Nec Corporation

Reishi Kondo

Tatsuya Komatsu

Masanori Kato

Yasuyuki Mitsui

Ryuuichi Ishige

Yumi Arai

Yuzo Senda

Masao Higuchi

Yukio Mitome

Mitsuru Noma

Sakiko Mishima

Eisuke Saneyoshi

Hiroaki Hattori

Yuko Ohta

A lung sound analysis system includes a storage means for storing therein the history of auscultation observations on lung sound data of each of auscultation positions of a subject who is a heart failure patient, a calculation means for calculating appearance frequency of abnormal sounds for each of the auscultation positions on the basis of the history, a determination means for determining the sequence of the auscultation positions at which lung sounds are to be auscultated from the subject, on the basis of the calculated appearance frequency, and an acquisition means for giving guidance on the auscultation positions of the subject to an operator according to the determined sequence, and acquiring time-series acoustic signals including lung sounds from a guided auscultation position. The system supports medical decision-making for healthcare professionals monitoring heart failure patients.

Lung sound analysis system

A lung sound analysis system includes an acquisition means for acquiring time-series acoustic signals including lung sounds of a subject who is a heart failure patient, a determination means for determining a pause phase of breathing of the subject, a dividing means for dividing the time-series acoustic signals into those in a period of the pause phase of breathing of the subject and those in a period other than the pause phase, according to a result of the determination, and a detection means for detecting abnormality in the lung sounds from the time-series acoustic signals in the period other than the pause phase after the division. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

A lung sound analysis system includes: an acquisition unit that obtains time-series acoustic signals including lung sounds from a heart failure patient; a determination unit that identifies the pause phase of the patient's breathing; a dividing unit that separates the time-series signals into those during the pause phase and those during other phases, based on the determination; a calculation unit that computes an index value representing the quality of the signals outside the pause phase, using the intensity of signals from both phases after separation; and a warning unit that issues alerts based on the calculated index value. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

A trajectory estimation device that includes an acquisition unit that acquires wave data based on a wave detected by a plurality of sensors; a generation unit that generates a spectrogram using the wave data; an extraction unit that extracts a Doppler shift from the spectrogram; a selection unit that selects, as a sensor pair, two of the sensors that satisfy a preset selection condition for the Doppler shift; and an estimation unit that estimates a trajectory of a wave source, which is a source generating the wave, based on a positional relationship between the sensors constituting the sensor pair and a relationship of the Doppler shifts between the two sensors constituting the sensor pair.

Trajectory estimation device, trajectory estimation system, trajectory estimation method, and program recording medium

This signal analysis device provides information to assist determining the state of a time series signal. An identification unit () identifies events included in the time series signal, and a ranking generation unit () generates a ranking among multiple event types.

Signal analysis device, signal analysis method, and recording medium

An information processing device is configured to receive a test signal related to a content. Accordingly, the information processing device can discriminate the status of a transmission path based on the state of the received test signal.

Information processing device, information processing system, information processing method, and program

An anomaly detection apparatus extracts a circumstantial feature value for anomaly detection corresponding to a circumstantial feature value for learning from other modal signal for anomaly detection different in modal from acoustic, calculates a signal pattern feature related to an acoustic signal of anomaly detection target based on the acoustic signal of anomaly detection target, the circumstantial feature value for anomaly detection and a signal pattern model learned based on an acoustic signal for learning and the circumstantial feature value for learning calculated from other modal signal for learning, and calculates an anomaly score for performing an anomaly detection of the acoustic signal of anomaly detection target based on the signal pattern feature.

Anomaly detection apparatus, anomaly detection method, and program

Provided is a noise estimation device capable of appropriately estimating the amount of noise in an observation signal. The noise estimation device includes: frequency analysis processing means for receiving an input of an observation signal that includes a moving object sound output from an moving object and noise and transforming the observation signal into a feature in each of time-frequency domains; noise range estimation means for estimating a first feature in a first time-frequency domain to which only the noise belongs based on acoustic characteristic information of the moving object sound and the feature; and amount-of-noise estimation means for estimating an amount of noise in a second time-frequency domain to which the moving object sound belongs based on the first feature.

Noise estimation device, moving object sound detection device, noise estimation method, moving object sound detection method, and non-transitory computer-readable medium

The acoustic feature extraction meansextracts an acoustic feature, using predetermined parameters, from an acoustic pattern obtained as a result of processing on an acoustic signal. The language vector calculation meanscalculates a language vector from a given label that represents an attribute of a source of the acoustic signal and that is associated with the acoustic pattern. The similarity calculation meanscalculates a similarity between the acoustic feature and the language vector. The parameter update meanslearns parameters so that the similarity becomes larger, and updates the predetermined parameters to the parameters obtained by learning.

Learning device and pattern recognition device

A signal source identification device includes: a feature value calculation unit configured to calculate feature values corresponding to paths where signals from generation sources of the signals transmit, based on signals received by a plurality of sensors; and an identification unit configured to identify whether or not a feature value calculated by the feature value calculation unit is a signal from a predetermined signal source by using an identification range that is a range within which feature values based on signals from the predetermined signal source fall and that is previously determined based on the feature values calculated by the feature value calculation unit.

Signal source identification device, signal source identification method, and program

A pattern recognition apparatus includes: a model storage part that stores a model(s) generated by using transfer path information indicating a difference of transfer paths of a signal(s) for training, additional to the signal(s) for training, and a pattern recognition part that inputs an input signal and transfer path information indicating a difference of transfer paths of the input signal, and performs pattern recognition of the input signal by using the model(s).

Pattern recognition robust to influence of a transfer path

A wave-source-direction estimation device includes: a plurality of input units that acquires, as input signals, electrical signals based on waves detected by a plurality of sensors; a signal selection unit that selects a plurality of pairs that are each a combination of two input signals from among a plurality of the input signals; a relative delay time calculation unit that calculates, as relative delay times, arrival time differences of the waves at the sensors that are supply sources of the two input signals composing each of the pairs, for each wave source direction; and an integrated-estimated-direction-information calculation unit that generates per-frequency estimated direction information for each of the pairs using the input signals composing each of the pairs and the relative delay times of each of the pairs and generates integrated estimated direction information by assigning a weight to and integrating the estimated direction information on all the pairs.

Wave-source-direction estimation device, wave-source-direction estimation method, and program storage medium

An anomaly detecting device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; calculating a series probability distribution being a probability distribution which the series feature amount follows; storing a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to the reference probability distribution; and detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data.

Anomaly detecting device, anomaly detecting method, and recording medium

A signal processing device according to an exemplary aspect of the present invention includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: extract, from a target signal, a feature value representing a feature of the target signal; calculate, based on the extracted feature value, signal element bases capable of representing a plurality of object signals by linear combination, and information of the linear combination, weights each representing intensities of the plurality of object signals included in the target signal; derive, based on the weights, information of a target object signal included in the target signal, the target object signal being at least one type of the plurality of object signals; and output information of the target object signal.

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Tokyo, Japan

Reishi Kondo