Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Policy learning with partial observation and mechanical constraints for multi-person modeling Reviewed

Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

CoRR Vol. abs/2007.03155 2020.7

Publishing type：Research paper (scientific journal)

Extracting the rules of real-world biological multi-agent behaviors is a
current challenge in various scientific and engineering fields. Biological
agents generally have limited observation and mechanical constraints; however,
most of the conventional data-driven models ignore such assumptions, resulting
in lack of biological plausibility and model interpretability for behavioral
analyses in biological and cognitive science. Here we propose sequential
generative models with partial observation and mechanical constraints, which
can visualize whose information the agents utilize and can generate
biologically plausible actions. We formulate this as a decentralized
multi-agent imitation learning problem, leveraging binary partial observation
models with a Gumbel-Softmax reparameterization and policy models based on
hierarchical variational recurrent neural networks with physical and
biomechanical constraints. We investigate the empirical performances using
real-world multi-person motion datasets from basketball and soccer games.

arXiv

Other Link： http://arxiv.org/pdf/2007.03155v1

Personalized Subjective Driving Risk: Analysis and Prediction Reviewed

Bao Naren, Carballo Alexander, Miyajima Chiyomi, Takeuchi Eijiro, Takeda Kazuya

JOURNAL OF ROBOTICS AND MECHATRONICS Vol. 32 ( 3 ) page： 503-519 2020.6

Convolution augmented transformer for semi-supervised sound event detection Reviewed

K Miyazaki, T Komatsu, T Hayashi, S Watanabe, T Toda, K Takeda

Proc. Workshop Detection Classification Acoust. Scenes Events (DCASE) page： 100 - 104 2020.6

Weakly-Supervised Sound Event Detection with Self-Attention Reviewed

Koichi Miyazaki, Tatsuya Komatsu, Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2020-May page： 66 - 70 2020.5

Publishing type：Research paper (international conference proceedings)

© 2020 IEEE. In this paper, we propose a novel sound event detection (SED) method that incorporates a self-attention mechanism of the Transformer for a weakly-supervised learning scenario. The proposed method utilizes the Transformer encoder, which consists of multiple self-attention modules, allowing to take both local and global context information of the input feature sequence into account. Furthermore, inspired by the great success of BERT in the natural language processing field, the proposed method introduces a special tag token into the input sequence for weak label prediction, which enables the aggregation of the whole sequence information. To demonstrate the performance of the proposed method, we conduct the experimental evaluation using the DCASE2019 Task4 dataset. The experimental results demonstrate that the proposed method outperforms the DCASE2019 Task4 baseline method, which is based on the convolutional recurrent neural network, and the self-attention mechanism effectively works for SED.

DOI： 10.1109/ICASSP40776.2020.9053609

Scopus

Other Link： https://dblp.uni-trier.de/db/conf/icassp/icassp2020.html#MiyazakiKH0TT20

Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform Reviewed

Alexander Carballo, Abraham Monrroy, David Wong, Patiphon Narksri, Jacob Lambert, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

arXiv preprint arXiv:2004.01374 page： - 2020.4

LIBRE: The multiple 3d lidar dataset Reviewed

Alexander Carballo, Jacob Lambert, Abraham Monrroy, David Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

arXiv preprint arXiv:2003.06129 page： - 2020.3

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

Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda

IEEE Access Vol. 8 page： 58443 - 58469 2020

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

© 2013 IEEE. Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

DOI： 10.1109/ACCESS.2020.2983149

Web of Science

Scopus

arXiv

Other Link： http://arxiv.org/pdf/1906.05113v3

Performance Analysis of 10 Models of 3D LiDARs for Automated Driving Reviewed

Jacob Lambert, Alexander Carballo, Abraham Monrroy Cano, Patiphon Narksri, David Wong, Eijiro Takeuchi, Kazuya Takeda

IEEE Access Vol. 8 page： 131699 - 131722 2020

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

© 2013 IEEE. Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers releasing new sensors regularly. With this increased variety of LiDARs, each with different properties such as number of laser emitters, resolution, field-of-view, and price tags, a more in-depth comparison of their characteristics and performance is required. This work compares 10 commonly used 3D LiDARs, establishing several metrics to assess their performance. Various outstanding issues with specific LiDARs were qualitatively identified. The accuracy and precision of individual LiDAR beams and accumulated point clouds are evaluated in a controlled environment at distances from 5 to 180 meters. Reflective targets were used to characterize intensity patterns and quantify the impact of surface reflectivity on accuracy and precision. A vehicle and pedestrian mannequin were also used as additional targets of interest. A thorough assessment of these LiDARs is given with their potential applicability for automated driving tasks. The data collected in these experiments and analysis tools are all shared openly.

DOI： 10.1109/ACCESS.2020.3009680

Web of Science

Scopus

END-TO-END AUTOMATIC SPEECH RECOGNITION INTEGRATED WITH CTC-BASED VOICE ACTIVITY DETECTION Reviewed

Yoshimura Takenori, Hayashi Tomoki, Takeda Kazuya, Watanabe Shinji

2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING page： 6999 - 7003 2020

Point Grid Map-Based Mid-To-Mid Driving without Object Detection Reviewed

Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

IEEE Intelligent Vehicles Symposium, Proceedings page： 2044 - 2051 2020

Publishing type：Research paper (international conference proceedings)

© 2020 IEEE. Teaching autonomous vehicles to imitate human driving in complex, urban traffic scenarios is a difficult task. 'End-to-end' autonomous driving systems, based on 'imitation learning', are an expecting approach. A model learns the relationships between sensing input and vehicle control signal outputs. These methods can successfully achieve driving in simple scenarios such as lane keeping. In contrast, the 'mid-to-mid' autonomous driving methods now being proposed. In such framework, the model learns the relationships between pre-processed feature maps from the model-based system as input and the future position of the ego vehicle as the output. Mid-to-mid driving methods can direct vehicles more robustly than end-to-end driving methods in some complex driving environments. However, mid-to-mid driving methods use the results of the object detection module to create the feature map. If object detection fails, or detection performance is poor due to changes in the driving environment, prediction performance may also be degraded. Our proposed method uses a prediction module that outputs point grid maps directly, without the use of an object detection module, which are then incorporated into the feature map. Point grid maps represent the locations of surrounding vehicles and obstacles directly, based on LiDAR point cloud data. Since the results of object detection are not used by the prediction module, detection performance does not affect prediction performance. In this study we conduct two experiments, an off-line evaluation using a Lyft dataset, and an on-line evaluation using the CARLA simulator. The results show that our model can achieve the same level of ego-vehicle position prediction performance as a model using annotated object location information.

DOI： 10.1109/IV47402.2020.9304809

Scopus

Other Link： https://dblp.uni-trier.de/db/conf/ivs/ivs2020.html#SeiyaCTT20

Nagoya University at TRECVID 2014: The instance search task Reviewed

Cai Zhi Zhu, Yinqiang Zheng, Ichiro Ide, Shinichi Satoh, Kazuya Takeda

2014 TREC Video Retrieval Evaluation, TRECVID 2014 2020

Publishing type：Research paper (international conference proceedings)

© 2020 2014 TREC Video Retrieval Evaluation. All rights reserved. This paper presents our recent progress on a video object retrieval system that participated in the Instance Search (INS) task of the TRECVID 2014. Basically the system is a further extension of our previous Bag-of-Words (BOW) framework, with emphasis on pursuing a practical spatial re-ranking method scalable to large video database this year. We take the asymmetrical dissimilarities based system, which performed best in the INS2013 task, as the baseline, and re-rank with an improved spatial verification method. Experiments carried out the TRECVID INS2013 and INS2014 consistently show that, our re-ranking algorithm is able to further improve the baseline system at a rather fast speed.

Scopus

Intervention Force-based Imitation Learning for Autonomous Navigation in Dynamic Environments. Reviewed

Tomoya Yokoyama, Shunya Seiya, Eijiro Takeuchi, Kazuya Takeda

page： 1679 - 1688 2020

Intelligibility enhancement based on speech waveform modification using hearing impairment Reviewed

Shu Hikosaka, Shogo Seki, Tomoki Hayashi, Kazuhiro Kobayashi, Kazuya Takeda, Hideki Banno, Tomoki Toda

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH Vol. 2020-October page： 4059 - 4063 2020

Publishing type：Research paper (international conference proceedings)

Copyright © 2020 ISCA In this paper, we propose a speech waveform modification method which incorporates a hearing impairment simulator, to improve speech intelligibility for the hearing-impaired. The settings of hearing aid devices usually need to be manually adjusted to suit the needs of each user, which creates a significant burden. To address this issue, the proposed method creates a spectral shaping filter, using a hearing impairment simulator capable of estimating speech signals as perceived by a specific hearing-impaired person. We conduct objective and subjective evaluations through simulations using the hearing impairment simulator. Our experimental results demonstrate that; 1) the proposed spectral shaping filter can significantly improve both speech intelligibility and quality, 2) the filter can be combined with a well-known speech intelligibility enhancement technique based on power compensation using dynamic range compression (DRC), and 3) speech intelligibility can be further improved by controlling the trade-off between filtering and DRC-based power compensation.

DOI： 10.21437/Interspeech.2020-2062

Scopus

Other Link： https://dblp.uni-trier.de/db/conf/interspeech/interspeech2020.html#HikosakaSHKTBT20

Attention-Based Speech Recognition Using Gaze Information

Osamu Segawa, Tomoki Hayashi, Kazuya Takeda

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) page： 465-470 2019.12

ITS+DM Hackathon (ITSC 2017): Lane Departure Prediction with Naturalistic Driving Data Reviewed

Andrey Alekseenko, Hien Q. Dang, Gaurav Bansal, Javier Sanchez-Medina, Chiyomi Miyajima, Takatsugu Hirayama, Kazuya Takeda, Ichiro Ide

IEEE Intelligent Transportation Systems Magazine Vol. 11 ( 4 ) page： 78 - 93 2019.12

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

© 2009-2012 IEEE. On October 16th, 2017, in Yokohama, Japan, from 8:00 to 18:00, the first Intelligent Transportation Systems plus Data Mining challenge was organized under the umbrella of the 2017 IEEE Intelligent Transportation Systems Conference, the flagship conference of the IEEE Intelligent Transportation Systems Society. This activity was organized thanks to a three way collaboration between the ITS Society, Nagoya University, and the IEEE ITSC 2017 organizers. The twenty-three contestants, coming from eleven different countries, faced a classic Naturalistic Driving problem: Lane Departure detection. This paper presents the three best solutions produced. The solutions submitted by most of the participants were very diverse and interesting, but overall, the top ones concurred in the use of ensemble learning after a very interesting feature engineering phase. This hackathon formulation was complex in several ways. It was complex in terms of class imbalance, the challenge time duration and the fact that the provided dataset included only numerical measurements coming from the inertial unit in the testing car. That restriction made it difficult to expect outstanding results ? the best one was only slightly over 3% above baseline. However, the organizers thought that such complexities pushed participants to show their repertoire as data scientists, taking into consideration for example computer power load of the different algorithms tested, and overall yielding more interesting approaches to share with the community. Additionally, the most interesting learned lessons were shared, from both an organizational and technical point of view.

DOI： 10.1109/MITS.2018.2880264

Web of Science

Scopus

Motion Analysis and Performance Improved Method for 3D LiDAR Sensor Data Compression

Chenxi Tu,Eijiro Takeuchi,Alexander Carballo,Chiyomi Miyajima,Kazuya Takeda

IEEE transction on ITS page： - 2019.11

Underdetermined Source Separation Based on Generalized Multichannel Variational Autoencoder Reviewed

Shogo Seki, Hirokazu Kameoka, Li Li, Tomoki Toda, and Kazuya Takeda

IEEE access page： - 2019.11

Estimating the Probabilities of Surrounding Vehicles' Intentions and Trajectories using a Behavior Planner

Hatem Darweesh, Eijiro Takeuchi, Kazuya Takeda

International journal of automotive engineering Vol. 10 ( 4 ) page： 299-308 2019.11

ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit Reviewed

Tomoki Hayashi, Ryuichi Yamamoto, Katsuki Inoue, Takenori Yoshimura, Shinji Watanabe, Tomoki Toda, Kazuya Takeda, Yu Zhang, Xu Tan

arXiv preprint arXiv:1910.10909 page： 00 2019.10

Effects on user perception of a'modified'speed experience through in-vehicle virtual reality Reviewed

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

Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings page： 166-170 2019.9

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Papers - TAKEDA, Kazuya