Using Convolutional Autoencoders to Improve Classification Performance

Abstract

This thesis combines convolutional neural networks with autoencoders, to form a convolutional autoencoder. Several techniques related to the realisation of a convolutional autoencoder are investigated, and an attempt is made to use these models to improve performance on an audio-based phone classification task.