The viking HRTF dataset

Simone Spagnol; Kristján Bjarki Purkhús; Rúnar Unnthórsson; Sverrir Karl Björnsson

The viking HRTF dataset

Simone Spagnol, Kristján Bjarki Purkhús, Rúnar Unnthórsson, Sverrir Karl Björnsson

Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

This paper describes the Viking HRTF dataset, a collection of head-related transfer functions (HRTFs) measured at the University of Iceland. The dataset includes full-sphere HRTFs measured on a dense spatial grid (1513 positions) with a KEMAR mannequin with 20 different artificial left pinnae attached, one at a time. The artificial pinnae were previously obtained through a custom molding procedure from 20 different lifelike human heads. The analyses of results reported here suggest that the collected acoustical measurements are robust, reproducible, and faithful to reference KEMAR HRTFs, and that material hardness has a negligible impact on the measurements compared to pinna shape. The purpose of the present collection, which is available for free download, is to provide accurate input data for future investigations on the relation between HRTFs and anthropometric data through machine learning techniques or other state-of-the-art methodologies.

Original language	English
Title of host publication	Proceedings of the 16th Sound and Music Computing Conference, SMC 2019
Editors	Isabel Barbancho, Lorenzo J. Tardon, Alberto Peinado, Ana M. Barbancho
Publisher	CERN Publishing
Pages	55-60
Number of pages	6
ISBN (Electronic)	9788409085187
Publication status	Published - 20 May 2019
Event	16th Sound and Music Computing Conference, SMC 2019 - Malaga, Spain Duration: 28 May 2019 → 31 May 2019

Publication series

Name	Proceedings of the Sound and Music Computing Conferences
ISSN (Print)	2518-3672

Conference

Conference	16th Sound and Music Computing Conference, SMC 2019
Country/Territory	Spain
City	Malaga
Period	28/05/19 → 31/05/19

Bibliographical note

Publisher Copyright:
Copyright: © 2019 Simone Spagnol et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cite this

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title = "The viking HRTF dataset",

abstract = "This paper describes the Viking HRTF dataset, a collection of head-related transfer functions (HRTFs) measured at the University of Iceland. The dataset includes full-sphere HRTFs measured on a dense spatial grid (1513 positions) with a KEMAR mannequin with 20 different artificial left pinnae attached, one at a time. The artificial pinnae were previously obtained through a custom molding procedure from 20 different lifelike human heads. The analyses of results reported here suggest that the collected acoustical measurements are robust, reproducible, and faithful to reference KEMAR HRTFs, and that material hardness has a negligible impact on the measurements compared to pinna shape. The purpose of the present collection, which is available for free download, is to provide accurate input data for future investigations on the relation between HRTFs and anthropometric data through machine learning techniques or other state-of-the-art methodologies.",

author = "Simone Spagnol and Purkh{\'u}s, {Kristj{\'a}n Bjarki} and R{\'u}nar Unnth{\'o}rsson and Bj{\"o}rnsson, {Sverrir Karl}",

note = "Publisher Copyright: Copyright: {\textcopyright} 2019 Simone Spagnol et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.; 16th Sound and Music Computing Conference, SMC 2019 ; Conference date: 28-05-2019 Through 31-05-2019",

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series = "Proceedings of the Sound and Music Computing Conferences",

publisher = "CERN Publishing",

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address = "Switzerland",

}

Spagnol, S, Purkhús, KB, Unnthórsson, R & Björnsson, SK 2019, The viking HRTF dataset. in I Barbancho, LJ Tardon, A Peinado & AM Barbancho (eds), Proceedings of the 16th Sound and Music Computing Conference, SMC 2019. Proceedings of the Sound and Music Computing Conferences, CERN Publishing, pp. 55-60, 16th Sound and Music Computing Conference, SMC 2019, Malaga, Spain, 28/05/19.

The viking HRTF dataset. / Spagnol, Simone; Purkhús, Kristján Bjarki; Unnthórsson, Rúnar et al.
Proceedings of the 16th Sound and Music Computing Conference, SMC 2019. ed. / Isabel Barbancho; Lorenzo J. Tardon; Alberto Peinado; Ana M. Barbancho. CERN Publishing, 2019. p. 55-60 (Proceedings of the Sound and Music Computing Conferences).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Purkhús, Kristján Bjarki

AU - Unnthórsson, Rúnar

AU - Björnsson, Sverrir Karl

N1 - Publisher Copyright: Copyright: © 2019 Simone Spagnol et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2019/5/20

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AB - This paper describes the Viking HRTF dataset, a collection of head-related transfer functions (HRTFs) measured at the University of Iceland. The dataset includes full-sphere HRTFs measured on a dense spatial grid (1513 positions) with a KEMAR mannequin with 20 different artificial left pinnae attached, one at a time. The artificial pinnae were previously obtained through a custom molding procedure from 20 different lifelike human heads. The analyses of results reported here suggest that the collected acoustical measurements are robust, reproducible, and faithful to reference KEMAR HRTFs, and that material hardness has a negligible impact on the measurements compared to pinna shape. The purpose of the present collection, which is available for free download, is to provide accurate input data for future investigations on the relation between HRTFs and anthropometric data through machine learning techniques or other state-of-the-art methodologies.

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ER -

The viking HRTF dataset

Abstract

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Bibliographical note

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