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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MBE, MICT, IEE-MBE [detail] | 2023-01-17 09:50 |
Saga | Oral Cytology Based on Representation Learning of Visually Salient Cells Kazuki Matsuo, Eiji Mitate, Tomoya Sakai (Nagasaki Univ.)  MICT2022-44 MBE2022-44 |
We classify microscopically photographed cells for screening tests to find oral cancer in its early stages. Oral cancer ... [more] | MICT2022-44 MBE2022-44 pp.7-12 |
|
| CCS | 2022-11-17 14:55 |
Mie | (Primary: On-site, Secondary: Online) |
Long-term modeling of financial machine learning with multiple time scales Kazuki Amagai (Ibaraki Univ.), Riku Tanaka (Daiwa Asset Management), Tomoya Suzuki (Ibaraki Univ.) CCS2022-47 |
In asset management businesses such as operating mutual funds, medium or long-term investments are common in terms of op... [more] | CCS2022-47 pp.19-24 |
| SR | 2022-11-07 09:55 |
Fukuoka | Fukuoka University (Primary: On-site, Secondary: Online) |
Performance comparisons of OFDM communication system with autoencoder Takao Touma, Tomohisa Wada (Ryukyu Univ.) SR2022-46 |
This paper is a follow-up to "A study of OFDM communication system with autoencoder" presented at Technical Committee on... [more] | SR2022-46 pp.7-14 |
| PRMU | 2022-09-15 10:45 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Effect validation of adversarial auxiliary classifier for video disentanglement Takeshi Haga, Hiroshi Kera, Kazuhiko Kawamoto (Chiba Univ) PRMU2022-21 |
The Disentanglement of sequential data such as video requires inductive biases to separate static latent variables from ... [more] | PRMU2022-21 pp.67-71 |
| SIP | 2022-08-25 13:21 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Style Feature Extraction by Contrastive Learning and Mutual Information Constraints Suguru Yasutomi, Toshihisa Tanaka (TUAT) SIP2022-52 |
Extracting style features is crucial for analyzing data. This paper proposes a style feature extraction using variationa... [more] | SIP2022-52 pp.13-18 |
| SIP | 2022-08-26 14:08 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Study on Bone-conducted Speech Enhancement Using Vector-quantized Variational Autoencoder and Gammachirp Filterbank Cepstral Coefficients Quoc-Huy Nguyen, Masashi Unoki (JAIST) SIP2022-71 |
Bone-conducted (BC) speech potentially avoids the undesired effects on recorded speech due to background noise or reverb... [more] | SIP2022-71 pp.109-114 |
| SeMI, IPSJ-DPS, IPSJ-MBL, IPSJ-ITS | 2022-05-26 13:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment Xintong Shi, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.), Yutaka Matsui, Kazunari Owada (Atom Medical Co., Ltd.) SeMI2022-4 |
For fetal heart rate (FHR) monitoring, the non-invasive fetal electrocardiogram (FECG) obtained from abdomen surface ele... [more] | SeMI2022-4 pp.15-19 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 09:40 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Variational Autoencoders Conditioned by Contrastive Features as Style-Feature Extractors Suguru Yasutomi, Toshihisa Tanaka (TUAT) SIP2022-3 BioX2022-3 IE2022-3 MI2022-3 |
Extracting style features is crucial for investigating the characteristics of data. This paper proposes a variational au... [more] | SIP2022-3 BioX2022-3 IE2022-3 MI2022-3 pp.13-18 |
| SR | 2022-05-12 10:55 |
Tokyo | NICT Koganei (Primary: On-site, Secondary: Online) |
A study of OFDM communication system with autoencoder Takao Touma, Tomohisa Wada (Ryukyu Univ.) SR2022-6 |
Among deep neuralnetworks, autoencoders have the property of matching input and output. Recently, research has been cond... [more] | SR2022-6 pp.27-33 |
| EMM | 2022-03-08 09:55 |
Online | (Primary: Online, Secondary: On-site) (Primary: Online, Secondary: On-site) |
[Poster Presentation]
Study on JPEG Compression Resistant Watermarking Method Trained with Quantized Activation Function Shingo Yamauchi, Masaki Kawamura (Yamaguchi Univ.) EMM2021-110 |
We propose a watermarking method that introduces a quantized activation function to acquire robustness against quantizat... [more] | EMM2021-110 pp.95-100 |
| MW | 2022-03-04 11:10 |
Online | Online | Deep-Learning Based Anomaly Detection Method for Microwave Non-destructive Road Monitoring Takahide Morooka, Shouhei Kidera (Univ. of Electro-Communications) MW2021-134 |
Microwave radar is promising as large-scale and speedy non-destructive monitoring tool for aging road or tunnel because ... [more] | MW2021-134 pp.128-133 |
| MBE, NC (Joint) |
2022-03-02 11:00 |
Online | Online | Learning of a stacked autoencoder with regularizers added to the cost function, evaluation of their effectiveness, and clarification of its information compression mechanism Masumi Ishikawa (Kyutech) NC2021-49 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] | NC2021-49 pp.17-22 |
| MBE, NC (Joint) |
2022-03-03 15:30 |
Online | Online | EEG style transfer for sleep stage scoring using deep learning Naoki Omiya (Univ. of Tsukuba), Kazumasa Horie (CCS), Hiroyuki Kitagawa (IIIS) NC2021-66 |
Sleep stage scoring is a clinical inspection to identify in which sleep stages the patients are from their biological si... [more] | NC2021-66 pp.106-111 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-01 14:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
Target speaker extraction based on conditional variational autoencoder and directional information in underdetermined condition Rui Wang, Li Li, Tomoki Toda (Nagoya Univ) EA2021-76 SIP2021-103 SP2021-61 |
This paper deals with a dual-channel target speaker extraction problem in underdetermined conditions. A blind source sep... [more] | EA2021-76 SIP2021-103 SP2021-61 pp.76-81 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-02 11:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
Study of Method for Improving Speech Intelligibility in Glossectomy Patients by Knowledge Distillation via Lip Features Kazushi Takashima, Masanobu Abe, Sunao Hara (Okayama Univ.) EA2021-81 SIP2021-108 SP2021-66 |
In this paper, we propose a voice conversion method for improving speech intelligibility uttered by glossectomy patients... [more] | EA2021-81 SIP2021-108 SP2021-66 pp.108-113 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 16:45 |
Online | Online | A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder
-- Introduction of Regularization Losses Based on Metrics of Disentangled Representation -- Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study disentangled representation learning using a deep generative model based on Variational Autoenco... [more] | |
| 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 |
| MW | 2021-12-17 11:15 |
Kanagawa | Kawasaki City Industrial Promotion Hall (Primary: On-site, Secondary: Online) |
Building Surrogate Model Using Convolutional Autoencoder for Fast Frequency Response Calculation of Planar BPFs Ren Shibata, Masataka Ohira, Ma Zhewang (Saitama Univ.) MW2021-99 |
Recently, surrogate models using deep learning are introduced to speed up electromagnetic (EM) analysis. For instance, a... [more] | MW2021-99 pp.85-90 |
| SIS | 2021-12-03 13:00 |
Online | Online | [Tutorial Lecture]
A study of anomalous sound detection using autoencoder for quality determination and condition diagnosis Takashi Sudo, Yasuhiro Kanishima, Hiroyuki Yanagihashi (Toshiba) SIS2021-25 |
In the quality inspection of the product manufacture in a mass-production line or the apparatus preservation for produc... [more] | SIS2021-25 pp.20-25 |
| SDM | 2021-11-12 17:10 |
Online | Online | Inference of MOSFET Characteristics and Parameters with Machine Learning Kohei Akazawa, Yuigo Nakanishi, Yuhei Suzuki, Yoshinari Kamakura (Osaka Inst. Technol.) SDM2021-67 |
A machine learning method to extract SPICE model parameters is discussed. The data set is obtained from SPICE simulatio... [more] | SDM2021-67 pp.77-80 |
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