Active odor cancellation

Kush R. Varshney, Lav R Varshney

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

Abstract

Noise cancellation is a traditional problem in statistical signal processing that has not been studied in the olfactory domain for unwanted odors. In this paper, we use the newly discovered olfactory white signal class to formulate optimal active odor cancellation using both nuclear norm-regularized multivariate regression and simultaneous sparsity or group lasso-regularized non-negative regression. As an example, we show the proposed technique on real-world data to cancel the odor of durian, katsuobushi, sauerkraut, and onion.

Original languageEnglish (US)
Title of host publication2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
PublisherIEEE Computer Society
Pages25-28
Number of pages4
ISBN (Print)9781479949755
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 - Gold Coast, QLD, Australia
Duration: Jun 29 2014Jul 2 2014

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
Country/TerritoryAustralia
CityGold Coast, QLD
Period6/29/147/2/14

Keywords

  • noise cancellation
  • olfactory signal processing
  • structured sparsity

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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