Deep learning has emerged as a powerful technique for speech enhancement, particularly in security systems where audio signals are often degraded by non-stationary noise. Traditional signal processing methods struggle in such conditions, making it difficult to detect critical sounds like gunshots, alarms, and unauthorized speech. This study investigates a hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) to enhance speech quality and improve sound classification accuracy in noisy security environments. The proposed model is trained and validated using real-world datasets containing diverse noise distortions, including VoxCeleb for benchmarking speech enhancement and UrbanSound8K and ESC-50 for sound classification. Performance is evaluated using industry-standard metrics such as Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and Signal-to-Noise Ratio (SNR). The architecture includes multi-layered neural networks, residual connections, and dropout regularization to ensure robustness and generalizability. Additionally, the paper addresses key challenges in deploying deep learning models for security applications, such as computational complexity, latency, and vulnerability to adversarial attacks. Experimental results demonstrate that the proposed DNN + GAN-based approach significantly improves speech intelligibility and classification performance in high-interference scenarios, offering a scalable solution for enhancing the reliability of audio-based 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.

Real-time acoustic signal enhancement in non-stationary noise remains challenging, especially for sensing systems that must be causal, low latency, and interpretable. This paper proposes a unified Bayesian–Kalman estimator (UBKE) that analytically fuses a spectral Bayesian MMSE estimator with a temporal Kalman state-space tracker via a variance optimal fusion weight α(k). The UBKE is derived in closed form from a shared probabilistic model, yielding an estimator that adaptively balances spectral and temporal information as noise statistics evolve. We establish theoretical properties including bias–variance behavior, stability conditions, and analytical expressions for output SNR, SNR improvement, and log-spectral distortion. Under typical short-time processing (32 ms frame, 50% overlap), the proposed method operates causally with an algorithmic delay of 16 ms and real-time factors below 0.5 on a modern CPU. Analytical and empirical results show that UBKE achieves up to +9.8 dB ΔSNR and approximately +17% PESQ improvement over a baseline MMSE estimator in highly non-stationary noise, while also reducing log-spectral distortion. Experiments on standard speech corpora with real-world noise confirm that the empirical trends closely follow the analytical predictions, with small mismatch between theoretical and measured gains. The UBKE thus offers an interpretable, low-latency, and quantitatively validated framework for real-time acoustic sensing and speech enhancement, and can serve as a foundation for future hybrid model-driven and learning-augmented systems.

Reverberation and background noise remain persistent obstacles to achieving clear and intelligible speech in enclosed environments. Conventional data-driven or purely empirical dereverberation systems often perform well only under training conditions but lack robustness and physical interpretability when exposed to new acoustic spaces. To address these limitations, this paper proposes a physics-informed speech enhancement algorithm that integrates analytical room acoustics modeling with a descriptor-guided optimization framework. The method employs virtual field simulations based on the Helmholtz equation to estimate key acoustic descriptors, reverberation time (RT60), direct-to-reverberant ratio (DRR), and clarity index (C50), which are then used to adaptively control a model-informed dereverberation filter. This hybrid formulation bridges physical modeling and signal processing, allowing the algorithm to minimize late reverberation energy while maintaining spectral fidelity. Experimental results across multiple simulated and real-room conditions demonstrate measurable improvements over baseline methods, achieving average gains of +6.4 dB in SNR, +1.2 in PESQ, and +0.13 in STOI, along with reduced RT60 and enhanced clarity. The proposed approach offers both computational efficiency and interpretability, making it suitable for real-time deployment in teleconferencing, hearing-assistive, and smart audio applications.