Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011, Article ID 193797, 16 pages doi: Research Article Time-Frequency Detection of Slowly Varying Periodic Signals with Harmonics: Methods and Performance Evaluation John M. O’Toole (EURASIP Member)1 and Boualem Boashash1, 2 1 Perinatal Research Centre and UQ Centre for Clinical Research, Royal Brisbane and Women’s Hospital, The University of Queensland, Herston, QLD 4029, Australia 2 College of Engineering, Qatar University, Qatar Correspondence should be addressed to John M. O’Toole, Received 16 August 2010; Accepted 3 December 2010 Academic Editor: Antonio Napolitano Copyright © 2011 J. M. O’Toole and B. Boashash. This is. | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 193797 16 pages doi 2011 193797 Research Article Time-Frequency Detection of Slowly Varying Periodic Signals with Harmonics Methods and Performance Evaluation John M. O Toole EURASIP Member 1 and Boualem Boashash1 2 1 Perinatal Research Centre and UQ Centre for Clinical Research Royal Brisbane and Women s Hospital The University of Queensland Herston QLD 4029 Australia 2 College of Engineering Qatar University Qatar Correspondence should be addressed to John M. O Toole Received 16 August 2010 Accepted 3 December 2010 Academic Editor Antonio Napolitano Copyright 2011 J. M. O Toole and B. Boashash. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We consider the problem of detecting an unknown signal from an unknown noise type. We restrict the signal type to a class of slowly varying periodic signals with harmonic components a class which includes real signals such as the electroencephalogram or speech signals. This paper presents two methods designed to detect these signal types the ambiguity filter and the time-frequency correlator. Both methods are based on different modifications of the time-frequency-matched filter and both methods attempt to overcome the problem of predefining the template set for the matched filter. The ambiguity filter method reduces the number of required templates by one half the time-frequency correlator method does not require a predefined template set at all. To evaluate their detection performance we test the methods using simulated and real data sets. Experiential results showthat the two proposed methods relative to the time-frequency-matched filter can more accurately detect speech signals and other simulated signals in the presence of coloured .
