Computational gastronomy – part 2 – ‘Active Odor Cancellation’

The Varshney twins – Dr. Kush Varshney (currently at IBM) and Professor Lav Varshney (previously at IBM) – have authored a series of papers on the theme of computational gastronomy, one of which, on Food Steganography, we looked at recently.

Example 2. Active Odor Cancellation. (IEEE International Workshop on Statistical Signal Processing, Gold Coast, Australia, June-July 2014.)

“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.”

Coming soon: Computational gastronomy part 3