In this paper, we use kernel principal component analysis (kPCA) for speech enhancement. To synthesize the de-noised audio signal we rely on an iterative pre-image method. In order to gain better understanding about the pre-image step we performed experiments with different pre-image methods, first on synthetic data and then on audio data. The results of these experiments led to a reduction of artifacts in the original speech enhancement method, tested on speech corrupted by additive white Gaussian noise at several SNR levels. The evaluation with perceptually motivated quality measures confirms the improvement. © 2011 Springer-Verlag.
CITATION STYLE
Leitner, C., & Pernkopf, F. (2011). The pre-image problem and kernel PCA for speech enhancement. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7015 LNAI, pp. 199–206). https://doi.org/10.1007/978-3-642-25020-0_26
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