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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2021-03-03 14:00 |
Online | Online | Learning coefficients of normal mixture models in one dimension. Genki Watanabe, Ryuji Ito, Miki Aoyagi (Nihon Univ.)  IBISML2020-48 |
Hierarchical learning models are widely used for data analysis in image or speech recognition, economics and so on. How... [more] | IBISML2020-48 pp.40-46 |
| 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 |
| 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 |
|
| 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 |
| 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 |
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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 |
| 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 | 2016-03-29 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 |
| 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 |
| EMM | 2016-01-18 17:00 |
Miyagi | Katahira Campus, Tohoku University | On Voice Quality Transformations for the English Pronunciation Software Aimed at Effective Presentations Yuki Nakahira, Tetsuya Kojima, Tomoko Hori, Sadanobu Yoshimoto (NIT, Tokyo College), Kouichi Suzuki (Kouichi Suzuki's Office) EMM2015-68 |
A computer software to learn English pronunciation aimed at effective presentations in English has been proposed. One of... [more] | EMM2015-68 pp.43-48 |
| SP | 2015-10-15 13:25 |
Hyogo | Kobe Univ. | Statistical singing voice conversion based on direct waveform modification and its parameter generation algorithms Kazuhiro Kobayashi, Tomoki Toda, Satoshi Nakamura (NAIST) SP2015-60 |
This report presents a novel statistical singing voice conversion (SVC) technique with direct waveform modification base... [more] | SP2015-60 pp.7-12 |
| RCC, ASN, RCS, NS, SR (Joint) |
2015-07-30 10:30 |
Nagano | JA Naganoken Bldg. | [Poster Presentation]
Parameter Control Method for Physical Wireless Parameter Conversion Sensor Networks based on Estimated Mixture Distribution Taiki Nakayama, Takeo Fujii (UEC), Osamu Takyu (Shinshu Univ.), Mai Ohta (Fukuoka Univ.) RCC2015-33 NS2015-53 RCS2015-116 SR2015-34 ASN2015-43 |
We have proposed a physical wireless parameter conversion method for Wireless Sensor Networks (WSNs). In this method, ea... [more] | RCC2015-33 NS2015-53 RCS2015-116 SR2015-34 ASN2015-43 pp.91-96(RCC), pp.95-100(NS), pp.93-98(RCS), pp.109-114(SR), pp.131-136(ASN) |
| 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 |
|
| MI | 2015-03-03 13:40 |
Okinawa | Hotel Miyahira | Target Extraction from X-ray Image Sequence by using Gaussian Mixture Model for Lung Tumor Tracking Naoki Shibusawa, Kei Ichiji, Yusuke Yoshida, Xiaoyang Zhang, Noriyasu Homma (Tohoku Univ.), Yoshihiro Takai (Hirosaki Univ.), Makoto Yoshizawa (Tohoku Univ.) MI2014-110 |
During treatment fraction, accurate tracking of moving tumor by using X-ray imaging is important for radiation therapy. ... [more] |
MI2014-110 pp.277-282 |
| SP | 2014-11-14 10:55 |
Fukuoka | Kyushu Univ. Chikushi Campus | Design of control parameters for voice quality control based on multiple-regression Gaussian mixture model Kazutaka Kubo, Kazuhiro Kobayashi, Tomoki Toda, Graham Neubig, Sakriani Sakti, Satoshi Nakamura (NAIST) SP2014-101 |
This report presents a method for designing control parameters in statistical voice quality control. As a method for int... [more] | SP2014-101 pp.65-70 |
| COMP | 2014-10-08 13:30 |
Tokyo | Chuo University | [Invited Talk]
Statistical Maximum and Minimum Operations for Gaussian Mixture Model and Their Applications Shuji Tsukiyama (Chuo Univ.) COMP2014-28 |
Due to the progress of micro-technology, process variability is increasing in not only inter-die but also intra-die, and... [more] | COMP2014-28 pp.17-18 |
| SP, IPSJ-MUS | 2014-05-25 11:30 |
Tokyo | Statistical bandwidth extension using sub-band basis spectrum model Yamato Ohtani, Masatsune Tamura, Masahiro Morita, Masami Akamine (Toshiba) SP2014-27 |
This paper describes a novel statistical bandwidth extension (BWE) method based on a Gaussian mixture model (GMM) and a ... [more] | SP2014-27 pp.303-308 |
|
| MBE, NC (Joint) |
2013-03-15 14:35 |
Tokyo | Tamagawa University | Signal separation of EEG using multivariate probabilistic model Yusuke Kurihana, Shigeki Miyabe, Tomasz M. Rutkowski, Yoshihiro Matsumoto, Takeshi Yamada, Shoji Makino (Univ. of Tsukuba) MBE2012-119 |
With independent component analysis (ICA), one promising source separation framework, it is difficult to separate desire... [more] | MBE2012-119 pp.161-166 |
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