PERICASA

This paper presents a novel architecture PERICASA, PERturbed frequent Itemset based classification for Computational Auditory Scene Analysis(CASA). A novel approach for perception of sound waves has been developed. Our aim is to develop a classifier which can correctly identify sound waves from noisy sound mixtures i.e. to solve the classical `Cocktail Party Problem´. The architecture is based on Gestalt principles of grouping like Pragnanz, Proximity, Common Fate and Similarity. These grouping cues are incorporated into a new Classification approach which is based on a concept namely Perturbed Frequent Itemsets. The primary idea is more the ease with which we can identify different feature values, easier it is to identify the sound wave.