In this paper, we investigate various methods of classifying time– varying signals. In particular, we are interested in detecting acoustic emissions that may occur in concrete structures during imminent failure. This important classification problem will result in detecting and separating the distress signal from other natural or man made acoustic signals. Due to the time–varying nature of the ...

In this paper we propose an efficient method for S4 heart sound segmentation based on the Matching Pursuit algorithm and Gabor Dicitonaries. An evaluation of this algorithm, through the use of different cardiac cycle events for S4 heart sound signals, showed a high performance, achieving a detection rate of 100% with the use of Gabor dictionary. The proposed method is practical, thus making it ...

We introduce a dictionary of elementary waveforms, called harmonic atoms, that extends the Gabor dictionary and fits well the natural harmonic structures of audio signals. By modifying the “standard” matching pursuit, we define a new pursuit along with a fast algorithm, namely the Fast Harmonic Matching Pursuit, to approximate N-dimensional audio signals with a linear combination of M harmonic ...

the focus of this paper is to consider the compressed sensing problem. it is stated that the compressed sensing theory, under certain conditions, helps relax the nyquist sampling theory and takes smaller samples. one of the important tasks in this theory is to carefully design measurement matrix (sampling operator). most existing methods in the literature attempt to optimize a randomly initiali...

We introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines...

Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient. In this paper, the performance of OMP is shown to be dependent on how well information of the underlying signals is...

We introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions , a matching pursuit deene...

Computing the optimal expansion of a signal in a redundant dictionary of waveforms is an NP-complete problem. We introduce a greedy algorithm called a matching pursuit which computes a sub-optimal expansion. The dictionary waveforms which best match a signal's structures are chosen iteratively. An orthogonalized version of the matching pursuit is also developed. Matching pursuits are general pr...