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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...
This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach...
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...
In this paper, we propose a method for lossy audio signal compression via structured sparse decomposition and compressed sensing (CS). In this method, a least absolute shrinkage and selection operator (LASSO) is employed to sparse and structured decompose the audio signals into tonal and transient layers, and then, both resulting layers are compressed by a CS method. By employing a new penalty term,...
In harsh and complex electric power system environments of Smart Grid, the observed signal is always disturbed by multiple signal sources, environmental noise or sudden packet loss, generating a significant challenge for reliable remote monitoring. In this paper, we propose a new post-processing detecting scheme for wireless data transmission in the distributed monitoring system to highly tolerate...
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