3,036 research outputs found
The Viking HRTF dataset v2
Full text linkThe 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).
An overview of the methods and procedures of the HRTF measurement sessions can be found in the papers
Simone Spagnol, Kristján Bjarki Purkhús, Sverrir Karl Björnsson, and Rúnar Unnthórsson (2019). The Viking HRTF dataset. In: Proceedings of the 16th Sound & Music Computing Conference (SMC 2019), pages 55-60, Málaga, Spain.
Marius George Onofrei, Riccardo Miccini, Rúnar Unnthórsson, Stefania Serafin, and Simone Spagnol (2020). 3D ear shape as an estimator of HRTF notch frequency. In: Proceedings of the 17th Sound & Music Computing Conference (SMC 2020), pages 131-137, Torino, Italy.
A first version of the dataset has been released in May 2019. In this second version, the used artificial pinnae were re-casted from the existing inverse molds with 35 Shore OO silicone for both the left and right channels of the KEMAR. Furthermore, the HRTF measurements have been taken inside the anechoic chamber of the University of Iceland in Reykjavík and free-field compensated.
The dataset, available in SOFA format, contains measurements for 20 different pairs of articial pinna replicas (subjects A to T, where T is a pair of standard large KEMAR anthropometric pinnae replicas) plus a pair of flat baffles simulating a "pinna-less" condition (subject Z).
3D scans of the 20 left pinna replicas are available as STL files. The scans were captured at 1mm resolution with a Creaform Go!SCAN 20 (courtesy of AAU Create Prototyping Lab).
The data is provided under the CC-BY 4.0 license that grants unlimited access for everyone. If you use this data please reference
Simone Spagnol, Kristján Bjarki Purkhús, Sverrir Karl Björnsson, and Rúnar Unnthórsson (2019). The Viking HRTF dataset. In: Proceedings of the 16th Sound & Music Computing Conference (SMC 2019), pages 55-60, Málaga, Spain.
Simone Spagnol, Riccardo Miccini and Rúnar Unnthórsson (2020). The Viking HRTF dataset v2. DOI: 10.5281/zenodo.416040
A hybrid approach to structural modeling of individualized HRTFs
Full text linkWe present a hybrid approach to individualized head-related transfer function (HRTF) modeling which requires only 3 anthropometric measurements and an image of the pinna. A prediction algorithm based on variational autoencoders synthesizes a pinna-related response from the image, which is used to filter a measured head-andtorso response. The interaural time difference is then manipulated to match that of the HUTUBS dataset subject minimizing the predicted localization error. The results are evaluated using spectral distortion and an auditory localization model. While the latter is inconclusive regarding the efficacy of the structural model, the former metric shows promising results with encoding HRTFs. Index Terms: Hardware - Digital signal processing; Computing methodologies - Neural networks; Applied computing - Sound and music computing</p
Auditory model based subsetting of Head-Related Transfer Function datasets
Full text linkThe 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
HRTF selection by anthropometric regression for improving horizontal localization accuracy
Full text linkThis 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 intersubject 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
Are spectral elevation cues in head-related transfer functions distance-independent?
Full text linkSince its title, this paper addresses one of the still open questions in sound localization: is our own perception of the elevation of a sound source affected by the distance of the source itself? The problem is addressed through the analysis of a recently published distance-dependent head-related transfer function (HRTF) database, which includes the responses of a single subject on a spatial grid spanning 14 elevation angles, 72 azimuth angles, and 8 distances comprised between 20 and 160 cm. Different HRTFs sharing the same angular coordinates are compared through spectral distortion and notch frequency deviation measurements. Results indicate that, even though the independence of spectral elevation cues fromdistance of the source can be assumed for the majority of all possible source directions, near-field HRTFs for sources close to the contralateral ear or around the horizontal plane in the ipsilateral side of the head are significantly affected by distancedependent pinna reflections and spectral modifications
Individual Three-dimensional Spatial Auditory Displays for Immersive Virtual Environments
Full text linkEstimation of pinna notch frequency from anthropometry: An improved linear model based on principal component analysis and feature selection
Full text linkIn this paper, anthropometric data from a database of Head-Related Transfer Functions (HRTFs) is used to estimate the frequency of the first pinna notch in the frontal part of the median plane. Given the presence of high correlations between some of the anthropometric features, as well as repeated values for the same subject observations, we propose the introduction of Principal Component Analysis (PCA) to project the features onto a space where they are more separated. We then construct a regression model employing forward step-wise feature selection to choose the principal components most capable of predicting notch frequencies. Our results show that by using a linear regression model with as few as three principal components, we can predict notch frequencies with a cross-validation mean absolute error of just about 600 Hz
ITSADIVE - Hybrid Structural Model for HRTF individualization v1.0
No full textThis repository includes the code for a hybrid structural HRTF model combining measured, synthesised, and selected components. In particular, its three components are:
A generic head-and-torso component, taken from the "pinna-less" KEMAR set included in the Viking HRTF dataset v2 with ITD removed (measured component);
A fully customized pinna component, built using features related to the shape of the user’s pinnae through deep learning (synthesized component);
The best-match ITD from an available HRTF dataset obtained by regression on anthropometric parameters of the head and torso (selected component).
The model, implemented in MATLAB/Python, directly outputs a SOFA file.
If you use this code in a scientific publication, please reference the following works:
@inproceedings{micciniHybridApproachStructural2021,
title = {A hybrid approach to structural modeling of individualized {HRTFs}},
booktitle = {2021 {IEEE} {Conference} on {Virtual} {Reality} and {3D} {User} {Interfaces} {Abstracts} and {Workshops} ({VRW} 2021)},
author = {Miccini, R. and Spagnol, S.},
month = mar,
year = {2021}
}
@misc{spagnolVikingHRTFDataset2020,
title = {The {Viking} {HRTF} dataset v2},
url = {https://zenodo.org/record/4160401},
publisher = {Zenodo},
author = {Spagnol, Simone and Miccini, Riccardo and Unnthorsson, Runar},
month = oct,
year = {2020},
doi = {10.5281/zenodo.4160401},
note = {type: dataset},
HRTF individualization using deep learning
Full text linkThe 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
Musiche liquide. XX Colloquio di Informatica Musicale. 20Th Colloquium on Music Informatics. Liquid music
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