NDLI: Time-frequency analysis and noise suppression with shift-invariant wavelet packets

Content Provider	IEEE Xplore Digital Library
Author	Cohen, I. Raz, S. Malah, D.
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel (Cohen, I.)
Abstract	Cross terms associated with bilinear distributions are not necessarily interpretable as interference terms. Any signal can be broken up in an infinite number of ways, each of which generates different cross terms. Therefore, it is important to choose an appropriate decomposition that separates the parts which are well delineated in the time-frequency plane. We have presented a modified Wigner distribution, where undesirable interference-terms can be eliminated while still retaining high energy concentration. A prescribed signal is expanded into a redundant library of orthonormal wavelet packet bases, from which the best decomposition is selected, and subsequently transformed into the Wigner domain. The discrimination between beneficial cross terms, which primarily enhance the useful properties of the time-frequency representation, and undesirable interference terms is determined according to the degree of adjacency and relative amplitudes of the interacting basis functions; only adjacent pairs whose coefficients are large enough are related to the same component of the signal. The balance between interference terms, concentration and computational complexity is achieved by adjusting the distance and amplitude thresholds.
Starting Page	401
Ending Page	408
File Size	525945
Page Count	8
File Format	PDF
ISBN	0306462060
DOI	10.1109/UWBSP.1998.818974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-06-14
Access Restriction	Subscribed
Rights Holder	Kluwer Academic/Plenum Publ.
Subject Keyword	Noise reduction Interference Signal processing Cities and towns Time frequency analysis Wavelet analysis Wavelet packets Libraries Signal analysis Signal resolution
Content Type	Text
Resource Type	Article

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