An experimental study of the concept drift challenge in farm intrusion detection using audio

Abstract

Intrusion detection in farm settings is a challenging task. The data distribution suffers significant drift due to variations in environmental sounds. In this paper, we study the effect of this drift on state-of-the-art deep models. We experimentally found that the traditional models fail to deal with such variations and exhibit performance degradation. VGG16 turns out to be the best deep model, which shows an improvement of 2.76% over the best-performing state-of-the-art deep model on parameter F1-score. Consequently, we make an initial attempt to overcome this drift by integrating an unsupervised background noise component with standard models. On denoised signals, we obtained an average improvement of 50% on parameter accuracy for VGG16. The experimental analysis demonstrates the need for adaptive learning to handle the Spatio-temporal drifts in outdoor farm settings.