Nippon Telegraph and Telephone Corporation

Nippon Steel Sumitomo Metal Corporation

Nippon Steel Corporation

Kaoru Hiramatsu

Kunio Kashino

Takahito Kawanishi

Yasunori Oishi

Naoki Maruyama

Hiroyuki Kawata

Takuya Kuwayama

Takeshi Yasui

Akinobu Murasato

Akinobu Minami

Hiroyuki Ban

Takuhiro Kaneko

Go Irie

Chihiro Watanabe

Kiyoharu Aizawa

Xiaomeng Wu

Hidehisa Nagano

Masataka Yamaguchi

Masaru Tsuchida

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.

Signal generation device, signal generation learning device, method, and program

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 unitcalculates 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 unitcalculates 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.

Analysis device, method, and program for analyzing neural network structure

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.

Device, method, and program for quantitatively analyzing structure of a neural network

The present invention enables detection of a sponsorship credit display in a broadcast program with higher precision. A sponsorship credit display detection deviceaccording to the present invention includes: a learning data creation unitthat 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 unitthat uses the learning data created by the learning data creation unitto 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 unitthat uses the detection model to which the parameter learned by the learning unithas been applied to detect a sponsorship credit display in a recognition target broadcast program.

Device and method for detecting display of provided credit, and program

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 unitlearns 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. The parameter learning unitlearns the parameters such that a loss function value obtained by calculating a loss function expressed by using a distance between a first feature vector of a first image and a second feature vector of a second image, which are relevant images and are obtained by applying the convolutional neural network, becomes smaller.

Image feature learning device, image feature learning method, image feature extraction device, image feature extraction method, and program

The present invention enables detection of a sponsorship credit display segment in a broadcast program with higher precision. A sponsorship credit display detection deviceaccording to the present invention includes: a related phrase storage unitthat stores related phrases included in announcement of a sponsorship credit display and related to the sponsorship credit display, in association with estimation periods; a voice recognition unitthat performs voice recognition on an audio signal of the broadcast program; a related phrase detection unitthat detects a stored related phrase from results of the voice recognition; a sponsorship credit display segment estimation unitthat, using an appearance time of the detected related phrase as a start point, estimates a period corresponding to the estimation period stored in association with the detected related phrase as a sponsorship credit display segment; and a detection unitthat, among estimated sponsorship credit display segments, detects a segment that continues for at least a predetermined time period as a sponsorship credit display segment.

The present invention enables detection of a sponsorship credit display in a broadcast program with higher precision. A sponsorship credit display detection deviceaccording to the present invention includes: a CM segment detection unitthat extracts a cut point, which is a time point where a frame in which the volume of an audio signal of a broadcast program is less than a volume threshold value and the amount of change from a previous frame is at least a pixel change threshold value is played, and detects a CM segment by comparing an interval of the extracted cut point with a CM defined length; a sponsorship credit display segment estimation unitthat estimates, as a sponsorship credit display segment, a predetermined time period before or after at least one continuous CM segment detected by the CM segment detection unit; and an output unitthat outputs information indicating the sponsorship credit display segment.

Low-dimensional feature values with which semantic factors of content are ascertained are generated from relevance between sets of two types of content. Based on a relation indicator indicating a pair of groups indicating which groups are related to first types of content groups among second types of content groups, an initial feature value extracting unitextracts initial feature values of the first type of content and the second type of content. A content pair selecting unitselects a content pair by selecting one first type of content and one second type of content from each pair of groups indicated by the relation indicator. A feature value conversion function generating unitgenerates feature conversion functionsof converting the initial feature values into low-dimensional feature values based on the content pair selected from each pair of groups.

Feature amount generation method, feature amount generation device, and feature amount generation program

A signal retrieval device includes a modification unit and a signal retrieval unit. The modification unit modifies a value of an attribute of a target represented by an input signal or a stored signals stored in a signal storage unit or a value of an attribute relating to a signal generation source of the input signal to acquire a plurality of modified values of the attribute. The signal retrieval unit retrieves a stored signal of the stored signals similar to the input signal using the input signal or the stored signals in which the attribute is modified according to each of the plurality of modified values of the attribute acquired by the modification unit.

Signal retrieval device, method, and program

Clustering can be performed using a self-expression matrix in which noise is suppressed. A self-expression matrix is calculated that minimizes an objective function that is for obtaining, from among matrices included in a predetermined matrix set, a self-expression matrix whose elements are linear weights when data points in a data set are expressed by linear combinations of points, the objective function being represented by a term for obtaining the residual between data points in the data set and data points expressed by linear combinations of points using the self-expression matrix, a first regularization term that is multiplied by a predetermined weight and is for reducing linear weights of the data points that have a large Euclidean norm in the self-expression matrix, and a second regularization term for the self-expression matrix. A similarity matrix defined by the calculated self-expression matrix is then calculated. Then a clustering result is obtained by clustering the data set based on the similarity matrix.

Clustering device, method and program

Information related to CMs included in a broadcast program can be automatically added. A CM information generation deviceincludes: a CM section detection unitthat detects one or more CM sections within a broadcast program by comparing the volume of the broadcast program with a volume threshold; a CM detection list generation unitthat generates a CM detection list describing company names of companies that have advertised detected CMs, which are CMs in the CM sections detected by the CM section detection unit, by cross-referencing the detected CMs with CM masters that have been associated with company names of advertisers in advance; a company name list generation unitthat generates a company name list describing company names that are specified by a sponsorship credit display indicating sponsors of the broadcast program; and a CM information generation unitthat generates CM information related to the detected CMs by comparing the CM detection list with the company name list.

Commercial information generating device, commercial information generating method, and program

The chroma of a specific color can be emphasized with respect to an object without affecting the color of illumination light. An illumination light generator generates illumination light to be radiated to the object by adding or subtracting, to or from a reference illumination light spectrum that is an illumination spectrum serving as a reference, an element spectrum in accordance with designated conditions with respect to chroma adjustment from among a plurality of element spectra that are spectra for being added or subtracted to or from the reference illumination light spectrum and for performing chroma emphasis of a specific color without affecting an illumination light color.

Lighting control device, lighting control method, and program

A CM section within a broadcast program is detected with high accuracy. A CM section detection deviceincludes: a CM section detection unitthat detects one or more CM sections by comparing a volume of a broadcast program with a volume threshold, and generates detected CM sections representing the CM sections that have been detected; and a CM section correction unitthat corrects the detected CM sections based on a sponsorship credit display section that is a section which is included in the broadcast program and in which a sponsorship credit indicating a sponsor of the broadcast program is displayed.

Commercial section detection device, commercial section detection method, and program

A signal output unit outputs an acquired signal. A signal accumulation unit accumulates signals. A signal attribute value display unit displays a value of an attribute related to an element constituting a target represented by the acquired signal or a signal generation source in a state in which an instruction for changing the value of the attribute is able to be received. A changed attribute value acquisition unit acquires a changed value of the attribute when the instruction for changing the value of the attribute is received. A signal retrieval unit retrieves, from the signals accumulated in the signal accumulation unit, a signal similar to a changed signal when the value of the attribute has been changed on the basis of the changed value of the attribute acquired by the changed attribute value acquisition unit.

Signal retrieval apparatus, method, and program

A signal output unit outputs an acquired signal. A signal attribute value display unit displays a value of an attribute related to an element constituting a target represented by the acquired signal or a signal generation source in a state in which a change instruction of the value of the attribute is able to be received. A changed attribute value acquisition unit acquires a changed value of the attribute when the change instruction of the value of the attribute is received. A change unit changes the value of the attribute for which the change instruction has been received on the basis of the changed value of the attribute acquired by the changed attribute value acquisition unit. A changed signal output unit outputs a changed signal in which the value of the attribute has been changed.

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Tokyo, Japan

Kaoru Hiramatsu