A New Speech Denoising Method Based on WPD-ICA Feature Extraction
Independent Component Analysis (ICA) feature extraction is an efficient sparse coding method for noise suppression. However, single channel signal can not be directly applied in ICA feature extraction. In this paper, we propose a new method using wavelet packet decomposition (WPD) as preprocessing for single channel data. Wavelet packet coefficients (WPCs) provide multi-channel data as input data to learn ICA basis vectors. Furthermore we project input data onto the basis vectors to get sparser and independent coefficients. Appropriate nonlinear shrinkage function is used onto the components of sparse coefficients so as to reduce noise. The proposed approach is very efficient with respect to signal recovery from noisy data because not only the projection coefficients are sparser based on WPCs but both the features and the shrinkage function are directly estimated from the observed data. The experimental results have shown that it has excellent performance on signal to noise ratio (SNR) enhancement compared with other filtering methods.
KeywordsRoot Mean Square Error Independent Component Analysis Speech Signal Wavelet Packet Independent Component Analysis
Unable to display preview. Download preview PDF.
- 3.Roberts, S., Everson, R.: Independent Component Analysis: Principles and Practice. Cambridge University Press, Cambridge (2001)Google Scholar
- 4.Lee, T.-W., Jang, G.-J.: The Statistical Structures of Male and Female Speech Signals. Proc. ICASSP, Salt Lack City, Utah, May (2001) 105–108Google Scholar
- 5.Lee, J.-H., Jung H.-Y., Lee, T.-W., Lee, S.-Y.: Speech Feature Extraction Using Independent Component Analysis. Proc. ICASSP, Istanbul, Turkey, Vol. 3, June (2000) 1631–1634Google Scholar
- 6.Hyvärinen, A.: Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation. Technical Report A51, Helsinki University of Technology, Laboratory of Computer and Information Science (1998)Google Scholar
- 7.Mallet, S.: A Wavelet Tour of Signal Processing. Academic Press, second edition (1999)Google Scholar
- 10.Lee, T.-W., Lewicki, M.-S.: The Generalized Gaussian Mixture Model Using ICA. International workshop on Independent Component Analysis (ICA’00), Helsinki, Finland, June (2000) 239–244Google Scholar