Abstract: An electric work vehicle includes an on-board charger, a power reception connector enabling attachment and detachment of a charging cable that is to be connected to the on-board charger, and an energizing cable that connects the power reception connector and the on-board charger to each other. Charging ports that enable attachment and detachment of the power reception connector are provided in both a left side portion of a body and a right side portion of the body. The charging ports are provided at positions higher than upper ends of travel devices.

Abstract: The acoustic feature extraction means 82 extracts an acoustic feature, using predetermined parameters, from an acoustic pattern obtained as a result of processing on an acoustic signal. The language vector calculation means 83 calculates a language vector from a given label that represents an attribute of a source of the acoustic signal and that is associated with the acoustic pattern. The similarity calculation means 84 calculates a similarity between the acoustic feature and the language vector. The parameter update means 85 learns parameters so that the similarity becomes larger, and updates the predetermined parameters to the parameters obtained by learning.

Abstract: An anomaly detection apparatus extracts a circumstantial feature value for anomaly detection corresponding to a circumstantial feature value for learning from other modal signal for anomaly detection different in modal from acoustic, calculates a signal pattern feature related to an acoustic signal of anomaly detection target based on the acoustic signal of anomaly detection target, the circumstantial feature value for anomaly detection and a signal pattern model learned based on an acoustic signal for learning and the circumstantial feature value for learning calculated from other modal signal for learning, and calculates an anomaly score for performing an anomaly detection of the acoustic signal of anomaly detection target based on the signal pattern feature.

Abstract: An anomaly detection apparatus extracts a circumstantial feature value for anomaly detection corresponding to a circumstantial feature value for learning from other modal signal for anomaly detection different in modal from acoustic, calculates a signal pattern feature related to an acoustic signal of anomaly detection target based on the acoustic signal of anomaly detection target, the circumstantial feature value for anomaly detection and a signal pattern model learned based on an acoustic signal for learning and the circumstantial feature value for learning calculated from other modal signal for learning, and calculates an anomaly score for performing an anomaly detection of the acoustic signal of anomaly detection target based on the signal pattern feature.

Abstract: A pattern recognition apparatus includes: a model storage part that stores a model(s) generated by using transfer path information indicating a difference of transfer paths of a signal(s) for training, additional to the signal(s) for training, and a pattern recognition part that inputs an input signal and transfer path information indicating a difference of transfer paths of the input signal, and performs pattern recognition of the input signal by using the model(s).

Abstract: An anomaly detecting device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; calculating a series probability distribution being a probability distribution which the series feature amount follows; storing a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to the reference probability distribution; and detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data.

Abstract: A sound signal corresponding to a sound emitted by a sound source is inputted to a frequency analysis unit (101), and the frequency analysis unit (101) performs frequency analysis of the sound signal and generates a spectrogram. Spectrogram template information indicating spectrograms corresponding to each of at least a plurality of predetermined movement speeds, the spectrograms being for cases in which the sound source is moved, is stored in a model storage unit (102). An estimation unit (103) estimates the movement speed of the sound source on the basis of the spectrogram generated by the frequency analysis unit (101) and the plurality of spectrograms indicated by the spectrogram template information stored in the model storage unit (102).

Abstract: The acoustic feature extraction means 82 extracts an acoustic feature, using predetermined parameters, from an acoustic pattern obtained as a result of processing on an acoustic signal. The language vector calculation means 83 calculates a language vector from a given label that represents an attribute of a source of the acoustic signal and that is associated with the acoustic pattern. The similarity calculation means 84 calculates a similarity between the acoustic feature and the language vector. The parameter update means 85 learns parameters so that the similarity becomes larger, and updates the predetermined parameters to the parameters obtained by learning.

Abstract: A signal processing device according to an exemplary aspect of the present invention includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: extract, from a target signal, a feature value representing a feature of the target signal; calculate, based on the extracted feature value, signal element bases capable of representing a plurality of object signals by linear combination, and information of the linear combination, weights each representing intensities of the plurality of object signals included in the target signal; derive, based on the weights, information of a target object signal included in the target signal, the target object signal being at least one type of the plurality of object signals; and output information of the target object signal.

Abstract: A purpose of the present disclosure is to provide a source separation method, a non-transitory computer readable medium, and a source separation apparatus. The source separation apparatus includes an input means for inputting mixture data obtained by mixing a plurality of data; and a matrix decomposition means for separating the input mixture data by estimating a mixing/unmixing matrix, a basis matrix for each source, an activations matrix for each source and a reliability vector for each source, and a means for unmixing of input mixture data using the estimated matrices from the matrix decomposition means to estimate the sources.

Abstract: An anomaly detection apparatus comprises a pattern storage part, first long time-span feature extraction part, a pattern feature calculation part, and a score calculation part. The pattern storage part stores a signal pattern model trained based on an acoustic signal for training in a first time-span and a feature for training being calculated from signal for training in a second time-span that is longer than first time-span. The first long time-span feature extraction part extracts a long time-span feature for detection associated with the feature for training from a signal being a detection target. The pattern feature calculation part calculates a signal pattern feature related to a signal being a detection target based on the signal being a detection target, the feature for detection, and the signal pattern model. The score calculation part calculates score to detect anomaly in the signal being detection target.

Abstract: A signal processing device according to an exemplary aspect of the present invention includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: extract, from a target signal, a feature amount of the target signal; repeatedly calculate, based on the extracted feature amount, a signal element basis representing a plurality of types of object signals by linear combination, and information of the linear combination, a weight representing an intensity of an object signal included in the target signal, and update information of the linear combination, based on the feature amount, the signal element basis, and the weight, until a predetermined condition is satisfied; derive, based on the weight, information of a target object signal being at least one type of the object signal included in the target signal; and output information of the target object signal.

Abstract: An anomaly detection apparatus extracts a circumstantial feature value for anomaly detection corresponding to a circumstantial feature value for learning from other modal signal for anomaly detection different in modal from acoustic, calculates a signal pattern feature related to an acoustic signal of anomaly detection target based on the acoustic signal of anomaly detection target, the circumstantial feature value for anomaly detection and a signal pattern model learned based on an acoustic signal for learning and the circumstantial feature value for learning calculated from other modal signal for learning, and calculates an anomaly score for performing an anomaly detection of the acoustic signal of anomaly detection target based on the signal pattern feature.

Abstract: A pattern recognition apparatus includes: a model storage part that stores a model(s) generated by using transfer path information indicating a difference of transfer paths of a signal(s) for training, additional to the signal(s) for training, and a pattern recognition part that inputs an input signal and transfer path information indicating a difference of transfer paths of the input signal, and performs pattern recognition of the input signal by using the model(s).

Abstract: The information processing apparatus acquires a plurality of training data, extracts feature data from each training data, and divides the training data using the extracted feature data into a plurality of data clusters. The information processing apparatus performs for each data cluster: extracting a feature matrix from the training data in the data cluster; and perform matrix decomposition on the feature matrix to generate a first basis matrix. The information processing apparatus performs dimensionality reduction on a concatenation of a plurality of the first basis matrices to generate a second basis matrix. The information processing apparatus performs matrix decomposition on a concatenation of a plurality of the feature matrices using the second basis matrix, thereby generating an activations matrix.

Abstract: A memory is configured to store instructions and a processor is configured to execute the instructions, and the instructions comprise: propagation path estimation apparatus comprises calculating feature value from output signals from a plurality of sensors, and determining a propagation path corresponding to the feature value.

Abstract: A signal processing device includes: a basis storage that stores an acoustic event basis group; a model storage that stores an identification model, as a feature amount, a combination of activation levels of spectral; an identification signal analysis unit that, upon input of a spectrogram of an acoustic signal for identification, performs sound source separation on the spectrogram by using a spectral basis set that is obtained by appending spectral bases corresponding to an unknown acoustic event that is an acoustic event other than the acoustic event specified as a detection target to the acoustic event basis group and causing only unknown spectral bases within the spectral basis set to be learned, and thereby calculating activation levels of spectral bases of the acoustic events in the spectrogram of the acoustic signal for identification; and a signal identify unit that identifies an acoustic event included in the acoustic signal for identification.

Abstract: A signal processing device includes a prior distribution storage that stores a prior distribution group that is a set of prior distributions representing representative spectral shapes of spectral bases of an acoustic event specified as a detection target, and a basis generation unit that, using as input a spectrogram for basis generation, generates an acoustic event basis group that is a set of the spectral bases of the acoustic event specified as a detection target. The basis generation unit performs sound source separation on the spectrogram for basis generation and thereby generates an acoustic event basis group, using respective prior distributions included in a prior distribution group as a constraint for spectral bases of a corresponding acoustic event.

Abstract: A signal processing device according to an exemplary aspect of the present invention includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: extract, from a target signal, a feature value representing a feature of the target signal; calculate, based on the extracted feature value, signal element bases capable of representing a plurality of object signals by linear combination, and information of the linear combination, weights each representing intensities of the plurality of object signals included in the target signal; derive, based on the weights, information of a target object signal included in the target signal, the target object signal being at least one type of the plurality of object signals; and output information of the target object signal.

Abstract: A signal detection device 10 includes a compression unit 11 which compresses an activation matrix by adding to each column an element of the row corresponding to a basis mapped to the information of the same acoustic element in an activation matrix computed by non-negative matrix factorization using a basis matrix, using the information of acoustic elements making up an acoustic event, mapped to the basis constituting the basis matrix.