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  • Research Article
  • Open Access

Time-Frequency Detection of Slowly Varying Periodic Signals with Harmonics: Methods and Performance Evaluation

EURASIP Journal on Advances in Signal Processing20102011:193797

  • Received: 16 August 2010
  • Accepted: 3 December 2010
  • Published:


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 show that 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 Gaussian noise. Results also show that all time-frequency methods outperform the classical time-domain-matched filter for both simulated and real signals, thus demonstrating the utility of the time-frequency detection approach.


  • Speech Signal
  • Detection Performance
  • Signal Type
  • Periodic Signal
  • Harmonic Component

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Authors’ Affiliations

Perinatal Research Centre and UQ Centre for Clinical Research, Royal Brisbane and Women's Hospital, The University of Queensland, Herston, QLD, 4029, Australia
College of Engineering, Qatar University, Qatar


© John M. O'Toole and Boualem Boashash. 2011

This article is published under license to BioMed Central Ltd. 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.