PERICASA

RaghvenPhDa Mall, Prakhar Jain, Vikram Pudi, Bipin Indurkiya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010
Pages888-893
Number of pages6
DOIs
Publication statusPublished - 13 Dec 2010
Externally publishedYes
Event9th IEEE International Conference on Cognitive Informatics, ICCI 2010 - Beijing, China
Duration: 7 Jul 20109 Jul 2010

Other

Other9th IEEE International Conference on Cognitive Informatics, ICCI 2010
CountryChina
CityBeijing
Period7/7/109/7/10

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Keywords

  • Frequent itemsets
  • Gestalt theory and CASA

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Mall, R., Jain, P., Pudi, V., & Indurkiya, B. (2010). PERICASA. In Proceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010 (pp. 888-893). [5599785] https://doi.org/10.1109/COGINF.2010.5599785