Frequency Estimation Of The First Pinna Notch In Head-Related Transfer Functions With A Linear Anthropometric Model

The relation between anthropometric parameters and Head-Related Transfer Function (HRTF) features, especially those due to the pinna, are not fully understood yet. In this paper we apply signal processing techniques to extract the frequencies of the main pinna notches (known as N1, N2, and N3) in the frontal part of the median plane and build a model relating them to 13 different anthropometric parameters of the pinna, some of which depend on the elevation angle of the sound source. Results show that while the considered anthropometric parameters are not able to approximate with sufficient accuracy neither the N2 nor the N3 frequency, eight of them are sufficient for modeling the frequency of N1 within a psychoacoustically acceptable margin of error. In particular, distances between the ear canal and the outer helix border are the most important parameters for predicting N1

HRTF selection by anthropometric regression for improving horizontal localization accuracy

This work focuses on objective Head-Related Transfer Function (HRTF) selection from anthropometric measurements for minimizing localization error in the frontal half of the horizontal plane. Localization predictions for every pair of 90 subjects in the HUTUBS database are first computed through an interaural time difference-based auditory model, and an error metric based on the predicted lateral error is derived. A multiple stepwise linear regression model for predicting error from inter-subject anthropometric differences is then built on a subset of subjects and evaluated on a complementary test set. Results show that by using just three anthropometric parameters of the head and torso (head width, head depth, and shoulder circumference) the model is able to identify non-individual HRTFs whose predicted horizontal localization error generally lies below the localization blur. When using a lower number of anthropometric parameters, this result is not guaranteed

Relative Auditory Distance Discrimination With Virtual Nearby Sound Sources

In this paper a psychophysical experiment targeted at exploring relative distance discrimination thresholds with binaurally rendered virtual sound sources in the near field is described. Pairs of virtual sources are spatialized around 6 different spatial locations (2 directions 7 3 reference distances) through a set of generic far-field Head-Related Transfer Functions (HRTFs) coupled with a near-field correction model proposed in the literature, known as DVF (Distance Variation Function). Individual discrimination thresholds for each spatial location and for each of the two orders of presentation of stimuli (approaching or receding) are calculated on 20 subjects through an adaptive procedure. Results show that thresholds are higher than those reported in the literature for real sound sources, and that approaching and receding stimuli behave differently. In particular, when the virtual source is close (< 25 cm) thresholds for the approaching condition are significantly lower compared to thresholds for the receding condition, while the opposite behaviour appears for greater distances (~ 1 m). We hypothesize such an asymmetric bias to be due to variations in the absolute stimulus level

HRTF individualization using deep learning

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

A Music Programming Course for Undergraduate Music Conservatory Students: Evaluation and Lessons Learnt

This paper introduces the content and organisation of a music programming course offered to undergraduate Conservatory students in the spring of 2022. A number of evaluation procedures, including pre- and post-course questionnaires and exercises, and a final assignment have been administered by the teacher. Results indicate an increased confidence in the use of computers and programming, although some aspects of creativity and computational thinking need further revision. The authors examine the course content in light of the results obtained, discuss the followed approach, and make assumptions for the improvement of both course content and assessment methods

The Viking HRTF dataset v2

The Viking HRTF dataset v2 is a collection of head-related transfer functions (HRTFs) measured at the University of Iceland. It includes full-sphere HRTFs measured on a dense spatial grid (1513 positions) with a KEMAR mannequin with different pairs of artificial pinnae attached. The artificial pinnae were previously obtained through a custom molding procedure from different lifelike human heads (courtesy of Ernst Backman, Saga Museum Reykjavík)

Evaluation of an Audio-haptic Sensory Substitution Device for Enhancing Spatial Awareness for the Visually Impaired

SIGNIFICANCE: Visually impaired participants were surprisingly fast in learning a new sensory substitution device, which allows them to detect obstacles within a 3.5-m radius and to find the optimal path in between. Within a few hours of training, participants successfully performed complex navigation as well as with the white cane. PURPOSE: Globally, millions of people live with vision impairment, yet effective assistive devices to increase their independence remain scarce. A promising method is the use of sensory substitution devices, which are human-machine interfaces transforming visual into auditory or tactile information. The Sound of Vision (SoV) system continuously encodes visual elements of the environment into audio-haptic signals. Here, we evaluated the SoV system in complex navigation tasks, to compare performance with the SoV system with the white cane, quantify training effects, and collect user feedback. METHODS: Six visually impaired participants received eight hours of training with the SoV system, completed a usability questionnaire, and repeatedly performed assessments, for which they navigated through standardized scenes. In each assessment, participants had to avoid collisions with obstacles, using the SoV system, the white cane, or both assistive devices. RESULTS: The results show rapid and substantial learning with the SoV system, with less collisions and higher obstacle awareness. After four hours of training, visually impaired people were able to successfully avoid collisions in a difficult navigation task as well as when using the cane, although they still needed more time. Overall, participants rated the SoV system's usability favorably. CONCLUSIONS: Contrary to the cane, the SoV system enables users to detect the best free space between objects within a 3.5-m (up to 10-m) radius and, importantly, elevated and dynamic obstacles. All in all, we consider that visually impaired people can learn to adapt to the haptic-auditory representation and achieve expertise in usage through well-defined training within acceptable time