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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 184  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SIS 2023-03-02
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
CAS, CS 2023-03-01
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
CAS, CS 2023-03-02
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
SP, IPSJ-SLP, EA, SIP [detail] 2023-03-01
(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
ICTSSL, CAS 2023-01-26
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
ICTSSL, CAS 2023-01-26
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
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
EA, EMM, ASJ-H 2022-11-21
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
EA, ASJ-H 2022-08-04
(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
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-17
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
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-18
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
EA 2022-05-13
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
EA 2022-05-13
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
(Joint) [detail]
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
RCS, SIP, IT 2022-01-21
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
ITE-ME, EMM, IE, LOIS, IEE-CMN, IPSJ-AVM [detail] 2021-08-25
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
SIP 2021-08-24
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
SP, IPSJ-SLP, IPSJ-MUS 2021-06-19
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
EA, US, SP, SIP, IPSJ-SLP [detail] 2021-03-03
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
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
 Results 1 - 20 of 184  /  [Next]  
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