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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.\n\n## 🎯 Main Objective\n- 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.\n\n## 🔍 Use Case\n- This project simulates a naval surveillance system that can:\n- Distinguish between natural marine sounds (e.g., dolphins, ships)\n- Identify suspicious patterns like torpedo or submarine movement\n- Alert when sound frequency breaks the “safe zone” of underwater activity\n\n\n## 🧠 Features\n-  Processes .wav files containing marine acoustic data\n- Extracts MFCC features and visualizes spectrograms\n- Uses a CRNN (CNN + LSTM) architecture for spatial + temporal learning\n- Detects when a frequency threshold is crossed\n- Classifies sounds as torpedo, ship, dolphin, submarines etc.\n\n## 🛠️ Technologies\n- Python\n- Librosa (audio feature extraction)\n- TensorFlow/Keras (deep learning model)\n- Scikit-learn (label processing)\n- Matplotlib (visualization)\n \n## 🔐 Real-World Applications\n- Naval submarine detection systems\n- Underwater mine or torpedo tracking\n- Marine research and anomaly detection\n- Coastal security monitoring\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdhanushi2620%2Faquasignature","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdhanushi2620%2Faquasignature","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdhanushi2620%2Faquasignature/lists"}