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EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

Chatlani, Navin and Soraghan, J.J. (2012) EMD-based filtering (EMDF) of low-frequency noise for speech enhancement. IEEE Transactions on Audio, Speech and Language Processing, 20 (4). 1158 - 1166.

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An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results.

Item type: Article
ID code: 14870
Keywords: noise estimation, speech enhancement, empirical mode decomposition, Bioengineering, Electrical engineering. Electronics Nuclear engineering, Acoustics and Ultrasonics, Electrical and Electronic Engineering
Subjects: Technology > Engineering (General). Civil engineering (General) > Bioengineering
Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
Depositing user: Strathprints Administrator
Date Deposited: 21 Jun 2010 13:27
Last modified: 31 Jan 2016 07:26

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