An anomalous sound detection training apparatus comprising: a frequency domain conversion unit adapted to convert each of input training data for normal sound and every kind of sound input data into frequency domains; a first function updating unit adapted to receive the input training data for normal sound and to update a feature amount extraction function and a feature amount inverse transformation function, the feature amount extraction function and the feature amount inverse transformation function being updated, based on an optimization index of a variational autoencoder; an acoustic feature extraction unit adapted to extract an acoustic feature of normal sound based on the input training data for normal sound by using the updated feature amount extraction function; a normal sound model updating unit adapted to update a normal sound model by using the acoustic feature that is extracted; a threshold updating unit adapted to obtain a threshold φ ρ corresponding to a false positive rate p, the false positive rate ρ having a predetermined value, by using the input training data for normal sound and the updated feature amount extraction function; an anomalous sound data sampling unit adapted to generate an acoustic feature of anomalous sound, whereby the acoustic feature of anomalous sound is generated by using probability distribution followed by anomalous sound being a probability distribution obtained by removing a probability distribution modeling normal sound from a probability distribution modeling every kind of mechanical sound and by using the updated inverse transformation function; a second function updating unit adapted to update the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φ ρ that is obtained, by using the acoustic feature of normal sound that is extracted and the acoustic feature of anomalous sound, the anomalous sound detection training apparatus being adapted to repeatedly perform processing of the first function updating unit, processing of the acoustic feature extraction unit, processing of the normal sound model updating unit, processing of the second function updating unit, processing of the threshold updating unit and processing of the anomalous sound data sampling unit by using the feature amount extraction function, the feature amount extraction function being updated by the second function updating unit, as an input. a frequency domain conversion unit adapted to convert each of input training data for normal sound and every kind of sound input data into frequency domains; a first function updating unit adapted to receive the input training data for normal sound and to update a feature amount extraction function and a feature amount inverse transformation function, the feature amount extraction function and the feature amount inverse transformation function being updated, based on an optimization index of a variational autoencoder; an acoustic feature extraction unit adapted to extract an acoustic feature of normal sound based on the input training data for normal sound by using the updated feature amount extraction function; a normal sound model updating unit adapted to update a normal sound model by using the acoustic feature that is extracted; a threshold updating unit adapted to obtain a threshold φ ρ corresponding to a false positive rate p, the false positive rate ρ having a predetermined value, by using the input training data for normal sound and the updated feature amount extraction function; an anomalous sound data sampling unit adapted to generate an acoustic feature of anomalous sound, whereby the acoustic feature of anomalous sound is generated by using probability distribution followed by anomalous sound being a probability distribution obtained by removing a probability distribution modeling normal sound from a probability distribution modeling every kind of mechanical sound and by using the updated inverse transformation function; a second function updating unit adapted to update the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φ ρ that is obtained, by using the acoustic feature of normal sound that is extracted and the acoustic feature of anomalous sound, the anomalous sound detection training apparatus being adapted to repeatedly perform processing of the first function updating unit, processing of the acoustic feature extraction unit, processing of the normal sound model updating unit, processing of the second function updating unit, processing of the threshold updating unit and processing of the anomalous sound data sampling unit by using the feature amount extraction function, the feature amount extraction function being updated by the second function updating unit, as an input.