Neary: Conversational Field Detection Based on Situated Sound Similarity

Toshiya NAKAKURA; Yasuyuki SUMI; Toyoaki NISHIDA

doi:10.1587/transinf.E94.D.1164

Neary: Conversational Field Detection Based on Situated Sound Similarity

Toshiya NAKAKURA, Yasuyuki SUMI, Toyoaki NISHIDA

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This paper proposes a system called Neary that detects conversational fields based on similarity of auditory situation among users. The similarity of auditory situation between each pair of the users is measured by the similarity of frequency property of sound captured by head-worn microphones of the individual users. Neary is implemented with a simple algorithm and runs on portable PCs. Experimental result shows Neary can successfully distinguish groups of conversations and track dynamic changes of them. This paper also presents two examples of Neary deployment to detect user contexts during experience sharing in touring at the zoo and attending an academic conference.

Publication: IEICE TRANSACTIONS on Information Vol.E94-D No.6 pp.1164-1172

Publication Date: 2011/06/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E94.D.1164

Type of Manuscript: Special Section PAPER (Special Section on Emerging Technologies of Ubiquitous Computing Systems)

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Toshiya NAKAKURA, Yasuyuki SUMI, Toyoaki NISHIDA, "Neary: Conversational Field Detection Based on Situated Sound Similarity" in IEICE TRANSACTIONS on Information, vol. E94-D, no. 6, pp. 1164-1172, June 2011, doi: 10.1587/transinf.E94.D.1164.
Abstract: This paper proposes a system called Neary that detects conversational fields based on similarity of auditory situation among users. The similarity of auditory situation between each pair of the users is measured by the similarity of frequency property of sound captured by head-worn microphones of the individual users. Neary is implemented with a simple algorithm and runs on portable PCs. Experimental result shows Neary can successfully distinguish groups of conversations and track dynamic changes of them. This paper also presents two examples of Neary deployment to detect user contexts during experience sharing in touring at the zoo and attending an academic conference.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1164/_p

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@ARTICLE{e94-d_6_1164,
author={Toshiya NAKAKURA, Yasuyuki SUMI, Toyoaki NISHIDA, },
journal={IEICE TRANSACTIONS on Information},
title={Neary: Conversational Field Detection Based on Situated Sound Similarity},
year={2011},
volume={E94-D},
number={6},
pages={1164-1172},
abstract={This paper proposes a system called Neary that detects conversational fields based on similarity of auditory situation among users. The similarity of auditory situation between each pair of the users is measured by the similarity of frequency property of sound captured by head-worn microphones of the individual users. Neary is implemented with a simple algorithm and runs on portable PCs. Experimental result shows Neary can successfully distinguish groups of conversations and track dynamic changes of them. This paper also presents two examples of Neary deployment to detect user contexts during experience sharing in touring at the zoo and attending an academic conference.},
keywords={},
doi={10.1587/transinf.E94.D.1164},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Neary: Conversational Field Detection Based on Situated Sound Similarity
T2 - IEICE TRANSACTIONS on Information
SP - 1164
EP - 1172
AU - Toshiya NAKAKURA
AU - Yasuyuki SUMI
AU - Toyoaki NISHIDA
PY - 2011
DO - 10.1587/transinf.E94.D.1164
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2011
AB - This paper proposes a system called Neary that detects conversational fields based on similarity of auditory situation among users. The similarity of auditory situation between each pair of the users is measured by the similarity of frequency property of sound captured by head-worn microphones of the individual users. Neary is implemented with a simple algorithm and runs on portable PCs. Experimental result shows Neary can successfully distinguish groups of conversations and track dynamic changes of them. This paper also presents two examples of Neary deployment to detect user contexts during experience sharing in touring at the zoo and attending an academic conference.
ER -