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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, NC (Joint) |
2020-01-25 15:25 |
Okinawa | Miyakojima Marine Terminal | A study on detection method for localized vibrations using energy distribution in a nonlinear coupled resonators Hikaru Furuta, Masayuki Kimura, Shinji Doi (Kyoto Univ.)  NLP2019-108 |
Several moving intrinsic localized modes (ILMs) are created via modulational instability of the zone boundary mode in a ... [more] | NLP2019-108 pp.117-120 |
| PRMU | 2019-12-19 10:30 |
Oita | A switching Markov model for evaluation of food functionality Tsukasa Hokimoto, Toshio Uchiyama (HIU) PRMU2019-46 |
For analytic purposes of the scientific processes on how food influence our health, the measurement data on medical or g... [more] | PRMU2019-46 pp.1-6 |
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| R | 2019-12-13 15:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Lindley Type Distributions and Software Reliability Assessment Qi Xiao, Tadashi Dohi, Hiroyuki Okamura (Hiroshima Univ.) R2019-53 |
Dennis Victor Lindley proposed an interesting one-parameter continuous probability distribition, which is called Lindley... [more] | R2019-53 pp.19-24 |
| HWS, VLD | 2019-02-28 13:30 |
Okinawa | Okinawa Ken Seinen Kaikan | Selection of Gaussian Mixture Reduction Methods Using Machine Learning Haruki Kazama, Shuji Tsukiyama (Chuo Univ.) VLD2018-113 HWS2018-76 |
Gaussian mixture model is a useful distribution for statistical methods such as statistical static timing analysis, but ... [more] | VLD2018-113 HWS2018-76 pp.121-126 |
| SR | 2019-01-25 16:40 |
Fukushima | Corasse, Fukushima city (Fukushima prefecture) | A study on widely acceptable model for spectrum usage Kento Yamada, Kenta Umebayashi (TUAT) SR2018-119 |
We investigate a flexible and scalable spectrum usage model in time domain for an enhanced dynamic spectrum access (DSA)... [more] | SR2018-119 pp.141-147 |
| NLP, CCS | 2018-06-10 15:50 |
Kyoto | Kyoto Terrsa | A study on reproducing probability density function of human balancing motions Ryoma Omori, Yoshikazu Yamanaka, Katsutoshi Yoshida (Utsunomiya Univ) NLP2018-50 CCS2018-23 |
Human balancing motions, such as during quiet standing, stick balancing on the fingertip, and so on, generally exhibit r... [more] | NLP2018-50 CCS2018-23 pp.125-129 |
| RCS, SR, SRW (Joint) |
2018-03-02 10:50 |
Kanagawa | YRP | Spectrum usage model for Smart Spectrum Access Kento Yamada, Kenta Umebayashi (TUAT), Janne Lehtomaki, Shashika Manosha Kapuruhamy Badalge (Univ. of Oulu) SR2017-133 |
In a smart spectrum access, the statistical information in terms of spectrum usage can enhance a spectrum sharing dramat... [more] | SR2017-133 pp.109-116 |
| EMM | 2018-01-29 15:30 |
Miyagi | Tohoku Univ. (Aobayama Campus) | Note estimation by contaminated normal distribution for audio watermarking method using non-negative matrix factorization Harumi Murata (Chukyo Univ.), Akio Ogihara (Kindai Univ.) EMM2017-69 |
For audio signals, the sound quality of stego signal should not deteriorate. With current methods, high sound quality me... [more] | EMM2017-69 pp.19-24 |
| MBE, NC (Joint) |
2017-12-16 13:25 |
Aichi | Nagoya University | Extraction of Color Regions on a Color Glove Using Gaussian Mixture Model Estimation Noriaki Fujishima, Shun Nishikori (NIT, Matsue College) MBE2017-59 |
In this study, the authors have researched the extraction accuracy of color regions using Gaussian Mixture Model Estimat... [more] | MBE2017-59 pp.35-38 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 13:00 |
Tokyo | On MDL Learning of Gaussian Mixture Modlels Kohei Miyamoto, Masanori Kawakita, Jun'ichi Takeuchi (Kyushu Univ.) PRMU2017-47 IBISML2017-19 |
The final goal of this work is model sellection for gaussian mixture models(GMM) based on the minimum description length... [more] | PRMU2017-47 IBISML2017-19 pp.59-66 |
|
| IT | 2017-09-08 14:50 |
Yamaguchi | Centcore Yamaguchi Hotel | On Two Part Coding of Gaussian Mixture Models Kohei Miyamoto, Masanori Kawakita, Jun'ichi Takeuchi (Kyushu Univ.) IT2017-47 |
The final goal of this work is model sellection for gaussian mixture models(GMM) based on the minimum description leng... [more] |
IT2017-47 pp.49-54 |
| PRMU, IE, MI, SIP | 2017-05-26 12:00 |
Aichi | Background Modeling based on Gaussian Mixture Model using Spatial Features Kan Zheng, Toshio Kondo, Yuki Fukazawa, Takahiro Sasaki (Mie Univ.) SIP2017-24 IE2017-24 PRMU2017-24 MI2017-24 |
Many methods for detecting a moving object from surveillance video using a background model have been proposed. Mixed Ga... [more] | SIP2017-24 IE2017-24 PRMU2017-24 MI2017-24 pp.125-130 |
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| VLD | 2017-03-02 16:15 |
Okinawa | Okinawa Seinen Kaikan | An algorithm to compute covariance for finding distribution of the maximum Daiki Azuma, Shuji Tsukiyama (Chuo Univ.), Masahiro Fukui (Ritsumeikan Univ.), Takashi Kambe (Kinki Univ.) VLD2016-121 |
In statistical approaches such as statistical static timing analysis, the distribution of the maximum of plural distribu... [more] | VLD2016-121 pp.103-108 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Policy search based on sample clustering with Gaussian mixture model Taiki Yano, Shinichi Maeda (Kyoto Univ.) IBISML2016-46 |
EM-based Policy Hyper Parameter Exploration (EPHE)(Wang et al., 2016) is a method that kills two birds with one stone; ... [more] | IBISML2016-46 pp.9-15 |
| SP | 2016-08-24 16:15 |
Kyoto | ACCMS, Kyoto Univ. | [Poster Presentation]
Joint Enhancement of Spectral and Cepstral Sequences of Noisy Speech Li Li (Univ.Tsukuba), Hirokazu Kameoka, Takuya Higuchi (NTT), Hiroshi Saruwatari (Univ.Tokyo), Shoji Makino (Univ.Tsukuba) SP2016-32 |
While spectral domain speech enhancement algorithms using non-negative matrix factorization (NMF) are powerful in terms ... [more] | SP2016-32 pp.29-32 |
| EA, SP, SIP | 2016-03-28 13:15 |
Oita | Beppu International Convention Center B-ConPlaza | [Poster Presentation]
An evaluation of acoustic-to-articulatory inversion mapping with latent trajectory Gaussian mixture model Patrick Lumban Tobing (NAIST), Tomoki Toda (Nagoya Univ./NAIST), Hirokazu Kameoka (NTT), Satoshi Nakamura (NAIST) EA2015-85 SIP2015-134 SP2015-113 |
In this report, we present an evaluation of acoustic-to-articulatory inversion mapping based on latent trajectory Gauss... [more] |
EA2015-85 SIP2015-134 SP2015-113 pp.111-116 |
| VLD | 2016-03-02 13:00 |
Okinawa | Okinawa Seinen Kaikan | An Algorithm for Reducing Components of a Gaussian Mixture Model 1
-- A Partitioning Method of Components -- Naoya Yokoyama, Shuji Tsukiyama (Chuo Univ.), Masahiro Fukui (Ritsumeikan Univ.) VLD2015-138 |
In statistical methods, such as statistical static timing analysis (S-STA), Gaussian mixture model (GMM) is a useful too... [more] | VLD2015-138 pp.155-160 |
| VLD | 2016-03-02 13:25 |
Okinawa | Okinawa Seinen Kaikan | An Algorithm for Reducing Components of a Gaussian Mixture Model 2
-- A Method for Calculating Sensitivities -- Daiki Azuma, Shuji Tsukiyama (Chuo Univ.), Masahiro Fukui (Ritsumeikan Univ.), Takashi Kambe (Kinki Univ.) VLD2015-139 |
In statistical methods, such as statistical static timing analysis (S-STA), Gaussian mixture model (GMM) is a useful too... [more] | VLD2015-139 pp.161-166 |
| PRMU, CNR | 2016-02-21 14:00 |
Fukuoka | Parameter sharing structures of separable lattice HMMs using mixture output distributions for image recognition Masato Sukegawa, Kei Sawada, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) PRMU2015-138 CNR2015-39 |
In image recognition systems, it is important to deal with geometrical variations such as size and location. Separable l... [more] | PRMU2015-138 CNR2015-39 pp.37-42 |
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| CPSY, IPSJ-EMB, IPSJ-SLDM, DC [detail] | 2015-03-06 16:40 |
Kagoshima | An Algorithm to Reduce Components of a Gaussian Mixture Model Considering Distribution Shape of Each Component Naoya Yokoyama, Shuji Tsukiyama (Chuo Univ.), Masahiro Fukui (Ritsumeikan Univ.) CPSY2014-170 DC2014-96 |
In statistical methods, such as statistical static timing analysis (S-STA) algorithm, summation and minimum or maximum o... [more] | CPSY2014-170 DC2014-96 pp.49-54 |
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