Feature Extraction of Engine Sound with Time Variability by Machine Learning

Abstract

The time-frequency analysis images are classified based on a machine learning model that combines a Convolutional Neural Network and a Quasi-Recurrent Neural Network, and features in each frequency component of engine sound with time variability are visualized by using Gradient-weighted Class Activation Mapping, which is a middle layer extraction method of a neural network.