Abnormal drone noise detection system based on the microphone array and self-supervised learning

Abstract

The drone noise mainly comes from its rotating blades, providing plentiful information of the status of the drone. In the production line, the abnormal sound detection system has the advantages of no contact and simple deployment and can help to locate the fault products at relatively low costs. Therefore, this paper develops an abnormal drone noise detection system based on the microphone array and self-supervised learning. The microphone array is a part of the data acquisition module to pick up the drone noise. There are eight microphones in the array, forming four differential microphone pairs. Each of them is pointing to a blade of the drone. A four-channel noise sample is recorded and then analyzed. It is worth noting that drone noise samples are extremely unbalanced, because abnormal samples are difficult to encounter. Hence, a self-supervised learning strategy is adopted by creating auxiliary classification tasks to fine tune representations of the normal drone noise samples. With the consideration of low-complexity, the trained neural network models can be finally deployed even on a Raspberry Pi system with no graphic cards.