An exciting possibility for compensating for loss of sensory function is to augment deficient senses by conveying missing information through an intact sense. Here we present an overview of techniques that have been developed for sensory substitution

This paper presents the results of a set of experiments in which we used continuous auditory feedback to augment motor training exercises. This feedback modality is mostly underexploited in current robotic rehabilitation systems, which usually implement only very basic auditory interfaces. Our hypothesis is that properly designed continuous auditory feedback could be used to represent temporal and spatial information that could in turn, improve performance and motor learning.

This paper studies the relationship between head-related transfer functions (HRTFs) and pinna reflection patterns in the frontal hemispace. A pre-processed database of HRTFs allows extraction of up to three spectral notches from each response taken in the median sagittal plane. Ray-tracing analysis performed on the obtained notches' central frequencies is compared with a set of possible reflection surfaces directly recognizeable from the corresponding pinna picture. Results of such analysis are discussed in terms of the reflection coefficient sign, which is found to be most likely negative. Based on this finding, a model for real-time HRTF synthesis that allows to control separately the evolution of different acoustic phenomena such as head diffraction, ear resonances, and reflections is proposed through the design of distinct filter blocks. Parameters to be fed to the model are derived either from analysis or from specific anthropometric features of the subject. Finally, objective evaluations of reconstructed HRTFs in the chosen spatial range are performed through spectral distortion measurements.

This study investigates how complementary auditory feedback may affect short-term gait modifications induced by four training sessions with a robotic exoskeleton. Healthy subjects walked on a treadmill and were instructed to match a modified gait pattern derived from their natural one, while receiving assistance by the robot (kinetic guidance). The main question we wanted to answer is whether the most commonly used combination of feedback (i.e., haptic and visual) could be either enhanced by adding auditory feedback or successfully substituted with a combination of kinetic guidance and auditory feedback. Participants were randomly assigned to one of four groups, all of which received kinetic guidance. The control group received additional visual feedback, while the three experimental groups were each provided with a different modality of auditory feedback. The third experimental group also received the same visual feedback as the control group. Differences among the training modalities in gait kinematics, timing and symmetry were assessed in three post-training sessions.

The research presented in this paper focuses on Head-Related Transfer Function (HRTF) individualization using deep learning techniques. HRTF individualization is paramount for accurate binaural rendering, which is used in XR technologies, tools for the visually impaired, and many other applications. The rising availability of public HRTF data currently allows experimentation with different input data formats and various computational models. Accordingly, three research directions are investigated here: (1) extraction of predictors from user data; (2) unsupervised learning of HRTFs based on autoencoder networks; and (3) synthesis of HRTFs from anthropometric data using deep multilayer perceptrons and principal component analysis. While none of the aforementioned investigations has shown outstanding results to date, the knowledge acquired throughout the development and troubleshooting phases highlights areas of improvement which are expected to pave the way to more accurate models for HRTF individualization.

This paper deals with the issue of individualizing the head-related transfer function (HRTF) rendering process for auditory elevation perception. Is it possible to find a nonindividual, personalized HRTF set that allows a listener to have an equally accurate localization performance than with his/her individual HRTFs? We propose a psychoacoustically motivated, anthropometry based mismatch function between HRTF pairs that exploits the close relation between the listener's pinna geometry and localization cues. This is evaluated using an auditory model that computes a mapping between HRTF spectra and perceived spatial locations. Results on a large number of subjects in the center for image processing and integrated computing (CIPIC) and acoustics research institute (ARI) HRTF databases suggest that there exists a nonindividual HRTF set, which allows a listener to have an equally accurate vertical localization than with individual HRTFs. Furthermore, we find the optimal parameterization of the proposed mismatch function, i.e., the one that best reflects the information given by the auditory model. Our findings show that the selection procedure yields statistically significant improvements with respect to dummy-head HRTFs or random HRTF selection, with potentially high impact from an applicative point of view.