# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 7 Conclusions  
In this paper, a beat tracking system for musical audio was developed using a parallel architecture of a Temporal Convolutional Network (TCN) and Transformer network to learn beat activations from the log Mel spectrogram representation of audio. Following this, a Dynamic Bayesian Network (DBN) was employed in the decoding stage to model beat intervals of varying lengths and determine globally optimal beat positions. The model was trained on four datasets--Ballroom, Harmonix, SMC, and Hainsworth--using an eight-fold cross-validation setup, while the GTZAN dataset was used exclusively for testing. The system's evaluation demonstrates performance comparable to the state of the art, even without data augmentations or modifications to the loss function, and achieves this with a reduced parameter count (3,683,855) compared with other Transformer-based architectures. We also used Grad-CAM to visualize the features and found that the proposed model can generalize across the time frames.  
Several potential improvements and extensions could further enhance the proposed beat tracking system. One promising direction is to incorporate source separation, allowing the model to apply distinct processing branches for different audio types or genres. This could include genre identification in early layers and directing subsequent branches for genre-specific beat tracking. Another area for exploration is the use of Transformers that treat time and frequency axes independently--such as frequency-directed and time-directed Transformers. Additionally, developing alternative loss functions that enforce periodicity during training and exploring methods to reduce model parameters could make the system more efficient. Further work could also include integrating data augmentation techniques to enhance robustness across diverse sound conditions and fine-tuning specific genres to improve genre-based accuracy. Testing on larger and more varied datasets could provide a more comprehensive understanding of the model's generalization capabilities, potentially advancing its applicability to real-time and live music settings.  
Conceptualization, N.T. and J.L.; methodology, N.T.; writing--original draft, N.T.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Research Foundation of Korea (NRF) under the Development of AI-Based Analysis/Synthesis Techniques for Korean Traditional Music Project (Funding Number: RS-2024-00340948).  
Not applicable.  
Figure 7: A diagram summarizing the Grad-CAM analysis in one of the audio samples from GTZAN dataset: (**a**) spectrogram, (**b**) ground truth annotations, (**c**) Grad-CAM activations generated from convolution block, (**d**) Grad-CAM activations generated from the final layer, (**e**) frames with beat probability \(>\) 0.5 marked in red dotted line, before DBN post-processing.  
Data Availability Statement: Data were derived from the following resources available in the public domain: [[https://ismir.net/resources/datasets/](https://ismir.net/resources/datasets/)]([https://ismir.net/resources/datasets/](https://ismir.net/resources/datasets/)) (accessed on 15 December 2024).  
The authors declare no conflicts of interest.