Abstract
In recent years, environmental sound classification has been a burgeoning subject of study. The unstructured nature of environmental sounds makes analysis challenging. However, sound signals have spectro-temporal patterns makes analysis easier using deep learning algorithms. Based on created spectrogram images and several feature extraction techniques Mel Spectrogram and Mel Frequency Cepstral Coefficients, we shall analyze sound signals using Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) in this paper. All the three models use UrbanSound8k based modified dataset and compared for the accuracy achieved on train and test datasets are: 98% and 88% for CNN, 95% and 86% for LSTM and 81% and 76% for ANN
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