Time-Frequency Fused Underwater Acoustic Source Localization Based on Contrastive Predictive Coding

Abstract

We propose a time-frequency fused underwater acoustic source localization method based on self-supervised learning with contrastive predictive coding. Firstly, two feature extractors are trained to solve the pretext task (predicting the future) based on the unlabeled acoustic signals in the time and frequency domains, respectively. Next, encoders with frozen parameters are taken from the trained feature extractors for extracting the high-level features in the time and frequency domains. During the training stage of the source localizer, features extracted by two encoders are concatenated together as a time-frequency fused feature vector and fed into a 3-layer multi-layer perceptron for solving the downstream task (source localization) based on a tiny labeled dataset. This method is assessed on the SWellEx-96 Experiment and compared with several alternative methods. The performance analysis confirms the promising performance of our proposed method.