A novel element detection method in audio sensor networks

Audio element detection in wireless sensor networks (WSNs) has great significance in our lives (e.g., in detecting traffic jam and accident, gun shots and explosion, and hurricane). It is particularly useful when video cameras cannot be used effectively (e.g., in darkness, with a wide range to cover); audio sensors are also much cheaper. However, most previous works on audio element detection require a large number of training examples to obtain satisfactory results. This becomes even more infeasible for audio sensors in WSNs where small energy consumption is required. In this paper, we propose a novel approach to solve this difficult problem. We first break down audio clips into a collection of simple "audio elements," and train these audio elements offline using statistical learning. Then, we train a weighted association graph using the trained audio element models online. This greatly reduces the amount of online training without sacrificing accuracy. We deploy our approach in an audio sensor network for traffic monitoring and venue monitoring to evaluate its performance. The experiments demonstrate that our proposed method achieves better results compared to the state-of-the-art methods while using smaller online training sets.