Disruptive Situations Detection on Public Transports through Speech Emotion Recognition

In this thesis, we describe a study on the application of Machine Learning and Deep Learning methods for Voice Activity Detection (VAD) and Speech Emotion Recognition (SER). The study is in the context of a European project whose objective is to detect disruptive situations in public transports. To this end, we developed an architecture, implemented a prototype and ran validation tests on a variety of options. The architecture consists of several modules. The denoising module was realized through the use of a filter and the VAD module through an open-source toolkit, while the SER system was entirely developed in this thesis. For SER architecture we adopted the use of two audio features (MFCC and RMS) and two kind of classifiers, namely CNN and SVM, to detect emotions indicative of disruptive situations such as fighting or shouting. We aggregated several models through ensemble learning. The ensemble was evaluated on several datasets and showed encouraging experimental results, even compared to the baselines of the state-of the-art. The code is available at: https://github.com/helemanc/ambient-intelligence