Data Selection for Reduced Training Effort in Vandalism Sound Event Detection

Typical sound event detection (SED) applications, employed in real environments, generate huge amounts of unlabeled data each day. These data can potentially be used to re-train the underlying machine learning models. However, as the labeling budget is usually restricted, active learning plays a vital role in re-training. Especially for applications with sparse event occurrence, a data selection process is paramount. In this paper we (i) introduce a novel application for vandalism SED, and (ii) analyze an active learning scheme for reduced training and annotation effort. In the presented system, the employed machine learning classifier shall recognize various acts of vandalism, i.e., glass breakage and graffiti spraying. To this end, we utilize embeddings generated with a pre-trained network and train a recurrent neural network for event detection. The applied data selection strategy is based on a mismatch-first, farthest-traversal approach and is compared to an upper bound by using all available data. Furthermore, results for the active learning scheme are evaluated with respect to different labeling budgets and compared to an active learning scheme with a random sampling scheme.