Kernel PCA and Pre-Image Iterations for Speech Enhancement (2013)
Abstract / truncated to 115 words
In this thesis, we present novel methods to enhance speech corrupted by noise. All methods are based on the processing of complex-valued spectral data. First, kernel principal component analysis (PCA) for speech enhancement is proposed. Subsequently, a simplification of kernel PCA, called pre-image iterations (PI), is derived. This method computes enhanced feature vectors iteratively by linear combination of noisy feature vectors. The weighting for the linear combination is found by a kernel function that measures the similarity between the feature vectors. The kernel variance is a key parameter for the degree of de-noising and has to be set according to the signal-to-noise ratio (SNR). Initially, PI were proposed for speech corrupted by additive white Gaussian ...
speech enhancement – speech de-noising – kernel pca – automatic speech recognition
Information
- Author
- Leitner, Christina
- Institution
- Graz University of Technology
- Supervisors
- Publication Year
- 2013
- Upload Date
- Nov. 10, 2015
The current layout is optimized for mobile phones. Page previews, thumbnails, and full abstracts will remain hidden until the browser window grows in width.
The current layout is optimized for tablet devices. Page previews and some thumbnails will remain hidden until the browser window grows in width.