Robust audio sensing with multi-sound classification

Audio data is a highly rich form of information, often containing patterns with unique acoustic signatures. In pervasive sensing environments, because of the empowered smart devices, we have witnessed an increasing research interest in sound sensing to detect ambient environment, recognise users' daily activities, and infer their health conditions. However, the main challenge is that the real-world environment often contains multiple sound sources, which can significantly compromise the robustness of the above environment, event, and activity detection applications. In this paper, we explore different approaches in multi-sound classification, and propose a stacked classifier based on the recent advance in deep learning. We evaluate our proposed approach in a comprehensive set of experiments on both sound effect and real-world datasets. The results have demonstrated that our approach can robustly identify each sound category among mixed acoustic signals, without the need of any a priori knowledge about the number and signature of sounds in the mixed signals.