Abstract: An image processing apparatus capable of preventing failure in decoding additional information embedded in a print. The apparatus performs image processing for generating a print in which additional information is embedded. A halftone pattern used for halftone processing is set out of a plurality of halftone patterns. It is determined whether or not, in a case where the set halftone pattern is used for the halftone processing, encoded patterns generated based on the additional information can be uniformly reproduced from a color plane with which the encoded patterns are synthesized. In a case where it is determined that the encoded patterns cannot be uniformly reproduced, a warning notification is performed for notifying before outputting a print in which the additional information is embedded, a user that the print is a print which can cause failure in decoding the additional information.

Abstract: An estimation apparatus 10 is a signal processing apparatus for processing an acoustic signal and estimates an observation signal of a virtual microphone arranged virtually from an input observation signal of a real microphone using a deep learning model having a neural network (NN) 11.

Abstract: An image processing apparatus capable of changing a halftone pattern set to a coding pattern that will be synthesized with an image to another halftone pattern that can uniformly generate the coding pattern when the coding pattern cannot be uniformly generated in performing a halftone process using the halftone pattern set to the cording pattern. A setting unit sets a halftone pattern used in a halftone process from among a plurality of halftone patterns. A determination unit determines whether a coding pattern can be uniformly generated when the halftone process is performed using the halftone pattern set by the setting unit as the coding pattern to be synthesized with an image to be printed. A changing unit changes a halftone pattern used in the halftone process to another halftone pattern capable of uniformly generating the coding pattern when the determination unit determines that the coding pattern cannot be uniformly generated.

Abstract: A learning device includes a conversion unit, a combination unit, an extraction unit, and an update unit. The conversion unit converts a mixed sound, of which sound sources for each component are known, into embedding vectors for each sound source using an embedding neural network. The combination unit combines the embedding vectors using a combination neural network to obtain a combined vector. The extraction unit extracts a target sound from the mixed sound and the combined vector using an extraction neural network. The update unit updates parameters of the embedding neural network such that a loss function calculated based on information regarding the sound sources for each component of the mixed sound and the target sound extracted by the extraction unit is optimized.

Abstract: A signal processing device includes processing circuitry configured to receive an input of extraction target information indicating which audio class of an audio signal is to be extracted from a mixture audio signal constituted by a mixture of audio signals of a plurality of audio classes, and output a result of extracting the audio signal of the audio class indicated by the extraction target information from the mixture audio signal, with a neural network by using a feature value of the mixture audio signal and the extraction target information.

Abstract: An image processing device configured to convert image data of a first resolution into image data of a second resolution higher than the first resolution includes at least one memory, and at least one processor in communication with the at least one memory and configured to cooperate with the at least one memory to calculate a direction and an intensity of an edge from the image data of the first resolution, and determine a pattern for the image data of the second resolution to be replaced by pixels of the image data of the first resolution, based on the direction and the intensity of the edge.

Abstract: A signal processing apparatus includes a neural network (“NN”), a sorting unit, and a spatial covariance matrix calculation unit. The NN converts a mixed signal, in which sounds of a plurality of sound sources input by a plurality of channels are mixed, into a separated signal separated into a signal for each sound source as a signal in a time domain as it is and outputs the separated signal. The sorting unit sorts, for the separated signal of each channel output from the NN, the separated signal of each channel such that the plurality of sound sources of a plurality of the separated signals are aligned among the plurality of channels. The spatial covariance matrix calculation unit calculates a spatial covariance matrix corresponding to each sound source in accordance with the separated signal for each channel output from the sorting unit and sorted.

Abstract: An audio signal processing apparatus (10) includes a first auxiliary feature conversion unit (12) and a second auxiliary feature conversion unit (13) that convert a plurality of signals relating to processing of an audio signal of a target speaker into a plurality of auxiliary features for the plurality of signals using a plurality of auxiliary neural networks corresponding to the plurality of signals, and an audio signal processing unit (11) that estimates information regarding an audio signal of the target speaker included in a mixed audio signal using a main neural network based on an input feature of the mixed audio signal and the plurality of auxiliary features, wherein the plurality of signals relating to processing of the audio signal of the target speaker are two or more pieces of information of different modalities.

Abstract: To make it possible to set an appropriate threshold value for detecting a print defect by taking into consideration an impurity included in a sheet. A lower limit value of a threshold value used for detection of a print defect is found and stored in advance. Then, whether or not a threshold value for detecting a print defect, which is set based on user instructions, is an appropriate threshold value is determined by using the lower limit value stored in advance.

Abstract: A signal processing device according to an embodiment of the present invention includes: a conversion unit configured to convert an input mixed acoustic signal into a plurality of first internal states, a weighting unit configured to generate a second internal state which is a weighted sum of the plurality of first internal states based on auxiliary information regarding an acoustic signal of a target sound source when the auxiliary information is input, and generate the second internal state by selecting one of the plurality of first internal states when the auxiliary information is not input, and a mask estimation unit configured to estimate a mask based on the second internal state.

Abstract: A speech recognition system includes a plurality of microphones to receive acoustic signals including speech signals, an input interface to generate multichannel inputs from the acoustic signals, one or more storages to store a multichannel speech recognition network, wherein the multichannel speech recognition network comprises mask estimation networks to generate time-frequency masks from the multichannel inputs, a beamformer network trained to select a reference channel input from the multichannel inputs using the time-frequency masks and generate an enhanced speech dataset based on the reference channel input and an encoder-decoder network trained to transform the enhanced speech dataset into a text. The system further includes one or more processors, using the multichannel speech recognition network in association with the one or more storages, to generate the text from the multichannel inputs, and an output interface to render the text.

Abstract: A speech recognition system includes a plurality of microphones to receive acoustic signals including speech signals, an input interface to generate multichannel inputs from the acoustic signals, one or more storages to store a multichannel speech recognition network, wherein the multichannel speech recognition network comprises mask estimation networks to generate time-frequency masks from the multichannel inputs, a beamformer network trained to select a reference channel input from the multichannel inputs using the time-frequency masks and generate an enhanced speech dataset based on the reference channel input and an encoder-decoder network trained to transform the enhanced speech dataset into a text. The system further includes one or more processors, using the multichannel speech recognition network in association with the one or more storages, to generate the text from the multichannel inputs, and an output interface to render the text.