An audio attention computational model based on information entropy of two channels and exponential moving average

Abstract

The main down-top attention model usually uses some characteristics of audio signal to extract the auditory saliency map at present. But existing audio attention computational model based image saliency mostly doesn’t consider the continuity and attenuation mechanism of the human brain on paying attention to some occurred events in our real environment. To address these issues, we propose a model based on information entropy of two channels and exponential moving average (EMA) to simulate the processing of sound signals from the basilar membrane to the cochlear nucleus, and get the local information entropy of image and audio channel, finally we use the relevant EMA methods to calculate the auditory attention map. Some experimental results on artificial audio signal and real-world audio signal belonging to a public corpus show that the proposed model not only can detect the attention event, but also reflect the mechanism of human brain, and the experimental results of talk show in real environment are better.