Abstract: A learning device including a control unit that performs learning of a mathematical model of a learning target using a first audio signal and a second audio signal, in which the learning target is a disentangling model that estimates, on the basis of an embedding vector of one of two input audio signals and an embedding vector of an other, a high similarity set that is a set in which a degree of similarity between an embedding vector of the one and an embedding vector of the other in an audio feature amount space that is a space indicating a distribution of a feature amount of an embedding vector of an audio signal is relatively high, and a low similarity set that is a set in which a degree of the similarity in the audio feature amount space is relatively low.

Abstract: An envelope sequence is provided that can improve approximation accuracy near peaks caused by the pitch period of an audio signal. A periodic-combined-envelope-sequence generation device according to the present invention takes, as an input audio signal, a time-domain audio digital signal in each frame, which is a predetermined time segment, and generates a periodic combined envelope sequence as an envelope sequence. The periodic-combined-envelope-sequence generation device according to the present invention comprises at least a spectral-envelope-sequence calculating part and a periodic-combined-envelope generating part. The spectral-envelope-sequence calculating part calculates a spectral envelope sequence of the input audio signal on the basis of time-domain linear prediction of the input audio signal.

Abstract: A sound signal processing device that obtains a two-channel stereo encoding target signal to be subjected to stereo encoding from a two-channel stereo input sound signal, the sound signal processing device including a signal mixing unit that obtains, as an encoding target signal, for each channel, a signal obtained by performing weighted addition on an input sound signal of the channel and an input sound signal of the other channel, the signal being closer to the input sound signal of the channel as an absolute value of an inter-channel time difference of the two-channel stereo input sound signal is smaller.

Abstract: A sound signal processing device that obtains a two-channel stereo encoding target signal to be subjected to stereo encoding from a two-channel stereo input sound signal, the sound signal processing device including a signal mixing unit that obtains, as an encoding target signal, for each channel, a signal obtained by performing weighted addition on an input sound signal of the channel and an input sound signal of the other channel, the signal being closer to the input sound signal of the channel as the two-channel stereo input sound signal is likely to be a single sound source.

Abstract: A sound signal processing device that obtains a two-channel stereo encoding target signal to be subjected to stereo encoding from a two-channel stereo input sound signal, the sound signal processing device including a signal mixing unit that obtains, as an encoding target signal, for each channel, a signal obtained by performing weighted addition on an input sound signal of the channel and an input sound signal of the other channel, the signal being closer to the input sound signal of the channel as a bit rate of the stereo encoding is higher.

Abstract: An aspect of the present invention provides a learning device including: a neural network that converts an acoustic time series that is a time series representing a sound into feature data expressed in a predetermined format required by a downstream task; and an update unit that updates the neural network on the basis of an execution result of the downstream task using the feature data, in which the neural network includes: a feature extraction unit that converts an input acoustic time series into an intermediate feature tensor that is a third-order tensor indicating features of the acoustic time series and is a tensor having time, frequency, and channel; and an intermediate network that executes processing of converting a representation of the intermediate feature tensor into a representation of a second-order tensor having time and direct product amounts that are amounts indicating a direct product of frequency and channel, and processing of acquiring, for each direct product amount of the second-order tens

Abstract: A coding method and a decoding method are provided which can use in combination a predictive coding and decoding method which is a coding and decoding method that can accurately express coefficients which are convertible into linear prediction coefficients with a small code amount and a coding and decoding method that can obtain correctly, by decoding, coefficients which are convertible into linear prediction coefficients of the present frame if a linear prediction coefficient code of the present frame is correctly input to a decoding device.

Abstract: An autocorrelation calculation unit 21 calculates an autocorrelation RO(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R?O(i) obtained by multiplying a coefficient wO(i) by the autocorrelation RO(i). It is assumed here, for each order i of some orders i at least, that the coefficient wO(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

Abstract: A coding method and a decoding method are provided which can use in combination a predictive coding and decoding method which is a coding and decoding method that can accurately express coefficients which are convertible into linear prediction coefficients with a small code amount and a coding and decoding method that can obtain correctly, by decoding, coefficients which are convertible into linear prediction coefficients of the present frame if a linear prediction coefficient code of the present frame is correctly input to a decoding device.

Abstract: A learning device includes: spectrogram generation circuitry 1 that generates a spectrogram from an input sound signal; patch generation circuitry 2 that divides the generated spectrogram to generate a plurality of patches; a mask processing circuitry 3 that selects some patches as masked patches; reconstruction circuitry 4 that obtains a plurality of reconstructed patches by reconstructing the plurality of patches by processing of an encoder and a decoder in a transformer serving as a deep learning model by using visible patches other than the masked patches among the plurality of patches and mask tokens corresponding to the masked patches; and parameter update circuitry 5 that updates a parameter of the encoder and a parameter of the decoder such that the masked patches approach reconstructed patches corresponding to the masked patches. The number of layers of the decoder is three or more.

Abstract: A learning device includes: spectrogram generation circuitry 1 that generates a spectrogram from a first sound signal and generates a target spectrogram from a second sound signal; patch generation circuitry 2 that divides the spectrogram to generate a plurality of patches and divides the target spectrogram to generate a plurality of target patches; mask processing circuitry 3 that selects some patches as masked patches; reconstruction circuitry 4 that obtains a plurality of reconstructed patches by reconstructing the plurality of patches by processing of an encoder and a decoder by using visible patches other than some patches among the plurality of patches and mask tokens; and parameter update circuitry 5 that updates parameters such that target patches corresponding to the masked patches among the plurality of target patches approach reconstructed patches corresponding to the masked patches among the plurality of reconstructed patches.

Abstract: An autocorrelation calculation unit 21 calculates an autocorrelation RO(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R?O(i) obtained by multiplying a coefficient wO(i) by the autocorrelation RO(i). It is assumed here, for each order i of some orders i at least, that the coefficient wO(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

Abstract: Provided is an abnormality estimation device capable of appropriately determining normal data appearing less frequently as normal. The abnormality estimation device includes an estimation unit that estimates an anomaly degree of an acoustic signal, by using an abnormality estimation model that is optimized while using a set of normal sounds and is optimized so as to minimize a difference between an anomaly degree of a normal sound appearing more frequently and an anomaly degree of a normal sound appearing less frequently.

Abstract: An aspect of the present invention provides a learning device including: a neural network that converts an acoustic time series that is a time series representing a sound into feature data expressed in a predetermined format required by a downstream task; and an update unit that updates the neural network on the basis of an execution result of the downstream task using the feature data, in which the neural network includes: a feature extraction unit that converts an input acoustic time series into an intermediate feature tensor that is a third-order tensor indicating features of the acoustic time series and is a tensor having time, frequency, and channel; and an intermediate network that executes processing of converting a representation of the intermediate feature tensor into a representation of a second-order tensor having time and direct product amounts that are amounts indicating a direct product of frequency and channel, and processing of acquiring, for each direct product amount of the second-order tens

Abstract: According to an aspect of the present invention, there is provided a sound estimation model acquisition device including a model acquisition unit configured to acquire a mathematical model that estimates an estimated time series that is a time series satisfying a predetermined estimation condition from one or a plurality of time series on a basis of a first sound time series that is a time series indicating a first sound, a second sound time series that is a time series indicating a second sound, and first difference information indicating at least a partial difference between the first sound and the second sound, in which the mathematical model is a mathematical model that estimates the estimated time series on the basis of an input time series that is a time series indicating an input sound and second difference information that is information indicating at least a partial difference between the input time series and the estimated time series, and the estimation condition is a condition that a difference be

Abstract: The number of conversational tests required for evaluation of acoustic quality of the ICC system is reduced. An evaluation value converting device 3 evaluates the quality of a conversation made across a near-end acoustic area 100 and a far-end acoustic area 200 inside a vehicle in which a plurality of acoustic areas are predetermined. A voice signal collected by a microphone M2 disposed in the far-end acoustic area 200 is emitted from a speaker S1 disposed in the near-end acoustic area 100.

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: An anomaly detection technique which realizes high accuracy while reducing cost required for normal model learning is provided. An anomaly detection apparatus includes an anomaly degree estimating unit configured to estimate an anomaly degree indicating a degree of anomaly of anomaly detection target equipment from sound emitted from the anomaly detection target equipment (hereinafter, referred to as anomaly detection target sound) based on association between a first probability distribution indicating distribution of normal sound emitted from one or more pieces of equipment different from the anomaly detection target equipment and normal sound emitted from the anomaly detection target equipment (hereinafter, referred to as normal sound for adaptive learning).

Abstract: A technology of accurately coding and decoding coefficients which are convertible into linear prediction coefficients even for a frame in which the spectrum variation is great while suppressing an increase in the code amount as a whole is provided. A coding device includes: a first coding unit that obtains a first code by coding coefficients which are convertible into linear prediction coefficients of more than one order; and a second coding unit that obtains a second code by coding at least quantization errors of the first coding unit if (A?1) an index Q commensurate with how high the peak-to-valley height of a spectral envelope is, the spectral envelope corresponding to the coefficients which are convertible into the linear prediction coefficients of more than one order, is larger than or equal to a predetermined threshold value Th1 and/or (B?1) an index Q? commensurate with how short the peak-to-valley height of the spectral envelope is, is smaller than or equal to a predetermined threshold value Th1?.