Abstract: An aspect of the present invention provides a learning device including: a neural network that converts an acoustic time series that is a time series representing a sound into feature data expressed in a predetermined format required by a downstream task; and an update unit that updates the neural network on the basis of an execution result of the downstream task using the feature data, in which the neural network includes: a feature extraction unit that converts an input acoustic time series into an intermediate feature tensor that is a third-order tensor indicating features of the acoustic time series and is a tensor having time, frequency, and channel; and an intermediate network that executes processing of converting a representation of the intermediate feature tensor into a representation of a second-order tensor having time and direct product amounts that are amounts indicating a direct product of frequency and channel, and processing of acquiring, for each direct product amount of the second-order tens

Abstract: A learning device includes: spectrogram generation circuitry 1 that generates a spectrogram from an input sound signal; patch generation circuitry 2 that divides the generated spectrogram to generate a plurality of patches; a mask processing circuitry 3 that selects some patches as masked patches; reconstruction circuitry 4 that obtains a plurality of reconstructed patches by reconstructing the plurality of patches by processing of an encoder and a decoder in a transformer serving as a deep learning model by using visible patches other than the masked patches among the plurality of patches and mask tokens corresponding to the masked patches; and parameter update circuitry 5 that updates a parameter of the encoder and a parameter of the decoder such that the masked patches approach reconstructed patches corresponding to the masked patches. The number of layers of the decoder is three or more.

Abstract: A learning device includes: spectrogram generation circuitry 1 that generates a spectrogram from a first sound signal and generates a target spectrogram from a second sound signal; patch generation circuitry 2 that divides the spectrogram to generate a plurality of patches and divides the target spectrogram to generate a plurality of target patches; mask processing circuitry 3 that selects some patches as masked patches; reconstruction circuitry 4 that obtains a plurality of reconstructed patches by reconstructing the plurality of patches by processing of an encoder and a decoder by using visible patches other than some patches among the plurality of patches and mask tokens; and parameter update circuitry 5 that updates parameters such that target patches corresponding to the masked patches among the plurality of target patches approach reconstructed patches corresponding to the masked patches among the plurality of reconstructed patches.

Abstract: A medical image diagnosis apparatus according to an embodiment includes movable member which moves in a bore along the central axis of the bore formed in a gantry provided with a medical imaging mechanism concerning imaging a subject, couchtop which inserts into the bore, screen provided for the member and having a picture from a projector projected thereon, first frame connected to the member and positioned on the projector side for the screen, a second frame supporting a reflection board and being movable along the first frame, and processing circuitry which estimates a posture of the examined subject based on one of the position of the reflection board concerning the screen and the orientation of the head of the examined subject, determines a rotation amount of the picture on the basis of the posture, and controls the projector so as to rotate the picture according to the rotation amount.

Abstract: An aspect of the present invention provides a learning device including: a neural network that converts an acoustic time series that is a time series representing a sound into feature data expressed in a predetermined format required by a downstream task; and an update unit that updates the neural network on the basis of an execution result of the downstream task using the feature data, in which the neural network includes: a feature extraction unit that converts an input acoustic time series into an intermediate feature tensor that is a third-order tensor indicating features of the acoustic time series and is a tensor having time, frequency, and channel; and an intermediate network that executes processing of converting a representation of the intermediate feature tensor into a representation of a second-order tensor having time and direct product amounts that are amounts indicating a direct product of frequency and channel, and processing of acquiring, for each direct product amount of the second-order tens

Abstract: According to an aspect of the present invention, there is provided a sound estimation model acquisition device including a model acquisition unit configured to acquire a mathematical model that estimates an estimated time series that is a time series satisfying a predetermined estimation condition from one or a plurality of time series on a basis of a first sound time series that is a time series indicating a first sound, a second sound time series that is a time series indicating a second sound, and first difference information indicating at least a partial difference between the first sound and the second sound, in which the mathematical model is a mathematical model that estimates the estimated time series on the basis of an input time series that is a time series indicating an input sound and second difference information that is information indicating at least a partial difference between the input time series and the estimated time series, and the estimation condition is a condition that a difference be

Abstract: A signal filtering device includes: an information generation unit that generates feature information on related information on a target signal; an extraction unit that extracts mask information from a mixed signal including the target signal on the basis of the feature information; and a mask processing unit that estimates the target signal from the mixed signal using the mask information. The information generation unit may encode the related information into a multidimensional vector and generate a linear transformation result of the multidimensional vector as the feature information. The information generation unit may encode the related information into a first multidimensional vector, encode the mixed signal into a second multidimensional vector, derive a similarity in time series between the first multidimensional vector and the second multidimensional vector, and generate a result of a weighted sum of the similarity in time series and the mixed signal as the feature information.

Abstract: A signal filtering device includes: a separation unit that separates a predetermined number of possibility signals from a mixed signal as possibilities of a target signal; an encoding unit that encodes related information of the target signal into a first feature vector and encodes the predetermined number of possibility signals into the predetermined number of second feature vectors; and a selection unit that derives a similarity between the first feature vector and the second feature vector for each of the possibility signals, and selects a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals. The selection unit may derive an inner product of the first feature vector and the second feature vector as the similarity. The predetermined number of possibility signals may be voice signals associated with the predetermined number of sound sources.

Abstract: One aspect of the present invention is a learning device including a self-learning unit that updates content of main conversion processing for converting data to be processed into data in a predetermined format by executing self-supervised learning, and a data augmentation unit that executes data augmentation processing of generating data to be processed in the main conversion processing based on an acoustic time series, in which the data augmentation unit performs acoustic time series clipping processing of clipping a partial time series that is a time series of a part of the acoustic time series, duplication processing of duplicating the partial time series, and conversion processing of converting one and the other of the partial time series according to a predetermined rule, and the self-learning unit updates the content of the main conversion processing by self-supervised learning based on a result obtained by the conversion processing.

Abstract: In one embodiment, An MRI apparatus comprising processing circuitry configured to: acquire an optical image of an object placed on a table of a bed; acquire information on an examination portion of the object; and select a specific RF coil to be used for imaging the examination portion of the object from a plurality of available RF coils based on the optical image and information on the plurality of available RF coils specified from the information on the examination portion.

Abstract: A medical image diagnosis apparatus according to an embodiment includes movable member which moves in a bore along the central axis of the bore formed in a gantry provided with a medical imaging mechanism concerning imaging a subject, couchtop which inserts into the bore, screen provided for the member and having a picture from a projector projected thereon, first frame connected to the member and positioned on the projector side for the screen, a second frame supporting a reflection board and being movable along the first frame, and processing circuitry which estimates a posture of the examined subject based on one of the position of the reflection board concerning the screen and the orientation of the head of the examined subject, determines a rotation amount of the picture on the basis of the posture, and controls the projector so as to rotate the picture according to the rotation amount.