Effect of signal to noise ratio on a convolutional neural network for source ranging and environmental classification

Abstract

In ocean acoustics, simultaneous estimation of both source-receiver range and environment are complicated by low signal-to-noise ratio (SNR). Range and environment class can be found with a convolutional neural network (CNN), which is chosen because of its ability to find patterns in grid-structured data. The CNN acts on synthetic pressure time series data from a single receiver generated for four canonical environments: deep mud, mud over sand, sandy silt, and sand. Data were split into training and validation sets. The CNN is trained to identify source range and environmental class. The change in performance for different SNR values is evaluated by adding Gaussian-distributed noise. A study is done regarding the impact of having different SNR values for the training and validation datasets. The trained CNN is applied to pressure time series data measured on the APL-UW Intensity Vector Autonomous Receiver system at SBCEX. This study shows that performance depends more on the suitability of the training dataset than on the SNR value, implying that a CNN has potential to both estimate range and environmental class, even when there is low SNR. [Work supported from Office of Naval Research.]