Our paper "Automatic Speech Detection on a Smart Beehive's Raspberry Pi" has been accepted to LWDA KDML 2023
31.08.2023In this technical report, we detail the process of setting an automated speech detection pipeline on a low-power device. We expand work done previously at our chair and deploy a trained Siamese network together with a k-NN-classifier.
The we4bee project has deployed 100 smart hives all over Germany. These hives are equipped with microphones, among other sensors. Beekeepers and bee researchers have observed the importance of sounds when monitoring bee hives, but audio can only be recorded in accordance with privacy laws. To prevent saving recordings of human voices, our aim is to deploy a pre-trained deep learning model on the Raspberry Pi 3B computer controlling the smart hive. This model has to classify recorded data in real-time.
In this technical report, we document the process of setting up the software on the Raspberry Pi, the adaptations required for existing code to run in the new environment, and the necessity of modifying the trained models for deployment on the mini computer. We find that in both standard operation conditions and under various artificial levels of high CPU and I/O load, the model’s inference runs in real-time.
