This paper considers the problem of modeling pinna-related transfer functions (PRTFs) for 3-D sound rendering. Following a structural modus operandi, we present an algorithm for the decomposition of PRTFs into ear resonances and frequency notches due to reflections over pinna cavities. Such an approach allows to control the evolution of each physical phenomenon separately through the design of two distinct filter blocks during PRTF synthesis. The resulting model is suitable for future integration into a structural head-related transfer function model, and for parametrization over anthropometrical measurements of a wide range of subjects.

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Pinna-Related Transfer Functions (PRTFs) reflect the modifications undergone by an acoustic signal as it interacts with the listener’s outer ear. These can be seen as the pinna contribution to the Head-Related Transfer Function (HRTF). This paper describes a database of PRTFs collected from measurements performed at the Department of Signal Processing and Acoustics, Aalto University. Median-plane PRTFs at 61 different elevation angles from 25 subjects are included. Such data collection falls into a broader project in which evidence of the correspondence between PRTF features and anthropometry is being investigated.

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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 .

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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 ×3 reference distances) through a set of generic far-field Head-Related Transfer Functions (HRTFs) coupled with a nearfield 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.

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This paper proposes a sonification model for encoding visual 3D information into sounds, inspired by the impact properties of the objects encountered during blind navigation. The proposed model is compared against two sonification models developed for orientation and mobility, chosen based on their common technical requirements. An extensive validation of the proposed model is reported; five legally blind and five normally sighted participants evaluated the proposed model as compared to the two competitive models on a simplified experimental navigation scenario. The evaluation addressed not only the accuracy of the responses in terms of psychophysical measurements but also the cognitive load and emotional stress of the participants by means of biophysiological signals and evaluation questionnaires. Results show that the proposed impact sound model adequately conveys the relevant information to the participants with low cognitive load, following a short training session.

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