Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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畳み込み雑音除去自己符号化器と対数周波数領域スペクトル特徴を用いた楽曲音源強調 Reviewed

大谷健登, 丹羽健太, 西野隆典, 武田一哉

電子情報通信学会論文誌D Vol. J101-D ( 3 ) page： 00 2018.3

ITSS Technical Activities Spotlight: Getting to Know the Naturalistic Driving Data Analytics Technical Committee [Technical Activities]

Pujitha Gunaratne, Kazuya Takeda

IEEE Intelligent Transportation Systems Magazine page： 167-167 2018.1

Daily activity recognition with large-scaled real-life recording datasets based on deep neural network using multi-modal signals Reviewed

Tomoki Hayashi, Masafumi Nishida, Norihide Kitaoka, Tomoki Toda, Kazuya Takeda

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E101A ( 1 ) page： 199 - 210 2018.1

Language：English Publishing type：Research paper (international conference proceedings) Publisher：Institute of Electronics, Information and Communication, Engineers, IEICE

© 2018 The Institute of Electronics, Information and Communication Engineers. In this study, toward the development of smartphone-based monitoring system for life logging, we collect over 1,400 hours of data by recording including both the outdoor and indoor daily activities of 19 subjects, under practical conditions with a smartphone and a small camera. We then construct a huge human activity database which consists of an environmental sound signal, triaxial acceleration signals and manually annotated activity tags. Using our constructed database, we evaluate the activity recognition performance of deep neural networks (DNNs), which have achieved great performance in various fields, and apply DNN-based adaptation techniques to improve the performance with only a small amount of subject-specific training data. We experimentally demonstrate that; 1) the use of multi-modal signal, including environmental sound and triaxial acceleration signals with a DNN is effective for the improvement of activity recognition performance, 2) the DNN can discriminate specified activities from a mixture of ambiguous activities, and 3) DNN-based adaptation methods are effective even if only a small amount of subject-specific training data is available.

DOI： 10.1587/transfun.E101.A.199

Web of Science

Scopus

Modeling Driver Risk Perception on City Roads Using Deep Learning Reviewed

Peng Ping, Yuan Sheng, Wenhu Qin, Chiyomi Miyajima, Kazuya Takeda

IEEE Access Vol. 6 page： 68850 - 68866 2018

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

© 2013 IEEE. Research on how risk is perceived by drivers is vital to driving behavior research and driving safety. As risk can be divided into subjective and objective risk, in this paper, we focus on modeling subjective risk perception by drivers using a deep learning method. Different drivers often perceive different levels of subjective risk under the same driving conditions. In addition, different driving conditions or driving events will have different effects on drivers. Based on these two risk perception features, in this paper, we first design an experiment on a city road with two lanes to assess the level of subjective risk perceived by drivers belonging to different groups. We then use a deep learning network-based method to abstract features of the driving environment. These environmental features are integrated with driver risk perception data and this information is used as training and testing data for the learning network. Finally, a long-short-term memory-based method is adopted to model the subjective risk perception of individual drivers based on traffic conditions and vehicle operation data from the driver's vehicle. Our results show that the proposed method can effectively model the subjective risk perception behavior of drivers, allowing for end-to-end risk perception prediction in future driving assistance systems.

DOI： 10.1109/ACCESS.2018.2879887

Web of Science

Scopus

Recognizing emotions from speech using a physical model Reviewed

Norihide Kitaoka, Shuhei Segawa, Ryota Nishimura, Kazuya Takeda

Acoustical Science and Technology Vol. 39 ( 2 ) page： 167 - 170 2018

Learning How to Drive in Blind Intersections from Human Data Reviewed

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

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 page： 317 - 324 2018

Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE

© 2018 IEEE. In this paper we present a method to learn how to drive in different types of blind intersections using expert driving data. We cluster different intersections based on the velocity of how drivers approach them, and train a linear SVM classifier for each class of intersection. Through clustering we found that there were three different classes of intersections in typical residential areas in Japan. We used inverse reinforcement learning (IRL) to build a driving model for each type of intersection. The models were trained from 308 trajectories traversed by 5 different drivers. The models and policies were implemented and evaluated in a ROS simulator where the agent is provided a global path, and upon it reaching an intersection, it selects the appropriate trained policy. By doing this, the simulated autonomous vehicle can perform proactive safe driving behaviors when approaching blind intersections.

DOI： 10.1109/SMC.2018.00064

Web of Science

Scopus

Multi-head decoder for end-to-end speech recognition Reviewed

Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH Vol. 2018-September page： 801 - 805 2018

Language：English Publishing type：Research paper (international conference proceedings) Publisher：ISCA

© 2018 International Speech Communication Association. All rights reserved. This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model. In the multi-head attention model, multiple attentions are calculated, and then, they are integrated into a single attention. On the other hand, instead of the integration in the attention level, our proposed method uses multiple decoders for each attention and integrates their outputs to generate a final output. Furthermore, in order to make each head to capture the different modalities, different attention functions are used for each head, leading to the improvement of the recognition performance with an ensemble effect. To evaluate the effectiveness of our proposed method, we conduct an experimental evaluation using Corpus of Spontaneous Japanese. Experimental results demonstrate that our proposed method outperforms the conventional methods such as location-based and multi-head attention models, and that it can capture different speech/linguistic contexts within the attention-based encoder-decoder framework.

DOI： 10.21437/Interspeech.2018-1655

Web of Science

Scopus

arXiv

Other Link： http://arxiv.org/pdf/1804.08050v2

An investigation of multi-speaker training for WaveNet vocoder

T. Hayashi, A. Tamamori, K. Kobayashi, K. Takeda, T. Toda

Proc. ASRU page： 712-718 2017.12

Investigation of effectiveness on recurrent neural network for daily activity recognition using multi-modal signals

A. Tamamori, T. Hayashi, T. Toda, K. Takeda

Proc. APSIPA page： 7 2017.12

An Investigation of Recurrent Neural Network for Daily Activity Recognition using Multi-modal Signals

Tamamori Akira, Hayashi Tomoki, Toda Tomoki, Takeda Kazuya

2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017) page： 1334-1340 2017.12

Involvement of poly-rC binding proteins in posttranscriptional regulation of Sortilin, the cytokine trafficking mediator Reviewed

Toshiki Yabe-Wada, Shintaro Matsuba, Kazuya Takeda, Akira Nakamura, Caroline C. Philpott, Nobuyuki Onai

CYTOKINE Vol. 100 page： 145 - 146 2017.12

Duration-controlled LSTM for polyphonic sound event detection

T. Hayashi, S. Watanabe, T. Toda, T. Hori, J. Le Roux, K. Takeda.

IEEE/ACM Transactions on Audio, Speech, and Language Processing Vol. 25 ( 11 ) page： 2059-2070 2017.11

Duration-Controlled LSTM for Polyphonic Sound Event Detection Reviewed

Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Takaaki Hori, Jonathan Le Roux, Kazuya Takeda

IEEE/ACM Transactions on Audio Speech and Language Processing Vol. 25 ( 11 ) page： 2059 - 2070 2017.11

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

© 2014 IEEE. This paper presents a new hybrid approach called duration-controlled long short-term memory (LSTM) for polyphonic sound event detection (SED). It builds upon a state-of-the-art SED method that performs frame-by-frame detection using a bidirectional LSTM recurrent neural network (BLSTM), and incorporates a duration-controlled modeling technique based on a hidden semi-Markov model. The proposed approach makes it possible to model the duration of each sound event precisely and to perform sequence-by-sequence detection without having to resort to thresholding, as in conventional frame-by-frame methods. Furthermore, to effectively reduce sound event insertion errors, which often occur under noisy conditions, we also introduce a binary-mask-based postprocessing that relies on a sound activity detection network to identify segments with any sound event activity, an approach inspired by the well-known benefits of voice activity detection in speech recognition systems. We conduct an experiment using the DCASE2016 task 2 dataset to compare our proposed method with typical conventional methods, such as nonnegative matrix factorization and standard BLSTM. Our proposed method outperforms the conventional methods both in an event-based evaluation, achieving a 75.3% F1 score and a 44.2% error rate, and in a segment-based evaluation, achieving an 81.1% F1 score, and a 32.9% error rate, outperforming the best results reported in the DCASE2016 task 2 Challenge.

DOI： 10.1109/TASLP.2017.2740002

Web of Science

Scopus

A Single-Dimensional Interface for Arranging Multiple Audio Sources in Three-Dimensional Space

Kento Ohtani, Kenta Niwa, Kazuya Takeda

IEICE TRANSACTIONS on Information and Systems (0.411), Vol. E100-D ( 10 ) page： pp. 2635-264 2017.10

A single-dimensional interface for arranging multiple audio sources in three-dimensional space Reviewed

Kento Ohtani, Kenta Niwa, Kazuya Takeda

IEICE Transactions on Information and Systems Vol. E100D ( 10 ) page： 2635 - 2643 2017.10

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

Copyright © 2017 The Institute of Electronics, Information and Communication Engineers. A single-dimensional interface which enables users to obtain diverse localizations of audio sources is proposed. In many conventional interfaces for arranging audio sources, there are multiple arrangement parameters, some of which allow users to control positions of audio sources. However, it is difficult for users who are unfamiliar with these systems to optimize the arrangement parameters since the number of possible settings is huge. We propose a simple, single-dimensional interface for adjusting arrangement parameters, allowing users to sample several diverse audio source arrangements and easily find their preferred auditory localizations. To select subsets of arrangement parameters from all of the possible choices, auditory-localization space vectors (ASVs) are defined to represent the auditory localization of each arrangement parameter. By selecting subsets of ASVs which are approximately orthogonal, we can choose arrangement parameters which will produce diverse auditory localizations. Experimental evaluations were conducted using music composed of three audio sources. Subjective evaluations confirmed that novice users can obtain diverse localizations using the proposed interface.

DOI： 10.1587/transinf.2017EDP7028

Web of Science

Scopus

Prediction Method for .Continuous Point Cloud Data Compression Using SLAM Information

Chenxi Tu, Eijiro Takeuchi, Chiyomi Miyajima, and Kazuya Takeda

fast-zero page： SLAM Information," fast-zero, Sep., 2017 2017.9

Evaluation of Deep Learning-Based Driving Signal Generation Methods for Vehicle Control

Shunya Seiya, Daiki Hayashi, Eijiro Takeuchi, Chiyomi Miyajima and Kazuya Takeda

Fast-ZERO, 2017 page： 00 2017.9

Estimating Risk Levels Perceived by Individuals for Lane Change Scenes,"The fourth International Symposium on Future Active Safety Technology Toward zero traffic accidents

Naren Bao, Chiyomi Miyajima, Eijiro Takeuchi, Kazuya Takeda, Shinichiro Honda, Toshiya Yoshitani, and Masayoshi Ito

The fourth International Symposium on Future Active Safety Technology Toward zero traffic accidents page： 00 2017.9

Missing component restoration for masked speech signals based on time-domain spectrogram factorization

Shogo Seki, Hirokazu Kameoka, Tomoki Toda, Kazuya Takeda

MLSP2017 page： 6 2017.9

Estimation of driver's risk feeling toward driving environment using neural network

Yuan Sheng, Yiyang Li, Chiyomi Miyajima, Eijiro Takeuchi, Kazuya Takeda, Shinichiro Honda, Toshiya Yoshitani, and Masayoshi Ito

FAST-zero '17 page： 00 2017.9

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Papers - TAKEDA, Kazuya