Anomalous sound detection apparatus, degree-of-anomaly calculation apparatus, anomalous sound generation apparatus, anomalous sound detection training apparatus, anomalous signal detection apparatus, anomalous signal detection training apparatus, and methods and programs therefor

A degree-of-anomaly calculation apparatus that calculates a degree of anomaly of input sound, the input sound being input, so as to detect whether or not the input sound is anomalous sound, the degree-of-anomaly calculation apparatus comprising:

• an acoustic feature extraction unit that extracts an acoustic feature of the input sound by using a feature amount extraction function;
• and
• a degree-of-anomaly calculation unit that calculates a degree of anomaly of the input sound by using the acoustic feature that is extracted, wherein
• the feature amount extraction function is based on probability distribution modeling sound which can include normal sound and anomalous sound, probability distribution modeling normal sound, and probability distribution modeling anomalous sound that is input, and
• the threshold is set by using degrees of anomaly obtained from the normal sound.

An anomalous sound detection training apparatus comprising:

• a first function updating unit that updates a feature amount extraction function and an feature amount inverse transformation function, the feature amount extraction function and the feature amount inverse transformation function being input, based on an optimization index of a variational autoencoder;
• an acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using the feature amount extraction function that is input;
• a normal sound model updating unit that updates a normal sound model by using the acoustic feature that is extracted;
• a threshold updating unit that obtains a threshold φρ corresponding to a false positive rate ρ, the false positive rate p having a predetermined value, by using the training data for normal sound and the feature amount extraction function that is input;
• and
• a second function updating unit that updates the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φρ that is obtained, by using the acoustic feature of normal sound that is extracted and an acoustic feature of anomalous sound that is input, wherein
• processing of the first function updating unit, processing of the acoustic feature extraction unit, processing of the normal sound model updating unit, and processing of the second function updating unit are repeatedly performed by using the feature amount extraction function, the feature amount extraction function being updated by the second function updating unit, as an input.

An anomalous signal detection apparatus that detects whether or not an input signal, the input signal being input, is an anomalous signal, the anomalous signal detection apparatus comprising:

• a feature amount extraction unit that extracts a feature amount of the input signal by using a feature amount extraction function;
• a degree-of-anomaly calculation unit that calculates a degree of anomaly of the input signal by using the feature amount that is extracted;
• and
• a determination unit that determines whether or not the input signal is an anomalous signal based on the degree of anomaly that is obtained and a threshold, wherein
• the feature amount extraction function is based on probability distribution modeling signals which can include a normal signal and an anomalous signal, probability distribution modeling normal signals, and probability distribution modeling anomalous signals that are input, and
• the threshold is set by using degrees of anomaly obtained from the normal signal.

An anomalous signal detection training apparatus comprising:

• a first function updating unit that updates a feature amount extraction function and an feature amount inverse transformation function, the feature amount extraction function and the feature amount inverse transformation function being input, based on an optimization index of a variational autoencoder;
• a feature amount extraction unit that extracts a feature amount of a normal signal based on training data for normal signals by using the feature amount extraction function that is input;
• a normal signal model updating unit that updates a normal signal model by using the feature amount that is extracted;
• a threshold updating unit that obtains a threshold φρ corresponding to a false positive rate ρ, the false positive rate p having a predetermined value, by using the training data for normal signals and the feature amount extraction function that is input;
• and
• a second function updating unit that updates the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φρ that is obtained, by using the feature amount of a normal signal that is extracted and a feature amount of an anomalous signal that is input, wherein
• processing of the first function updating unit, processing of the feature amount extraction unit, processing of the normal signal model updating unit, and processing of the second function updating unit are repeatedly performed by using the feature amount extraction function, the feature amount extraction function being updated by the second function updating unit, as an input.

An anomalous sound detection method for detecting whether or not input sound, the input sound being input, is anomalous sound, the anomalous sound detection method comprising:

• an acoustic feature extraction step in which an acoustic feature extraction unit extracts an acoustic feature of the input sound by using a feature amount extraction function;
• a degree-of-anomaly calculation step in which a degree-of-anomaly calculation unit calculates a degree of anomaly of the input sound by using the acoustic feature that is extracted;
• and
• a determination step in which a determination unit determines whether or not the input sound is anomalous sound based on the degree of anomaly that is obtained and a threshold, wherein
• the feature amount extraction function is based on probability distribution modeling sound which can include normal sound and anomalous sound, probability distribution modeling normal sound, and probability distribution modeling anomalous sound that is input, and
• the threshold is set by using degrees of anomaly obtained from the normal sound.

An anomalous sound detection training method comprising:

• a first function updating step in which a first function updating unit updates a feature amount extraction function and an feature amount inverse transformation function, the feature amount extraction function and the feature amount inverse transformation function being input, based on an optimization index of a variational autoencoder;
• an acoustic feature extraction step in which an acoustic feature extraction unit extracts an acoustic feature of normal sound based on training data for normal sound by using the feature amount extraction function that is input;
• a normal sound model updating step in which a normal sound model updating unit updates a normal sound model by using the acoustic feature that is extracted;
• a threshold updating step in which a threshold updating unit obtains a threshold φρ corresponding to a false positive rate ρ, the false positive rate p having a predetermined value, by using the training data for normal sound and the feature amount extraction function that is input;
• and
• a second function updating step in which a second function updating unit updates at least one of the feature amount extraction function that is updated and the feature amount inverse transformation function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φρ that is obtained, by using the acoustic feature of normal sound that is extracted and an acoustic feature of anomalous sound that is input, wherein
• processing of the first function updating step, processing of the acoustic feature extraction step, processing of the normal sound model updating step, and processing of the second function updating step are repeatedly performed by using the feature amount extraction function and the feature amount inverse transformation function, the feature amount extraction function and the feature amount inverse transformation function being updated by the first function updating step, as inputs.

An anomalous signal detection method for detecting whether or not an input signal, the input signal being input, is an anomalous signal, the anomalous signal detection method comprising:

• a feature amount extraction step in which a feature amount extraction unit extracts a feature amount of the input signal by using a feature amount extraction function;
• a degree-of-anomaly calculation step in which a degree-of-anomaly calculation unit calculates a degree of anomaly of the input signal by using the feature amount that is extracted;
• and
• a determination step in which a determination unit determines whether or not the input signal is an anomalous signal based on the degree of anomaly that is obtained and a threshold, wherein
• the feature amount extraction function is based on probability distribution modeling signals which can include a normal signal and an anomalous signal, probability distribution modeling normal signals, and probability distribution modeling anomalous signals that are input, and
• the threshold is set by using degrees of anomaly obtained from the normal signal.