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: An aspect of the present invention is a learning apparatus including a time series acquisition unit that is configured to, with a time series of an amplitude of a fluctuating oscillator whose amplitude changes periodically being defined as an oscillator time series, acquire an observed time series which is a time series represented by an oscillator linear sum which is a linear sum of the oscillator time series and a learning processing execution unit that is configured to use an expression representing a generation mechanism of the observed time series and a mathematical model representing a relationship between a probabilistic state transition of a state of a generation source of the observed time series and a symbol output which is information probabilistically output in the state to execute a linear sum estimation learning model which is a mathematical model that is configured to estimate the oscillator linear sum of the observed time series on the basis of the observed time series, wherein the learning pr

Abstract: One aspect of the present invention is an image adjustment method including: deriving a statistic from a first pixel having a first luminance and a second pixel having a second luminance co-occurring in a local region in an input image, and deriving a two-dimensional luminance histogram having the statistic as a numerical value corresponding to the first luminance and the second luminance; deriving a numerical value by weighting the two-dimensional luminance histogram by using a weighting coefficient determined according to a third luminance greater than the first luminance and less than or equal to the second luminance, and deriving a one-dimensional luminance histogram having the numerical value as a numerical value corresponding to the third luminance; and deriving a luminance conversion function to cause the one-dimensional luminance histogram derived to be converted into a predetermined histogram, and converting a luminance of each of pixels of the input image by using the luminance conversion function.

Abstract: An acquiring unit of a depth estimation apparatus acquires an input image. In addition, a depth map generating unit inputs the input image acquired by the acquiring unit into a depth estimator for generating, from an image, a depth map in which a depth of a space that appears on the image is imparted to each pixel of the image, and generates an estimated depth map that represents a depth map corresponding to the input image. The depth estimator is a model having been learned in advance so as to reduce, with respect to each error between a depth of the estimated depth map and a depth of a correct-answer depth map that presents the depth map of a correct answer, a value of a loss function set such that a degree of increase of a loss value with respect to a pixel at which the error is larger than a threshold is smaller than a degree of increase of a loss value with respect to a pixel at which the error is equal to or smaller than the threshold.

Abstract: To provide translation techniques for translating a natural language representation in one language into a natural language representation in another language using sound. With L1 and L2 being different languages from each other, a translation data generation apparatus includes: a latent variable generation unit that generates, from a natural language representation in the language L1, a latent variable corresponding to the natural language representation in the language L1 using a language L1 encoder; an index calculation unit that calculates an index for the natural language representation in the language L1 from the natural language representation in the language L1; and a natural language representation generation unit that generates a natural language representation in the language L2 corresponding to the natural language representation in the language L1 from the latent variable and the index for the natural language representation in the language L1 using a language L2 decoder.

Abstract: A signal generation device includes a variable generation unit and a signal generation unit. The variable generation unit generates a plurality of latent variables corresponding to a plurality of features of a signal. The signal generation unit inputs, to at least one neural network learned in advance, a latent variable representing attributes obtained by converting a part of the plurality of latent variables by an attribute vector representing attributes of a signal to be generated and the other part of the plurality of latent variables representing an identity and generates the signal to be generated using the at least one neural network.

Abstract: It is possible to analyze what type of input/output mapping is mainly performed in the entire hidden layer of a neural network. A relationship analysis unit 30 calculates strength of a relationship of each combination of a dimension of the input data and a unit of the neural network and calculates strength of a relationship of each combination of the unit and a dimension of the output data. A role analysis unit 32 calculates a relationship between a prescribed number of types of roles and the unit and a relationship between the prescribed number of types of roles, the dimension of the input data, and the dimension of the output data on the basis of the strength of the relationship of each combination of the dimension of the input data and the unit of the neural network and the strength of the relationship of each combination of the unit and the dimension of the output data.

Abstract: To provide techniques for generating, from a sound signal, a natural language representation corresponding to the sound signal while controlling a predetermined index for a natural language representation. A data generation apparatus 200 includes: a latent variable generation unit 210 that generates, from a sound signal, a latent variable corresponding to the sound signal using an encoder; and a data generation unit 220 that generates a natural language representation corresponding to the sound signal from the latent variable and a condition concerning an index for the natural language representation using a decoder.

Abstract: An image adjustment device includes: an illumination component derivation unit that derives an illumination component of a grayscale image; a reflectance component derivation unit that derives a reflectance component image that is a resulting image in which the illumination component is removed from the grayscale image; a contrast component derivation unit that derives a contrast component based on a contrast value between a pixel of the reflectance component image and a peripheral area of the pixel; a histogram derivation unit that derives a luminance histogram of the grayscale image weighted according to the contrast value for each pixel of the contrast component; a conversion function derivation unit that derives a luminance conversion function for converting a luminance such that a luminance histogram of a converted grayscale image in which the grayscale image is converted by the luminance conversion function and a predetermined histogram are matched with or similar to each other; and a luminance conversi

Abstract: The present invention enables the structure of a neural network to be quantitatively analyzed. An analyzing unit calculates, for each of combinations of a dimension of input data and a cluster, a sum of squared errors between an output of each unit belonging to the cluster when a value of the dimension of the input data is replaced with an average value of the dimension of the input data included in learning data and an output of each unit belonging to the cluster for the input data before replacement as a relationship between the combinations, and calculates, for each of combinations of the cluster and a dimension of output data, a squared error between the value of the dimension of the output data when an output value of each unit belonging to the cluster is replaced with an average output value of each unit of the cluster when the input data included in the learning data was input and the value of the dimension of the output data before replacement as a relationship between the combinations.

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: To provide database generation techniques that can accurately and efficiently generate a database useable in text-based sound signal search. A sound signal database generation apparatus includes: a latent variable generation unit that generates, from a sound signal, a latent variable corresponding to the sound signal using a sound signal encoder; a data generation unit that generates a natural language representation corresponding to the sound signal from the latent variable and a condition concerning an index for a natural language representation using a natural language representation decoder; and a sound signal database generation unit that generates a record including the natural language representation corresponding to the sound signal and the sound signal from the natural language representation corresponding to the sound signal and the sound signal, and generates a sound signal database made up of the record.

Abstract: The present invention enables detection of a sponsorship credit display in a broadcast program with higher precision. A sponsorship credit display detection device 100 according to the present invention includes: a learning data creation unit 140 that creates, as learning data, from a broadcast program in which the sponsorship credit display has been detected, a still image where the sponsorship credit display is displayed and a still image where the sponsorship credit display is not displayed; a learning unit 150 that uses the learning data created by the learning data creation unit 140 to learn a parameter to be applied to a detection model for detecting a sponsorship credit display in a broadcast program; and a sponsorship credit display detection unit 170 that uses the detection model to which the parameter learned by the learning unit 150 has been applied to detect a sponsorship credit display in a recognition target broadcast program.

Abstract: According to an aspect of the present invention, there is provided a terminal apparatus including an operator that receives information indicating a position in a body of a subject from a user, in which biological information corresponding to the position received by the operator is output on the basis of biological information obtained by a plurality of sensors of which positions are substantially fixedly maintained with respect to the body of the subject.

Abstract: A learning device calculates a feature of each data included in a pair of datasets in which two modalities among a plurality of modalities are combined, using a model that receives data on a corresponding modality among the modalities and outputs a feature obtained by mapping the received data into an embedding space. The learning device then selects similar data similar to each target data that is data on a first modality in a first dataset of the datasets, from data on a second modality included in a second dataset of the datasets. The learning device further updates a parameter of the model such that the features of the data in the pair included in the first and the second datasets are similar to one another, and the feature of data paired with the target data is similar to the feature of data paired with the similar data.

Abstract: A learning device calculates an image feature using a model (image encoder) that receives an image and outputs the image feature obtained by mapping the image into a latent space. The learning device calculates an audio feature using a model (audio encoder) that receives a speech in a predetermined language and outputs the audio feature obtained by mapping the speech into the latent space, and that includes a neural network provided with a self-attention mechanism. The learning device updates parameters of the models used by an image feature calculation unit and an audio feature calculation unit such that the image feature of a first image is similar to the audio feature of a speech corresponding to the first image.

Abstract: The purpose of the present invention is to enable learning of a neural network for extracting features of images having high robustness from an undiscriminating image region while minimizing the number of parameters of a pooling layer. A parameter learning unit 130 learns parameters of each layer in a convolutional neural network configured by including a fully convolutional layer for performing convolution of an input image to output a feature tensor of the input image, a weighting matrix estimation layer for estimating a weighting matrix indicating a weighting of each element of the feature tensor, and a pooling layer for extracting a feature vector of the input image based on the feature tensor and the weighting matrix.

Abstract: The present invention enables detection of a sponsorship credit display segment in a broadcast program with higher precision.