Sparse signal representation and approximation has received a lot of attention during the last few years. This is due to its applicability and high performance in many signal processing areas. In this paper, we propose a new detection method based on sparse decomposition in a union of subspaces (UoS) model. Our proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalization for detectors. Low-rank assumption for the desired signals implies that the representations of these signals in terms of proper basis vectors would be sparse. Our proposed detector also exploits sparsity in its decision rule. We demonstrate the high efficiency of our method in the cases of voice activity detection in speech processing.
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