Abstract: A mask estimation apparatus includes processing circuitry configured to estimate, for a target segment to be processed among a plurality of segments of a continuous time, a first mask which is an occupancy ratio of a target signal to an observation signal of the target segment, based on a first feature obtained from a plurality of the observation signals of the target segment recorded at a plurality of locations, and estimate a parameter for modeling a second feature and a second mask which is an occupancy ratio of the target signal to the observation signal based on an estimation result of the first mask in the target segment and the second feature obtained from the plurality of the observation signals of the target segment.

Abstract: A signal filtering device includes: an information generation unit that generates feature information on related information on a target signal; an extraction unit that extracts mask information from a mixed signal including the target signal on the basis of the feature information; and a mask processing unit that estimates the target signal from the mixed signal using the mask information. The information generation unit may encode the related information into a multidimensional vector and generate a linear transformation result of the multidimensional vector as the feature information. The information generation unit may encode the related information into a first multidimensional vector, encode the mixed signal into a second multidimensional vector, derive a similarity in time series between the first multidimensional vector and the second multidimensional vector, and generate a result of a weighted sum of the similarity in time series and the mixed signal as the feature information.

Abstract: A signal filtering device includes: a separation unit that separates a predetermined number of possibility signals from a mixed signal as possibilities of a target signal; an encoding unit that encodes related information of the target signal into a first feature vector and encodes the predetermined number of possibility signals into the predetermined number of second feature vectors; and a selection unit that derives a similarity between the first feature vector and the second feature vector for each of the possibility signals, and selects a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals. The selection unit may derive an inner product of the first feature vector and the second feature vector as the similarity. The predetermined number of possibility signals may be voice signals associated with the predetermined number of sound sources.

Abstract: A signal processing device (10) includes: a speech enhancement unit (11) that generates, from an observation signal, an enhancement signal in which a voice of a speaker is enhanced; an original sound addition unit (12) that adds the observation signal to the enhancement signal; and a speech recognition unit (13) that performs speech recognition on the enhancement signal to which the observation signal is added by the original sound addition unit (12).

Abstract: There is provided an acoustic signal enhancement device that receives, as an input, a recording sound obtained by frequency division and updates parameters, the device including: assuming that a switch weight is a weight indicating a ratio of a classification to which a recording sound at each timing belongs in classifications of spatial states where a recording sound temporally changes, a beamformer unit that performs beamformer processing based on a weighted spatial covariance matrix which is updated and updates an auxiliary estimation value of a target sound; a switch unit that updates the switch weight and power of a target sound based on the updated auxiliary estimation value and outputs an estimation value of the target sound; and a weighted spatial covariance estimation unit that updates the weighted spatial covariance matrix based on the updated switch weight and the power.

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 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: 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 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: 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.

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 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: A signal analysis device includes a memory and processing circuitry coupled to the memory and configured to obtain, for a spatial covariance matrix Rj (j is an integral number equal to or larger than 1 and equal to or smaller than J) for modeling spatial characteristics of J (J is an integral number equal to or larger than 2) source signals that are present in a mixed manner, a simultaneous decorrelation matrix P as a matrix in which all PHRjP are diagonal matrices, or/and Hermitian transposition PH thereof, as a parameter for decorrelating components corresponding to the J source signals for observation signal vectors based on observation signals acquired at I (I is an integral number equal to or larger than 2) different positions.

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 analysis device includes an estimation unit that models a sound source position occurrence probability matrix Q using a product of a sound source position probability matrix B and a sound source existence probability matrix A, and estimates at least one of the sound source position probability matrix B and the sound source existence probability matrix A based on the modeling, the sound source position occurrence probability matrix Q being composed of probabilities of arrival of a signal from each sound source position candidate per frame, which is a time section, with respect to a plurality of sound source position candidates. The sound source position probability matrix B being composed of probabilities of arrival of a signal from each sound source position candidate per sound source with respect to a plurality of sound sources.

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.