Abstract: A sound source signal is estimated with high accuracy in a noise environment. A sound source signal estimation unit (15) estimates each sound source signal using a separation matrix from an observation signal obtained by collecting a mixed acoustic signal in which a plurality of sound source signals and diffusive noise are mixed by a microphone array formed by a plurality of microphones The separation matrix is configured to convert steering vectors from each sound source to the microphone into unit vectors and convert a spatial covariance matrix of the diffusive noise into a matrix including a diagonal matrix with a size of the number of sound sources.

Abstract: A sound source separation filter information estimation device (10) estimates a covariance matrix having information on a correlation between sound source spectra and information on a correlation between channels as information on sound source separation filter information for separating an individual sound source signal from a mixed acoustic signal.

Abstract: The signal separation device includes: cross product calculation means receiving an input of an observed signal that is a mixture of a plurality of target signals, and calculating a cross product of the observed signal; model calculation means updating a parameter of a model for estimating the cross product with a predetermined algorithm using an inverse matrix of a matrix that represents an estimate of the cross product; inverse matrix calculation means calculating the inverse matrix of a matrix by a SIMD command when the parameter is updated; and separation means calculating the target signals using a matrix representing an estimate of the cross product, the updated parameter, and the observed signal.

Abstract: A sound source separation filter information estimation device (10) estimates a covariance matrix having information on a correlation between sound source spectra and information on a correlation between channels as information on sound source separation filter information for separating an individual sound source signal from a mixed acoustic signal.

Abstract: A signal analysis device includes a memory and processing circuitry coupled to the memory and configured to obtain, for a spatial covariance matrix Rj (j is an integral number equal to or larger than 1 and equal to or smaller than J) for modeling spatial characteristics of J (J is an integral number equal to or larger than 2) source signals that are present in a mixed manner, a simultaneous decorrelation matrix P as a matrix in which all PHRjP are diagonal matrices, or/and Hermitian transposition PH thereof, as a parameter for decorrelating components corresponding to the J source signals for observation signal vectors based on observation signals acquired at I (I is an integral number equal to or larger than 2) different positions.

Abstract: A signal analysis device includes an estimation unit that models a sound source position occurrence probability matrix Q using a product of a sound source position probability matrix B and a sound source existence probability matrix A, and estimates at least one of the sound source position probability matrix B and the sound source existence probability matrix A based on the modeling, the sound source position occurrence probability matrix Q being composed of probabilities of arrival of a signal from each sound source position candidate per frame, which is a time section, with respect to a plurality of sound source position candidates. The sound source position probability matrix B being composed of probabilities of arrival of a signal from each sound source position candidate per sound source with respect to a plurality of sound sources.

Abstract: A browser management system according to one embodiment includes at least one processor. The at least one processor is configured to: (A) in response to processing for displaying a webpage on a browser of a user terminal, generate a data sequence for identifying the browser; (B) generate an image file including a plurality of pixels representing the data sequence; and (C) transmit the image file to the user terminal in order to store the data sequence into a cache of the user terminal.

Abstract: The signal separation device includes: cross product calculation means receiving an input of an observed signal that is a mixture of a plurality of target signals, and calculating a cross product of the observed signal; model calculation means updating a parameter of a model for estimating the cross product with a predetermined algorithm using an inverse matrix of a matrix that represents an estimate of the cross product; inverse matrix calculation means calculating the inverse matrix of a matrix by a SIMD command when the parameter is updated; and separation means calculating the target signals using a matrix representing an estimate of the cross product, the updated parameter, and the observed signal.

Abstract: A mask estimation apparatus includes processing circuitry configured to estimate, for a target segment to be processed among a plurality of segments of a continuous time, a first mask which is an occupancy ratio of a target signal to an observation signal of the target segment, based on a first feature obtained from a plurality of the observation signals of the target segment recorded at a plurality of locations, and estimate a parameter for modeling a second feature and a second mask which is an occupancy ratio of the target signal to the observation signal based on an estimation result of the first mask in the target segment and the second feature obtained from the plurality of the observation signals of the target segment.

Abstract: A browser management system according to one embodiment includes at least one processor. The at least one processor is configured to: (A) in response to processing for displaying a webpage on a browser of a user terminal, generate a data sequence for identifying the browser; (B) generate an image file including a plurality of pixels representing the data sequence; and (C) transmit the image file to the user terminal in order to store the data sequence into a cache of the user terminal.

Abstract: A signal analysis device (1) includes an estimation unit (10) that, when a parameter for modeling spatial characteristics of signals from N signal sources (where N is an integer equal to or larger than 2) is a spatial parameter, estimates a signal source position prior probability which is a mixture weight for modeling a prior distribution of the spatial parameter with respect to each signal source using a mixture distribution that is a linear combination of prior distributions of the spatial parameter with respect to K signal source position candidates (where K is an integer equal to or larger than 2), and which is a probability that a signal arrives from each signal source position candidate per signal source.

Abstract: A signal analysis device includes an estimation unit that models a sound source position occurrence probability matrix Q using a product of a sound source position probability matrix B and a sound source existence probability matrix A, and estimates at least one of the sound source position probability matrix B and the sound source existence probability matrix A based on the modeling, the sound source position occurrence probability matrix Q being composed of probabilities of arrival of a signal from each sound source position candidate per frame, which is a time section, with respect to a plurality of sound source position candidates. The sound source position probability matrix B being composed of probabilities of arrival of a signal from each sound source position candidate per sound source with respect to a plurality of sound sources.

Abstract: A signal analysis device includes a memory and processing circuitry coupled to the memory and configured to obtain, for a spatial covariance matrix Rj (j is an integral number equal to or larger than 1 and equal to or smaller than J) for modeling spatial characteristics of J (J is an integral number equal to or larger than 2) source signals that are present in a mixed manner, a simultaneous decorrelation matrix P as a matrix in which all PHRjP are diagonal matrices, or/and Hermitian transposition PH thereof, as a parameter for decorrelating components corresponding to the J source signals for observation signal vectors based on observation signals acquired at I (I is an integral number equal to or larger than 2) different positions.

Abstract: A feature extraction unit in a mask estimation apparatus extracts, from a plurality of observation signals obtained by observing a plurality of acoustic signals at different positions, feature vectors obtained by collecting time-frequency components of the observation signals for each time-frequency point. A mask update unit uses the feature vectors, a mixture weight of each component distribution, and a shape parameter that is a model parameter capable of controlling a shape of each component distribution, where a probability distribution of the feature vectors is modeled by a mixture distribution consisting of a plurality of component distributions, to estimate masks indicating a proportion in which each component distribution contributes to each time-frequency point. A mixture weight update unit updates the mixture weight based on the updated masks. A parameter update unit updates the shape parameter by using the feature vectors and the masks.

Abstract: An observation feature value vector is calculated based on observation signals recorded at different positions in a situation in which target sound sources and background noise are present in a mixed manner; masks associated with the target sound sources and a mask associated with the background noise are estimated; a spatial correlation matrix of the target sound sources that includes the background noise is calculated based on the masks associated with the observation signals and the target sound sources; a spatial correlation matrix of the background noise is calculated based on the masks associated with the observation signals and the background noise; and a spatial correlation matrix of the target sound sources is estimated based on the matrix obtained by weighting each of the spatial correlation matrices by predetermined coefficients.

Abstract: A feature extraction unit in a mask estimation apparatus extracts, from a plurality of observation signals obtained by observing a plurality of acoustic signals at different positions, feature vectors obtained by collecting time-frequency components of the observation signals for each time-frequency point. A mask update unit uses the feature vectors, a mixture weight of each component distribution, and a shape parameter that is a model parameter capable of controlling a shape of each component distribution, where a probability distribution of the feature vectors is modeled by a mixture distribution consisting of a plurality of component distributions, to estimate masks indicating a proportion in which each component distribution contributes to each time-frequency point. A mixture weight update unit updates the mixture weight based on the updated masks. A parameter update unit updates the shape parameter by using the feature vectors and the masks.

Abstract: An observation feature value vector is calculated based on observation signals recorded at different positions in a situation in which target sound sources and background noise are present in a mixed manner; masks associated with the target sound sources and a mask associated with the background noise are estimated; a spatial correlation matrix of the target sound sources that includes the background noise is calculated based on the masks associated with the observation signals and the target sound sources; a spatial correlation matrix of the background noise is calculated based on the masks associated with the observation signals and the background noise; and a spatial correlation matrix of the target sound sources is estimated based on the matrix obtained by weighting each of the spatial correlation matrices by predetermined coefficients.

Abstract: A first generating part assumes that a circuit board is a laminated body of a lower layer portion and an upper layer portion and sets a thermal expansion coefficient of the circuit board itself having been actually measured in advance as a thermal expansion coefficient ?1 of the lower layer portion. Further, the first generating portion sets a value obtained by a Stoney's equation as a thermal expansion coefficient ?2 of the upper layer portion. Then, the laminated body of the lower layer portion and the upper layer portion is segmented into a plurality of grid data and element segment data in which a position and a material of grid data are made to correspond to each other is generated. The second calculating part calculates a physical amount occurring in an analysis object based on a finite element with a variety of solvers, and outputs an analysis result. In other words, the second calculating part performs a simulation of behavior of the analysis object.

Abstract: With a view to shortening the cable laying time there is provided a magnetic resonance imaging apparatus for generating an image of a subject in accordance with a magnetic resonance signal received after application of a high-frequency magnetic field to the subject under a static magnetic field and a gradient magnetic field, the magnetic resonance imaging apparatus comprising a base, operating portions provided in the base and performing predetermined operations, such as a data collector and a controller, two pipes projecting from the base, and plural cables wound in the figure of 8 onto projecting portions of the two pipes projecting from the base, one end of the cables being connected electrically to the operating portions.

Abstract: With a view to shortening the cable laying time there is provided a magnetic resonance imaging apparatus for generating an image of a subject in accordance with a magnetic resonance signal received after application of a high-frequency magnetic field to the subject under a static magnetic field and a gradient magnetic field, the magnetic resonance imaging apparatus comprising a base, operating portions provided in the base and performing predetermined operations, such as a data collector and a controller, two pipes projecting from the base, and plural cables wound in the figure of 8 onto projecting portions of the two pipes projecting from the base, one end of the cables being connected electrically to the operating portions.