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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kong, W., Yang, B. (2006). Higher-Order Feature Extraction of Non-Gaussian Acoustic Signals Using GGM-Based ICA. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_105
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DOI: https://doi.org/10.1007/11760023_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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