Violence Detection in Real-Life Audio Signals Using Lightweight Deep Neural Networks

Abstract

The automatic detection of violent behavior in acoustic data has become an important research area due to its growing application potential in various surveillance and behavior monitoring tasks where it causes fewer privacy issues than video. In this paper, we propose two deep learning approaches of different characteristics to classify speech data containing violent behavior against data that only includes patterns of non-violent behavior. The first approach is based on conventional deep neural networks, while the second approach uses lightweight deep neural networks. Both utilize Mel-spectrogram images of speech signals as input to fine-tuned models. Our best lightweight model's classification accuracy is about 8% better than the previous state-of-the-art result using the same benchmark data. Lightweight models have fewer parameters and require fewer computing resources than conventional deep models. This can be an important advantage when being deployed on mobile or edge devices.