תמונות העטיפה

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

שמור ב:
הוצא לאור ב:Electronics vol. 14, no. 13 (2025), p. 2643-2668
מחבר ראשי: Mensah, Samuel Yaw
מחברים אחרים: Zhang, Tao, Mahmud, Nahid AI, Geng Yanzhang
יצא לאור:
MDPI AG
נושאים:
Mean square errors
Accuracy
Datasets
Deep learning
Performance evaluation
Classification
Multilayers
Artificial neural networks
Real time
Signal processing
Generative adversarial networks
Speech processing
Audio recordings
Machine learning
Access control
Sound
Statistical analysis
Regularization
Artificial intelligence
Security systems
Fourier transforms
Signal to noise ratio
Intelligibility
Neural networks
Decision making
Network latency
Recurrent neural networks
Methods
Audio signals
Surveillance
Kalman filters
Speech
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Resumen: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.
ISSN:2079-9292
DOI:10.3390/electronics14132643
Fuente:Advanced Technologies & Aerospace Database