研究者詳細 - 武田　一哉

論文 - 武田　一哉

分割表示 689 件中 201 - 220 件目／全件表示 >>

Compressing Continuous Point Cloud Data Using Image Compression Methods 査読有り

Chenxi Tu, Eijiro Takeuchi, Chiyomi Miyajima, Kazuya Takeda

ITSC2016 頁： 00 2016年11月

AI framework to arrange audio objects according to listener preferences 査読有り

Kento Ohtani, Kenta Niwa, Kazuya Takeda

5th Joint Meeting of the Acoustical Society of America and Acoustical Society of Japan 頁： 00 2016年11月

Stereo Channel Music Signal Separation Based on Nonnegative Tensor Factorization with Cepstrum Regularization 査読有り

Shogo Seki, Kento Ohtani, Tomoki Toda, Kazuya Takeda

5th Joint Meeting of the ASA and ASJ 頁： 00 2016年11月

Driver-Behavior Modeling Using On-Road Driving Data A new application for behavior signal processing 査読有り

Miyajima Chiyomi, Takeda Kazuya

IEEE SIGNAL PROCESSING MAGAZINE 33 巻 ( 6 ) 頁： 14 - 21 2016年11月

Analysis of driver workload when using speech interfaces 査読有り

D Hayashi, C Miyajima, K Takeda

The Journal of the Acoustical Society of America 140 巻 ( 4 ) 頁： 2961 - 2961 2016年10月

Projection of a virtual speaker into a vehicle using sound field control 査読有り

T Yamamura, Y Ishiguro, T Nishino, K Takeda

The Journal of the Acoustical Society of America 140 巻 ( 4 ) 頁： 3063 - 3063 2016年10月

Emotion recognition from speech using a physical model 査読有り

Norihide Kitaoka, Shuhei Segawa, Kazuya Takeda

Proc. ICA2016 頁： 00 2016年9月

Bidirectional LSTM-HMM Hybrid System for Polyphonic Sound Event Detection 査読有り

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

DCASE2016 頁： 00 2016年9月

Investigation of DNN-based modeling for audio-visual speech recognition 査読有り

Satoshi Tamura, Hiroshi Ninomiya, Norihide Kitaoka, Shin Osuga, Yurie Iribe, Kazuya Takeda, Satoru Hayamizu

2015 First International Workshop on Spoken Language Processing (MLSLP2015) 頁： 00 2016年9月

Investigation on Recurrent Neural Network Architectures for Daily Activity Recognition 査読有り

Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda

UV2016 頁： 00 2016年9月

Recurrent Neural Networkに基づく日常生活行動認識

玉森聡、林知樹、戸田智基、武田一哉

電子情報通信学会技術報告 116(189) 巻 ( 7 ) 頁： 12 2016年8月

Accelerated Deformable Part Models on GPUs 査読有り

Hirabayashi Manato, Kato Shinpei, Edahiro Masato, Takeda Kazuya, Mita Seiichi

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 27 巻 ( 6 ) 頁： 1589 - 1602 2016年6月

Prediction of Individual Driving Behavior on Highway Curves

Naren Bao, Daiki Hayashi, Chiyomi Miyajima, and Kazuya Takeda

The third Workshop on Natural- istic Driving Data Analysitics, IEEE Intelligent Vehicles Symposium 頁： 00 2016年6月

Integrating driving behavior and traffic context through signal symbolization. 査読有り

Yamazaki, S., Miyajima, C., Yurtsever, E., Takeda, K., Mori, M., Hitomi, K., & Egawa, M.

In Intelligent Vehicles Symposium (IV), 2016 IEEE. 頁： pp. 642-647 2016年6月

Classification of driver's neutral and cognitive distraction states based on peripheral vehicle behavior in driver's gaze transition 査読有り

Takatsugu Hirayama, Kenji Mase, Chiyomi Miyajima, Kazuya Takeda

IEEE Transactions on Intelligent Vehicles 1 巻 ( 2 ) 頁： 148 - 157 2016年6月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2016 IEEE. To support safe driving, numerous methods of detecting distractions using measurements of a driver's gaze have been proposed. These methods empirically focused on certain driving contexts and analyzed gaze behavior under particular peripheral vehicle conditions; therefore, numerous driving situations were not considered. To address this problem with hypothesis-testing approaches, we turn the problem around and propose a data-mining approach that analyzes peripheral vehicle behavior during gaze transitions of drivers in order to compare their neutral driving state with a cognitive distraction state. This change in thinking is the first contribution of this paper. The analysis results show that under the neutral condition, drivers generally turned their gaze to peripheral vehicles to be focused on; however, they did not do this consistently under the distracted condition. As the second contribution, we propose a simple classifier to discriminate between the cognitive distraction and neutral states by analyzing the peripheral vehicle behavior. The proposed classifier can manage various situations and provide high classification accuracy by focusing on gaze transitions from the front view toward other directions.

DOI： 10.1109/TIV.2016.2599786

Scopus

Daily activity recognition based on acoustic signals and acceleration signals estimated with Gaussian process 査読有り

Masafumi Nishida, Norihide Kitaoka, Kazuya Takeda

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 頁： 279 - 282 2016年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

© 2015 Asia-Pacific Signal and Information Processing Association. We have created corpus of daily activities using wearable sensors. The corpus consists of sound and image data from a camera and motion signals from a smartphone for both indoor and outdoor activities over 72 continuous hours. We propose a method that can interpolate acceleration signals to any sample points with a Gaussian process in order to recognize daily activities. We conducted recognition experiments of daily activities using our corpus. Experimental results showed that the proposed method can improve recognition accuracy compared to a conventional method. This demonstrates the effectiveness of estimating acceleration signals with a Gaussian process to recognize daily activities.

DOI： 10.1109/APSIPA.2015.7415520

Web of Science

Scopus

Audio-visual speech recognition using deep bottleneck features and high-performance lipreading 査読有り

Satoshi Tamura, Hiroshi Ninomiya, Norihide Kitaoka, Shin Osuga, Yurie Iribe, Kazuya Takeda, Satoru Hayamizu

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 頁： 575 - 582 2016年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

© 2015 Asia-Pacific Signal and Information Processing Association. This paper develops an Audio-Visual Speech Recognition (AVSR) method, by (1) exploring high-performance visual features, (2) applying audio and visual deep bottleneck features to improve AVSR performance, and (3) investigating effectiveness of voice activity detection in a visual modality. In our approach, many kinds of visual features are incorporated, subsequently converted into bottleneck features by deep learning technology. By using proposed features, we successfully achieved 73.66% lipreading accuracy in speaker-independent open condition, and about 90% AVSR accuracy on average in noisy environments. In addition, we extracted speech segments from visual features, resulting 77.80% lipreading accuracy. It is found VAD is useful in both audio and visual modalities, for better lipreading and AVSR.

DOI： 10.1109/APSIPA.2015.7415335

Web of Science

Scopus

Tracking driver signage observation using local feature matching and optical flow 査読有り

Chiyomi Miyajima, Katsuya Sakoyama, Kazuya Takeda

2015 IEEE/SICE International Symposium on System Integration, SII 2015 頁： 479 - 482 2016年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2015 IEEE. We investigate a method for identifying objects observed by drivers. Here we focus on roadside signage as an example, and track the driver's observation of signage while driving. A gaze tracking system and a forward-directed video camera are used to determine the driver's region of interest (ROI). The driver's observation of signage is detected by tracking the driver's ROI using optical flow, and by matching the driver's ROI with template images of signboards in a signage database using local feature matching. Driver and signage location information are used to limit candidate signboards for reducing computational cost for image matching. We conduct an experiment to evaluate our method and achieve a 66.2% detection rate of drivers' signboard observation with a false positive rate of 6.6%.

DOI： 10.1109/SII.2015.7405026

Scopus

Modeling and detecting excessive trust from behavior signals: Overview of research project and results 査読有り

Kazuya Takeda

Human-Harmonized Information Technology, Volume 1: Vertical Impact 頁： 57 - 75 2016年1月

記述言語：英語掲載種別：論文集(書籍)内論文

© Springer Japan 2016. An approach which would allow us to better understand behavioral states inherent in observed behaviors is proposed, based on the development of a mathematical representation of driving behaviors signals using our large driving behavior signal corpus. In particular, the project is aimed at developing technologies for preventing excessive trust in users of automated systems. Misuse/disuse of automation is introduced as a cognitive model of excessive trust, and methods of quantitative measurement are devised. PWARX and GMM models are proposed to represent discrete and continuous information in the cognition/decision/action process. We also develop a method of modeling visual behavior aiming at understanding environmental awareness while driving. We showed the effectiveness of the model experimentally through risky lane change detection. Finally, we show the effectiveness of the method to quantify excessive trust based on developed technology.

DOI： 10.1007/978-4-431-55867-5_3

Scopus

Robust Example Search Using Bottleneck Features for Example-based Speech Enhancement 査読有り

Ogawa Atsunori, Seki Shogo, Kinoshita Keisuke, Delcroix Marc, Yoshioka Takuya, Nakatani Tomohiro, Takeda Kazuya

17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5 08-12-September-2016 巻頁： 3733 - 3737 2016年

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