Live music performances and music production often involve the manipulation of several parameters during sound generation, processing, and mixing. In hardware layouts, those parameters are usually controlled using knobs, sliders and buttons. When these layouts are virtualized, the use of physical (e.g. MIDI) controllers can make interaction easier and reduce the cognitive load associated to sound manipulation. The addition of haptic feedback can further improve such interaction by facilitating the detection of the nature (continuous / discrete) and value of a parameter. To this end, we have realized an endless-knob controller prototype with programmable resistance to rotation, able to render various haptic effects. Ten subjects assessed the effectiveness of the provided haptic feedback in a target-matching task where either visual-only or visual-haptic feedback was provided; the experiment reported significantly lower errors in presence of haptic feedback. Finally, the knob was configured as a multi-parametric controller for a real-time audio effect software written in Python, simulating the voltage-controlled filter aboard the EMS VCS3. The integration of the sound algorithm and the haptic knob is discussed, together with various haptic feedback effects in response to control actions.

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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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