# Vehicle Detection using Machine Listening and Deep Learning

Machine Learning can work very well with image recognition, but it can also be used to recognize audio patterns. Machine listening can be used to identify audio patterns of different entities like car engine, human speaking, nature sounds etc. Aim of this thesis is to create a program which will read the labelled audio files, extract features from them, feed features to a sequential model, which will then classify these audio files of vehicles based on their sounds and then further categorize them as either light weight, medium weight, heavy weight, rail-bound or two-wheeled vehicle using the applications of machine listening and deep learning in the field of acoustics. It will also classify unlabelled test data files on a pre-trained model. Additionally, to increase the speed and performance of the software program and algorithm, the program could be executed on a High Performance Computing (HPC) system containing cluster which in turn will have many compute servers also called as nodes which will enable faster and parallel computing. This thesis provides an base model for the vehicle classification giving both advantages and disadvantages along with possibility for future extensions.