Environmental Noise

Environmental noise research focuses on understanding and mitigating the impact of unwanted sounds on various aspects of human life and technological systems. Current research employs machine learning models, including deep neural networks (like autoencoders and ResNets) and novel architectures based on Fourier analysis, to analyze and reduce noise in audio recordings, create dynamic noise maps for urban planning, and improve the robustness of sound-based applications. This work is significant for improving the accuracy of speech recognition, enhancing the reliability of machine health monitoring, and informing urban planning strategies to reduce noise pollution and its associated health and economic consequences.