International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Al-Powered Noise Pollution Monitoring and Reduction System

Author(s) Mr. Guduru Harshavardhan, Mr. Teneti Shiva Reddy, Mr. Kurchetti Sai Manishankar, Dr.S.Ramchandra Reddy, h M.pramod
Country India
Abstract Audio classification is an important aspect of numerous applications, such as environmental monitoring, noise pollution control, and intelligent acoustic systems. In this work, a deep learning-based method for classifying audio signals through spectrogram-based feature extraction is investigated. The audio signals are converted into Mel-spectrogram representations using the melspectrogram function of Librosa, yielding a time-frequency representation that corresponds to human hearing. These spectrograms are considered image- like data, allowing Convolutional Neural Networks (CNNs) to be applied for classification. The audio tags are mapped into categorical data, allowing for multi-class classification. The deep learning model is trained to identify patterns in spectrograms, distinguishing correctly between different environmental sounds, like natural sounds (e.g., rain, birdsong) and dangerous noise (e.g., traffic, industrial noise). This approach allows for efficient and automatic detection of noise sources, supporting effective noise pollution management. By combining spectrogram-based feature extraction with machine learning algorithms, this research proves the feasibility of creating intelligent noise monitoring systems. The results show the power of deep learning in environmental sound classification, opening up possibilities for applications in urban planning, smart cities, and real-time noise control systems.
Keywords Deep Learning, Melspectogram, Librosa, Convolutional Neural Network.
Field Sociology > Health
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-23
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E-ISSN 2582-2160
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