Аңдатпа
Introduction: Hazardous acoustic event detection is critically important for intelligent surveillance, emergency response systems, and public safety monitoring applications. Accurate and real-time identification of dangerous sound events such as explosions, alarms, screaming, and weapon-related sounds can significantly improve situational awareness and accelerate emergency response in safety-critical environments. Methods: This study proposes a lightweight deep learning architecture for hazardous sound classification based on convolutional feature extraction and channel attention mechanisms. The proposed framework utilizes log-mel spectrogram representations as input and incorporates a TinyCNN backbone enhanced with squeeze-and-excitation channel attention modules to improve discriminative spectral feature learning while preserving computational efficiency. A custom balanced dataset consisting of eight hazardous acoustic classes, including crying, dog barking, emergency alarm, explosion, fire, glass breaking, screaming, and weapon-related sounds, was constructed with one thousand audio samples per class. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Results: Experimental results demonstrate that the proposed architecture achieves strong multi-class classification performance while maintaining real-time inference capability suitable for edge deployment scenarios. Quantitative evaluations confirm the effectiveness of the lightweight framework for hazardous acoustic event detection. Additional ablation studies indicate that the integration of channel attention mechanisms and spectrogram-based augmentation strategies substantially improves model robustness, feature discrimination, and generalization performance. Discussion: The obtained findings demonstrate that the proposed lightweight channel-attention-enhanced architecture provides an efficient and reliable solution for real-time hazardous sound detection in intelligent monitoring and public safety systems. The combination of computational efficiency and robust classification performance highlights the suitability of the proposed framework for deployment in resource-constrained and edge-based environments.