Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Jump Function Kolmogorov for Audio Classification in Noise-Mismatch Conditions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Huy Dat Tran ; Inst. for Infocomm Res., Singapore, Singapore ; Haizhou Li

We present jump function Kolmogorov (JFK), a novel signal representation, which is (a) additive, thus the sum of signal and noise yields the sum of their JFKs; (b) sparse, therefore the signal and noise are separable in this domain. In this paper, the proposed signal representation is used in developing a classification system under noise-mismatch conditions. In this framework, we estimate JFKs from noisy signals in wavelet domain and compare them with the templates trained in clean condition. As the JFK is additive and sparse, the noise is simply eliminated by limiting JFKs only within the confidence intervals. The experiments show that the JFK-driven method significantly outperforms the conventional ones in three different classification tasks. The proposed method is further improved by adopting a discriminative feature selection for the classification.

Published in:

Signal Processing, IEEE Transactions on  (Volume:57 ,  Issue: 8 )