Reliable detection of audio events in highly noisy environments

The authors propose an audio events detection system tailored to surveillance applications.The method has been tested on a huge and challenging data set, made publicly available.The performance analysis has been done for low SNR values and under various conditions.A comparative analysis with other methods from the literature has been performed. In this paper we propose a novel method for the detection of audio events for surveillance applications. The method is based on the bag of words approach, adapted to deal with the specific issues of audio surveillance: the need to recognize both short and long sounds, the presence of a significant noise level and of superimposed background sounds of intensity comparable to the audio events to be detected. In order to test the proposed method in complex, realistic scenarios, we have built a large, publicly available dataset of audio events. The dataset has allowed us to evaluate the robustness of our method with respect to varying levels of the Signal-to-Noise Ratio; the experimentation has confirmed its applicability under real world conditions, and has shown a significant performance improvement with respect to other methods from the literature.