Kernel PCA for Speech Enhancement
Publikation aus Digital
Christina Leitner , Franz Pernkopf and Gernot Kubin
12th Annual Conference of the International Speech Communication Association (Interspeech) , 1/2011
In this paper, we apply kernel principal component analysis (kPCA), which has been successfully used for image denoising, to speech enhancement. In contrast to other enhancement methods which are based on the magnitude spectrum, we rather apply kPCA to complex spectral data. This is facilitated by Gaussian kernels. In the experiments, we show good noise reduction with few artifacts for noise corrupted speech at different SNR levels using additive white Gaussian noise. We compared
kPCA with linear PCA and spectral subtraction and evaluated all algorithms with perceptually motivated quality measures.