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Empirical mode decomposition for joint denoising and dereverberation

Jan, T and Wang, W (2011) Empirical mode decomposition for joint denoising and dereverberation

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We propose a novel algorithm for the enhancement of noisy reverberant speech using empirical-mode-decomposition (EMD) based subband processing. The proposed algorithm is a one-microphone multistage algorithm. In the first step, noisy reverberant speech is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) via an EMD algorithm. Denoising is then applied to selected high frequency IMFs using EMD-based minimum mean-squared error (MMSE) filter, followed by spectral subtraction of the resulting denoised high-frequency IMFs and low-frequency IMFs. Finally, the enhanced speech signal is reconstructed from the processed IMFs. The method was motivated by our observation that the noise and reverberations are disproportionally distributed across the IMF components. Therefore, different levels of suppression can be applied to the additive noise and reverberation in each IMF. This leads to an improved enhancement performance as shown in comparison to a related recent approach, based on the measurements by the signal-to-noise ratio (SNR). © 2011 EURASIP.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Jan, T
Wang, W
Date : 2011
Additional Information : First published in the Proceedings of the 19th European Signal Processing Conference (EUSIPCO-2011) in 2011, published by EURASIP
Depositing User : Symplectic Elements
Date Deposited : 17 Dec 2013 16:51
Last Modified : 31 Oct 2017 16:28

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