Higher-Order feature extraction of non-gaussian acoustic signals using GGM-Based ICA

Authors:
Wei Kong;Bin Yang
Affiliations:
Information Engineering College, Shanghai Maritime University, Shanghai, China;Logistics Research Center, Shanghai Maritime University, Shanghai, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper, independent component analysis (ICA) is applied for feature extraction of non-Gaussian acoustic signals. The generalized Gaussian model (GGM) is introduced as the p.d.f. estimator in ICA because it can provide a general method for modeling non-Gaussian statistical structure of univariate distributions. It is demonstrated that the proposed method can efficiently extract ICA features for not only sup-Gaussian but also sub-Gaussian signals. The basis vectors are localized in both time and frequency domain and the resulting coefficients are statistically independent and sparse. The experiments of Chinese speech and the underwater signals show that the proposed method is more efficient than conventional methods.