of different loss functions, such as Binary Cross-Entropy or others that focus on specific objectives (e.g., shift tolerance), can greatly impact how the model converges during training and its ability to generalize. Loss functions tailored to specific challenges--like timing errors in beat tracking--might lead to improved performance in those contexts but could also introduce biases or limitations in other evaluation scenarios. A deeper analysis of how loss function choice affects performance across different music genres or datasets would help contextualize the reported results and provide a clearer understanding of the performance trade-offs. Data augmentation and preprocessing techniques, such as time-stretching, pitch-shifting, or source separation, aim to make the model more robust to variations in input data. However, these strategies introduce an additional layer of complexity, which could lead to overfitting if not properly managed. Without augmentations or source separation, our model might exhibit better generalization on unseen data but potentially struggle with certain variations, such as timing or pitch shifts. Investigating how the inclusion or exclusion of these techniques influences model robustness across different test conditions could provide useful insights for future improvements. Post-processing strategies, such as filtering or heuristic-based refinements, are commonly employed to further enhance the accuracy of predictions. The absence of these in our approach means that our model's raw predictions are subject to greater variability, as the lack of smoothing or correction can introduce noise or inconsistencies in the final output. Further research into how various post-processing methods, including those used in prior studies, affect prediction accuracy and whether they should be incorporated into the experimental setup could provide valuable guidance for future model refinement. In conclusion, to better compare and benchmark model performance across studies, it would be beneficial to standardize certain elements of the experimental setup, such as dataset composition, loss functions, and augmentation strategies. A more detailed investigation into how these factors influence performance metrics could help clarify the true effectiveness of different approaches, offering a more nuanced understanding of model behavior and facilitating more meaningful comparisons across studies.