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 sound source signal is estimated with high accuracy in a noise environment. A sound source signal estimation unit (15) estimates each sound source signal using a separation matrix from an observation signal obtained by collecting a mixed acoustic signal in which a plurality of sound source signals and diffusive noise are mixed by a microphone array formed by a plurality of microphones The separation matrix is configured to convert steering vectors from each sound source to the microphone into unit vectors and convert a spatial covariance matrix of the diffusive noise into a matrix including a diagonal matrix with a size of the number of sound sources.

Abstract: Provided is a cooking utensil having an excellent design. The cooking utensil includes a bottom and a side extending substantially upward from a perimeter of the bottom. The bottom includes a bottom laminate film on an inner surface thereof. The side includes a side laminate film that is different from the bottom laminate film on an inner surface thereof. The bottom laminate film and the side laminate film have different color tones.

Abstract: A sound source separation filter information estimation device (10) estimates a covariance matrix having information on a correlation between sound source spectra and information on a correlation between channels as information on sound source separation filter information for separating an individual sound source signal from a mixed acoustic signal.

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: A data processing system according to the present disclosure includes an acquisition unit and a production unit. The acquisition unit acquires on-site investigation data related to the space. The production unit changes, with respect to article data of the article to be placed in the space, at least one of a dimension, design, and a function on the basis of the on-site investigation data so as to achieve harmony with the space and produces article processing data for processing the article according to the article data after the change. The on-site investigation data includes space-related data related to the space. The production unit produces the article processing data on the basis of the space-related data.

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: Provided is a reverberation removal device that is highly accurate even in noisy environments and underdetermined conditions. Reverberation is removed by applying a plurality of reverberation prediction filters to an observation signal while switching the plurality of reverberation prediction filters according to each time frequency bin of the observation signal.

Abstract: The signal separation device includes: cross product calculation means receiving an input of an observed signal that is a mixture of a plurality of target signals, and calculating a cross product of the observed signal; model calculation means updating a parameter of a model for estimating the cross product with a predetermined algorithm using an inverse matrix of a matrix that represents an estimate of the cross product; inverse matrix calculation means calculating the inverse matrix of a matrix by a SIMD command when the parameter is updated; and separation means calculating the target signals using a matrix representing an estimate of the cross product, the updated parameter, and the observed signal.

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 sound source signal generation technology based on an optimization algorithm that enables high-speed processing of sound source extraction is provided. A sound source signal generation device includes an optimization unit that optimizes a separation matrix W(f)=[w1(f), . . . , wK(f), WZ(f)] using an observed signal x(f, t), the optimization unit includes an auxiliary function calculation unit that calculates an auxiliary function Vi(f) (i=1, . . . , K) according to a predetermined equation, a first separation filter calculation unit that calculates a separation filters wi(f) (i=1, . . . , K) using auxiliary functions Vi(f) (i=1, . . . , K) and Vz(f), and a second separation filter calculation unit that calculates a separation filter WZ(f) according to a predetermined equation when a convergence condition is satisfied.

Abstract: A sucking apparatus for a sheet having adhesiveness includes a first member including a first surface that sucks the sheet, and a second member including a second surface that surrounds an outer periphery of the first surface and is in contact with the sheet. The first surface includes a plurality of holes opened, and the first surface is subjected to surface treatment to allow the sheet to be less likely to stick to the first surface than to the second surface.

Abstract: A speech recognition unit converts an input utterance sequence into a confusion network sequence constituted by a k-best of candidate words of speech recognition results; a lattice generating unit generates a lattice sequence having the candidate words as internal nodes and a combination of k words among the candidate words for an identical speech as an external node, in which edges are extended between internal nodes other than internal nodes included in an identical external node, from the confusion network sequence; an integer programming problem generating unit generates an integer programming problem for selecting a path that maximizes an objective function including at least a coverage score of an important word, of paths following the internal nodes with the edges extended, in the lattice sequence; and the summary generating unit generates a high-quality summary having less speech recognition errors and low redundancy using candidate words indicated by the internal nodes included in the path selected b

Abstract: A determination device includes a memory, and processing circuitry coupled to the memory and configured to accept input of a plurality of sequences provided as candidates for a solution to one given input, and determine, for two sequences of the plurality of sequences, a sequence that has a higher accuracy than the other sequence of the two sequences, using a model expressed as a neural network.

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 device applies a convolutional separation filter, which is a combined filter of: a rear reverberation removal filter for suppressing a rear reverberation component from a mixed acoustic signal obtained by converting an observed mixed acoustic signal obtained by observing a source signal into a time-frequency domain; and a sound source separation filter for emphasizing components corresponding to source signals from the mixed acoustic signal, to a mixed acoustic signal string including the mixed acoustic signal and a delay signal of the mixed acoustic signal and estimates model parameters of a model for obtaining information corresponding to signals in which the rear reverberation component is suppressed and target signals emitted from target sound sources in the source signal are emphasized.