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 Results 1 - 20 of 44  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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 2021-08-24
10:00
Online Online [Invited Talk] Audio source separation based on independent low-rank matrix analysis and its extensions
Daichi Kitamura (NIT Kagawa) SIP2021-32
Audio source separation is a technique for separating individual audio sources from an observed mixture signal. In parti... [more] SIP2021-32
pp.19-24
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2021-06-28
13:50
Online Online Simplification of Average Consensus Algorithm in Distributed HALS Algorithm for NMF
Keiju Hayashi, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2021-3 IBISML2021-3
Nonnegative Matrix Factorization (NMF) is the process of approximating a given nonnegative matrix by the product of two ... [more] NC2021-3 IBISML2021-3
pp.15-22
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2021-06-28
14:15
Online Online Modification of Optimization Problem in Randomized NMF and Design of Optimization Method based on HALS Algorithm
Takao Masuda, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2021-4 IBISML2021-4
Nonnegative matrix factorization (NMF) is the process of decomposing a given nonnegative matrix into two nonnegative fac... [more] NC2021-4 IBISML2021-4
pp.23-30
SIS, IPSJ-AVM 2021-06-24
14:15
Online Online [Tutorial Lecture] Noise Reduction using Nonnegative Matrix Factorization
Motoaki Mour (Aichi Univ.) SIS2021-9
Nonnegative matrix factorization (NMF) is a general term for methods that factorize a matrix into two or more matrices w... [more] SIS2021-9
pp.49-54
EA, US, SP, SIP, IPSJ-SLP [detail] 2021-03-04
10:15
Online Online A quantitative measure of discriminability between NMF dictionaries
Eisuke Konno, Daisuke Saito, Nobuaki Minematsu (UTokyo) EA2020-82 SIP2020-113 SP2020-47
Supervised nonnegative matrix factorization (NMF) is a popular approach for monaural audio source separation. It realize... [more] EA2020-82 SIP2020-113 SP2020-47
pp.134-139
MSS, CAS, IPSJ-AL [detail] 2020-11-25
17:00
Online Online Distributed Algorithms based on Multiplicative Update Rules for Nonnegative Matrix Factorization
Yohei Domen, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) CAS2020-24 MSS2020-16
Nonnegative matrix factorization (NMF) is a multivariate method that approximates a given nonnegative matrix by the prod... [more] CAS2020-24 MSS2020-16
pp.28-33
NC, MBE
(Joint)
2020-03-05
14:40
Tokyo University of Electro Communications
(Cancelled but technical report was issued)
A method for sound source separation using the onset information based on the NMF with deformable bases
Shota Uchida, Susumu Kuroyanagi (NITech) NC2019-98
Currently, the NMF with deformable bases has been proposed as a model for learning the sequences of frequency spectrum f... [more] NC2019-98
pp.131-136
SIS 2019-12-12
15:45
Okayama Okayama University of Science [Invited Talk] Consensus-Based Distributed Algorithms and Applications to Machine Learning
Norikazu Takahashi (Okayama Univ.) SIS2019-29
Recently, consensus-based distributed optimization methods for multi-agent systems have been vigorously studied in the f... [more] SIS2019-29
p.35
IE, EMM, LOIS, IEE-CMN, ITE-ME, IPSJ-AVM [detail] 2019-09-20
15:25
Niigata Tokimeito, Niigata University Analysis of Daily Activities and Intervention Acceptability Using Nonnegative Tensor Factorization
Masahiro Kohjima, Masami Takahashi, Takeshi Kurashima, Tatsushi Matsubayashi, Hiroyuki Toda (NTT) LOIS2019-18 IE2019-31 EMM2019-75
In order to improve people's lifestyles to prevent lifestyle-related diseases, it is important to understand not only th... [more] LOIS2019-18 IE2019-31 EMM2019-75
pp.97-102
EA, SIP, SP 2019-03-14
13:30
Nagasaki i+Land nagasaki (Nagasaki-shi) [Poster Presentation] An Initialization Method for Multichannel Nonnegative Matrix Factorization using Nonnegative Independent Component Analysis
Takahiro Ushijima, Takanobu Uramoto, Shingo Uenohara, Ken'ichi Furuya (Oita Univ.) EA2018-105 SIP2018-111 SP2018-67
Recently, devices that handle voice have become widely used, and there is a demand for a technique to extract only the t... [more] EA2018-105 SIP2018-111 SP2018-67
pp.37-42
OFT, OCS, OPE
(Joint) [detail]
2019-02-15
14:25
Fukuoka   Maximum and Minimum strain extraction from BGS observations including noise and optical loss using nonnegative matrix factorization
Takuya Fujimoto, Hiroshi Naruse (Mie Univ.), Takanori Nishino (Meijo Univ.) OFT2018-81 OPE2018-210
We have proposed a method for extracting the maximum and minimum strains produced in a Brillouin gain spectrum (BGS) obs... [more] OFT2018-81 OPE2018-210
pp.57-62(OFT), pp.89-94(OPE)
NLP, NC
(Joint)
2019-01-24
15:20
Hokkaido The Centennial Hall, Hokkaido Univ. A New Method for Deriving Multiplicative Update Rules for NMF with Error Functions Containing Logarithm
Akihiro Koso, Norikazu Takahashi (Okayama Univ.) NLP2018-122
Nonnegative Matrix Factorization (NMF) is an operation that decomposes a given nonnegative matrix X into two nonnegative... [more] NLP2018-122
pp.137-142
OFT 2018-10-11
16:00
Miyagi Touhoku Univ. [Poster Presentation] Maximum and Minimum strain extraction from Brillouin gain spectrum using nonnegative matrix factorization
Takuya Fujimoto, Hiroshi Naruse (Mie Univ.), Takanori Nishino (Meijo Univ.) OFT2018-48
A fiber optic strain measurement based on the Brillouin scattering phenomenon enables a distributed strain measurement a... [more] OFT2018-48
pp.157-161
NLP 2018-08-09
09:30
Kagawa Saiwai-cho Campus, Kagawa Univ. Derivation and Experimental Evaluation of a Novel Nonnegative Matrix Factorization Algorithm for Discovering Communities
Yoshito Usuzaka, Norikazu Takahashi (Okayama Univ.) NLP2018-64
Community discovery is an important technique for a better understanding of the structure of a network. We consider the ... [more] NLP2018-64
pp.57-62
SIP, EA, SP, MI
(Joint) [detail]
2018-03-19
13:00
Okinawa   [Poster Presentation] Performance Evaluation of Initial Value Setting Method for Spatial Correlation Matrices in Multi-channel NMF
Yu Tajima, Akira Tanaka (Hokkaido Univ.) EA2017-130 SIP2017-139 SP2017-113
The multi-channel nonnegative matrix factorization (MNMF) is an extension of the single-channel nonnegative matrix facto... [more] EA2017-130 SIP2017-139 SP2017-113
pp.161-162
MBE, NC
(Joint)
2018-03-13
10:00
Tokyo Kikai-Shinko-Kaikan Bldg.
Yuma Saito, Tsubasa Ito (Tokyo Tech), Keisuke Ota, Masanori Murayama (RIKEN), Toru Aonishi (Tokyo Tech) NC2017-68
Recent rapid progress of imaging techniques such as two-photon microscopes causes the extreme increase in amount of acqu... [more] NC2017-68
pp.3-8
EA 2018-02-16
13:10
Hiroshima Pref. Univ. Hiroshima The effect of increasing the number of channels with multi-channel non-negative matrix factorization for noisy speech recognition
Takanobu Uramoto (Oita Univ.), Youhei Okato, Toshiyuki Hanazawa (Mitsubishi Electric), Iori Miura, Shingo Uenohara, Ken'ich Furuya (Oita Univ.) EA2017-99
Nonnegative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of ... [more] EA2017-99
pp.33-38
EA, ASJ-H 2017-10-22
09:00
Toyama Ushidake-Onsen [Invited Talk] Blind source separation based on independent low-rank matrix analysis
Daichi Kitamura (UT), Nobutaka Ono (NII), Hiroshi Sawada, Hirokazu Kameoka (NTT), Hiroshi Saruwatari (UT) EA2017-56
In this paper, we propose a new effective algorithm for blind source separation problem (BSS) called independent low-ran... [more] EA2017-56
pp.73-80
WIT, SP 2017-10-19
13:20
Fukuoka Tobata Library of Kyutech (Kitakyushu) Speech enhancement of utterance while playing with werewolf game "JINRO" based on NMF
Shunsuke Kawano, Toru Takahashi (OSU) SP2017-35 WIT2017-31
We describe that speech enhancement for natural and multi speaker dialognue. To record natural and multi speaker dialogn... [more] SP2017-35 WIT2017-31
pp.7-12
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