In our earlier work, we have measured human intelligibility of stimuli reconstructed either from the short-time magnitude spectra or short-time phase spectra of a speech signal. We demonstrated that, even for small analysis window durations of 20-40 ms (of relevance to automatic speech recognition), the short-time phase spectrum can contribute to speech intelligibility as much as the short-time...

The discrete Fourier transform (FT) is a conventional method for spatial reconstruction of chemical shifting imaging (CSI) data. Due to point spread function (PSF) effects, FT reconstruction leads to intervoxel signal leakage (Gibbs ringing). Spectral localization by imaging (SLIM) reconstruction was previously proposed to overcome this intervoxel signal contamination. However, the existence of...

For distributed optical fiber pipeline pre-warning system, the sampling rate used is very high and thus huge data will be generated, which makes it difficult to transfer and store. Compressive sensing is a new compressed sampling method in the field of signal processing which compresses and samples the signal simultaneously. In this paper, an adaptive compressive sensing method is presented for...

This paper presents a new method for signal reconstruction by leveraging sampled-data control theory. We formulate the signal reconstruction problem in terms of an analog performance optimization problem using a stable discrete-time filter. The proposed performance criterion naturally takes intersample behavior into account, reflecting the energy distributions of the signal. We present methods ...

In Wireless Sensor Networks (WSN), the effective detection and reconstruction of the event signal is mainly based on the regulation of sampling and communication parameters used by the sensor nodes. The aim of this paper is to understand the effect of these parameters on the reconstruction performance of event signal in WSN. Theoretical analysis and results show that with proper selection of sa...

Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced signal. Instead, they directly employ the mixed-signal phase for signal reconstruction which leads to undesired traces of the interfering source in the target signal. In this paper, assuming a given knowledge of signal spectrum amplitude, we present a solution t...

Intensively growing approach in signal processing and acquisition, the Compressive Sensing approach, allows sparse signals to be recovered from small number of randomly acquired signal coefficients. This paper analyses some of the commonly used threshold-based algorithms for sparse signal reconstruction. Signals satisfy the conditions required by the Compressive Sensing theory. The Orthogonal M...

The novel theory of Compressed Sensing (CS) reduces the samples of compressible signal sharply by information sampling. In order to improve reconstruction accuracy of noise signal for CS, a Singular Value Decomposition (SVD) noise signal reconstruction algorithm is presented in this paper. This algorithm decomposes the random measurement matrix, modifies the diagonal matrix Eigen values by mean...