103 research outputs found
A hybrid approach to structural modeling of individualized HRTFs
No full textWe 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
The Viking HRTF dataset v2
No full textThe 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
Evaluation of Individualized HRTFs in a 3D Shooter Game
No full textPrevious research stresses the importance of Head-Related Transfer Function (HRTF) individualization approaches for accurately locating sound sources in virtual 3D spaces. However, in the realm of interactive experiences, methods for assessing whether individualized HRTFs bring a benefit to the player experience are rarely investigated. Methods to improve spatial audio rendering are needed now than ever since Virtual Reality (VR) is becoming a mainstream technology for interactive experiences. This paper proposes a method of using in-game metrics to test the hypothesis that individualized HRTFs improve the experience of both expert and novice players in a First-Person Shooter (FPS) game on a desktop environment. The FPS game provides players with a localization task across three different audio renderings using the same acoustic spaces: stereo panning (control condition), generic binaural rendering, and individualized binaural rendering. Collected metrics from the game include localization error, spatial quality attributes, and an extensive questionnaire. The individualized HRTFs for each participant were synthesized using a hybrid structural model. The model employs a deep learning architecture to synthesize a pinna-related response from a pinna image, and combines it with a measured generic head-and-torso response. The interaural time difference (ITD) is then adjusted to match that of an HRTF dataset subject minimizing a localization error metric. The results show that the 22 participants performed significantly better in the localization task with their individualized HRTF. Increased localization accuracy with respect to the generic HRTF was recorded both in azimuth and elevation perception, and especially in the case of expert game players.Accepted Author ManuscriptDesign Aesthetic
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},
Estimation of Spectral Notches from Pinna Meshes: Insights from a Simple Computational Model
No full textWhile 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.Design Aesthetic
Predictive factors of deep abdominal complications after hydatid cysts of the liver: 15 years of experience with 672 patients.
No full textWe read with interest the article by El Malki and collaborators, 1 and we congratulate them for the excellent general results and their ability to share with us their extensive experience in a field that is getting renewed interest in western European countries and the US, mainly because of the increasing phenomenon of immigration from North African countries
Estimation of pinna notch frequency from anthropometry: An improved linear model based on principal component analysis and feature selection
No full textIn 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
Treatment of giant intramuscular hemangioma: a multistep approach in three patients
No full textBACKGROUND:
Giant intramuscular hemangioma (GIH) is a rare, progressively enlarging benign tumor, characterized by variable presentation and usually initially diagnosed in childhood. Large volume, rapid enlargement and particular radiologic imaging create suspicion of malignancy. Radiologic investigations and needle or small excisional biopsy are not always reliable for an accurate diagnosis; therefore, histology on a large surgical specimen is often requested. The timing and modality of treatment of these tumors is a matter of debate.
PATIENTS AND METHODS:
Data on 3 patients with GIH of the upper trunk and neck are reported. Associated vascular anomalies were found in all patients. All 3 patients had surgery because of the effect of the growth on their function, the severe symptoms and suspicion of malignancy.
RESULTS:
A one-step excision of an enormous tumor was carried out in one patient, who died from severe postoperative complications. A second patient was successfully treated by a multistep surgical and multidisciplinary approach. An uneventful removal of part of the tumor was performed on the third patient, who is currently in follow-up for completion of treatment.
CONCLUSION:
Surgery remains the most effective mode of treatment for GIH and often results in permanent cure. The authors suggest performing the surgical removal of these tumors at first diagnosis, when their smaller size requires less demanding procedures, presents lower rates of morbidity and offers a better chance of complete excision
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