In this paper, we are interested in multichannel speech denoising in the context of mobile communications. The conventional method exploits the "similarity" between the observations expressed by the coherence function. In this work, we aimt at alleviating the drawbacks of this approach. More precisely, a wavelet or a multiwavelet transform is used to generate a multiresolution representation of the observations and the coherence between the resulting coefficients is computed. Experiments carried out on artificially and naturally noisy signals indicate that significant gains are achieved by the proposed method relatively to the classical coherence denoising technique.

The difficulties of having expertise in expert systems, the increasing of the data volume, self adaptation and prediction , all those problems are solved in the presence of learning. The classical definition of learning in cognitive science is the ability to improve the performance as the exercise of an activity. With learning, knowledge is automatically extracted from a data set. In this paper, we are interested to study efficient active learning methods. These methods are based on the definition of an efficient training set by iteratively adapting it through adding the most informative unlabeled instances. The selection of these instances are generally based on an uncertainty and diversity criteria. This study is focused on the uncertainty criterion. A review of the principal families of active learning algorithms is presented. Then the large-margin active learning techniques are detailed and evaluations of the contribution of large margin uncertainty criteria are presented.Index Terms-Active learning (AL), large margin, uncertainty, support vector machine (SVM), hyperspectral image (IHS).

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