Abstract: A threshold acquisition apparatus acquires a threshold for determining whether an anomaly score acquired from a target sound is normal or anomalous. The threshold acquisition apparatus includes: an allowable number setting unit that sets an allowable number of times such that the number of anomaly scores determined to be anomalous included in a set of anomaly scores per predetermined section length, which is a part of time-series acoustic signals that do not include an anomalous sound, does not exceed the allowable number of times; and a threshold estimation unit that estimates a threshold candidate such that the number of sections determined to be anomalous per predetermined section length, which is a part of time-series acoustic signals, satisfies a predetermined criterion by using the allowable number of times, and acquires the threshold candidate as the threshold.

Abstract: By appropriately registering a sound with an arbitrary length to be registered, erroneous determination is suppressed. A normal sound registration apparatus 1 extracts a feature amount with a fixed-length from a time-series acoustic signal with a variable arbitrary length. A frequency conversion unit 12 acquires a time-series frequency signal obtained by frequency-converting the time-series acoustic signal. A feature extraction unit 14 extracts a feature amount from the time-series acoustic signal. The feature extraction unit 14 is optimized to extract, from the time-series acoustic signal including at least a known normal sound and an unknown normal sound, a feature amount which reflects a feature of the unknown normal sound, and to extract, from the time-series acoustic signal including at least an anomalous sound and a normal sound, a feature amount which reflects a feature of the anomalous sound.

Abstract: A direction-of-arrival estimation device for achieving direction-of-arrival estimation which is robust against an SNR and in which an application range of a learning model is specific is provided.

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 input of a first observation signal corresponding to an incoming signal from a first direction is received, an angular rotation operation of the first observation signal is performed to obtain a second observation signal corresponding to an incoming signal from a second direction that is different from the first direction and the second observation signal is added to a set of training data.

Abstract: An estimation apparatus includes a state estimation unit that estimates a state from an observed amount using an encoder, an observed amount estimation unit that estimates an observed amount from a state using a decoder, and a future observed amount estimation unit that estimates a future observed amount, which is a value to which the observed amount changes with time, using a parameter K representing time evolution, where a parameter of the encoder, a parameter of the decoder, and the parameter K are optimized simultaneously.

Abstract: Even in a case where an amount of training data is small, a caption for an audio signal is generated with high accuracy. An audio caption generation apparatus (1) generates a caption for an input target audio. A training data storage (10) stores a training data set including a set of an audio signal and a caption corresponding thereto. An audio similarity calculation unit (11) calculates similarity between the target audio and each audio signal of training data. A guidance caption retrieval unit (12) acquires a plurality of captions corresponding to an audio signal similar to the target audio. A caption generation unit (13) generates a caption for the target audio by determining words in order from the head on the basis of the acquired captions.

Abstract: Erroneous detection of erroneously determining a normal sound to be anomalous is suppressed. A registered normal sound detection apparatus 2 calculates anomaly score of an observed signal. A feature extraction unit 24 extracts a feature amount with a fixed-length from a time-series acoustic signal with an arbitrary length. An anomaly score calculation unit 25 corrects the anomaly score calculated from the observed signal so that the higher similarity score between the observed signal and a registered normal sound is, the smaller a value of the anomaly score is. The anomaly score calculation unit 25 calculates the similarity score by a similarity score function learned by using the feature amount extracted from the time-series acoustic signal including at least a normal sound by the feature extraction unit 24.

Abstract: Provided is an acoustic signal processing technique for performing a signal transformation suitable for desired signal processing (e.g., sound source enhancement processing) on a signal, and then performing the desired signal processing on the transformed signal. An acoustic signal processing device performs signal processing M which is a desired target on an input acoustic signal x.

Abstract: To highly accurately estimate an environment in which an acoustic signal is collected without inputting auxiliary information. An input circuitry (21) inputs a target acoustic signal, which is an estimation target. An estimation circuitry (22) correlates an acoustic signal and an explanatory text for explaining the acoustic signal to estimate an environment in which the target acoustic signal is collected. The environment is an explanatory text for explaining the target acoustic signal obtained by the correlation. The correlation is so trained as to minimize a difference between an explanatory text assigned to the acoustic signal and an explanatory text obtained from the acoustic signal by the correlation.

Abstract: Provided is an abnormal data generation device capable of generating highly accurate abnormal data. The abnormal data generation device includes an abnormal data generation unit for generating pseudo generated data of abnormal data that has, in the same latent space, a normal distribution as a normal data generation model and an abnormal distribution expressed as a complementary set of the normal distribution and that is optimized such that pseudo generated data cannot be discriminated from observed actual abnormal data by a latent variable sampled from the abnormal distribution.

Abstract: An anomaly degree calculation device 200 includes an anomaly degree calculation unit 201 that calculates an anomaly degree on a basis of a feature amount extracted from target data that is a calculation target of the anomaly degree. The anomaly degree calculation unit 201 calculates the anomaly degree on a basis of a similarity degree of the target data and registration data registered in advance. The similarity degree is calculated in consideration of a degree to which a frame constituting the target data and a frame constituting the registration data are similar to each other.

Abstract: To provide an anomalous sound detection training technique by which a feature amount extraction function for detecting anomalous sound can be generated irrespective of whether training data for anomalous signals is available or not.

Abstract: A mask to enhance speech emitted from a speaker is estimated from an observation signal, the mask is applied to the observation signal, and thereby a post-mask speech signal is acquired. The mask is estimated from a feature obtained by combining a feature for speaker recognition extracted from the observation signal and a feature for generalized mask estimation extracted from the observation signal.

Abstract: An input of a first observation signal corresponding to an incoming signal from a first direction is received, an angular rotation operation of the first observation signal is performed to obtain a second observation signal corresponding to an incoming signal from a second direction that is different from the first direction and the second observation signal is added to a set of training data.

Abstract: An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

Abstract: There is provided a neural network learning technique for learning a parameter of a probability density function representing the distribution of data with high accuracy using an autoencoder. A neural network learning apparatus, wherein ? is a parameter of a probability density function q?(x) representing distribution of data x, and M? is a neural network that is an autoencoder that learns the parameter ?, the neural network learning apparatus including: a neural network calculation unit that calculates an output value M?(xn) of the neural network from learning data xn using the parameter ? for n=1, . . .

Abstract: Accuracy of unsupervised anomalous sound detection is improved using a small number of pieces of anomalous sound data. A threshold deciding part (13) calculates an anomaly score for each of a plurality of pieces of anomalous sound data, using a normal model learned with normal sound data and an anomaly model expressing the pieces of anomalous sound data, and decides a minimum value among the anomaly scores as a threshold. A weight updating part (14) updates, using a plurality of pieces of normal sound data, the pieces of anomalous sound data and the threshold, weights of the anomaly model so that all the pieces of anomalous sound data are judged as anomalous, and probability of the pieces of normal sound data being judged as anomalous is minimized.

Abstract: A direction-of-arrival estimation device for achieving direction-of-arrival estimation which is robust against an SNR and in which an application range of a learning model is specific is provided.

Abstract: Erroneous detection of erroneously determining a normal sound to be anomalous is suppressed. A registered normal sound detection apparatus 2 calculates anomaly score of an observed signal. A feature extraction unit 24 extracts a feature amount with a fixed-length from a time-series acoustic signal with an arbitrary length. An anomaly score calculation unit 25 corrects the anomaly score calculated from the observed signal so that the higher similarity score between the observed signal and a registered normal sound is, the smaller a value of the anomaly score is. The anomaly score calculation unit 25 calculates the similarity score by a similarity score function learned by using the feature amount extracted from the time-series acoustic signal including at least a normal sound by the feature extraction unit 24.