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Proceedings Paper

Signal-adaptive decomposition of multicomponent signals
Author(s): Khaled T. Assaleh; Richard J. Mammone
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Paper Abstract

In this paper we present a new method for adaptively decomposing a multicomponent signal into its components. This method is based on fitting an autoregressive (AR) model to the short-time spectra of the signal. The AR parameters represent the coefficients of the linear predictive (LP) polynomial. The roots of this polynomial constitute a set of center frequencies and bandwidths that characterize the modes of the signal. The decomposition process is achieved by applying a time-varying filter bank to the original multicomponent signal. The characteristics of this filter bank are derived from a subset of the roots of the LP polynomial. We have developed a constraining algorithm to determine that subset based on the boundedness of the bandwidths, and the temporal continuity of the center frequencies of the components. We have applied the proposed decomposition method for the separation of the formants of speech signals.

Paper Details

Date Published: 1 November 1993
PDF: 9 pages
Proc. SPIE 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV, (1 November 1993); doi: 10.1117/12.160441
Show Author Affiliations
Khaled T. Assaleh, Rutgers Univ. (United States)
Richard J. Mammone, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 2027:
Advanced Signal Processing Algorithms, Architectures, and Implementations IV
Franklin T. Luk, Editor(s)

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