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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-27 17:50 |
Okinawa | (Primary: On-site, Secondary: Online) |
Comparison of Variational Bayes and Gibbs Sampling for Normal Inverse Gaussian Mixture Models Takashi Takekawa (Kogakuin Univ.)  NC2022-9 IBISML2022-9 |
Mixture models for multivariate normal distributions (GMM) are widely used for data clustering. To compensate for the s... [more] | NC2022-9 IBISML2022-9 pp.76-79 |
| PRMU, IPSJ-CVIM | 2022-03-11 09:45 |
Online | Online | A Study on Understanding the Meaning of Adverbs by Frequency Spectrum Analysis Using Gaussian Process Tomoe Taniguchi (Ochanomizu Univ.), Daichi Mochihashi (ISM), Masatoshi Nagano, Tomoaki Nakamura (UEC), Takayuki Nagai (Osaka Univ.), Tetsunari Inamura (NII), Ichiro Kobayashi (Ochanomizu Univ.) PRMU2021-74 |
In this study, we attempt to understand the meaning of adverbs through the features of human actions. Specifically, the... [more] |
PRMU2021-74 pp.91-96 |
| SIS, ITE-BCT | 2021-10-07 11:40 |
Online | Online | Wireless Channel Prediction with Gaussian Process Yitu Wang (NTT), Takayuki Nakachi (former NTT), Takeru Inoue, Toru Mano, Kudo Riichi (NTT) SIS2021-12 |
With accurate knowledge of future Channel State Information (CSI), it becomes possible to better manipulate the wireless... [more] | SIS2021-12 pp.11-16 |
| SIS | 2021-03-05 10:00 |
Online | Online | Prediction of Network Traffic through Gaussian Process Yitu Wang, Takayuki Nakachi (NTT) SIS2020-54 |
With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligen... [more] | SIS2020-54 pp.103-108 |
| 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 |
| 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 |
| EA, US (Joint) |
2019-01-22 14:00 |
Kyoto | Doshisha Univ. | [Poster Presentation]
On speaker identification under multiple-talker condition using frequency domain binaural model Kai Kiyota, Irwansyah, Kousuke Matsuoka, Tsuyoshi Usagawa (Kumamoto Univ.) EA2018-94 |
In order to realize the speech recognition system suitable for a small meeting logging with speaker identification, it i... [more] | EA2018-94 pp.7-12 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-20 10:20 |
Fukuoka | Hideaki Hayashi, Seiichi Uchida (Kyushu Univ.) PRMU2018-41 IBISML2018-18 |
(To be available after the conference date) [more] | PRMU2018-41 IBISML2018-18 pp.37-40 |
|
| EA, ASJ-H | 2018-08-23 15:10 |
Miyagi | Tohoku Gakuin Univ. | Investigation of abnormal sound detectionusing occurrence probability of regularsound based on Gaussian Mixture Model Koji Abe, Moeko Hara, Shouichi Takane, Masayuki Nishiguchi, Kanji Watanabe (Akita Pref. Univ.) EA2018-34 |
In most abnormal sound detection systems, abnormal sounds are defined in advance and abnormal sounds are detected by mat... [more] | EA2018-34 pp.37-44 |
| 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 10:30 |
Tokyo | Experimental Analysis of Variational Bayesian Method in Model Selection of Gaussian Mixture Model by Singular Bayesian Information Criterion Naoki Hayashi (Tokyo Tech), Fumito Nakamura (Bosch) PRMU2017-41 IBISML2017-13 |
A Gaussian mixture model (GMM) is a statistical model used in various fields such a pattern recognition, thus, it is imp... [more] | PRMU2017-41 IBISML2017-13 pp.19-26 |
|
| 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 |
|
| 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 |
| EA, SP, SIP | 2016-03-29 10:45 |
Oita | Beppu International Convention Center B-ConPlaza | Tensor-based Speech Representation and its Application to Identification of Languages and Speakers So Suzuki, Daisuke Saito, Nobuaki Minematsu (UTokyo) EA2015-127 SIP2015-176 SP2015-155 |
This paper proposes a novel approach to speech representation for automatic identification of languages and speakers by ... [more] | EA2015-127 SIP2015-176 SP2015-155 pp.341-346 |
| 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 |
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