Abstract: A rolling diaphragm pump includes a housing, a rolling diaphragm, a piston, a motor, and a ball screw. Rotational motion of the motor is converted into linear motion by the ball screw to cause the piston to reciprocate, and a volume of a pump chamber is changed by deformation of the rolling diaphragm, thereby sucking and discharging a transport fluid. The ball screw includes a screw shaft placed coaxially with an axis and rotationally driven about the axis relative to the housing by the motor, and a nut portion screwed to the screw shaft so as to be reciprocatable in an axial direction and having an insertion hole formed over an entirety thereof in the axial direction at a position eccentric to the axis. A guide shaft configured to guide reciprocation in the axial direction of the nut portion is inserted through the insertion hole of the nut portion and fixed to the housing.

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: An acoustic signal enhancement device includes: a spatiotemporal covariance matrix estimation unit 2 configured to estimate spatiotemporal covariance matrices Rf(j) and Pf(j); a reverberation suppression unit 3 configured to obtain a reverberation suppression filter Gf(j) of the sound source j using the estimated spatiotemporal covariance matrices Rf(j) and Pf(j) for each sound source j and to generate a reverberation suppression signal vector using the obtained reverberation suppression filter Gf(j) and the observation signal vector Xt,f; a sound source separation unit 4 configured to obtain an enhanced sound yt,f(j) of the sound source j and power of the sound source j using the generated reverberation suppression signal vector for each sound source j (where 1?j?J) corresponding to the target sound; and a control unit 5 configured to perform control such that processes of these units are repeatedly performed.

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: The connector includes a first insulator including a first retaining surface and a protrusion portion formed on and protruding from the first retaining surface, and a second insulator including a second retaining surface opposed to the first retaining surface and a recess portion formed in the second retaining surface and corresponding to the protrusion portion, the first insulator and the second insulator are connected to each other with the sheet type conductive member and the electric wire being held between the first retaining surface and the second retaining surface, and at least part of the protrusion portion is housed in the recess portion, whereby the conductor portion of the electric wire is electrically connected to the flexible conductor of the sheet type conductive member in the recess portion.

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: To sufficiently suppress noise and reverberation, a convolutional beamformer for calculating, at each time point, a weighted sum of a current signal and a past signal sequence having a predetermined delay and a length of 0 or more such that it increases a probability expressing a speech-likeness of an estimation signals based on a predetermined probability model is acquired where the estimation signals are acquired by applying the convolutional beamformer to frequency-divided observation signals corresponding respectively to a plurality of frequency bands of observation signals acquired by picking up acoustic signals emitted from a sound source, whereupon target signals are acquired by applying the acquired convolutional beamformer to the frequency-divided observation signals.

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: Provided is an acoustic signal enhancement device, including a time-space covariance matrix estimation unit 2 configured to estimate a time-space covariance matrix Rf(n),Pf(n) corresponding to a sound source n, using a power ?t,f(n) of the sound source n and an observation signal vector Xt,f composed of an observation signal xm,t,f from a microphone m; a reverberation suppression unit 3 configured to obtain a reverberation removal filter Gf(n) of the sound source n using the time-space covariance matrix Rf(n),Pf(n), and to generate a reverberation suppression signal vector Zt,f(n) corresponding to the observation signal xm,t,f for an emphasized sound of the sound source n using the reverberation removal filter Gf(n) and the observation signal vector Xt,f; and a sound source separation unit 4 configured to obtain an emphatic sound yt,f(n) of the sound source n and the power ?t,f(n) of the sound source n using the reverberation suppression signal vector Zt,f(n).

Abstract: Features are extracted from an observed speech signal including at least speech of multiple speakers including a target speaker. A mask is calculated for extracting speech of the target speaker based on the features of the observed speech signal and a speech signal of the target speaker serving as adaptation data of the target speaker. The signal of the speech of the target speaker is calculated from the observed speech signal based on the mask. Speech of the target speaker can be extracted from observed speech that includes speech of multiple speakers.

Abstract: Provided is a target sound extraction technique based on a steering vector generation method enabling instability in a calculation to be prevented when a neural network is trained by using an error back propagation method to reduce an estimation error of a beamformer. A target sound signal generation apparatus generates a target sound signal yt,f corresponding to a target sound included in an observed sound from an observed signal vector xt,f corresponding to the observed sound collected by using a plurality of microphones. The target sound signal generation apparatus includes a mask generation unit, a steering vector generation unit, a beamformer vector generation unit, and a target sound signal generation unit. The mask generation unit is configured as a neural network trained by using an error back propagation method.

Abstract: A time-variant noise spatial covariance matrix is estimated effectively. Using time-frequency-divided observation signals based on observation signals acquired by collecting acoustic signals emitted from one or a plurality of sound sources and mask information expressing the occupancy probability of a component of each of the time-frequency-divided observation signals that corresponds to each noise source, a time-independent first noise spatial covariance matrix corresponding to the time-frequency-divided observation signals and the mask information belonging to a long time interval is acquired for each noise source. Further, using the mask information of each of a plurality of different short time intervals, a mixture weight corresponding to each noise source in each short time interval is acquired.

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: A mask estimation apparatus for estimating mask information for specifying a mask used to extract a signal of a specific sound source from an input audio signal includes a converter which converts the input audio signal into embedded vectors of a predetermined dimension using a trained neural network model and a mask calculator which calculates the mask information by fitting the embedded vectors to a mixed Gaussian model.

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: A speech intelligibility calculating method is a method executed by a speech intelligibility calculating apparatus, the speech intelligibility calculating method including: a speech intelligibility calculating step of calculating a speech intelligibility that is an objective assessment index of a speech quality, based on a difference component between features found through an analysis of an input clean speech and an input enhanced speech, using one or more filter banks; and a step of outputting the speech intelligibility calculated at the speech intelligibility calculating step. This speech intelligibility calculating method is capable of calculating a speech intelligibility without any dependency on a speech enhancement method.

Abstract: An estimation device includes a memory, and processing circuitry coupled to the memory and configured to receive an input of an input audio signal that is an audio signal in which sounds from a plurality of sound sources are mixed, and an input of supplemental information, and output an estimation result of mask information that identifies a mask for extracting a sound of any one of the sound sources included in an entire or a part of a signal included in the input audio signal, the signal being identified by the supplemental information, cause a neural network to iterate a process of outputting the estimation result of the mask information, and cause the neural network to output an estimation result of the mask information for a different sound source, by inputting a different piece of the supplemental information to the neural network at each iteration.

Abstract: Provided are a measuring mechanism for measuring a predetermined fixed amount or an arbitrary amount of a liquid composition and discharging it, and a quantitative dispensing container and a quantitative applicator including such a mechanism. A measuring mechanism is provided that includes a base and a head defining a measurement space between the base and itself, the base and the head configured to be capable of changing the volume of the measurement space in response to a predetermined operation of the head including a measurement operation for increasing the volume of the measurement space and a discharge operation for reducing the volume of the measurement space. The base includes a first passageway for allowing the composition to flow from the outside into the measurement space, and a first check valve located on the first passageway. The first check valve of the base increases its sealability if there is a positive pressure in the measurement space.

Abstract: A time-variant noise spatial covariance matrix is estimated effectively. Using time-frequency-divided observation signals based on observation signals acquired by collecting acoustic signals emitted from one or a plurality of sound sources and mask information expressing the occupancy probability of a component of each of the time-frequency-divided observation signals that corresponds to each noise source, a time-independent first noise spatial covariance matrix corresponding to the time-frequency-divided observation signals and the mask information belonging to a long time interval is acquired for each noise source. Further, using the mask information of each of a plurality of different short time intervals, a mixture weight corresponding to each noise source in each short time interval is acquired.

Abstract: A signal processing device includes a power estimating unit that treats the feature quantity of a signal including reverberation as the input; inputs an observation feature quantity corresponding to an observation signal to a neural network which is learnt in such a way that the estimate value of the feature quantity corresponding to the power of the signal having reduced reverberation, from among the input signal, is output; and estimates the estimate value of the feature quantity corresponding to the power of the signal having reduced reverberation and corresponding to the observation signal. Moreover, the signal processing device includes a regression coefficient estimating unit that uses the estimate value of the feature quantity corresponding to the power as obtained as the estimation result by the power estimating unit, and estimates a regression coefficient of the autoregressive process for generating the observation signal.