Deep-learning beat-tracking algorithms have achieved remarkable accuracy in recent years. However, despite these advancements, challenges persist with musical examples featuring complex rhythmic structures, especially given their under-representation in training corpora. Expanding on our prior work, this paper demonstrates how our user-centred beat-tracking methodology effectively handles increasingly demanding musical scenarios. We evaluate its adaptability and robustness through musical pieces that exhibit rhythmic dissonance, while maintaining ease of integration with leading methods through minimal user annotations. The selected musical works—Uruguayan Candombe, Colombian Bambuco, and Steve Reich’s Piano Phase—present escalating levels of rhythmic complexity through their respective polyrhythm, polymetre, and polytempo characteristics. These examples not only validate our method’s effectiveness but also demonstrate its capability across increasingly challenging scenarios, culminating in the novel application of beat tracking to polytempo contexts. The results show notable improvements in terms of the F-measure, ranging from 2 to 5 times the state-of-the-art performance. The beat annotations used in fine-tuning reduce the correction edit operations from 1.4 to 2.8 times, while reducing the global annotation effort to between 16% and 37% of the baseline approach. Our experiments demonstrate the broad applicability of our human-in-the-loop strategy in the domain of Computational Ethnomusicology, confronting the prevalent Music Information Retrieval (MIR) constraints found in non-Western musical scenarios. Beyond beat tracking and computational rhythm analysis, this user-driven adaptation framework suggests wider implications for various MIR technologies, particularly in scenarios where musical signal ambiguity and human subjectivity challenge conventional algorithms.