Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Compressing Continuous Point Cloud Data Using Image Compression Methods Reviewed

Chenxi Tu, Eijiro Takeuchi, Chiyomi Miyajima, Kazuya Takeda

ITSC2016 page： 00 2016.11

AI framework to arrange audio objects according to listener preferences Reviewed

Kento Ohtani, Kenta Niwa, Kazuya Takeda

5th Joint Meeting of the Acoustical Society of America and Acoustical Society of Japan page： 00 2016.11

Stereo Channel Music Signal Separation Based on Nonnegative Tensor Factorization with Cepstrum Regularization Reviewed

Shogo Seki, Kento Ohtani, Tomoki Toda, Kazuya Takeda

5th Joint Meeting of the ASA and ASJ page： 00 2016.11

Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing Reviewed

Chiyomi Miyajima, Kazuya Takeda

IEEE Signal Processing Magazine Vol. 33 ( 6 ) page： 14 - 21 2016.11

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

© 2016 IEEE. This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.

DOI： 10.1109/MSP.2016.2602377

Web of Science

Scopus

Analysis of driver workload when using speech interfaces Reviewed

D Hayashi, C Miyajima, K Takeda

The Journal of the Acoustical Society of America Vol. 140 ( 4 ) page： 2961 - 2961 2016.10

Projection of a virtual speaker into a vehicle using sound field control Reviewed

T Yamamura, Y Ishiguro, T Nishino, K Takeda

The Journal of the Acoustical Society of America Vol. 140 ( 4 ) page： 3063 - 3063 2016.10

Emotion recognition from speech using a physical model Reviewed

Norihide Kitaoka, Shuhei Segawa, Kazuya Takeda

Proc. ICA2016 page： 00 2016.9

Bidirectional LSTM-HMM Hybrid System for Polyphonic Sound Event Detection Reviewed

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

DCASE2016 page： 00 2016.9

Investigation of DNN-based modeling for audio-visual speech recognition Reviewed

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

2015 First International Workshop on Spoken Language Processing (MLSLP2015) page： 00 2016.9

Investigation on Recurrent Neural Network Architectures for Daily Activity Recognition Reviewed

Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda

UV2016 page： 00 2016.9

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 Vol. 116(189) ( 7 ) page： 12 2016.8

Accelerated Deformable Part Models on GPUs Reviewed

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

IEEE Transactions on Parallel and Distributed Systems Vol. 27 ( 6 ) page： 1589 - 1602 2016.6

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

© 2015 IEEE. Object detection is a fundamental challenge facing intelligent applications. Image processing is a promising approach to this end, but its computational cost is often a significant problem. This paper presents schemes for accelerating the deformable part models (DPM) on graphics processing units (GPUs). DPM is a well-known algorithm for image-based object detection, and it achieves high detection rates at the expense of computational cost. GPUs are massively parallel compute devices designed to accelerate data-parallel compute-intensive workload. According to an analysis of execution times, approximately 98 percent of DPM code exhibits loop processing, which means that DPM could be highly parallelized by GPUs. In this paper, we implement DPM on the GPU by exploiting multiple parallelization schemes. Results of an experimental evaluation of this GPU-accelerated DPM implementation demonstrate that the best scheme of GPU implementations using an NVIDIA GPU achieves a speed up of 8.6x over a naive CPU-based implementation.

DOI： 10.1109/TPDS.2015.2453962

Web of Science

Scopus

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 page： 00 2016.6

Integrating driving behavior and traffic context through signal symbolization. Reviewed

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

In Intelligent Vehicles Symposium (IV), 2016 IEEE. page： pp. 642-647 2016.6

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

Takatsugu Hirayama, Kenji Mase, Chiyomi Miyajima, Kazuya Takeda

IEEE Transactions on Intelligent Vehicles Vol. 1 ( 2 ) page： 148 - 157 2016.6

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

© 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 Reviewed

Masafumi Nishida, Norihide Kitaoka, Kazuya Takeda

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 page： 279 - 282 2016.2

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

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 page： 575 - 582 2016.2

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

Chiyomi Miyajima, Katsuya Sakoyama, Kazuya Takeda

2015 IEEE/SICE International Symposium on System Integration, SII 2015 page： 479 - 482 2016.2

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

© 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 Reviewed

Kazuya Takeda

Human-Harmonized Information Technology, Volume 1: Vertical Impact page： 57 - 75 2016.1

Language：English Publishing type：Part of collection (book)

© 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 Reviewed

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

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH Vol. 08-12-September-2016 page： 3733 - 3737 2016

Publishing type：Research paper (international conference proceedings) Publisher：ISCA-INT SPEECH COMMUNICATION ASSOC

Copyright © 2016 ISCA. Example-based speech enhancement is a promising approach for coping with highly non-stationary noise. Given a noisy speech input, it first searches in noisy speech corpora for the noisy speech examples that best match the input. Then, it concatenates the clean speech examples that are paired with the matched noisy examples to obtain an estimate of the underlying clean speech component in the input. This framework works well if the noisy speech corpora contain the noise included in the input. However, it is impossible to prepare corpora that cover all types of noisy environments. Moreover, the example search is usually performed using noise sensitive mel-frequency cepstral coefficient features (MFCCs). Consequently, a mismatch between an input and the corpora is inevitable. This paper proposes using bottleneck features (BNFs) extracted from a deep neural network (DNN) acoustic model for the example search. Since BNFs have good noise robustness (invariance), the mismatch is mitigated and thus a more accurate example search can be performed. Experimental results on the Aurora4 corpus show that the example-based approach using BNFs greatly improves the enhanced speech quality compared with that using MFCCs. It also consistently outperforms a conventional DNN-based approach, i.e. a denoising autoencoder.

DOI： 10.21437/Interspeech.2016-671

Web of Science

Scopus

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Papers - TAKEDA, Kazuya