Echoic log-surprise: A multi-scale scheme for acoustic saliency detection

This technique, we termed echoic log-surprise, combines an unsupervised statistical approach based on Bayesian log-surprise and the biological concept of echoic or Auditory Sensory Memory. Our algorithm computes several independent log-surprise cues in parallel considering a wide range of memory values, with the aim of leveraging saliency information from different temporal scales. Then, we explore several statistical metrics to combine these multi-scale signals in a single temporal saliency signal including Renyi entropy, Jensen-Shannon divergence, Cramer or Bhattacharyya distances. We have adopted Acoustic Event Detection tasks as adequate proxies to test its performance. Results show that the proposed echoic log-surprise method outperforms classical acoustic detection techniques commonly deployed in intelligent and expert systems, such as energy thresholding or voice activity detection, and it also achieves better results than some other state-of-the-art acoustic saliency algorithms, such as Kalinli's and conventional log-surprise.