Overheard: Audio-based Integral Event Inference

Abstract

There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy inferences on sensed data from smart devices via advanced deep-learning tools. Nonetheless, up to now, no work has investigated the possibility of riskier overheard, referring to inferring an integral event about humans by analyzing polyphonic audios. To this end, we propose an Audio-based integraL evenT infERence (ALTER) model and two upgraded models (ALTER-p and ALTER-pp) to achieve the integral event inference. Specifically, ALTER applies a link-like multi-label inference scheme to consider the short-term co-occurrence dependency among multiple labels for the event inference. Moreover, ALTER-p uses a newly designed attention mechanism, which fully exploits audio information and the importance of all data points, to mitigate information loss in audio data feature learning for the event inference performance improvement. Furthermore, ALTER-pp takes into account the long-term co-occurrence dependency among labels to infer an event with more diverse elements, where another devised attention mechanism is utilized to conduct a graph-like multi-label inference. Finally, extensive real-data experiments demonstrate that our models are effective in integral event inference and also outperform the state-of-the-art models.