Building artificial situational awareness: Soundscape classification in daily-life scenarios of gender based violence victims

Soundscapes are a fundamental component of the context of real-world scenarios necessary to provide situation awareness in cyberphysical systems. However, real life conditions challenge the achievements of most artificial intelligence based methods to characterize them. In this paper we have identified the challenges for Acoustic Scene Classification (ASC) in daily-life scenarios of Gender-based Violence Victims (GBVV) with the aim of providing context information for the multimodal detection of violent scenarios. Specifically, these entail data imbalance, low quality of recordings, low quality of labels, inter-subject variability, low inference time and robustness requirements to various changing conditions. The unique Women and Emotion in real LIfe affectiVE computing dataset (WELIVE) comprising unconstrained, user-centered, in-the-wild daily-life recordings of 14 gender-based violence victims for 7-10 days with self-reported semantic location labels is used to evaluate hand-crafted and learnt features using pre-trained models transferred from existing acoustic event classifiers. We propose a distilled version of the Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition (PANNs) trained on AudioSet and adapted to the ASC domain, achieving the best trade-off for the challenges identified. Our method demonstrates a high information transfer factor of 1.843 with a fast inference time of 1 . 989 ± 0 . 052 milliseconds per 24-sample batch, highlighting its potential for real-time, context-aware applications in sensitive, unconstrained environments.