Robust sound event recognition under TV playing conditions

The ability to automatically recognize sound events in real-life conditions is an important part of applications such as acoustic surveillance and smart home automation. The main challenge of these applications is that the sound sources often come from unknown distances under different acoustic environments, which are also noisy and reverberant. Among the noises in the home, the most difficult to deal with are non-stationary interference, such as TV, radio or music playing. In this paper, we address one of the hardest situations of sound event recognition: the presence of interference under reverberant conditions. Our system is a dual microphone approach and consists of a comprehensive combination of several modules: first, a novel regression-based noise cancellation (RNC), to reduce the interference, and second, an improved subband power distribution image feature (iSPD-IF) to classify the noise cancelled signals. A comprehensive experiment is carried out, which demonstrates nearly perfect classification accuracy under severe noisy and reverberant conditions.