Techniques for Recognising and Classifying Environmental Noise Using Deep Learning

Increasing urbanisation poses new challenges in mitigating noise pollution and preserving quality of life. In this study, we present an innovative approach for the classification of environmental noise, exploiting advanced Deep Learning (DL) techniques. By merging three different public datasets, we created a unified corpus to train and test a convolutional neural network (CNN), with the aim of efficiently recognising and classifying various noise events. The proposed approach overcomes the limitations of conventional methodologies, avoiding the need for data pre-processing that could alter sound characteristics. The experimental results demonstrate a significant improvement in classification accuracy, reaching 96.93% with the test set and 100% by applying a post-processing filter. These results emphasise the potential of DL in the treatment of environmental noise, offering new perspectives for signal processing and telecommunications.