Abstract
A novel speech enhancement method based on empirical mode decomposition is proposed. The method is a fully data driven approach. Noisy speech signal is decomposed adaptively into oscillatory components called Intrinsic Mode Functions (IMFs) using a process called sifting. The empirical mode decomposition denoising involves thresholding each IMFs. A nonlinear function is introduced for amplitude thresholding. And then reconstructs the estimated speech signal using the processed IMFs. The experimental results show significant improvement in output SNR and quality as compared to recently reported results.
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Shen, LR., Yin, QB., Li, XY., Wang, HQ. (2006). Speech Enhancement in Short-Wave Channel Based on Empirical Mode Decomposition. In: Grigoriev, D., Harrison, J., Hirsch, E.A. (eds) Computer Science – Theory and Applications. CSR 2006. Lecture Notes in Computer Science, vol 3967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11753728_59
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DOI: https://doi.org/10.1007/11753728_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34166-6
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