Abstract: A stress estimation apparatus that estimates stress of a measurement subject, obtains a body-movement data acquisition unit 101 for acquiring body-movement data, a body-movement data storage unit 102 for storing body-movement data, a body-movement feature calculation unit 103 for calculating a stress-related body-movement feature from the stored body-movement data, a going-out frequency calculation unit 104 for calculating an estimated value of a going-out frequency from the stored body-movement data, a body-movement feature correction unit 105 for correcting a correlation of the body-movement feature with stress using the body-movement feature and the going-out frequency, a corrected body-movement feature output unit 106 for outputting the corrected body-movement feature, and a stress estimation unit 107 for estimating stress using the corrected body-movement feature.

Abstract: This drowsiness estimating device estimates a subject's drowsiness from a time-series signal of the subject's eye-openness width. A filtering circuitry filters the time-series signal of the eye-openness width to eliminate signal changes due to the subject blanking and outputs the filtered time-series signal of eye-openness. A feature calculator calculates a feature from at least the filtered time-series signal of the eye-openness width. A drowsiness estimator estimates a drowsiness evaluated value from the feature and outputs an estimated result. The feature calculator includes at least a first feature calculation circuit that calculates a variation of the filtered time-series signal of the eye-openness width within a feature calculation window width and outputs the variation as a first feature.

Abstract: An alertness estimation apparatus 10, includes: an image extraction unit 11 that extracts an image of a portion including eyes from an image data of a face image of a human; a first alertness estimation unit 12 that estimates a first alertness based on the extracted image; a pulse wave detection unit 13 that determines whether or not the estimated first alertness satisfies a set condition, and detect a pulse wave of the human from the face image, when the first alertness satisfies the set condition; and a second alertness estimation unit 14 that estimates a second alertness of the human based on the detected pulse wave.

Abstract: A drowsiness estimation information correction device includes: a drowsiness estimation information acquisition unit that acquires drowsiness estimation information that is based on an eyelid movement of a subject; and a drowsiness estimation information correction unit that calculates corrected drowsiness estimation information obtained by correcting the drowsiness estimation information with humidity information indicating humidity in a surrounding environment of the subject.

Abstract: A blood volume pulse signal detection apparatus 300 includes a ROI decision unit 325 that determines a ROI in input movie data including image of human face, a sub-ROI decision unit 340 that determines a sub-ROI based on the ROI, a filter design unit 330 that designs a bandpass filter by performing the analysis at frequency domain and/or time domain using an average blood volume pulse signals at ROI according to the physiological characteristics of heart rate, and a noise reduction unit 350 that enhances a blood volume pulse signal at each sub-ROI using the bandpass filter.

Abstract: An environmental apparatus controller performs control over multiple types of environmental apparatuses, and includes a grasping unit, learning control plan output means, and a selection control unit. The grasping unit grasps current mental and physical condition information of a user, environmental situation information, and target relationship information between a target mental and physical condition and a present mental and physical condition. The learning control plan output means outputs a control change plan for each combination of multiple types of the environmental apparatuses according to the current mental and physical condition information, the environmental situation information, and the target relationship information. The selection control unit selects one of a plurality of the control change plans output by the learning control plan output means and executes the plan.

Abstract: A state estimation apparatus 10 includes: a filter generation unit 11 that generates a filter for emphasizing heart rate component and heart rate variable component by removing noise contained in a time series signal of a pulse wave obtained by an image data of a face image of a human; a noise removal unit 12 that performs a convolutional calculation between the bandpass filter generated by the filter generation unit 11 and the time-series signal of the pulse wave containing noise, thereby obtains a pulse wave after noise removal; a state estimation unit 13 that receives the time-series signal of the pulse wave after noise removal, which is the output of the noise removal unit 12, and outputs a state estimation value with reference to a state estimation model learned in advance.

Abstract: This drowsiness estimating device estimates a subject's drowsiness from a time-series signal of the subject's eye-openness width. A filtering circuitry filters the time-series signal of the eye-openness width to eliminate signal changes due to the subject blanking and outputs the filtered time-series signal of eye-openness. A feature calculator calculates a feature from at least the filtered time-series signal of the eye-openness width. A drowsiness estimator estimates a drowsiness evaluated value from the feature and outputs an estimated result. The feature calculator includes at least a first feature calculation circuit that calculates a variation of the filtered time-series signal of the eye-openness width within a feature calculation window width and outputs the variation as a first feature.

Abstract: A noise suppression system includes an a priori S/N ratio estimated value and expectation calculation unit that acquires an expectation of a priori S/N ratio, by correcting an estimated value of the a priori S/N ratio relating to a signal and a noise based on a priori S/N ratio model or based on a signal model and a noise model, the signal and the noise being estimated from an input signal in which the signal and the noise are mixed; a noise suppression coefficient calculation unit that calculates a noise suppression coefficient with use of the expectation of the a priori S/N ratio; and a noise suppression unit that suppresses the noise included in the input signal by multiplying the input signal by the noise suppression coefficient.

Abstract: An environmental apparatus controller performs control over multiple types of environmental apparatuses, and includes a grasping unit, learning control plan output means, and a selection control unit. The grasping unit grasps current mental and physical condition information of a user, environmental situation information, and target relationship information between a target mental and physical condition and a present mental and physical condition. The learning control plan output means outputs a control change plan for each combination of multiple types of the environmental apparatuses according to the current mental and physical condition information, the environmental situation information, and the target relationship information. The selection control unit selects one of a plurality of the control change plans output by the learning control plan output means and executes the plan.

Abstract: Provided an area-specific environment management system, which manages an environment in an individual one of a plurality of areas, the system including: a biological information sensor that acquires biological information about at least one of an individual and a group who belongs to an individual one of the areas; an environmental information sensor that acquires environmental information about an individual one of the areas; an arousal level estimation section that estimates an arousal level with respect to the biological information by using an arousal level estimation model; and an environment provision section that provides an environment to an individual one of the areas based on the arousal level and the environmental information, wherein the arousal level includes, as a state to which the arousal level belongs, a state from sleepy until awakened; a wakeful state; and an excessively wakeful state, and wherein the environment provision section includes an individual control mode corresponding to a stat

Abstract: A speech processing apparatus includes: an expectation value calculation unit configured to calculate, using an input signal spectrum and a speech model that models a feature quantity of speech, a spectrum expectation value which is an expectation value of a spectrum of an acoustic component included in the input signal spectrum; and an acoustic power estimation unit configured to estimate an acoustic power of the acoustic component of the input signal spectrum based on the input signal spectrum and the spectrum expectation value.

Abstract: To remove only noise components without removing desired signal components, a signal processing apparatus includes a noise decorrelator that removes noise signals having correlation between at least two input signals, in each of which a desired signal and a noise signal coexist, by receiving the at least two input signals from at least two channels, and a residual noise remover that removes residual noise included in an output signal of the noise decorrelator based on a phase difference between the output signal of the noise decorrelator and at least one input signal included in the at least two input signals.

Abstract: A speech processing system placed in a vehicle to effectively acquire a voice of a driver includes a first microphone that is provided on a surface facing the driver of the vehicle out of a steering wheel of the vehicle, and inputs a voice of the driver, a second microphone that is provided at a position where the voice of the driver of the vehicle is blocked by at least part of the steering wheel in the vehicle, and inputs noise in the vehicle, and a noise suppressor that suppresses an estimated noise signal based on a first signal corresponding to the sound input to the first microphone and a second signal corresponding to the sound input to the second microphone, and generates and outputs a pseudo speech signal.

Abstract: An apparatus of this invention is directed to a speech input apparatus for obtaining a piece of high-quality speech with included noise reduced. The speech input apparatus includes a sound insulating block attached to a ceiling member in a vehicle, a first microphone that is attached to a side surface of the sound insulating block or the ceiling member in the vicinity of the sound insulating block on a side close to a passenger of the vehicle, inputs a sound mixture including the voice of the passenger and noise in the vehicle, and outputs a first signal to a noise suppressor configured to output an enhanced speech signal, and a second microphone that is attached to a side surface of the sound insulating block or the ceiling member in the vicinity of the sound insulating block on a side far from the passenger, inputs the noise in the vehicle, and outputs a second signal to the noise suppressor.

Abstract: A system of this invention is directed to a speech processing system that efficiently performs noise suppression processing for a plurality of noise sources spreading in a lateral direction with respect to a speaker of interest.

Abstract: The purpose of the present invention is to provide a technology which is capable of appropriately evaluating a person's conduct with respect to another person. Provided is an information processing device, comprising a recognition unit 11, a detection unit 12, and an evaluation unit 13. The recognition unit 11 recognizes an evaluation subject's conduct. The detection unit 12 detects a trigger which is a state of a person other than the evaluation subject which triggers the evaluation subject's conduct. Using the detected trigger and the result of recognition by the recognition unit 13 relating to the evaluation subject's conduct, the evaluation unit 13 evaluates the evaluation subject's conduct.

Abstract: To input the voice of the passenger of a vehicle and output a piece of high-quality enhanced speech independently of the direction of a piece of speech or noise, a speech processing apparatus includes a first microphone that is provided on one of a ceiling member in a vehicle and an accessory thereof, inputs a sound mixture including a voice of a passenger of the vehicle and noise in the vehicle, and outputs a first signal, a second microphone that is provided on one of the ceiling member in the vehicle and the accessory thereof at a position farther than the first microphone when viewed from the passenger of the vehicle, inputs the noise in the vehicle while insulating the voice of the passenger of the vehicle using one of the ceiling member of the vehicle and the accessory thereof, and outputs a second signal, and a noise suppressor that outputs an enhanced speech signal based on the first signal and the second signal.

Abstract: A speech processing apparatus includes: an expectation value calculation unit configured to calculate, using an input signal spectrum and a speech model that models a feature quantity of speech, a spectrum expectation value which is an expectation value of a spectrum of an acoustic component included in the input signal spectrum; and an acoustic power estimation unit configured to estimate an acoustic power of the acoustic component of the input signal spectrum based on the input signal spectrum and the spectrum expectation value.

Abstract: A noise suppression system includes an a priori S/N ratio estimated value and expectation calculation unit that acquires an expectation of a priori S/N ratio, by correcting an estimated value of the a priori S/N ratio relating to a signal and a noise based on a priori S/N ratio model or based on a signal model and a noise model, the signal and the noise being estimated from an input signal in which the signal and the noise are mixed; a noise suppression coefficient calculation unit that calculates a noise suppression coefficient with use of the expectation of the a priori S/N ratio; and a noise suppression unit that suppresses the noise included in the input signal by multiplying the input signal by the noise suppression coefficient.