Abstract: An evaluation apparatus according to the present disclosure includes at least one memory storing a set of instructions, and at least one processor configured to execute the set of instructions and thereby to acquire, from a sensor, time-space data on vibrations that have occurred in an overhead optical fiber strung between poles, and evaluate, for each of predetermined analysis sections, a wind velocity and a propagation speed of wind in the analysis section based on the time-space data, and evaluate a wind direction in the analysis section based on the evaluated wind velocity and the propagation speed of the wind.

Abstract: A signal processing apparatus according to the present disclosure includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: acquire signals measured by a signal measurement apparatus at a plurality of points located along an optical fiber; and determine whether there is an event based on a degree of similarity between the signals measured at points adjacent points.

Abstract: The acoustic feature extraction means 82 extracts an acoustic feature, using predetermined parameters, from an acoustic pattern obtained as a result of processing on an acoustic signal. The language vector calculation means 83 calculates 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 means 84 calculates a similarity between the acoustic feature and the language vector. The parameter update means 85 learns parameters so that the similarity becomes larger, and updates the predetermined parameters to the parameters obtained by learning.

Abstract: An apparatus includes memory and processor configured to: input, from a sensor, a signal indicating a characteristic vibration occurring at each position of an optical fiber, and estimate, based on the input signal, an aerial section in which the optical fiber being aerially laid over a pole is present; calculate, based on the input signal, sensing data indicating a vibration characteristic of each position of the optical fiber being present in the aerial section, and calculate a difference degree of the sensing data between two neighboring points of the optical fiber; and temporally integrate the difference degree, calculate a time average of the difference degree, estimate, based on a time average value of the difference degree, a position of the pole and a position of an extra length section of the optical fiber, and output an estimation result of positions of the pole and the extra length section.

Abstract: An extraction unit extracts sound pressure information from a spectrogram. A binarization unit carries out binarization on the extracted sound pressure information in order to generate an event mask indicating a time period in which an audio event exists.

Abstract: A signal processing device according to an aspect of the present disclosure comprises: cutout means that cuts out a target signal in a predetermined section, and an overlap signal in a section not matching the predetermined section but overlapping with at least part of the predetermined section from an input signal; conversion means that converts the target signal and the overlap signal into a phase component signal in a frequency domain with respect to each frequency; gradient calculation means that, based on the phase component signal, calculates, at each frequency, a phase gradient that is the gradient of the phase component at the frequency; score calculation means that calculates a score relating to the sudden change characteristic of the input signal according to the phase gradients; and determination means that, based on the score, determines the presence or absence of a sudden change in the target signal.

Abstract: An abnormality estimation apparatus 10 includes: a detection unit 11 configured to detect a vibration response when a vehicle passes over an expansion and contraction apparatus 23, using vibration information indicating vibration that occurs in a bridge; a first index calculation unit 12 configured to calculate a first index for determining the presence or absence of an abnormality in the expansion and contraction apparatus 23, using the vibration response; and an estimation unit 13 configured to estimate the presence or absence of an abnormality in accordance with a change in the first index.

Abstract: Provided are a sound model generation device and the like that make it possible to more easily generate a sound model capable of distinguishing sound events using a plurality of features. A concatenating unit concatenates a plurality of features of sound signals that are learning data and generates a concatenated feature. A learning unit learns the generated concatenated feature for generating a sound model for distinguishing a sound event from a sound signal.

Abstract: An extraction unit extracts sound pressure information from a spectrogram. A binarization unit carries out binarization on the extracted sound pressure information in order to generate an event mask indicating a time period in which an audio event exists.

Abstract: 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.

Abstract: The acoustic feature extraction means 82 extracts an acoustic feature, using predetermined parameters, from an acoustic pattern obtained as a result of processing on an acoustic signal. The language vector calculation means 83 calculates 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 means 84 calculates a similarity between the acoustic feature and the language vector. The parameter update means 85 learns parameters so that the similarity becomes larger, and updates the predetermined parameters to the parameters obtained by learning.