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All Technical Committee Conferences (Searched in: Recent 10 Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EA, SIP, SP, IPSJ-SLP [detail] | 2025-03-03 09:45 |
Okinawa | [Poster Presentation]
Decentralized Independent Vector Analysis Based on Majorization-Minimization Algorithm for Distributed Microphone Arrays Katsuhiro Morita, Kouei Yamaoka, Norihiro Takamune, Hiroshi Saruwatari (UTokyo)  EA2024-100 SIP2024-135 SP2024-41 |
Blind source separation (BSS) is a technique for separating source signals from observed mixtures without any prior info... [more] | EA2024-100 SIP2024-135 SP2024-41 pp.138-145 |
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| EA, SIP, SP, IPSJ-SLP [detail] | 2025-03-03 11:20 |
Okinawa | Proposal and Analysis of Metric for Evaluating Sampling Frequency Independence Based on Local Equivariance Error Kanami Imamura (UTokyo/AIST), Tomohiko Nakamura (AIST), Norihiro Takamune (UTokyo), Kouhei Yatabe (TUAT), Hiroshi Saruwatari (UTokyo) EA2024-108 SIP2024-143 SP2024-49 |
Audio signal processing methods based on deep neural networks (DNNs) require additional steps, such as signal resampling... [more] | EA2024-108 SIP2024-143 SP2024-49 pp.193-198 |
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| EA, SIP, SP, IPSJ-SLP [detail] | 2025-03-04 11:05 |
Okinawa | [Poster Presentation]
Noise Self-Supervised Rank-Constrained Spatial Covariance Matrix Estimation Using Independent Deeply Learned Matrix Analysis for Real-Time Multichannel Speech Extraction in Diffuse Noise Environment Yuki Nakanishi, Yuto Ishikawa, Norihiro Takamune, Hiroshi Saruwatari (The Univ. of Tokyo) EA2024-146 SIP2024-181 SP2024-87 |
For real-time speech extraction in a diffuse noise environment, a method based on independent low-rank matrix analysis (... [more] | EA2024-146 SIP2024-181 SP2024-87 pp.415-422 |
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| EA, US (Joint) |
2024-12-20 13:45 |
Oita | OITAUniv | [Poster Presentation]
Investigation of Projection Back in Spatially Regularized Independent Low-Rank Matrix Analysis Using Simultaneous Steering Vector Estimation Sota Hirata, Norihiro Takamune, Kouei Yamaoka (The University of Tokyo), Daichi Kitamura (NIT, Kagawa), Hiroshi Saruwatari (The University of Tokyo), Yu Takahashi, Kazunobu Kondo (Yamaha Corp.) EA2024-68 |
Blind source separation (BSS) is a technique to separate each source signal from observed mixtures without any prior inf... [more] | EA2024-68 pp.26-33 |
| 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 |
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| 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 |
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| 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 |
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