Abstract
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-theloop 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.

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Abstract In this article, we formalize and analyze qualia motion, i.e., the process by which a composition transitions across distinct harmonic qualities through the Fourier qualia space (FQS)—a multidimensional and transposition-independent space based on the discrete Fourier transform (DFT) coefficients’ magnitude. In the FQS, the plot of set classes relies on their harmonic qualities—such as diatonicity and octatonicity—enabling us to (1) identify the pitch-class set in a musical phrase that best represents its qualia—a reference sonority; (2) define a harmonic progression using all sequential reference sonorities in a piece; (3) visualize trajectory in space; and (4) establish a statistical metric for the ambiguity of harmonic qualia. Finally, we discuss Schoenberg's Op. 19, No. 1, analyzing the sense of its harmonic path. The proposed space leverages a bipartite, symmetrical, and consequential structure and unveils ambiguity as an element of nondirected linearity and counterpoint.

Abstract
This paper introduces Motiv, a dataset of expert saxophonist recordings illustrating parallel, similar, oblique, and contrary motions. These motions are variations of three phrases from Jesus VillaRojo's Lamento, with controlled similarities. The dataset includes 116 audio samples recorded by four tenor saxophonists, each annotated with descriptions of motions, musical scores, and latent space vectors generated using the VocalSet RAVE model. Motiv enables the analysis of motion types and their geometric relationships in latent spaces. Our preliminary dataset analysis shows that parallel motions align closely with original phrases, while contrary motions exhibit the largest deviations, and oblique motions show mixed patterns. The dataset also highlights the impact of individual performer nuances. Motiv supports a variety of music information retrieval (MIR) tasks, including gesture-based recognition, performance analysis, and motion-driven retrieval. It also provides insights into the relationship between human motion and music, contributing to real-time music interaction and automated performance systems.