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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2024-03-11 16:50 |
Tokyo | The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
A Method of Timbre Synthesis Reflecting Impression Using Conditional-VAE
-- Applying the Temporal Information -- Miyu Yoshikawa, Susumu Kuroyanagi (NIT)  NC2023-49 |
It is difficult to systematically explain the relationship between tones and the impressions people have of them. In th... [more] | NC2023-49 pp.37-42 |
| SIP, IT, RCS | 2024-01-18 09:55 |
Miyagi | (Primary: On-site, Secondary: Online) |
A Study on Autoencoder-aided Data-Driven Tuning for Linear Dispersion Code Arata Kameda, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) IT2023-31 SIP2023-64 RCS2023-206 |
In this paper, we consider the application of wireless autoencoder (WAE) to a model of code generation rules for linear ... [more] | IT2023-31 SIP2023-64 RCS2023-206 pp.7-12 |
| SIP, IT, RCS | 2024-01-18 16:10 |
Miyagi | (Primary: On-site, Secondary: Online) |
A Study on Autoencoder for Iterative Signal Detection in MIMO Channels Yoshinori Ichihashi, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) IT2023-52 SIP2023-85 RCS2023-227 |
In this paper, we apply deep unfolding to uplink signal detection via probabilistic data association (PDA) and configure... [more] | IT2023-52 SIP2023-85 RCS2023-227 pp.115-120 |
| IBISML | 2023-12-20 16:25 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Anomaly detection by deep support data descriptions with pseudo-anomaly data Shuta Tsuchio, Takuya Kitamura (NIT, Toyama college) IBISML2023-34 |
This paper presents deep support vector data description (DSVDD) with pseudo-anomaly data that generated by generative m... [more] | IBISML2023-34 pp.25-30 |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-17 09:20 |
Tottori | (Primary: On-site, Secondary: Online) |
Co-speech Gesture Generation with Variational Auto Encoder Shihichi Ka, Koichi Shinoda (Tokyo Tech) PRMU2023-29 |
Co-speech gesture generation is the study of generating gestures from speech. In prior works, deterministic methods lear... [more] | PRMU2023-29 pp.74-79 |
| RCS | 2023-06-16 10:00 |
Hokkaido | Hokkaido University, and online (Primary: On-site, Secondary: Online) |
A Study on Autoencoder for ICA-Aided MIMO Blind Signal Separation Arata Kameda, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) RCS2023-68 |
[more] | RCS2023-68 pp.235-240 |
| NC, MBE (Joint) |
2023-03-14 15:25 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
A Method of Timbre Synthesis Reflecting Impression Using Conditional-VAE
-- Conditioning by Impression and Generating Sound Waveforms -- Takeru Watanabe, Susumu Kuroyanagi (NIT) NC2022-106 |
In This paper, we aim to propose a method of timbre synthesis based on impressions recalled by humans. We worked on this... [more] | NC2022-106 pp.84-89 |
| IT, RCS, SIP | 2023-01-25 12:20 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
A Study on Wireless Autoencoder for Data-Aided Channel Estimation Yoshinori Ichihashi, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Takanobu Doi, Kazushi Muraoka, Naoto Ishii (NEC), Hisato Iwai (Doshisha Univ.) IT2022-56 SIP2022-107 RCS2022-235 |
In MIMO (Multiple-Input Multiple-Output) channels, when the channel matrix is obtained by the Data-Aided Channel Estimat... [more] | IT2022-56 SIP2022-107 RCS2022-235 pp.154-159 |
| 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 |
| CS | 2020-11-05 16:00 |
Online | Online + Central Community Center, Nonoichi Community Center (Primary: Online, Secondary: On-site) |
[Invited Talk]
PAPR and OOBE Suppression of OFDM Signal Using AutoEncoder Masaya Ohta, Reiya Kuwahara (OPU) CS2020-52 |
OFDM (Orthogonal Frequency Division Multiplexing) signals have characteristics of both high OOBE (Out-of-Band Emission) ... [more] | CS2020-52 pp.30-34 |
| IBISML | 2020-10-21 15:35 |
Online | Online | IBISML2020-24 |
The goal of this study is to understand the information processing mechanism in a deep neural network (DNN) as a curve $... [more] | IBISML2020-24 p.42 |
| MI | 2020-09-03 13:10 |
Online | Online | [Invited Talk]
Manifold modeling in embedded space for image restoration Tatsuya Yokota (Nitech) MI2020-27 |
In this invited talk, I will discuss convolutional neural networks, which have achieved remarkable results in various im... [more] | MI2020-27 pp.43-44 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2020-06-29 13:50 |
Online | Online | Performance comparison of autoencoders and sparse PCAs Masumi Ishikawa (Kyutech) NC2020-4 IBISML2020-4 |
Principal component analysis (PCA) is an effective tool for clarifying data structure. Each principal component includes... [more] | NC2020-4 IBISML2020-4 pp.21-26 |
| SP, EA, SIP | 2020-03-03 09:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
Cross-Lingual Voice Conversion using Cyclic Variational Auto-encoder Hikaru Nakatani, Patrick Lumban Tobing, Kazuya Takeda, Tomoki Toda (Nagoya Univ.) EA2019-139 SIP2019-141 SP2019-88 |
In this report, we present a novel cross-lingual voice conversion (VC) method based on cyclic variational auto-encoder (... [more] | EA2019-139 SIP2019-141 SP2019-88 pp.219-224 |
| SP, EA, SIP | 2020-03-03 09:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
Semi-supervised Self-produced Speech Enhancement and Suppression Based on Joint Source Modeling of Air- and Body-conducted Signals Using Variational Autoencoder Shogo Seki, Moe Takada, Kazuya Takeda, Tomoki Toda (Nagoya Univ.) EA2019-140 SIP2019-142 SP2019-89 |
This paper proposes a semi-supervised method for enhancing and suppressing self-produced speech, using a variational aut... [more] | EA2019-140 SIP2019-142 SP2019-89 pp.225-230 |
| EA | 2019-12-13 13:00 |
Fukuoka | Kyushu Inst. Tech. | Speaker-independent source separation with multichannel variational autoencoder Li Li (Univ. Tsukuba), Hirokazu Kameoka (NTT), Shota Inoue, Shoji Makino (Univ. Tsukuba) EA2019-77 |
The multichannel variational autoencoder method (MVAE) is a recently proposed determined source separation method, which... [more] | EA2019-77 pp.79-84 |
| NC, MBE | 2019-12-06 15:40 |
Aichi | Toyohashi Tech | Prevention of redundant representations and of the black box in stacked autoencoders Masumi Ishikawa (Kyutech) MBE2019-56 NC2019-47 |
Recent progress in deep learning (DL) is remarkable and its recognition capability is said to surpass that of humans. Th... [more] | MBE2019-56 NC2019-47 pp.67-72 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2019-12-06 16:25 |
Tokyo | NHK Science & Technology Research Labs. | An evaluation of representation learning using phoneme posteriorgrams and data augmentation in speech emotion recognition Shintaro Okada (Nagoya Univ.), Atsushi Ando (Nagoya Univ./NTT), Tomoki Toda (Nagoya Univ.) SP2019-43 |
This paper presents a new speech emotion recognition method based on representation learning and data augmentation. To ... [more] |
SP2019-43 pp.91-96 |
| EMT, IEE-EMT | 2019-11-07 15:15 |
Saga | Hotel Syunkeiya | Land classification using unsupervised quaternion neural network with neighbor pixel information Jungmin Song, Ryo Natusaki, Akira Hirose (The Univ. of Tokyo) EMT2019-57 |
(To be available after the conference date) [more] | EMT2019-57 pp.117-122 |
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 10:40 |
Osaka | I-Site Nanba（Osaka） | [Poster Presentation]
A Study on Coded Modulation Using Autoencoders Akiho Nakata, Koji Ishii (Kagawa Univ.) RCC2019-24 NS2019-60 RCS2019-117 SR2019-36 SeMI2019-33 |
This study tries to design a coded modulation scheme with a desired transmission rate by use of autoencoder and evaluate... [more] | RCC2019-24 NS2019-60 RCS2019-117 SR2019-36 SeMI2019-33 pp.67-72(RCC), pp.93-98(NS), pp.89-94(RCS), pp.99-104(SR), pp.81-86(SeMI) |
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