# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 1 Introduction  
Beat tracking is the computational task of estimating the temporal locations of beats within an audio signal, and it is often complemented by downbeat tracking, which identifies the start of each musical measure or bar. While beat tracking is intuitive for human listeners, who easily tap along to a rhythm, automating this process remains a challenging task. In Music Information Retrieval (MIR), beat tracking is crucial not only for understanding rhythmic structure but also for applications in music production, live performance synchronization, and interactive music systems, all of which rely on accurate rhythm detection to function optimally [1]. The complexity of music significantly contributes to the difficulty of beat tracking. Music often involves polyphonic structures (multiple layers of sound), syncopation (when beats do not align directly with note onsets), and variations in tempo. Furthermore, expressive nuances and background noise add additional unpredictability. These factors, combined with the diversity of musical genres and rhythms, make beat tracking a non-trivial problem for computational models [2, 3]. In the early stages, beat tracking relied on rule-based or handcrafted algorithms that applied specific heuristics to infer beats. While these methods showed success in controlled environments, they struggled to generalize across different genres and adapt to unfamiliar music. The advent of machine learning brought significant improvements, with neural networks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) offering enhanced accuracy by learning from large, annotated datasets of diverse music. These models predict frame-wise probabilities of beats, which are then refinedthrough post-processing methods like the Dynamic Bayesian Network (DBN), a widely used tool for ensuring consistent beat structure and tempo regularity [1; 4; 5; 6]. More recently, Temporal Convolutional Networks (TCNs) have shown promise for beat tracking. TCNs capture long-term dependencies using dilated convolutions, which span across long sequences without requiring sequential processing. This approach offers faster training and allows the network to capture both short-term and medium-term patterns. As such, TCNs present an attractive alternative to traditional RNN-based models. However, TCNs still face challenges when dealing with complex rhythmic patterns, genre diversity, and the distinction between offline (using future information) and online (real-time) beat tracking [5; 6; 7]. The Transformer architecture, initially popularized for natural language processing tasks, has recently gained attention in MIR due to its strong performance. In MIR, Transformers are often used as temporal encoders to represent song-level features, which are beneficial for classification and chord recognition tasks. Despite their effectiveness, Transformers require substantial amounts of data, which poses a challenge in MIR due to the relatively small number of annotated datasets available. Therefore, the effective training of Transformer-based models in MIR often necessitates data augmentation or architectural modifications [8; 9; 10; 11; 12]. To address some of these challenges, recent models are focusing on jointly modeling both spectral and temporal features. The current state-of-the-art (SOTA) models process audio in slightly different ways. One approach focuses on frequency by treating each time frame as a sequence across different frequency bands, while another approach focuses on time, treating each frequency band as a sequence over time. This dual approach captures patterns across both the frequency and time dimensions, providing richer representations of the input signal [10; 12]. However, beat tracking presents unique challenges. While approaches such as those used in music tagging or source separation can benefit from modeling relationships between frequency bins within the same frame, beat tracking requires precise frame-level binary classification.