研究者詳細 - 武田　一哉

論文 - 武田　一哉

分割表示 689 件中 101 - 120 件目／全件表示 >>

Convolution augmented transformer for semi-supervised sound event detection 査読有り

頁： 100 - 104 2020年6月

Weakly-Supervised Sound Event Detection with Self-Attention 査読有り

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

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2020-May 巻頁： 66 - 70 2020年5月

掲載種別：研究論文（国際会議プロシーディングス）

© 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

その他リンク： https://dblp.uni-trier.de/db/conf/icassp/icassp2020.html#MiyazakiKH0TT20

Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform 査読有り

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

arXiv preprint arXiv:2004.01374 頁： - 2020年4月

LIBRE: The multiple 3d lidar dataset 査読有り

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

arXiv preprint arXiv:2003.06129 頁： - 2020年3月

A Survey of Autonomous Driving: Common Practices and Emerging Technologies 査読有り

Yurtsever Ekim, Lambert Jacob, Carballo Alexander, Takeda Kazuya

IEEE ACCESS 8 巻頁： 58443 - 58469 2020年

Performance Analysis of 10 Models of 3D LiDARs for Automated Driving 査読有り

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

IEEE ACCESS 8 巻頁： 131699 - 131722 2020年

END-TO-END AUTOMATIC SPEECH RECOGNITION INTEGRATED WITH CTC-BASED VOICE ACTIVITY DETECTION 査読有り

Yoshimura Takenori, Hayashi Tomoki, Takeda Kazuya, Watanabe Shinji

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

Point Grid Map-Based Mid-To-Mid Driving without Object Detection 査読有り

Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

IEEE Intelligent Vehicles Symposium, Proceedings 頁： 2044 - 2051 2020年

掲載種別：研究論文（国際会議プロシーディングス）

© 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

その他リンク： https://dblp.uni-trier.de/db/conf/ivs/ivs2020.html#SeiyaCTT20

Nagoya University at TRECVID 2014: The instance search task 査読有り

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

2014 TREC Video Retrieval Evaluation, TRECVID 2014 2020年

掲載種別：研究論文（国際会議プロシーディングス）

© 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. 査読有り

Tomoya Yokoyama, Shunya Seiya, Eijiro Takeuchi, Kazuya Takeda

頁： 1679 - 1688 2020年

Intelligibility enhancement based on speech waveform modification using hearing impairment 査読有り

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 2020-October 巻頁： 4059 - 4063 2020年

掲載種別：研究論文（国際会議プロシーディングス）

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

その他リンク： 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) 頁： 465-470 2019年12月

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

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

IEEE Intelligent Transportation Systems Magazine 11 巻 ( 4 ) 頁： 78 - 93 2019年12月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 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 頁： - 2019年11月

Underdetermined Source Separation Based on Generalized Multichannel Variational Autoencoder 査読有り

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

IEEE access 頁： - 2019年11月

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

Hatem Darweesh, Eijiro Takeuchi, Kazuya Takeda

International journal of automotive engineering 10 巻 ( 4 ) 頁： 299-308 2019年11月

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

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

arXiv preprint arXiv:1910.10909 頁： 00 2019年10月

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

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 頁： 166-170 2019年9月

LeadingDisplay: a versatile, robotic display for infotainment in autonomous vehicles 査読有り

Yoshio Ishiguro, Kazuya Takeda

Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings 頁： 405-409 2019年9月

Improving target selection accuracy for vehicle touch screens 査読有り

Kosuke Ito, Kento Ohtani, Yoshio Ishiguro, Takanori Nishino, Kazuya Takeda

Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings 頁： 176-180 2019年9月

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