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I. Engel, R. Daugintis, T. Vicente, AI. T.. Hogg, J. Pauwels, AR. J.. Tournier, and L. Picinali, "The SONICOM HRTF Dataset," J. Audio Eng. Soc., vol. 71, no. 5, pp. 241-253, (2023 May.). doi: https://doi.org/10.17743/jaes.2022.0066 I. Engel, R. Daugintis, T. Vicente, AI. T.. Hogg, J. Pauwels, AR. J.. Tournier, and L. Picinali, "The SONICOM HRTF Dataset," J. Audio Eng. Soc., vol. 71 Issue 5 pp. 241-253, (2023 May.). doi: https://doi.org/10.17743/jaes.2022.0066
Abstract: Immersive audio technologies, ranging from rendering spatialized sounds accurately to efficient room simulations, are vital to the success of augmented and virtual realities. To produce realistic sounds through headphones, the human body and head must both be taken into account. However, the measurement of the influence of the external human morphology on the sounds incoming to the ears, which is often referred to as head-related transfer function (HRTF), is expensive and time-consuming. Several datasets have been created over the years to help researcherswork on immersive audio; nevertheless, the number of individuals involved and amount of data collected is often insufficient for modern machine-learning approaches. Here, the SONICOM HRTF dataset is introduced to facilitate reproducible research in immersive audio. This dataset contains the HRTF of 120 subjects, as well as headphone transfer functions; 3D scans of ears, heads, and torsos; and depth pictures at different angles around subjects' heads.

@article{engel2023the,
author={engel, isaac and daugintis, rapolas and vicente, thibault and hogg, aidan o. t. and pauwels, johan and tournier, arnaud j. and picinali, lorenzo},
journal={journal of the audio engineering society},
title={the sonicom hrtf dataset},
year={2023},
volume={71},
number={5},
pages={241-253},
doi={https://doi.org/10.17743/jaes.2022.0066},
month={may},} @article{engel2023the,
author={engel, isaac and daugintis, rapolas and vicente, thibault and hogg, aidan o. t. and pauwels, johan and tournier, arnaud j. and picinali, lorenzo},
journal={journal of the audio engineering society},
title={the sonicom hrtf dataset},
year={2023},
volume={71},
number={5},
pages={241-253},
doi={https://doi.org/10.17743/jaes.2022.0066},
month={may},
abstract={immersive audio technologies, ranging from rendering spatialized sounds accurately to efficient room simulations, are vital to the success of augmented and virtual realities. to produce realistic sounds through headphones, the human body and head must both be taken into account. however, the measurement of the influence of the external human morphology on the sounds incoming to the ears, which is often referred to as head-related transfer function (hrtf), is expensive and time-consuming. several datasets have been created over the years to help researcherswork on immersive audio; nevertheless, the number of individuals involved and amount of data collected is often insufficient for modern machine-learning approaches. here, the sonicom hrtf dataset is introduced to facilitate reproducible research in immersive audio. this dataset contains the hrtf of 120 subjects, as well as headphone transfer functions; 3d scans of ears, heads, and torsos; and depth pictures at different angles around subjects' heads.},}

TY - paper
TI - The SONICOM HRTF Dataset
SP - 241 EP - 253
AU - Engel, Isaac
AU - Daugintis, Rapolas
AU - Vicente, Thibault
AU - Hogg, Aidan O. T.
AU - Pauwels, Johan
AU - Tournier, Arnaud J.
AU - Picinali, Lorenzo
PY - 2023
JO - Journal of the Audio Engineering Society
IS - 5
VO - 71
VL - 71
Y1 - May 2023 TY - paper
TI - The SONICOM HRTF Dataset
SP - 241 EP - 253
AU - Engel, Isaac
AU - Daugintis, Rapolas
AU - Vicente, Thibault
AU - Hogg, Aidan O. T.
AU - Pauwels, Johan
AU - Tournier, Arnaud J.
AU - Picinali, Lorenzo
PY - 2023
JO - Journal of the Audio Engineering Society
IS - 5
VO - 71
VL - 71
Y1 - May 2023
AB - Immersive audio technologies, ranging from rendering spatialized sounds accurately to efficient room simulations, are vital to the success of augmented and virtual realities. To produce realistic sounds through headphones, the human body and head must both be taken into account. However, the measurement of the influence of the external human morphology on the sounds incoming to the ears, which is often referred to as head-related transfer function (HRTF), is expensive and time-consuming. Several datasets have been created over the years to help researcherswork on immersive audio; nevertheless, the number of individuals involved and amount of data collected is often insufficient for modern machine-learning approaches. Here, the SONICOM HRTF dataset is introduced to facilitate reproducible research in immersive audio. This dataset contains the HRTF of 120 subjects, as well as headphone transfer functions; 3D scans of ears, heads, and torsos; and depth pictures at different angles around subjects' heads.