Deep Learning-Based Speech Enhancement for Robust Sound Classification in Security Systems

Deep learning has become an effective technique in speech enhancement that has enhanced the classification of sound in security systems. Traditional approaches are ineffective in noisy conditions, so detecting important sound events like gunshots, alarms, and unauthorized speeches becomes difficult. This work aims to investigate the use of deep learning methods such as CNN, RNN, and GAN to improve the sound classification in security solutions. The study focuses on the different datasets of real-world noise distortions and then applies signal processing techniques to the speech signals to classify them. There are some quantitative measures like perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and signal-to-noise ratio (SNR) enhancements. Also, the issues like computational, deployment, and security issues of AI models are discussed. In this way, the proposed deep learning framework for improving speech signals before classification is expected to increase the reliability of security systems in critical areas. The results presented in the paper can be used to design improved security systems that can function in conditions characterized by high interference.