@inproceedings{ddc05932898d4cc2bdc4b1a5c498e82c,
title = "Active odor cancellation",
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.",
keywords = "noise cancellation, olfactory signal processing, structured sparsity",
author = "Varshney, \{Kush R.\} and Varshney, \{Lav R.\}",
year = "2014",
doi = "10.1109/SSP.2014.6884566",
language = "English",
isbn = "9781479949755",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "25--28",
booktitle = "2014 IEEE Workshop on Statistical Signal Processing, SSP 2014",
note = "2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 ; Conference date: 29-06-2014 Through 02-07-2014",
}