Anomaly detection apparatus, probability distribution learning apparatus, autoencoder learning apparatus, data transformation apparatus, and program

A probability distribution learning apparatus comprising a processor configured to execute operations comprising:

• receiving normal sounds for learning, wherein the normal sounds for learning are normal sounds emitted from one or more pieces of equipment that are different from anomaly detection target equipment;
• and
• learning a first probability distribution, wherein the first probability distribution indicates distribution of normal sound emitted from one or more pieces of equipment that are different from the anomaly detection target equipment, from the normal sounds for learning,
• wherein a variable x of the first probability distribution q1(x;θ) is a variable indicating input data generated from the normal sound emitted from the one or more pieces of equipment different from the anomaly detection target equipment,
• the variable x is expressed as x=fK(fK-1( . . . (f1(z0)) . . . )) using transformations fi(i=1, . . . , K, K is an integer of 1 or greater, and inverse transformations fil exist for the transformations fi) and a latent variable z0,
• q0(z0) is set as a probability distribution of the latent variable z0,
• a probability density q1(x;θ) of the input data x is calculated using a probability density q0(z0) of the latent variable z0=fi−1(f2−1( . . . (fK−1(x)) . . . )) corresponding to the input data x, and
• at least one inverse transformation of the transformations fi(i=1, . . . , K) is adaptive batch normalization;
• and
• transmitting the first probability distribution to an application configured to train anomaly detection neural network using the first probability distribution as anomaly training data.

A data transformation apparatus comprising a processor configured to execute operations comprising:

• retrieving input data, wherein the input data corresponds to domain data of a first domain;
• calculating a latent variable from the input data using a first predetermined function having an inverse function;
• and
• calculating output data, wherein the output data correspond to domain data of a second domain, from the latent variable using a second predetermined function having an inverse function,
• wherein the first predetermined function and the second predetermined function are derived from a third predetermined function which transforms the latent variable z0 into a variable x;
• and
• transmitting the output data to an application configured to train neural network model using the output data as training data.