A multisensory virtual environment has been designed, aiming at recreating a realistic interaction with a set of vibrating strings. Haptic, auditory and visual cues progressively istantiate the environment: force and tactile feedback are provided by a robotic arm reporting for string reaction, string surface properties, and furthermore defining the physical touchpoint in form of a virtual plectrum embodied by the arm stylus. Auditory feedback is instantaneously synthesized as a result of the contacts of this plectrum against the strings, reproducing guitar sounds. A simple visual scenario contextualizes the plectrum in action along with the vibrating strings. Notes and chords are selected using a keyboard controller, in ways that one hand is engaged in the creation of a melody while the other hand plucks virtual strings. Such components have been integrated within the Unity3D simulation environment for game development, and run altogether on a PC. As also declared by a group of users testing a monophonic Keytar prototype with no keyboard control, the most significant contribution to the realism of the strings is given by the haptic feedback, in particular by the textural nuances that the robotic arm synthesizes while reproducing physical attributes of a metal surface. Their opinion, hence, argues in favor of the importance of factors others than auditory feedback for the design of new musical interfaces.

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Measuring the auditory lateralization elicited by interaural time difference (ITD) cues involves the estimation of a psychometric function (PF). The shape of this function usually follows from the analysis of the subjective data and models the probability of correctly localizing the angular position of a sound source. The present study describes and evaluates a procedure for progressively fitting a PF, using Gaussian process classification of the subjective responses produced during a binary decision experiment. The process refines adaptively an approximated PF, following Bayesian inference. At each trial, it suggests the most informative auditory stimulus for function refinement according to Bayesian active learning by disagreement (BALD) mutual information. In this paper, the procedure was modified to accommodate two-alternative forced choice (2AFC) experimental methods and then was compared with a standard adaptive “three-down, one-up” staircase procedure. Our process approximates the average threshold ITD 79.4% correct level of lateralization with a mean accuracy increase of 8.9% over the Weibull function fitted on the data of the same test. The final accuracy for the Just Noticeable Difference (JND) in ITD is achieved with only 37.6% of the trials needed by a standard lateralization test.

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