Nec Corporation

Kazutoshi Sagi

Takahiro Toizumi

Yuzo Senda

Hirofumi Aoki

Yu Kiyokawa

Sakiko Mishima

Madhuri Mahendra Nagare

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.

Sound model generation device, sound model generation method, and recording medium

Provided is a learning device that can generate a feature deriving device capable of deriving, for an identical object, feature amounts which respectively express a feature of the object in different forms and which are mutually related. This learning device comprises: an acquisition unit that acquires first data and second data, with different forms of the object recorded therein; an encoder that derives a first feature amount from the first data; a conversion unit that converts the first feature amount to a second feature amount; a decoder that generates third data from the second feature amount; and a parameter updating unit that updates, on the basis of a comparison between the second data and the third data, the value of a parameter used in the derivation of the first feature amount, and the value of a parameter used in the generation of the third data.

Learning device, learning method, and storage medium

A learning device comprises: an acquisition unit that acquires a first feature amount derived by an encoder from data with an identification object recorded therein, the encoder being configured so as to derive, from data with the identical object in various forms recorded therein, feature amounts which are mutually convertible by a conversion using a conversion parameter that takes a value according to the difference in the forms; a conversion unit that generates a second feature amount by performing a conversion on the first feature amount using the conversion parameter value; and a parameter updating unit that updates the value of a sorting parameter used in sorting by a sorting means, which is configured to sort second feature amounts as input, such that if the second feature amount has been input, the sorting means outputs a result indicating, as a sorting destination, a class associated with the identification object.

An image processing device which is capable of accurately detect pixels covered by cloud shadows and remove effects of the cloud shadows in an images are provided. The device includes: a cloud transmittance calculation unit that calculates transmittance of the one or more clouds in an input image, for each pixel; a cloud height estimation unit that determines estimation of a height from the ground to each cloud in the input image to detect position of corresponding one or more shadows; an attenuation factor estimation unit that calculates attenuation factors for the direct sun irradiance by applying an averaging filter to the cloud transmittance calculated; and a shadow removal unit that corrects pixels affected by the one or more shadows, based on a physical model of a cloud shadow formation by employing the attenuation factors calculated and the position, and outputs an image which includes the pixels corrected.

Image processing device, image processing method and storage medium

An identification device according to one embodiment comprises: an acquisition unit that uses an encoder configured to derive, from data in which a single subject under different conditions has been recorded, feature values as a first feature value derived from data in which a subject to be identified has been recorded; a conversion unit that generates a second feature value by carrying out conversion using the conversion parameter on the first feature value; a discrete classification unit that carries out discrete classification on each of a plurality of third feature values including the second feature value and generates a plurality of discrete classification results indicating the results of the classification; a result derivation unit that derives, on the basis of the plurality of discrete classification results, identification result information; and an output unit that outputs the identification result information.

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Tokyo, Japan

Kazutoshi Sagi