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Speech Signal Enhancement Using Empirical Mode Decomposition and Adaptive Method Based on the Signal for Noise Ratio Objective Evaluation


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DOI: https://doi.org/10.15866/irecos.v9i8.1588

Abstract


This paper introduces a new speech enhancement algorithm. It is mainly based on the empirical mode decomposition (EMD) and the improved thresholding technique applied on the selected intrinsic mode functions (IMFs). The proposed technique is a fully data driven approach. Firstly, the noisy speech signal is iteratively decomposed via the EMD algorithm into N oscillatory components called IMFs. Secondly, an adaptive method is based on the SNR_OUT. Objective evaluation is applied, to lead the IMFs to be thresholded by giving the highest SNR_OUT value. This is meant to reconstruct a set of N enhanced speech signals. The first is obtained by thresholding only the first IMF and by keeping the others unprocessed. The second is reconstructed by gathering the sum of the two first thresholded modes and the rest of the signal. This procedure is repeated consequently until obtaining a reconstructed signal from all thresholded IMFs and the residue. The SNR_OUT value is calculated then at every iteration. Once this thresholding procedure is completed, we retain only the case corresponding to the maximum SNR_OUT. The proposed algorithm is evaluated by using the speech signals taken from the NOISEUS database and corrupted with the additive white Gaussian noise. Our approach holds also a comparison with the other states of the art speech enhancement techniques based on the EMD analysis process.
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Keywords


Speech Signal; Empirical Mode Decomposition; Improved Thresholding; SNR Out

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References


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