Towards Robust Environmental Sound Classification: A Deep Learning Approach Leveraging Time-Frequency Representations

Intelligent sound classification is essential for a number of applications, from autonomous systems to smart city surveillance Recent advancements in The accuracy and efficiency of sound classification tasks can be significantly increased with machine learning. This paper presents a novel approach for environment sound classification that integrates the powerful audio processing capabilities of Librosa with the deep learning frameworks of PyTorch. Evaluate our method on the large dataset of ambient noises, and the findings demonstrate a significant improvement in classification accuracy compared to baseline approaches and how various feature extraction methods and network settings affect performance, as well as the difficulties that arise while training and evaluating models. The outcomes highlight how well Librosa's extensive audio feature extraction and PyTorch's adaptable deep learning capabilities work together to push the boundaries of environmental sound classification.