System identification with denoising

Document Type

Conference Proceeding

Publication Date

1-1-2000

Abstract

When the signal-To-noise ratio (SNR) is low, classical system identification methods can not produce accurate results. The results can be improved by using denoising methods with time-frequency decompositions. The chirp signal is used as a training sequence to make the time-frequency domain denoising possible. Chirplet decomposition is proposed for separation of signal and noise components. The results are compared with the Gabor transform denoising. The chirplet denoising method proposed here is less sensitive to SNR changes than the Gabor denoising proposed before. Also, the accuracy of the estimates in chirplet case is superior to the Gabor transform method.

Identifier

0033693084 (Scopus)

ISBN

[0780362934]

Publication Title

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

External Full Text Location

https://doi.org/10.1109/ICASSP.2000.862047

ISSN

15206149

First Page

576

Last Page

579

Volume

1

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