Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Pre-Trained Text Embeddings for Enhanced Text-to-Speech Synthesis Reviewed

Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda, Shubham Toshniwal, Karen Livescu

Proc. Interspeech 2019 page： 4430-4434 2019.9

Robustness of Statistical Voice Conversion Based on Direct Waveform Modification Against Background Sounds Reviewed

Yusuke Kurita, Kazuhiro Kobayashi, Kazuya Takeda, Tomoki Toda

Proc. Interspeech 2019 page： 684-688 2019.9

Effects on the Perception of Speed and Normality When Virtual Reality Scenes Reviewed

Yusuke Sakai, Toshimitsu Watanabe, Yoshio Ishiguro, Takanori Nishino, Kazuya Takeda

11th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (2019 Automotive User Interfaces) 2019.9

Real-time Streaming Point Cloud Compression for 3D LiDAR Sensor Using U-net

Chenxi Tu,Eijiro Takeuchi,Alexander Carballo,Kazuya Takeda

IEEE access page： - 2019.8

Overview of the five key research groups within the Behaviour Signal Processing Laboratory (Takeda Laboratory) at Nagoya University

Kazuya Takeda

Impact Vol. 2019 ( 5 ) page： 32-35 2019.6

A Survey of Autonomous Driving: Common Practices and Emerging Technologies Reviewed

Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda

arXiv preprint arXiv:1906.05113 page： 00 2019.6

Risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer Reviewed

Ekim Yurtsever, Yongkang Liu, Jacob Lambert, Chiyomi Miyajima, Eijiro Takeuchi, Kazuya Takeda, John HL Hansen

arXiv page： 1906.02859 2019.6

A Predictive Reward Function for Human-Like Driving Based on a Transition Model of Surrounding Environment Reviewed

Daiki Hayashi, Yunfei Xu, Takashi Bando, Kazuya Takeda

2019 International Conference on Robotics and Automation (ICRA) page： 7618-7624 2019.5

Point cloud compression for 3D LiDAR sensor using recurrent neural network with residual blocks Reviewed

Chenxi Tu, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda

2019 International Conference on Robotics and Automation (ICRA) page： 3274-3280 2019.5

SCENE-DEPENDENT ANOMALOUS ACOUSTIC-EVENT DETECTION BASED ON CONDITIONAL WAVENET AND I-VECTOR

Komatsu Tatsuya, Hayashi Tomoki, Kondo Reishi, Toda Tomoki, Takeda Kazuya

2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) page： 870-874 2019.5

Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks

Tu Chenxi, Takeuchi Eijiro, Carballo Alexander, Takeda Kazuya

2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) page： 3274-3280 2019.5

A Predictive Reward Function for Human-like Driving based on a Transition Model of Surrounding Environment

Hayashi Daiki, Xu Yunfei, Bando Takashi, Takeda Kazuya

2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) page： 7618-7624 2019.5

An Empirical Study of Adaptive Training of Daily Activity Classifier and Chat Application Designed for the Elderly to Go Out Reviewed

Akira Tamamori, Yoshio Ishiguro, Kei Hiroi, Nobuo Kawaguchi, Kazuya Takeda

IPSJ Transactions on Consumer Device & Systems (CDS) Vol. 9 ( 2 ) page： 33 - 46 2019.5

Environmental sound processing and its applications Reviewed

Koichi Miyazaki, Tomoki Toda, Tomoki Hayashi, Kazuya Takeda

IEEJ Transactions on Electrical and Electronic Engineering Vol. 14 ( 3 ) page： 340 - 351 2019.3

Language：English Publishing type：Research paper (scientific journal)

© 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. As part of the effort to develop techniques for understanding environments using sound, many studies in the field of computational auditory scene analysis have focused on using computers to perform functions carried out naturally by the human auditory system. Thanks to recent progress in machine-learning techniques, these environmental sound-processing techniques have significantly improved and a widening variety of applications has resulted in considerable interest in this field. In this review, we introduce the fundamental techniques of environmental sound processing, as well as recent advances in front-end and back-end processing and potential applications for these techniques. Prospects for further progress in the field of environmental sound processing and the challenges still to be overcome are also discussed. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

DOI： 10.1002/tee.22868

Web of Science

Scopus

A traffic flow simulation framework for learning driver heterogeneity from naturalistic driving data using autoencoders Reviewed

Ekim Yurtsever, Chiyomi Miyajima, Kazuya Takeda

International Journal of Automotive Engineering Vol. 10 ( 1 ) page： 86-93 2019.3

Real-Time Streaming Point Cloud Compression for 3D LiDAR Sensor Using U-Net

Tu Chenxi, Takeuchi Eijiro, Carballo Alexander, Takeda Kazuya

IEEE ACCESS Vol. 7 page： 113616-113625 2019

Impact of driver behavior on fuel consumption: Classification, evaluation and prediction using machine learning Reviewed

Peng Ping, Wenhu Qin, Yang Xu, Chiyomi Miyajima, Kazuya Takeda

IEEE Access Vol. 7 page： 78515 - 78532 2019

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

© 2013 IEEE. Driving behavior has a large impact on vehicle fuel consumption. Dedicated study on the relationship between the driving behavior and fuel consumption can contribute to decreasing the energy cost of transportation and the development of the behavior assessment technology for the ADAS system. Therefore, it is vital to evaluate this relationship in order to develop more ecological driving assistance systems and improve the vehicle fuel economy. However, modeling driving behavior under the dynamic driving conditions is complex, making a quantitative analysis of the relationship between the driving behavior and the fuel consumption difficult. In this paper, we introduce two kinds of machine learning methods for evaluating the fuel efficiency of driving behavior using the naturalistic driving data. In the first stage, we use an unsupervised spectral clustering algorithm to study the macroscopic relationship between driving behavior and fuel consumption, using the data collected during the natural driving process. In the second stage, the dynamic information from the driving environment and natural driving data is integrated to generate a model of the relationship between various driving behaviors and the corresponding fuel consumption features. The dynamic environment factors are coded into a processable, digital form using a deep learning-based object detection method so that the environmental data can be linked with the vehicle's operating signal data to provide the training data for the deep learning network. The training data are labeled according to its fuel consumption feature distribution, which is obtained from the road segment data and historical driving data. This deep learning-based model can then be used as a predictor of the fuel consumption associated with different driving behaviors. Our results show that the proposed method can effectively identify the relationship between the driving behavior and the fuel consumption on both macro and micro levels, allowing for end-to-end fuel consumption feature prediction, which can then be applied in the advanced driving assistance systems.

DOI： 10.1109/ACCESS.2019.2920489

Web of Science

Scopus

Training Engineers in Autonomous Driving Technologies using Autoware

Carballo Alexander, Wong David, Ninomiya Yoshiki, Kato Shinpei, Takeda Kazuya

2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) page： 3347-3354 2019

Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer

Yurtsever Ekim, Liu Yongkang, Lambert Jacob, Miyajima Chiyomi, Takeuchi Eijiro, Takeda Kazuya, Hansen John H. L.

2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) page： 3100-3107 2019

Personalized Safety-focused Control by Minimizing Subjective Risk

Bao Naren, Yang Dongfang, Carballo Alexander, Ozguner Umit, Takeda Kazuya

2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) page： 3853-3858 2019

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Papers - TAKEDA, Kazuya