Experimental design and analysis of audio tagging systems

Abstract

The goal of general-purpose audio tagging is to create systems capable of recognizing a variety of sounds. Including musical instruments, vehicles, animals, sounds generated by some sort of human activity etc. The motivation for research in the field of artificial sound understanding can be found in potential applications such as security, healthcare (hearing impairment), improvement in smart devices and various music related tasks. The main contribution of this work entails conducting extensive studies and comparisons between audio tagging systems using a huge dataset made of 11 073 audio recordings. In this thesis, we have carried out two sets of experiments. First, we have examined Deep Convolutional neural networks (CNN) and 3 of its variants (Convolutional Recurrent Neural Network (CRNN), Gated Convolutional Recurrent Neural Network (GCRNN) and Gated Convolutional Neural Networks (GCNN)) using Log-Mel Spectrogram features. We have supported our analysis and discussion with numerous statistical tests to analyze and compare the effect of the abovementioned features and models on the tagging performance. Our experimental findings indicate

that

our systems capture diverse set of sound events, with various confidences. Moreover, Convolutional Recurrent Neural Network (CRNN) significantly outperforms the other models. Second, motivated by the fact that the individual models produce diverse predictions, we have investigated the effect of ensemble learning using a technique known as stacking. Our analysis shows that stacking provides a proper amalgamation of the individual learners, resulting in better handling the diverse nature of the events. Keywords: Audio Tagging, Deep Learning, Machine leaning, Ensemble Learning, Stacking, Feature Extraction, Statistical Tests.