A general description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous S-transform spectrum nonredundantly

  • Authors:

  • Robert A. Brown;M. Louis Lauzon;Richard Frayne

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Calgary and with the Seaman Family MR Research Centre, Hotchkiss Brain Institute, Foothills Medical Centre, Calgary, AB, Canada;Department of Radiology and Department of Clinical Neurosciences, University of Calgary, and Seaman Family MR Research Centre, Hotchkiss Brain Institute, Foothills Medical Centre, Calgary, AB, Can ...;Department of Radiology, Department of Clinical Neurosciences and Department of Electrical and Computer Engineering, University of Calgary, and Seaman Family MR Research Centre, Hotchkiss Brain In ...

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2010

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Abstract

Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these,the S-transform, provides simultaneous time and frequency informationsimilar to the wavelet transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family transforms, including the Fourier, short-time Fourier, and S-transforms. A discrete, nonredundant formulation of this transform,as well as algorithms for calculating the forward and inverse transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately,this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques,both in biomedical and other applications.