Deep Learning for Acoustic Signal Analysis

Abstract

Acoustic signal analysis is essential to underwater monitoring, sonar imaging, and maritime security but suffers from persistent difficulties of high background noise, multipath propagation and limited labelled data. This chapter describes how recent developments in deep learning (DL) have overcome these limitations to providing robust features extraction and accurate prediction of complex acoustic patterns. It presents the fundamental properties of underwater signals and describes layers such as convolutional, recurrent LSTM (or long short-term memory), and transformer-based networks. Foundational mathematics and the noise reduction, spectral feature extraction, and data augmentation preprocessing strategies are presented along with publicly accessible databases. Case studies show that the proposed approach can be used in submarine detection, marine mammal identification, and anomaly monitoring, with the performance evaluated on some common metrics including accuracy, precision, recall, and F-measure. The comparison results clearly show the superiority of DL over traditional signal processing and attention is given to current bottlenecks, such as lack of data, generalization, and interpretability. The chapter finishes by discussing the way forward highlighting multimodal integration, federated learning, real-time edge deployment, and sustainable practices and ultimately providing a valuable reference for researchers and practitioners in underwater acoustics.