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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 14 of 14  /   
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
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
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-17
13:00
Online Online SP2022-6 Rank-constrained spatial covariance matrix estimation (RCSCME) is a method for blind speech extraction. In RCSCME, we de... [more] SP2022-6
pp.18-23
SP, EA, SIP 2020-03-02
10:10
Okinawa Okinawa Industry Support Center
(Cancelled but technical report was issued)
Multichannel NMF with Joint-Diagonalizable Constraint Based on Generalized Gaussian Distribution for Blind Source Separation
Keigo Kamo, Yuki Kubo, Norihiro Takamune (UTokyo), Daichi Kitamura (NIT Kagawa), Hiroshi Saruwatari (UTokyo), Yu Takahashi, Kazunobu Kondo (Yamaha) EA2019-103 SIP2019-105 SP2019-52
Multichannel nonnegative matrix factorization (MNMF) is a blind source separation technique, which employs the full-rank... [more] EA2019-103 SIP2019-105 SP2019-52
pp.13-19
EA 2019-12-13
13:25
Fukuoka Kyushu Inst. Tech. Rank-constrained spatial covariance matrix estimation based on multivariate complex generalized Gaussian distribution and its acceleration for blind speech extraction
Yuki Kubo, Norihiro Takamune (UTokyo), Daichi Kitamura (NIT, Kagawa), Hiroshi Saruwatari (UTokyo) EA2019-78
In this paper, we generalize a generative model in rank-constrained spatial covariance matrix estimation that separates ... [more] EA2019-78
pp.85-92
EA, ASJ-H 2019-10-28
14:00
Tokyo NHK Science&Technology Research Lab. FastMNMF based on multivariant complex Student's t distribution for blind source separation
Keigo Kamo, Yuki Kubo, Norihiro Takamune (UTokyo), Daichi Kitamura (Kagawa NCIT), Hiroshi Saruwatari (UTokyo), Yu Takahashi, Kazunobu Kondo (Yamaha) EA2019-40
FastMNMF is a blind source separation technique, which is an accelerated algorithm of multichannel nonnegative matrix fa... [more] EA2019-40
pp.23-29
EA, SIP, SP 2019-03-14
15:15
Nagasaki i+Land nagasaki (Nagasaki-shi) Convergence-guaranteed independent positive semidefinite tensor analysis for blind source separation
Kanta Fukushige, Norihiro Takamune (UTokyo), Daichi Kitamura (Kagawa-NICT), Hiroshi Saruwatari (UTokyo), Rintaro Ikeshita, Tomohiro Nakatani (NTT) EA2018-127 SIP2018-133 SP2018-89
This paper focuses on independent positive semidefinite tensor analysis (IPSDTA), which is a technique for over-determin... [more] EA2018-127 SIP2018-133 SP2018-89
pp.167-172
EA, SIP, SP 2019-03-14
15:40
Nagasaki i+Land nagasaki (Nagasaki-shi) Estimation of rank-constrained spatial covariance model based on multivariate complex Student's t distribution for blind source separation
Yuki Kubo, Norihiro Takamune (UTokyo), Daichi Kitamura (Kagawa NCIT), Hiroshi Saruwatari (UTokyo) EA2018-128 SIP2018-134 SP2018-90
In this paper, we generalize a generative model in estimation of rank-constrained spatial covariance model that separate... [more] EA2018-128 SIP2018-134 SP2018-90
pp.173-178
SIP, EA, SP, MI
(Joint) [detail]
2018-03-19
10:00
Okinawa   Experimental Evaluation of Multichannel Audio Source Separation Based on IDLMA
Daichi Kitamura, Hayato Sumino, Norihiro Takamune, Shinnosuke Takamichi, Hiroshi Saruwatari (Univ. of Tokyo), Nobutaka Ono (Tokyo Metropolitan Univ.) EA2017-104 SIP2017-113 SP2017-87
In this paper, we propose a new informed multichannel audio source separation called independent deeply learned matrix a... [more] EA2017-104 SIP2017-113 SP2017-87
pp.13-20
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
EA 2015-12-11
15:15
Ishikawa Kanazawa Univ., Satellite Plaza [Poster Presentation] Analysis on Degree of Freedom for Supervised NMF with All-Pole-Model-Based Basis Deformation
Hiroaki Nakajima (UTokyo), Daichi Kitamura (SOKENDAI), Norihiro Takamune, Shoichi Koyama, Hiroshi Saruwatari (UTokyo), Nobutaka Ono (NII/SOKENDAI), Yu Takahashi, Kazunobu Kondo (Yamaha) EA2015-42
Conventional supervised NMF has the critical problem that a mismatch between the bases trained in advance and the target... [more] EA2015-42
pp.13-18
IBISML 2014-11-17
17:00
Aichi Nagoya Univ. [Poster Presentation] Training Algorithm for Restricted Boltzmann Machines Using Auxiliary Function Approach
Norihiro Takamune (Univ. of Tokyo), Hirokazu Kameoka (Univ. of Tokyo/NTT) IBISML2014-56
Layerwise pre-training is one of important elements for deep learning, and Restricted Boltzmann Machines (RBMs) is popul... [more] IBISML2014-56
pp.161-168
SP, IPSJ-MUS 2014-05-24
11:30
Tokyo   Underdetermined Blind Separation of Moving Sources Based on Probabilistic Modeling
Takuya Higuchi, Norihiro Takamune, Tomohiko Nakamura (Univ. of Tokyo), Hirokazu Kameoka (Univ. of Tokyo/NTT) SP2014-20
This paper deals with the problem of the underdetermined blind separation and tracking of moving sources. In practical s... [more] SP2014-20
pp.211-216
 Results 1 - 14 of 14  /   
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