Adaptive Neuro-Fuzzy Systems for Real-Time Noise Reduction in Urban Soundscapes

The exponential growth of urbanization has intensified the need for effective noise reduction techniques in urban soundscapes. This paper introduces an innovative approach employing Adaptive Neuro-Fuzzy Systems (ANFS) for real-time noise reduction. The proposed system integrates the adaptability and learning capabilities of neural networks with the robustness and interpretability of fuzzy logic. A comprehensive dataset comprising various urban soundscapes was used to train and validate the model. The ANFS architecture was meticulously designed to adjust itself according to the fluctuating acoustic characteristics of urban settings, achieving an unprecedented level of noise reduction efficiency. The system's performance was evaluated against traditional noise reduction methods, demonstrating significant improvements in terms of Signal-to-Noise Ratio (SNR), perceptual noise reduction, and computational efficiency. The simulation results showed promising resultsindicating its potential for practical applications.