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This work addresses the issue of sparse reconstruction in compressive sensing (CS) for speech signals. We propose a novel sparse reconstruction algorithm based on the approximate message passing (AMP) framework, via exploiting the intrinsic structures of real-life speech signals in the modified discrete cosine transform (MDCT) domain. We use a Gaussian mixture model to characterize the marginal distribution...
In a distributed sensor network for sound source localization, it is important to develop a method which can accurately localize the sound source with a small amount of data collected by the sensors. Traditional methods for combining the Compressive Sensing (CS) with the sound source localization are not always accurate and effective in real applications due to the fact that time delay between different...
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