• DocumentCode
    1306751
  • Title

    Discrete multiwindow Gabor-type transforms

  • Author

    Zibulski, Meir ; Zeevi, Yehoshua Y.

  • Author_Institution
    Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel
  • Volume
    45
  • Issue
    6
  • fYear
    1997
  • fDate
    6/1/1997 12:00:00 AM
  • Firstpage
    1428
  • Lastpage
    1442
  • Abstract
    The discrete (finite) Gabor scheme is generalized by incorporating multiwindows. Two approaches are presented for the analysis of the multiwindow scheme: the signal domain approach and the Zak transform domain approach. Issues related to undersampling, critical sampling, and oversampling are considered. The analysis is based on the concept of frames and on generalized (Moore-Penrose) inverses. The approach based on representing the frame operator as a matrix-valued function is far less demanding from a computational complexity viewpoint than a straightforward matrix algebra in various operations such as the computation of the dual frame. DFT-based algorithms, including complexity analysis, are presented for the calculation of the expansion coefficients and for the reconstruction of the signal in both signal and transform domains. The scheme is further generalized and incorporates kernels other than the complex exponential. Representations other than those based on the dual frame and nonrectangular sampling of the combined space are considered as well. An example that illustrates the advantages of the multiwindow scheme over the single-window scheme is presented
  • Keywords
    computational complexity; discrete Fourier transforms; inverse problems; matrix algebra; signal reconstruction; signal representation; signal sampling; transforms; DFT-based algorithms; Zak transform domain approach; complexity analysis; computational complexity; critical sampling; discrete multiwindow Gabor-type transforms; dual frame; expansion coefficients; frame operator; generalized Moore-Penrose inverses; matrix algebra; matrix-valued function; oversampling; reconstruction; signal domain approach; undersampling; Discrete transforms; Equations; Hilbert space; Least squares approximation; Least squares methods; Matrices; Sampling methods;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.599955
  • Filename
    599955