Urban Sound Classification using CNN

This document illustrates a simple audio classification model based on deep learning. We address the problem of classifying the type of sound based on short audio signals and their generated spectrograms, from labeled sounds belonging to 10 different classes during model training. In order to meet this challenge, we use a model based on Convolutional Neural Network (CNN). The audio was processed with Mel-frequency Cepstral Coefficients (MFCC) into what's commonly known as Mel spectrograms, and hence, was transformed into an image. Our final CNN model achieved 91% accuracy on the testing dataset.