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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 33  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IA, ICSS 2022-06-24
10:25
Nagasaki Univ. of Nagasaki
(Primary: On-site, Secondary: Online)
Application of Adversarial Examples to Physical ECG Signals
Taiga Ono (Waseda Univ.), Takeshi Sugawara (UEC), Jun Sakuma (Tsukuba Univ./RIKEN), Tatsuya Mori (Waseda Univ./RIKEN/NICT) IA2022-11 ICSS2022-11
This work aims to assess the reality and feasibility of applying adversarial examples to attack cardiac diagnosis system... [more] IA2022-11 ICSS2022-11
pp.61-66
KBSE 2022-03-10
10:15
Online Online (Zoom) Safety risk analysis and evaluation method for DNN system in autonomous driving
Tomoko Kaneko, Yuji Takahashi (NII), Shinichi Yamaguchi (SDM), Junji Hashimoto (GREE), Nobukazu Yoshioka (Soudai) KBSE2021-51
There is a great concern about the quality of current AI, especially DNN (deep learning), especially about its safety an... [more] KBSE2021-51
pp.60-65
VLD, DC, RECONF, ICD, IPSJ-SLDM
(Joint) [detail]
2021-12-01
09:20
Online Online Block Sparse MLP-based Vision DNN Accelerators on Embedded FPGAs
Akira Jinguji, Hiroki Nakahara (Tokyo Tech) VLD2021-21 ICD2021-31 DC2021-27 RECONF2021-29
Since the advent of Vision Transformer, a deep learning model for image recognition without Convolution, MLP-based model... [more] VLD2021-21 ICD2021-31 DC2021-27 RECONF2021-29
pp.25-30
EMM, EA, ASJ-H 2021-11-15
09:00
Online Online [Poster Presentation] A Study on Convergency of DNN Watermarking without Embedding Loss Function
Takuro Tanaka, Tatsuya Yasui, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.) EA2021-35 EMM2021-62
Intellectual Property Rights protection related to Deep Neural Networks is an important issue due to the high cost of tr... [more] EA2021-35 EMM2021-62
pp.49-54
RISING
(3rd)
2021-11-16
11:30
Tokyo
(Primary: On-site, Secondary: Online)
Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning
Satoshi Nakaniida, Takeo Fujii (UEC)
We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) through... [more]
SR 2021-11-05
11:15
Online Online Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning
Satoshi Nakaniida, Takeo Fujii (UEC) SR2021-53
We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) through... [more] SR2021-53
pp.72-78
SP, WIT, IPSJ-SLP, ASJ-H [detail] 2021-10-19
15:10
Online Online A study on model training for DNN-HSMM-based speech synthesis using a large-scale speech corpus
Nobuyuki Nishizawa, Gen Hattori (KDDI Research) SP2021-34 WIT2021-27
In this study, an investigation into model training for DNN-HSMM-based speech synthesis using a large speech corpus coll... [more] SP2021-34 WIT2021-27
pp.52-57
PRMU, IPSJ-CVIM 2020-05-14
13:30
Online Online Human Action Recognition with Two-stream 3D BagNet
Junya Uchida, Yu Wang, Jien Kato (Ritsumeikan Univ.) PRMU2020-3
We propose the Two-stream 3D BagNet for the human action recognition task. The proposed architecture is inspired by the ... [more] PRMU2020-3
pp.13-17
PRMU, IPSJ-CVIM 2020-03-17
16:00
Kyoto
(Cancelled but technical report was issued)
Acceleration of Deep Learning Inference by Model Cascading
Shohei Enomoto, Takeharu Eda (NTT) PRMU2019-98
In recent years, various applications have appeared due to the development of deep learning and the spread of IoT device... [more] PRMU2019-98
pp.203-208
IE, IMQ, MVE, CQ
(Joint) [detail]
2020-03-06
10:10
Fukuoka Kyushu Institute of Technology
(Cancelled but technical report was issued)
A Comparison Study of Neural Sign Language Translation Methods with Spatio-Temporal Features
Kodai Watanabe, Wataru Kameyama (Waseda Univ.) IMQ2019-68 IE2019-150 MVE2019-89
In Neural Sign Language Translation, a model based on 2DCNN (2 Dimensional Convolutional Neural Network) called AlexNet ... [more] IMQ2019-68 IE2019-150 MVE2019-89
pp.273-278
SP, EA, SIP 2020-03-03
09:00
Okinawa Okinawa Industry Support Center
(Cancelled but technical report was issued)
[Poster Presentation] Automatic estimation of prosodic control made in English utterances using DNN-based acoustic models trained with prosodic features and labels
Yang Shen, Shintarou Ando, Nobuaki Minematsu, Daisuke Saito (UTokyo), Satoshi Kobashikawa (NTT) EA2019-136 SIP2019-138 SP2019-85
This paper investigate how to utilize DNN acoustic models trained with prosodic features and labels to detect prosodic e... [more] EA2019-136 SIP2019-138 SP2019-85
pp.201-206
NLC, IPSJ-NL, SP, IPSJ-SLP
(Joint) [detail]
2019-12-06
16:00
Tokyo NHK Science & Technology Research Labs. A comparison of neural vocoders in singing voice synthesis
Sota Wada, Yukiya Hono, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2019-42
In this study, we compare five types of vocoders based on neural networks (neural vocoders) for singing voice synthesis.... [more] SP2019-42
pp.85-90
SP 2019-06-13
13:30
Kanagawa Tokyo Institute of Technology A study on style transplantation modeling techniques for DNN-based speech synthesis
Yoshiki Hiruta (Tokyo Tech), Tomoki Koriyama (The Univ. of Tokyo), Yuuki Tachioka (Denso IT Lab), Takao Kobayashi (Tokyo Tech) SP2019-1
This paper investigates style transplantation modeling techniques for DNN-based statistical parametric speech synthesis.... [more] SP2019-1
pp.1-6
SP 2018-08-27
11:35
Kyoto Kyoto Univ. [Poster Presentation] A Study on Representation of Speaker Information for DNN Speech Synthesis
Lin Yuhan, Keisuke Imoto, Masahiro Niitsuma, Ryosuke Yamanishi, Yoichi Yamashita (Ritsumeikan Univ.) SP2018-25
Recent studies have shown that DNN speech synthesis can generate natural synthesized speech than HMM-based speech synthe... [more] SP2018-25
pp.15-18
SP, ASJ-H 2018-01-20
14:55
Tokyo The University of Tokyo [Poster Presentation] TRAJECTORY TRAINING CONSIDERING POWER FOR SPEECH SYNTHESIS BASED ON NEURAL NETWORKS
Ryohei Funato, Kei Hashimoto, keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2017-74
In statistical parametric speech synthesis, a relation between acoustic features and linguistic features is modeled by s... [more] SP2017-74
pp.43-48
SP, ASJ-H 2018-01-21
16:00
Tokyo The University of Tokyo A study on voice conversion based on WaveNet
Jumpei Niwa, Takenori Yoshimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (NIT) SP2017-84
This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acous... [more] SP2017-84
pp.99-104
PRMU 2017-10-13
13:10
Kumamoto   [Tutorial Lecture] Understanding Deep Neural Network to Solve the Technical and Social Issues
Takahiro Kubo (TIS) PRMU2017-92
Deep learning leaves many achievements in fields such as images, and the scope of its application is expanding more and ... [more] PRMU2017-92
pp.167-168
PRMU, SP 2017-06-22
15:15
Miyagi   Comparisons on Transplant Emotional Expressions in DNN-based TTS Synthesis
Katsuki Inoue, Sunao Hara, Masanobu Abe (Okayama Univ.), Nobukatsu Hojo, Yusuke Ijima (NTT) PRMU2017-29 SP2017-5
Recent studies have shown that DNN-based speech synthesis can generate more natural synthesized speech than the conventi... [more] PRMU2017-29 SP2017-5
pp.23-28
SP, SIP, EA 2017-03-01
12:40
Okinawa Okinawa Industry Support Center [Poster Presentation] Reverberant speech enhancement with deep auto encoder based on harmonic structure
Rikuto Ota, Yukoh Wakabayashi, Takahiro Fukumori, Masato Nakayama, Takanobu Nishiura (Ritsumeikan Univ.) EA2016-107 SIP2016-162 SP2016-102
This paper describes reverberant speech enhancement (RSE) with deep auto encoder (DAE) based on harmonic structure. DAEs... [more] EA2016-107 SIP2016-162 SP2016-102
pp.141-146
SP, SIP, EA 2017-03-01
12:40
Okinawa Okinawa Industry Support Center [Poster Presentation] An investigation of speaker adaptation method for DNN-based speech synthesis using speaker codes
Nobukatsu Hojo, Yusuke Ijima (NTT) EA2016-108 SIP2016-163 SP2016-103
In this work, we conducted objective evaluation experiments on the conventional speaker adaptation methods for DNN-based... [more] EA2016-108 SIP2016-163 SP2016-103
pp.147-152
 Results 1 - 20 of 33  /  [Next]  
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