AUTHOR=Sarasúa Álvaro , Urbano Julián , Gómez Emilia TITLE=Mapping by Observation: Building a User-Tailored Conducting System From Spontaneous Movements JOURNAL=Frontiers in Digital Humanities VOLUME=6 YEAR=2019 URL=https://www.frontiersin.org/journals/digital-humanities/articles/10.3389/fdigh.2019.00003 DOI=10.3389/fdigh.2019.00003 ISSN=2297-2668 ABSTRACT=

Metaphors are commonly used in interface design within Human-Computer Interaction (HCI). Interface metaphors provide users with a way to interact with the computer that resembles a known activity, giving instantaneous knowledge or intuition about how the interaction works. A widely used one in Digital Musical Instruments (DMIs) is the conductor-orchestra metaphor, where the orchestra is considered as an instrument controlled by the movements of the conductor. We propose a DMI based on the conductor metaphor that allows to control tempo and dynamics and adapts its mapping specifically for each user by observing spontaneous conducting movements (i.e., movements performed on top of fixed music without any instructions). We refer to this as mapping by observation given that, even though the system is trained specifically for each user, this training is not done explicitly and consciously by the user. More specifically, the system adapts its mapping based on the tendency of the user to anticipate or fall behind the beat and observing the Motion Capture descriptors that best correlate to loudness during spontaneous conducting. We evaluate the proposed system in an experiment with twenty four (24) participants where we compare it with a baseline that does not perform this user-specific adaptation. The comparison is done in a context where the user does not receive instructions and, instead, is allowed to discover by playing. We evaluate objective and subjective measures from tasks where participants have to make the orchestra play at different loudness levels or in synchrony with a metronome. Results of the experiment prove that the usability of the system that automatically learns its mapping from spontaneous movements is better both in terms of providing a more intuitive control over loudness and a more precise control over beat timing. Interestingly, the results also show a strong correlation between measures taken from the data used for training and the improvement introduced by the adapting system. This indicates that it is possible to estimate in advance how useful the observation of spontaneous movements is to build user-specific adaptations. This opens interesting directions for creating more intuitive and expressive DMIs, particularly in public installations.