Nippon Telegraph and Telephone Corporation

Maxell, Ltd.

Toshiba Elevator Kabushiki Kaisha

Chugai Seiyaku Kabushiki Kaisha

Other

Hitachi, Ltd.

Hitachi Maxell, Limited

Hitachi Consumer Electronics Co., Ltd.

Threebond Co., Ltd.

Mitsubishi Heavy Industries Limited

The Furukawa Electric Co., Ltd.

Bunker Ramo Corporation

The Kansai Electric Power Co., Inc.

Ntt Corporation

Raqualia Pharma Inc.

Hiroaki Ito

Masashi Takahashi

Kazunori Kobayashi

Muneaki Yamaguchi

Shin Murata

Takehiro Fujita

Daisuke Yoshida

Tadamitsu Kishimoto

Tomokazu Murakami

Shoichiro Saito

Isao Karube

Mimpei Morishita

Hidetomo Kitamura

Norihiro Nishimoto

Masamichi Sugimoto

Kazuyuki Yoshizaki

Kenichi Akamatsu

Shin Shimaoka

Yuma Koizumi

Yosuke Tonami

Mitsunari Yamamoto

Hiroshi Tanaka

Takuya Hirata

Hiroki Tanaka

Koichi Hamada

Tsuyoshi Oishi

Minoru Saito

Masaru Takahashi

Keisuke Kinoshita

Yoshiharu Sato

Yoshihiro Arashitani

Kiyoshi Kawamura

Taku Nakamura

Tatsuya Yamagishi

Yuji Shishido

Yusuke Yatabe

Marc Delcroix

Shoko Araki

Tadashi Inoue

Hirotaka Watanabe

Yutaka Saito

Takahiko Endo

Yuki Hasegawa

Katsuyuki Hasegawa

Hiromasa Inouye

Kenta Niwa

Shohei Saito

Matsuo Kato

Yukio Ishii

Tsubasa Ochiai

Yuto Komatsu

Hisashi Uematsu

Ryuji Izumino

A channel in which an utterance of a keyword is included is selected from acoustic signals of multiple channels. An addition unitadds all channels of input voice signals of multiple channels to generate a composite voice signal of one channel. A keyword detection unitgenerates a keyword detection result indicating a result of detecting an utterance of a predetermined keyword from a composite voice signal. A power calculation unitcalculates powers of channels based on input voice signals. A delay unitdelays the powers of the channels. When the keyword detection result indicates that the keyword was detected, a maximum power detection unitselects, as an output channel, a channel having the maximum power among the powers of the channels of the input voice signals. A channel selection unitselects the voice signal of the output channel from the input voice signals and outputs the selected voice signal.

Channel selection apparatus, channel selection method, and program

A signal processing device includes processing circuitry configured to receive an input of extraction target information indicating which audio class of an audio signal is to be extracted from a mixture audio signal constituted by a mixture of audio signals of a plurality of audio classes, and output a result of extracting the audio signal of the audio class indicated by the extraction target information from the mixture audio signal, with a neural network by using a feature value of the mixture audio signal and the extraction target information.

Signal processing device, signal processing method, signal processing program, learning device, learning method, and learning program

Audibility of an outside sound needed for a driver inside an automobile to apprehend a danger and obtain a grasp of a situation necessary for driving is improved. A sound collection and emission apparatus () emits, on the basis of an outside acoustic signal which emanates from a sound source outside an automobile () and arrives at the automobile (), an inside acoustic signal which is an acoustic signal derived from the outside acoustic signal to inside the automobile (). A sound collection unit (M) collects the outside acoustic signal. A sound emission unit (S) emits the inside acoustic signal. A danger sound detection unit () determines whether the outside acoustic signal has a feature representing a danger defined in advance. A control unit () performs control that emits the inside acoustic signal from the sound emission unit (S) such that a driver of the automobile () is capable of perceiving the danger if the outside acoustic signal is determined to represent the danger.

Sound collection and emission apparatus, sound collection and emission method, and program

Information regarding an impact occurring in a vehicle is analyzed from an observed signal by a sensor installed in the vehicle. An acquisition unit () acquires an observed signal by a sensor (S) installed at a predetermined position of the vehicle. The analysis unit () analyzes the type of impact at each time from the observed signal with respect to the impact occurring on the vehicle at a position different from the position at which the sensor (S) is installed in the vehicle. The type of impact includes at least one type of impact among impact from collision of an obstacle with the vehicle, impact from the vehicle driving over a curb, impact from collision of a flying object with the vehicle, impact from the vehicle rubbing against an object other than the vehicle, and impact from an object other than the vehicle rubbing against the vehicle. An output unit () outputs a result obtained by analyzing a time at which the impact occurs and the type of impact.

Impact analysis method, feature amount extraction method, impact analysis apparatus, and program

A command analysis device capable of shortening the time until a command is executed is provided. The command analysis device includes a speech recognition unit that performs, every time when a predetermined unit of a speech signal is input, speech recognition on the speech signal and acquires a partial speech recognition result which is an intermediate result, and a command analysis unit that verifies an intermediate result in light of a predetermined intermediate result recognition rule, and outputs an analysis result during the input of the speech signal when a command execution target and a command execution content are successfully analyzed are provided.

Command analysis device, command analysis method, and program

Provided is an anomaly detection device that acquires a degree of anomaly required for anomaly detection when a feature pattern of time series data changes over time. The anomaly detection device is a device that detects the degree of anomaly in the time series data. The anomaly detection device includes a first acquisition unit, a prediction unit, and an anomaly degree acquisition unit. The first acquisition unit acquires, from a first section that is a partial section of the time series data, a dynamic feature pattern of the first section. The prediction unit predicts data of a second section that is a partial section of the time series data later than the first section by using the feature pattern. The anomaly degree acquisition unit acquires the degree of anomaly based on a difference between the predicted second section data and actual data of the second section.

Abnormality estimating apparatus, abnormality detection apparatus, abnormality detection method, and abnormality detection program

A work supporting apparatus includes: a visible light camera (visible light image photographing unit) configured to photograph a visible light image; a far-infrared camera (thermal image photographing unit) configured to photograph a thermal image; a work progress status determining unit configured to determine a work progress status from the visible light image photographed by the visible light camera and the thermal image photographed by the far-infrared camera; and a display configured to display a determination result of the work progress status.

Work supporting apparatus and work supporting system

Operations are appropriately changed in accordance with the use method. A keyword detection unitgenerates a keyword detection result indicating a result of detecting an utterance of a predetermined keyword from an input voice. A voice detection unitgenerates a voice section detection result indicating a result of detecting a voice section from the input voice. A sequential utterance detection unitgenerates a sequential utterance detection result indicating that a sequential utterance has been made if the keyword detection result indicates that the keyword has been detected and if the voice section detection result indicates that the voice section has been detected.

Continuous utterance estimation apparatus, continuous utterance estimation method, and program

The present invention provides a cleaner composition having excellent cleaning properties and appropriate drying properties. The present invention is a cleaner composition including: (A) a hydrofluoroolefin having a boiling point of 30° C. or more and less than 100° C.; and (B) at least one of a perfluoropolyether having a boiling point of 100° C. or more and a hydrofluoroolefin having a boiling point of 100° C. or more, where a mass ratio of the component (A) and the component (B) is 99:1 to 55:45.

Cleaner composition, cleaning aerosol, and method for cleaning contaminated part

A danger detection system for detecting a danger on a road, the danger that changes over time and may not be a danger due to the change over time, the danger detection system including a vibration acquisition unitconfigured to acquire an acoustic signal collected by a microphone mounted on a vehicle when the vehicle passes through a region on a predetermined road, a danger determination unitconfigured to determine whether the acoustic signal is derived from the danger, a danger location detection unitconfigured to determine whether the danger is present in the region on the predetermined road based on the number of times an acoustic signal acquired from each of the plurality of vehicles is determined to be derived from the danger, and a danger information notification unitconfigured to notify a communication device that can pass through the region on the predetermined road of danger information that is information about a region on a road determined to have a danger.

Danger detection system, method and program, and danger location detection apparatus

An abnormality detection deviceincludes: a vibration acquisition unitconfigured to acquire an acoustic signal generated during passage of a vehicle on a road; a frequency domain conversion unitconfigured to convert the acquired acoustic signal into a frequency domain signal; an unexpectedness determination unitconfigured to determine whether there is unexpectedness at each predetermined frequency using the frequency domain signal; and an abnormality determination unitconfigured to determine whether there is an abnormality in the road based on the number of frequencies at which it is determined that there is the unexpectedness.

Anomaly detection apparatus, method, system and program

A road condition estimation deviceincludes: a reception unitthat receives pieces of sensor information from automobiles respectively, each piece of sensor information including positional information that is information regarding a position at which an automobile travels, and spectral information obtained by converting an acoustic signal generated when the automobile travels at the position into information in a frequency domain; a first classification unitthat classifies the pieces of spectral information respectively for roads on which the automobiles travel, based on pieces of positional information included in the pieces of sensor information thus received; and an estimation unitthat estimates a condition of a road for each of the pieces of spectral information classified by the first classification unit.

Road condition estimation apparatus, method and program

High-quality video is provided using a small amount of coded bits. The moving picture decoding method performs inter-frame prediction processing. With the aforementioned inter-frame prediction processing, blocks with similar motion vectors from among the motion vectors in multiple blocks that have already been decoded are combined and a combined area is computed. A predicted vector for a target block to be decoded is computed using the motion vector of the aforementioned combined area, and a motion vector for the aforementioned target block is computed based on the aforementioned predicted vector and a difference vector which is included in a coded stream that is input. A predicted image is generated using the aforementioned motion vector, and a difference image which is included in the aforementioned coded stream and the aforementioned predicted image are added to generate a decoded image.

Moving picture decoding method and moving picture encoding method

An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

Anomalous sound detection training apparatus, acoustic feature extraction apparatus, anomalous sound sampling apparatus, and methods and programs for the same

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Tokyo, Japan

Hiroaki Ito