A personality-based adaptive system for visualizing classical music performances

Schedl, Markus; Melenhorst, Mark; Liem, Cynthia C. S.; Martorell Domínguez, Agustín; Mayor, Oscar; Tkalčič, Marko

doi:http://dx.doi.org/10.1145/2910017.2910604

A personality-based adaptive system for visualizing classical music performances

Citació

Schedl M, Melenhorst M, Liem CCS, Martorell A, Mayor Ó, Tkalčič M. A personality-based adaptive system for visualizing classical music performances. In: Proceedings of the 7th International Conference on Multimedia Systems (MMSys'16); 2016 May 10-13; Klagenfurt, Austria. New York (NY): ACM; 2016. p. 157-63. DOI: 10.1145/2910017.2910604

Resum

To enhance the experience of listening to classical orchestra music, either in the concert hall or at home, we present a personalized system that integrates three visualization/interaction concepts: Score Follower (points to the current position in the score), Orchestra Layout (illustrates instruments that are currently playing and their dynamics), and Structure Visualization (visualizes structural elements such as themes or motifs). Motivated by previous literature that found evidence for connections between personality and music consumption and preference, we first assessed in a user study to which extent personality traits and music visualization preferences correlate. Measuring preference via pragmatic quality and personality traits according to the Big Five Inventory (BFI) questionnaire, we found substantial interconnections between them. These translate into rules relating certain personality traits (e.g., extraversion or agreeableness) to preference rankings of the visualizations. In the proposed personality-based system, users are grouped into four clusters according to their answers to the most significant personality questions determined in the study. The order of the visualizations for a given user is adapted with respect to the ranking preferred by other users in the same cluster. Evaluation of the system was carried out by a second user study that showed a significantly higher normalized discounted cumulative gain (NDCG) for the personalized system in comparison to a system with randomized order of the visualizations.