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Nowadays many systems use speech as a way to interact with them. Therefore, machine learning systems are needed to perform various tasks on these recordings. But speech signals in a real environment are usually mixed with some other signals, such as noise. This may interfere with posterior signal processing applied to the signals. In this work, a new technique of data denoising is presented using Multivariate Empirical Mode Decomposition. To analyse the efficiency of the proposed technique we perform experiments with two microphones and four speakers. Different signal-to-noise ratios are checked in order to study the evolution of the improvement of the recovered data. An improvement of the analyzed data is obtained in all the cases, suggesting that this method could be used as a pre-enhancement step in speech processing algorithms.
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