# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 5 Results and Discussions
We aim to address this as a standalone contribution in future work, as it warrants dedicated investigation. Furthermore, post-processing techniques vary widely across studies, adding another layer of complexity to the comparison. Some models apply post-processing filters or heuristics to refine the beat and downbeat predictions, aiming to enhance output accuracy. In contrast, some approaches do not use any post-processing at all, instead directly optimizing for raw predictions without additional refinement. This difference in post-processing approaches can lead to substantial variations in reported accuracy and performance metrics. In the Ballroom dataset, characterized by structured ballroom dance genres such as covers Cha-Cha-Ch'a, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz, and Slow Waltz that exhibit consistent rhythmic patterns, the proposed model achieved an F-Measure of 0.961. This score, while slightly below the top performance from \"Beat This\" (0.975), remained competitive, aligning closely with those of SpecTNT-TCN (0.962) and Demixed DSA (0.968). For CMLt and AMLt, the model recorded scores of 0.937 and 0.962, respectively, demonstrating competitive performance that reflected alignment with SpecTNT-TCN and Demixed DSA. In the [PERSON] dataset, which contained Rock/Pop (68), Dance (40), Jazz (40), Classical (30), Folk (22), and Choral (22), each file is between 30 and 60 s in length, and the model demonstrated notable improvements in CMLt and AMLt scores (0.916 and 0.906, respectively) compared with the TCN baseline. The model surpassed the F-Measure of SpecTNT-TCN (0.877) with a score of 0.883. Although Demixed DSA achieved a slightly higher F-Measure (0.902), the proposed model's strong CMLt score highlighted its effective continuity in beat tracking. The Harmion dataset presented a challenging range of genres, despite the presence of ballroom-type rhythmic patterns. Results indicate that the proposed model matched Demixed DSA with an F-Measure of 0.954. Furthermore, the model attained a higher AMLt score of 0.964 compared with other baselines, including SpecTNT-TCN and Demixed DSA, underscoring its ability to accurately detect beats even under looser temporal tolerances. For the SMC dataset, known for its challenges due to quiet accompaniment and expressive timing, the proposed model achieved an F-Measure of 0.582. While this was lower than the \"Beat This\" model, it fell within the range of scores seen across the baselines, which generally exhibited reduced performance on this dataset. The model also recorded CMLt and AMLt scores of 0.455 and 0.618, respectively, consistent with other methods on this complex dataset. In the GTZAN dataset, which contains genres such as Blues, Classical, Country, Disco, Hip-hop, Jazz, Metal, Pop, Reggae, and Rock, the proposed model attained a competitive F-Measure of 0.867. Although it did not surpass the highest scores achieved by SpecTNT-TCN and Demixed DSA, the model's AMLt score of 0.905 highlighted its accuracy in flexible beat placement. The performance across GTZAN reflected the model's robustness in a diverse genre environment. Overall, the proposed model demonstrated consistent performance across various datasets, achieving strong scores in both F-Measure and temporal accuracy metrics (CMLt and AMLt). While it was not always the top performer in absolute F-Measure, it showcased reliable beat-tracking capabilities, closely matching the high-performing methods like Demixed DSA and SpecTNT-TCN, particularly in complex datasets such as GTZAN and Harmonix. The model excelled in continuity metrics, indicating its ability to capture \"allowed\" metrical levels through continuous segments. The variations include the same metrical level (both in-phase and off-beat), twice the annotated level, and half the annotated level (starting on the first or second beat). These results underscore the model's ability to generalize well across varied musical styles, establishing it as a robust solution for beat-tracking tasks.