“Wow!” Bayesian surprise for salient acoustic event detection

Abstract

We extend our previous work and present how Bayesian surprise can be applied to detect salient acoustic events. Therefore, we use the Gamma distribution to model each frequencies spectrogram distribution. Then, we use the Kullback-Leibler divergence of the posterior and prior distribution to calculate how “unexpected” and thus surprising newly observed audio samples are. This way, we are able to efficiently detect arbitrary, unexpected and thus surprising acoustic events. Complementing our qualitative system evaluations for (humanoid) robots, we demonstrate the effectiveness and practical applicability of the approach on the CLEAR 2007 acoustic event detection data.