Noise reduction in state space using the focused gamma neural network

Author

Principe, Jose C. ; Kuo, Jyh-Ming

Author_Institution

Comput. NeuroEng. Lab., Florida Univ., Gainesville, FL, USA

Volume

ii

fYear

1994

fDate

19-22 Apr 1994

Abstract

In this paper we utilize the gamma neural model to improve the signal to noise ratio (SNR) of broadband signals corrupted by white noise. The projection of a noisy signal onto the signal subspace can not remove the noise in the subspace. A focus gamma network, when trained as a nonlinear predictor of the projected trajectory, reduces this noise further. The property of adaptive memory depth of the gamma model is utilized to decide when to stop the training of the network. The preliminary results show that the SNR can be improved significantly, preserving the broadband signal spectrum

Keywords

learning (artificial intelligence); neural nets; prediction theory; signal processing; spectral analysis; state-space methods; white noise; adaptive memory depth; broadband signal spectrum; focused gamma neural network; network training; noise reduction; noisy signal; nonlinear predictor; projected trajectory; signal subspace; signal to noise ratio; state space; white noise; Artificial neural networks; Filtering; Filters; Intelligent networks; Neural networks; Noise reduction; Signal to noise ratio; State-space methods; Trajectory; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389601

Filename

389601