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We develop a theory for functional independent component analysis in an infinite-dimensional framework using Sobolev spaces that accommodate smoother functions. The notion of penalized kurtosis is introduced motivated by Silverman's method for smoothing principal components. This approach allows for a classical definition of independent components obtained via projection onto the eigenfunctions of a smoothed kurtosis operator mapping a whitened functional random variable. We discuss the theoretical properties of this operator in relation to a generalized Fisher discriminant function and the relationship it entails with the Feldman-H\'ajek dichotomy for Gaussian measures, both of which are critical to the principles of functional classification. The proposed estimators are a particularly competitive alternative in binary classification of functional data and can eventually achieve the so-called near-perfect classification, which is a genuine phenomenon of high-dimensional data. Our methods are illustrated through simulations, various real datasets, and used to model electroencephalographic biomarkers for the diagnosis of depressive disorder., Comment: 23 pages, 1 table, 4 figures

Emerging technologies in the domain of extended reality offer rich, new possibilities for the study and practice of joint music performance. Apart from the technological challenges, bringing music players together in extended reality raises important questions on their performance and embodied coordination. In this study, we designed an extended reality platform to assess a remote, bidirectional polyrhythmic interaction between two players, mediated in real time by their three-dimensional embodied avatars and a shared, virtual drum circle. We leveraged a multi-layered analysis framework to assess their performance quality, embodied co-regulation and first-person interaction experience, using statistical techniques for time-series analysis and mixed-effect regression and focusing on contrasts of visual coupling (not seeing / seeing as avatars / seeing as real) and auditory context (metronome / music). Results reveal that an auditory context with music improved the performance output as measured by a prediction error, increased movement energy and levels of experienced agency. Visual coupling impacted experiential qualities and induced prosocial effects with increased levels of partner realism resulting in increased levels of shared agency and self-other merging. Embodied co-regulation between players was impacted by auditory context and visual coupling, suggesting prediction-based compensatory mechanisms to deal with the novelty, difficulty, and expressivity in the musical interaction. This study contributes to the understanding of music performance in extended reality by using a methodological approach to demonstrate how co-regulation between players is impacted by visual coupling and auditory context and provides a basis and future directions for further action-oriented research.

The Art and Science Interaction Lab (ASIL) is a unique, highly flexible and modular interaction science research facility to effectively bring, analyse and test experiences and interactions in mixed virtual/augmented contexts as well as to conduct research on next-gen immersive technologies. It brings together the expertise and creativity of engineers, performers, designers and scientists creating solutions and experiences shaping the lives of people. The lab is equipped with state-of-the-art visual, auditory and user-tracking equipment, fully synchronized and connected to a central backend. This synchronization allows for highly accurate multi-sensor measurements and analysis.

In this position paper, we frame the field of Visual Musicology by providing an overview of well-established musicological sub-domains and their corresponding analytic and visualization tasks. To foster collaborative, interdisciplinary research, we discuss relevant data and domain characteristics. We give a description of the problem space, as well as the design space of musicology and discuss how existing problem-design mappings or solutions from other fields can be transferred to musicology. We argue that, through methodology transfer, established methods can be exploited to solve current musicological problems and show exemplary mappings from analytics fields related to text, geospatial, time-series, and other high-dimensional data to musicology. Finally, we point out open challenges, discuss research gaps, and highlight future research opportunities., Comment: 5 pages, position paper, 3 figures, 4th Workshop on Visualization for the Digital Humanities, VIS4DH, IEEE VIS 2019