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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 10:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
Accelerating and stabilizing vectorwise coordinate descent for spatially regularized independent low-rank matrix analysis Yuto Ishikawa, Takuya Okubo, Norihiro Takamune (UTokyo), Tomohiko Nakamura (AIST), Daichi Kitamura (NIT Kagawa), Hiroshi Saruwatari (UTokyo), Yu Takahashi, Kazunobu Kondo (Yamaha)  EA2023-68 SIP2023-115 SP2023-50 |
Spatially regularized independent low-rank matrix analysis (SR-ILRMA) is the method that introduces the spatial prior in... [more] | EA2023-68 SIP2023-115 SP2023-50 pp.43-50 |
| EA, US (Joint) |
2023-12-22 13:00 |
Fukuoka | [Poster Presentation]
Multichannel Blind Source Separation Using Independent Low-Rank Matrix Analysis with Observed-Signal-Dependent Regularization Based on Spectrogram Consistency Takaaki Kojima, Norihiro Takamune, Sota Misawa (UTokyo), Daichi Kitamura (NIT,Kagawa), Hiroshi Saruwatari (UTokyo) EA2023-51 |
Independent low-rank matrix analysis (ILRMA) is the state-of-the-art technique for blind source separation under the ove... [more] | EA2023-51 pp.13-20 |
|
| CQ | 2023-07-12 14:00 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Study on the Effectiveness of Matrix Approximation without Rank Constraint Eriko Segawa, Yusuke Sakumto (Kwansei Gakuin Univ.) CQ2023-10 |
The proper selection of graph spectrum is crucial for constructing efficient graph algorithms. We have discussed a matri... [more] | CQ2023-10 pp.12-17 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-15 11:25 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
Study on the Importance of Each Eigenvalue and Eigenvector for Laplacian Matrix Using Matrix Approximation Eriko Segawa, Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2022-84 |
It is important for developing sophisticated graph algorithms to understand deeply the characteristics of the typical ma... [more] | CQ2022-84 pp.25-30 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:25 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Fast Identification of Possible Model Parameter Update for Low-Rank Update of Training Data Hiroyuki Hanada, Noriaki Hashimoto (RIKEN), Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) PRMU2022-123 IBISML2022-130 |
Machine learning methods often require re-training the training dataset with low-rank modifications (small number of ins... [more] | PRMU2022-123 IBISML2022-130 pp.347-354 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 09:50 |
Okinawa | (Primary: On-site, Secondary: Online) |
Regularization Term Design Based on Spectrogram Consistency in Independent Low-Rank Matrix Analysis for Multichannel Audio Source Separation Sota Misawa, Norihiro Takamune (UTokyo), Kohei Yatabe (TUAT), Daichi Kitamura (NIT, Kagawa), Hiroshi Saruwatari (UTokyo) EA2022-105 SIP2022-149 SP2022-69 |
It is known that block permutation occurs in the separated signals obtained by independent low-rank matrix analysis. Rec... [more] | EA2022-105 SIP2022-149 SP2022-69 pp.177-184 |
| EA, US (Joint) |
2022-12-23 09:00 |
Hiroshima | Satellite Campus Hiroshima | Proposal of Speech Decomposition Algorithm by Cepstral-Basis-Decomposed Nonnegative Matrix Factorization and Application to Speech Source Separation Technique Fuga Oshima, Masashi Nakayama (Hiroshima City) EA2022-69 |
Nonnegative matrix factorization (NMF) is the algorithm that effectively represents acoustical signals by inputting ampl... [more] | EA2022-69 pp.49-54 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 15:00 |
Online | Online | Blind Source Separation based on Independent Low-Rank Matrix Analysis using Restricted Boltzmann Machines Shotaro Furuta, Takuya Kishida, Toru Nakashika (UEC) SP2022-8 |
In this paper, we propose a new blind source separation method that combines independent low-rank source separation (ILR... [more] | SP2022-8 pp.26-29 |
| SIP, IT, RCS | 2021-01-22 13:20 |
Online | Online | Acceleration of Fast Multiple Singular Value Thresholding with Fast Inverse Square Root Takayuki Sasaki, Ryuichi Tanida, Kimata Hideaki (NTT) IT2020-104 SIP2020-82 RCS2020-195 |
In this paper, we propose a method for speeding up the singular value thresholding of a small many matrices using the fa... [more] | IT2020-104 SIP2020-82 RCS2020-195 pp.230-234 |
| SP, EA, SIP | 2020-03-03 16:15 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
A Portscan Detection Based on Low-rankness of Destination Port Matrices Hiroki Nousou, Masao Yamagishi, Isao Yamada (Tokyo Tech) EA2019-167 SIP2019-169 SP2019-116 |
The detection of port scans as possible preliminaries to more serious attacks is important for system administrators and... [more] | EA2019-167 SIP2019-169 SP2019-116 pp.385-390 |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 16:25 |
Nagasaki | Fukue Culture Center | [Invited Talk]
Image Processing Based on Sparse and Low-rank Modeling Seisuke Kyochi (The Univ. of Kitakyushu) SIS2019-10 |
This paper presents fundamental tools for image recovery by convex optimization and introduces some case study from the ... [more] | SIS2019-10 pp.55-60 |
| EA, ASJ-H | 2018-08-23 12:55 |
Miyagi | Tohoku Gakuin Univ. | Self-produced speech enhancement and suppression method with wearable air- and body-conductive microphones Moe Takada, Shogo Seki, Tomoki Toda (Nagoya Univ.) EA2018-29 |
This paper presents a self-produced speech enhancement and suppression method for multichannel signals recorded with bot... [more] | EA2018-29 pp.7-12 |
| PRMU, MI, IE, SIP | 2018-05-18 16:00 |
Gifu | [Short Paper]
Ryoma Bise (Kyushu Univ.), Zheng Yinqiang, Imari Sato (NII) SIP2018-17 IE2018-17 PRMU2018-17 MI2018-17 |
Photoacoustic imaging (PAI) is a new imaging technology that can non-invasively visualize blood vessels inside biologica... [more] | SIP2018-17 IE2018-17 PRMU2018-17 MI2018-17 pp.75-79 |
|
| SIP, EA, SP, MI (Joint) [detail] |
2018-03-20 09:00 |
Okinawa | [Poster Presentation]
Blind Source Separation Based on the Sparsity of Impulse Responses Ryota Oda, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.) EA2017-164 SIP2017-173 SP2017-147 |
We propose a blind source separation (BSS) algorithm using a priori information of the mixing process based on the state... [more] | EA2017-164 SIP2017-173 SP2017-147 pp.341-346 |
|
| SIP, IT, RCS | 2018-01-22 13:55 |
Kagawa | Sunport Hall Takamatsu | Hyperspectral Image Restoration Ryuji Kurihara, Masahiro Okuda (Kitayu U.) IT2017-73 SIP2017-81 RCS2017-287 |
We propose a new regularization function for hyperspectral image (HSI) restoration. Spatial-smoothness-based regularizat... [more] | IT2017-73 SIP2017-81 RCS2017-287 pp.107-111 |
| ITS, WBS, RCC | 2017-12-15 15:20 |
Okinawa | Tiruru/Okinawa Jichikaikan | Estimation of Three-Dimensional Received Power Distribution Using Unmanned Aircraft Naoya Kiyofuji (Osaka Univ.), Hiroto Nishioka (Osaka City Univ.), Takahiro Matsuda (Osaka Univ.), Shinsuke Hara (Osaka City Univ.), Fumie Ono, Ryu Miura, Fumihide Kojima (NICT) WBS2017-82 ITS2017-59 RCC2017-98 |
We consider wireless data transfer between UAs (Unmanned Aircrafts) in the air and ground nodes. The received powers of ... [more] | WBS2017-82 ITS2017-59 RCC2017-98 pp.269-274 |
| PRMU | 2017-10-12 09:30 |
Kumamoto | Accelerating Convolutional Neural Networks Using Low-Rank Tensor Decomposition Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota (Tokyo Inst. of Tech.) PRMU2017-63 |
In the image recognition using convolution neural networks (CNN), convolution operations occupies the majority of the co... [more] | PRMU2017-63 pp.1-6 |
|
| EA, ASJ-H | 2017-07-21 13:40 |
Hokkaido | Hokkaido Univ. | [Poster Presentation]
ILRMA with complex Student's t source model Shinichi Mogami, Daichi Kitamura, Norihiro Takamune, Yoshiki Mitsui, Hiroshi Saruwatari (Univ. of Tokyo), Nobutaka Ono (NII), Yu Takahashi, Kazunobu Kondo (Yamaha) EA2017-23 |
(To be available after the conference date) [more] | EA2017-23 pp.131-136 |
| PRMU, IE, MI, SIP | 2017-05-25 15:10 |
Aichi | Graph Learning for Spectral Clustering using Low-rank and Sparse Decomposition Taiju Kanada, Masaki Onuki, Yuichi Tanaka (TUAT) SIP2017-10 IE2017-10 PRMU2017-10 MI2017-10 |
Spectral clustering is a method of clustering using eigenvectors of graph Laplacian. By using appropriate graphs, it is ... [more] | SIP2017-10 IE2017-10 PRMU2017-10 MI2017-10 pp.55-60 |
|
| EA, US (Joint) |
2017-01-25 13:00 |
Kyoto | Doshisha Univ. | [Poster Presentation]
Study on efficient solver for independent low-rank matrix analysis with sparse time-series-activity regularization Yoshiki Mitsui (Univ. Tokyo), Daichi Kitamura (SOKENDAI), Shinnosuke Takamichi (Univ. Tokyo), Nobutaka Ono (NII/SOKENDAI), Hiroshi Saruwatari (Univ. Tokyo) EA2016-72 |
In this paper, we propose a new blind source separation (BSS) method based on independent low-rank matrix analysis (ILRM... [more] | EA2016-72 pp.25-30 |
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