Integrated Noise Reduction and Signal Classification Model for Rescue Operations in Noisy Disaster Scenarios

In this study, we propose a deep learning-based noise reduction and signal classification model designed to detect and classify victim signals in noisy disaster environments. The proposed model combines a CNN-based noise reduction model and an STFT-based signal classification model to enable real-time detection and classification of victim signals. This approach allows effective extraction of victim signals even in various noisy conditions, supporting rapid rescue operations in disaster situations. The experimental results show that the proposed model achieved 87.87% accuracy and an Fl-score of 0.8573, demonstrating high performance across a range of noisy con-ditions. Furthermore, the model was trained and evaluated on a dataset that reflects real disaster environments, proving its excellent performance in detecting victim signals. This study is expected to make a significant technical contribution to the prompt and accurate detection of victims in disaster situations.