Abstract:
Sonar Shield is an intelligent dual module AI solution developed with the primary objective of solving one of the key problems of contemporary signal processing technologies that relates to the loss of valuable information due to noise in two unique yet equally essential environments: under-water acoustic environment and satellite communication. Two unique modules are incorporated into one universal platform, which can be used by scientists, engineers, and specialists working in the corresponding fields of expertise. The Aqua Noise Reduction Module addresses underwater communication interference from sources like ocean currents, marine life, and vessel vibrations. It utilizes a CNN-based classifier to identify specific noise types within a signal, then automatically selects the most effective deep learning architecture such as Autoencoders, U-Nets, and WaveNet to perform denoising. The resulting highquality audio is then objectively validated using SNR, PESQ, and STOI performance metrics. The second module is the Satellite Signal Noise Reduction Module. The problem addressed in this module is that of error propagation within long-distance satellite communication that can be affected by environmental noise, electromagnetic interferences, and degraded communication channels. To solve this problem, BiGru Decoder sequence models along with FEC, namely Reed-Solomon coding algorithm, will be used to detect and correct errors in telemetry data obtained from satellite signals. The Sonar Shield project is a modular and scalable signal enhancement framework built using Python, TensorFlow, and PyTorch[3]. It integrates a user-friendly interface with advanced data visualization and automated reporting, providing a cross-platform solution for Windows, Linux, and macOS that demonstrates the practical effectiveness of AI in signal processing.
