Apparatus and method for sound anomaly detection based on non-compression convolutional neural network

Abstract

According to the present invention, a sound anomaly detection method includes acquiring, by an audio unit, a noise from an inspection target; generating, by a data processing unit, an input value that is a feature vector matrix including a plurality of feature vectors from the noise; generating, by a detection unit, a restored value imitating the input value through a detection neural network that is a deep neural network learned for the input value; determining, by the detection unit, whether a restoration error indicating a difference between the input value and the restored value is greater than or equal to a calculated reference value; and determining, by the detection unit, that there is an anomaly in the inspection target when determining that the restoration error is greater than or equal to the reference value.