🌊 AquaSignature: Underwater Threat Detection using CRNN

• AquaSignature is a deep learning-powered system designed to recognize and classify underwater acoustic signals such as submarines, torpedoes, and marine animals. The core objective is to detect foreign underwater threats that cross a defined acoustic frequency barrier, making it highly relevant for naval defense and surveillance.

🎯 Main Objective

• To automatically detect and classify acoustic anomalies — such as submarines or torpedoes from other countries — based on their marine sound signatures. If the barrier of predefined frequency is crossed, the system raises an alert, helping defense forces monitor underwater activities in real time.

🔍 Use Case

• This project simulates a naval surveillance system that can:
• Distinguish between natural marine sounds (e.g., dolphins, ships)
• Identify suspicious patterns like torpedo or submarine movement
• Alert when sound frequency breaks the “safe zone” of underwater activity

🧠 Features

• Processes .wav files containing marine acoustic data
• Extracts MFCC features and visualizes spectrograms
• Uses a CRNN (CNN + LSTM) architecture for spatial + temporal learning
• Detects when a frequency threshold is crossed
• Classifies sounds as torpedo, ship, dolphin, submarines etc.

🛠️ Technologies

• Python
• Librosa (audio feature extraction)
• TensorFlow/Keras (deep learning model)
• Scikit-learn (label processing)
• Matplotlib (visualization)

🔐 Real-World Applications

• Naval submarine detection systems
• Underwater mine or torpedo tracking
• Marine research and anomaly detection
• Coastal security monitoring