Presentation 2017-10-12
Indoor Area Estimation Using Convolutional Neural Network with Spectrogram of Environmental Ultrasound
Tatsuro Tsuchiya, Takeshi Umezawa, Noritaka Osawa,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Active methods for indoor position estimation have been proposed which are based on signal strength or propagation time of radio waves or ultrasounds. Those methods require transmitters to be installed in target areas. This paper proposes a passive method for indoor area estimation where a prediction model is constructed by a convolution neural network based on spectrogram of environmental ultrasound. No new transmitting devices are required in the method.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Spectrogram / ultrasound / Indoor Area Estimation / Convolutional Neural Network
Paper # PRMU2017-69
Date of Issue 2017-10-05 (PRMU)

Conference Information
Committee PRMU
Conference Date 2017/10/12(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Shinichi Sato(NII)
Vice Chair Hironobu Fujiyoshi(Chubu Univ.) / Yoshihisa Ijiri(Omron)
Secretary Hironobu Fujiyoshi(AIST) / Yoshihisa Ijiri(NAIST)
Assistant Masato Ishii(NEC) / Yusuke Sugano(Osaka Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Indoor Area Estimation Using Convolutional Neural Network with Spectrogram of Environmental Ultrasound
Sub Title (in English)
Keyword(1) Spectrogram
Keyword(2) ultrasound
Keyword(3) Indoor Area Estimation
Keyword(4) Convolutional Neural Network
1st Author's Name Tatsuro Tsuchiya
1st Author's Affiliation Chiba University(Chiba Univ.)
2nd Author's Name Takeshi Umezawa
2nd Author's Affiliation Chiba University(Chiba Univ.)
3rd Author's Name Noritaka Osawa
3rd Author's Affiliation Chiba University(Chiba Univ.)
Date 2017-10-12
Paper # PRMU2017-69
Volume (vol) vol.117
Number (no) PRMU-238
Page pp.pp.37-42(PRMU),
#Pages 6
Date of Issue 2017-10-05 (PRMU)