The integration of generative artificial intelligence (GenAI) into design processes raises fundamental questions about behavioral patterns in human-GenAI interaction. This study examines how 16 professional designers interact with GenAI tools during concept development through a mixed-methods approach including pre/post-task questionnaires, video-based behavior analysis, and digital interaction tracking. Results reveal a critical distinction between reflective usage modes and creative modes, with differentiated cognitive impacts. Analysis of communication loops shows significant correlations between interaction difficulties and final design output quality. Three distinct clusters emerge: designers with fluid, problematic, and adaptive interaction patterns. By providing a methodological framework for evaluating GenAI tool effectiveness in design practice, this research contributes to theoretical understanding of behavioral processes in human-GenAI co-creation. Findings reveal specific strategies and workflow adaptations that optimize designer-GenAI collaboration, informing both design methodology and human-computer interaction practice.

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.

While previous research on spatial sound perception investigated the physical mechanisms producing the most relevant elevation cues, how spectral notches are generated and related to the individual morphology of the human pinna is still a topic of debate. Correctly modeling these important elevation cues, and in particular the lowest frequency notches, is an essential step for individualizing Head-Related Transfer Functions (HRTFs). In this paper we propose a simple computational model able to predict the center frequencies of pinna notches from ear meshes. We apply such a model to a highly controlled HRTF dataset built with the specific purpose of understanding the contribution of the pinna to the HRTF. Results show that the computational model is able to approximate the lowest frequency notch with improved accuracy with respect to other state-of-the-art methods. By contrast, the model fails to predict higher-order pinna notches correctly. The proposed approximation supplements understanding of the morphology involved in generating spectral notches in experimental HRTFs.

The rising availability of public head-related transfer function (HRTF) data, measured on hundreds of different individuals, offers a user the possibility to select the best matching non-individual HRTF from a wide catalogue. To this end, reducing the number of alternatives to a small subset of candidate HRTFs is the first step towards an efficient selection process. In this article a novel HRTF subset selection algorithm based on auditory-model vertical localization predictions and a greedy heuristic is outlined, designed to identify a representative HRTF subset from a catalogue including the three biggest public datasets currently available (373 HRTFs overall). The so-resulting subset (6 HRTFs) is then evaluated on a fourth independent dataset. Auditory model predictions show that for over 95% of the subjects of this dataset there exists at least one HRTF out of the representative subset scoring minimal vertical localization error deviations compared to the best available non-individual HRTF out of the catalogue.

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.

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.

Electronic travel aids (ETAs) have been in focus since technology allowed designing relatively small, light, and mobile devices for assisting the visually impaired. Since visually impaired persons rely on spatial audio cues as their primary sense of orientation, providing an accurate virtual auditory representation of the environment is essential. This paper gives an overview of the current state of spatial audio technologies that can be incorporated in ETAs, with a focus on user requirements. Most currently available ETAs either fail to address user requirements or underestimate the potential of spatial sound itself, which may explain, among other reasons, why no single ETA has gained a widespread acceptance in the blind community. We believe there is ample space for applying the technologies presented in this paper, with the aim of progressively bridging the gap between accessibility and accuracy of spatial audio in ETAs.

A novel sensory substitution algorithm based on the sonification of depth maps into physically based fluid flow sounds is described. Spatial properties are extracted from depth maps and mapped into parameters of an empirical phenomenological model of bubble statistics, which manages the generation of the corresponding synthetic fluid flow sound. Following minimal training, the proposed approach was tested in a preliminary experiment with 20 normally sighted participants and compared against the well-known vOICe sensory substitution algorithm. Although the accuracy in recognizing visual sequences based on the corresponding sonification is comparable between the two systems, an overwhelming support for the fluid sounds compared to the vOICe output in terms of pleasantness was recorded. Collected data further suggests that ample margins of performance improvement are achievable following thorough training procedures.

A novel approach to the selection of generic head-related transfer functions (HRTFs) for binaural audio rendering through headphones is formalized and described in this paper. A reflection model applied to the user's ear picture facilitates extraction of the relevant anthropometric cues that are used for selecting two HRTF sets in a database fitting that user, whose localization performances are evaluated in a complete psychoacoustic experiment. The proposed selection increases the average elevation performances of 17% (with a peak of 34%) with respect to generic HRTFs from an anthropomorphic mannequin. It also significantly enhances externalization and reduces the number of up/down reversals.

The contribution of the external ear to the head-related transfer function (HRTF) heavily depends on the listener's unique anthropometry. In particular, the shape of the most prominent contours of the pinna defines the frequency location of the HRTF spectral notches along the elevation of the sound source. This paper addresses the issue of automatically estimating the location of pinna edges starting from a set of pictures produced by a multi-flash imaging device. A basic image processing algorithm designed to obtain the principal edges and their distance from the ear canal entrance is described. The effectiveness of the developed hardware and software is preliminarily evaluated on a small number of test subjects.

The paper presents a system for customized binaural audio delivery based on the extraction of the relevant features from a 2-D representation of the listener's pinna. A procedure based on multi-flash imaging for recognizing the main contours of the pinna and their position with respect to the ear canal entrance is detailed. The resulting contours drive the parametrization of a structural head-related transfer function model that performs in real time the spatialization of a desired sound file according to the listener's position with respect to the virtual sound source, tracked by sensor-equipped headphones. The low complexity of the model allows smooth implementation and delivery on any mobile device. The purpose of the desired system is to provide low-tech custom binaural audio to any user without the need of tedious and cumbersome subjective measurements.

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 paper faces the general problem of modeling pinna-related transfer functions (PRTFs) for 3-D sound rendering. Following a structural approach, we aim at constructing a model for PRTF synthesis which allows to control separately the evolution of ear resonances and spectral notches through the design of two distinct filter blocks. Taking such model as endpoint, we propose a method based on the McAulay-Quatieri partial tracking algorithm to extract the frequencies of the most important spectral notches. Ray-tracing analysis performed on the so obtained tracks reveals a convincing correspondence between extracted frequencies and pinna geometry of a bunch of subjects.