Noise Decomposition Based on VGG and LSTM Networks

This paper provides a sampling system noise decomposition strategy based on a visual geometry group (VGG) convolutional neural network and a long short-term memory (LSTM) prediction network. Three typical noise components in acquisition systems—Gaussian white noise, quantization noise, and device 1/f noise—are considered in our analysis. The proposed approach is separated into two parts. The first is the classification system, which adopts the independent component analysis (ICA) method to preprocess the samples before employing the VGG16 network to perform autonomous classification of noise components. The estimation of noise power is the second task, in which 14 features of the acquired signals are fed to the LSTM prediction network to estimate the power of each noise component. The simulation results demonstrate that the suggested method has reliable classification results for mixed signals comprising the three noise components and can estimate the power of each component accurately.