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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIS | 2023-03-02 11:00 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
Blink detection from one-dimensional face signal by using convolutional sparse dictionary learning Souichiro Maruyama, Makoto Nakashizuka (CIT)  SIS2022-40 |
In this report, a blink detection method from average intensities of whole facial videos using convolutional dictionary... [more] | SIS2022-40 pp.1-4 |
| CAS, CS | 2023-03-01 10:20 |
Fukuoka | Kitakyushu International Conference Center (Primary: On-site, Secondary: Online) |
Approximate joint diagonalization for blind separation of superimposed images Shinya Saito, Kunio Oishi (Tokyo University of Tech.) CAS2022-98 CS2022-75 |
This report presents blind separation of superimposed images. When we take a picture for panorama thought window glass a... [more] | CAS2022-98 CS2022-75 pp.12-17 |
| CAS, CS | 2023-03-02 15:40 |
Fukuoka | Kitakyushu International Conference Center (Primary: On-site, Secondary: Online) |
Sound Quality Improvement of Source Separation Signal by Binary Mask Taiga Saito, Kenji Suyama (Tokyo Denki Univ.) CAS2022-122 CS2022-99 |
A two-microphone source separation method using multiple complex weighted sum circuits (WSCs) has been proposed. However... [more] | CAS2022-122 CS2022-99 pp.150-154 |
| 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 |
| ICTSSL, CAS | 2023-01-26 14:20 |
Tokyo | TBD (Primary: On-site, Secondary: Online) |
Effects of Suppression Section Expansion in Actual Room Environment Sound Source Separation Tsukasa Hidaka, Kenji Suyama (Tokyo Denki Univ.) CAS2022-71 ICTSSL2022-35 |
In actual room environments, it is easy to assume that the sound source signal arrives with any spatial propagation spre... [more] | CAS2022-71 ICTSSL2022-35 pp.51-55 |
| ICTSSL, CAS | 2023-01-26 14:40 |
Tokyo | TBD (Primary: On-site, Secondary: Online) |
Sound Source Separation Avoiding Sound Quality Degradation by Spatial Propagation Kai Furusawa, Kenji Suyama (Tokyo Denki Univ.) CAS2022-72 ICTSSL2022-36 |
In general, the farther the distance between the microphone and the sound source, the greater the spatial propagation sp... [more] | CAS2022-72 ICTSSL2022-36 pp.56-61 |
| 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 |
| EA, EMM, ASJ-H | 2022-11-21 10:00 |
Online | Online | [Poster Presentation]
Sound signal mixing method using both source-separated and non-separated signals Yuto Nishitani, Kota Takahashi (UEC) EA2022-48 EMM2022-48 |
We are researching a smart mixer, a device that performs better sound mixing than conventional sound mixers. The smart ... [more] |
EA2022-48 EMM2022-48 pp.40-45 |
| EA, ASJ-H | 2022-08-04 15:15 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Audio Source Separation Combining Wavelet Transform and Deep Neural Network Tomohiko Nakamura (Univ. Tokyo) EA2022-32 |
Audio source separation is a technique of separating an observed audio signal into individual source signals. The use of... [more] | EA2022-32 p.25 |
| 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 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-18 15:00 |
Online | Online | Unsupervised Training of Sequential Neural Beamformer Using Blindly-separated and Non-separated Signals Kohei Saijo, Tetsuji Ogawa (Waseda Univ.) SP2022-25 |
We present an unsupervised training method of the sequential neural beamformer (Seq-NBF) using the separated signals fro... [more] | SP2022-25 pp.110-115 |
| EA | 2022-05-13 13:10 |
Online | Online | Fast Blind Source Separation in Noisy Reverberant Environments Using Independent Vector Extraction Rintaro Ikeshita, Tomohiro Nakatani (NTT) EA2022-5 |
Blind source separation (BSS) is a technique of separating and extracting individual source signals only from their mixt... [more] | EA2022-5 pp.20-25 |
| EA | 2022-05-13 16:50 |
Online | Online | Basic study for permutation solver based on deep neural networks Fumiya Hasuike, Rui Watanabe, Daichi Kitamura (NIT, Kagawa) EA2022-13 |
This paper focuses on a permutation problem associated with frequency-domain independent component analysis (FDICA) that... [more] | EA2022-13 pp.62-67 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 10:55 |
Online | Online | Fetal Heart Rate Detection via Maternal ECG Cancellation by Neural-Network Autoencoder Abuzar Ahmad Qureshi, Lu Wang, Tomoaki Ohtsuki (Keio Univ.), Kazunari Owada, Hayato Hayashi (Atom Medical Co.) NLP2021-123 MICT2021-98 MBE2021-84 |
Fetal heart rate (HR) monitoring is necessary for accessing the state of the fetus during pregnancy and labor. Non-invas... [more] | NLP2021-123 MICT2021-98 MBE2021-84 pp.243-247 |
| RCS, SIP, IT | 2022-01-21 11:45 |
Online | Online | Simultaneous matrix diagonalization using alternating least-squares algorithm Shinya Saito, Kunio Oishi (Tokyo University of Tech.) IT2021-73 SIP2021-81 RCS2021-241 |
This paper presents an approach for overdetermined blind source separation (BSS) using AJD. The approach is constructed ... [more] | IT2021-73 SIP2021-81 RCS2021-241 pp.252-257 |
| ITE-ME, EMM, IE, LOIS, IEE-CMN, IPSJ-AVM [detail] | 2021-08-25 13:25 |
Online | Online | Extraction of watermarks from video frames by using BSS Akane Yokota, Masaki Kawamura (Yamaguchi Univ.) LOIS2021-17 IE2021-12 EMM2021-47 |
We propose a method for extracting watermarks additively embedded in video frames by using blind source separation (BS... [more] |
LOIS2021-17 IE2021-12 EMM2021-47 pp.7-12 |
| 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 |
| SP, IPSJ-SLP, IPSJ-MUS | 2021-06-19 15:00 |
Online | Online | Source Separation for Asynchronous Recordings of Conversation Using Time-Frequency Masking and Independent Vector Analysis Haruki Nammoku, Kouei Yamaoka, Yukoh Wakabayashi, Nobutaka Ono (TMU) SP2021-22 |
In this study, we investigate the source separation for conversational speech recorded by multiple voice recorders that ... [more] | SP2021-22 pp.101-106 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 13:05 |
Online | Online | [Invited Talk]
* Masahito Togami (LINE) EA2020-64 SIP2020-95 SP2020-29 |
Recently, deep learning based speech source separation has been evolved rapidly. A neural network (NN) is usually learne... [more] | EA2020-64 SIP2020-95 SP2020-29 pp.27-32 |
| NC, MBE (Joint) |
2021-03-04 09:25 |
Online | Online | An Estimated Intersections Reduction Method for Percussion Source Separation Based on the U-Net Daisuke Tanaka, Susumu Kuroyanagi (NIT) NC2020-55 |
In the music information processing using drum information, the sound source separation is pre-processed to separate onl... [more] | NC2020-55 pp.71-76 |
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