Gunshot Detection from Audio Streams in Portable Devices

Abstract: Machine learning and artificial neural networks can be used to classify or detect specific sound events in audio signals. Gunshot detection is one use case for such networks and can be used to help law enforcement by alerting officers or triggering camera recordings. However, artificial neural networks with a high performance usually require large amounts of computational power, meaning that they do not work on smaller portable devices. This thesis shows that a small convolutional neural network (CNN) can be used for real-time gunshot detection on a portable camera without requiring too much memory, battery consumption, or CPU power. We implemented a CNN with four layers and 100k trainable parameters to detect gunshots. We could reach an average precision of 0.98 and an F1 score of 0.95. We benchmarked the runtime performance of this architecture on the Axis Body Worn Cameras (BWCs). For real-time gunshot detection, our system uses 11.9 MB RAM and requires 4.9 MB persistent memory; it decreases the battery time by only 8.4% and uses approximately 11.5% of the CPU. With our configuration, the real-time detection has a latency of 3.6 seconds on the BWC. The results of our Master’s thesis show that audio-based gunshot detection on portable devices is indeed viable. We hope it will encourage the research on simpler features for audio classification.