Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Crossing Blind Intersections from a Full Stop Using Estimated Visibility of Approaching Vehicles

Narksri Patiphon, Takeuchi Eijiro, Ninomiya Yoshiki, Takeda Kazuya

2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) page： 2427-2434 2019

ATTENTION-BASED SPEECH RECOGNITION USING GAZE INFORMATION

Segawa Osamu, Hayashi Tomoki, Takeda Kazuya

2019 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU 2019) page： 465-470 2019

Optimizing Learned Object Detection on Point Clouds from 3D Lidars Through Range and Sparsity Information

Lambert Jacob, Takeuchi Eijiro, Takeda Kazuya

2019 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) page： 407-413 2019

Daily activity recognition based on recurrent neural network using multi-modal signals Reviewed

Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda

APSIPA Transactions on Signal and Information Processing Vol. 7 ( e21 ) page： 1 - 11 2018.12

Language：English Publishing type：Research paper (scientific journal) Publisher：CAMBRIDGE UNIV PRESS

Copyright © 2018 The Authors. Our aim is to develop a smartphone-based life-logging system. Human activity recognition (HAR) is one of the core techniques to realize it. Recent studies reported the effectiveness of feed-forward neural network (FF-NN) and recurrent neural network (RNN) as a classifier for HAR task. However, there are still unresolved problems in those studies: (1) a life-logging system using only a smartphone for recording device has not been developed, (2) only indoor activities have been utilized for evaluation, (3) insufficient investigations/evaluations of RNN. In this study, we address these unresolved problems as follows: (1) we build a prototype system for life-logging and conduct data recording experiment on this system to include both indoor and outdoor activities. The experimental results of HAR on this new dataset showed that RNN-based classifier was still effective. (2) From the results of a HAR experiment, it was demonstrated that a multi-layered Simple Recurrent Unit with a non-linear transform at the bottom layer and a highway-connection was the most effective. (3) We could grasp the reason for the improvement of RNN from FF-NN by observing the posterior probabilities over test data.

DOI： 10.1017/ATSIP.2018.25

Web of Science

Scopus

BACK-TRANSLATION-STYLE DATA AUGMENTATION FOR END-TO-END ASR Reviewed

Hayashi Tomoki, Watanabe Shinji, Zhang Yu, Toda Tomoki, Hori Takaaki, Astudillo Ramon, Takeda Kazuya

2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018) page： 426-433 2018.12

Back-translation-style data augmentation for end-to-end ASR

Tomoki Hayashi, Shinji Watanabe, Yu Zhang, Tomoki Toda, Takaaki Hori, Ramon Astudillo, Kazuya Takeda

2018 IEEE Spoken Language Technology Workshop (SLT) page： 426-433 2018.12

End-to-End Navigation with Branch Turning Support using Convolutional Neural Network

Seiya Shunya, Carballo Alexander, Takeuchi Eijiro, Miyajima Chiyomi, Takeda Kazuya

2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO) page： 499-506 2018.12

ITS+ DM Hackathon (ITSC 2017): Lane departure prediction with naturalistic driving data

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

IEEE Intelligent Transportation Systems Magazine page： 00 2018.11

Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts Reviewed

Sama Kyle, Morales Yoichi, Akai Naoki, Liu Hailong, Takeuchi Eijiro, Takeda Kazuya

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) page： 1337-1343 2018.11

SecretSign: A Method of Finding an Off-line Target Object without Revealing the Target to Observers

Sakai Yusuke, Morita Hiromi, Ishiguro Yoshio, Nishino Takanori, Takeda Kazuya

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) page： 3651-3656 2018.11

High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles Reviewed

Carballo Alexander, Takeuchi Eijiro, Takeda Kazuya

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) page： 3811-3818 2018.11

SecretSign: A Method of Finding an Off-Line Target Object without Revealing the Target to Observers

Yusuke Sakai, Hiromi Morita, Yoshio Ishiguro, Takanori Nishino, Kazuya Takeda

2018 21st International Conference on Intelligent Transportation Systems (ITSC) page： 3651-3656 2018.11

Generalized multichannel variational autoencoder for underdetermined source separation

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

arXiv preprint arXiv:1810.00223 page： 00 2018.9

Connectionist Temporal Classification-based Sound Event Encoder for Converting Sound Events into Onomatopoeic Representations

Miyazaki Koichi, Hayashi Tomoki, Toda Tomoki, Takeda Kazuya

2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) page： 852-856 2018.9

Anomalous Sound Event Detection Based on WaveNet

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

2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) page： 2494-2498 2018.9

Tsukuba Challenge 2017 Dynamic Object Tracks Dataset for Pedestrian Behavior Analysis

Lambert Jacob, Liang Leslie, Morales Luis Yoichi, Akai Naoki, Carballo Alexander, Takeuchi Eijiro, Narksri Patiphon, Seiya Shunya, Takeda Kazuya

JOURNAL OF ROBOTICS AND MECHATRONICS Vol. 30 ( 4 ) page： 598-612 2018.8

End-to-End Autonomous Mobile Robot Navigation with Model-Based System Support

Carballo Alexander, Seiya Shunya, Lambert Jacob, Darweesh Hatem, Narksri Patiphon, Morales Luis Yoichi, Akai Naoki, Takeuchi Eijiro, Takeda Kazuya

JOURNAL OF ROBOTICS AND MECHATRONICS Vol. 30 ( 4 ) page： 563-583 2018.8

Stereophonic Music Separation Based on Non-Negative Tensor Factorization with Cepstral Distance Regularization Reviewed

Shogo Seki, Tomoki Toda, Kazuya Takeda

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES Vol. E101A ( 7 ) page： 1057 - 1064 2018.7

Language：English Publishing type：Research paper (scientific journal) Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. Non-negative Tensor Factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

DOI： 10.1587/transfun.E101.A.1057

Web of Science

Scopus

Integrating driving behavior and traffic context through signal symbolization for data reduction and risky lane change detection

Ekim Yurtever, Suguru Yamazaki, Chiyomi Miyajima, Chiyomi, Kazuya Takeda, Masataka Mori, Kentarou Hitomi, and Masumi Egawa

IEEE Transactions on Intelligent Vehicles page： 00 2018.6

Retrieving a Driving Model Based on Clustered Intersection Data

Sama Kyle, Morales Yoichi, Akai Naoki, Takeuchi Eijiro, Takeda Kazuya

2018 3RD INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING (ICCRE) page： 222-226 2018.4

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Papers - TAKEDA, Kazuya