Application of compressed sensing theory in the sampling and reconstruction of speech signals

Tang, Xin; Wu, Shao-Ling; Dong, Rui; Xia, Guang

doi:10.2991/ifmeita-17.2018.68

Cited by 3 publications

(3 citation statements)

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“…Another paper [28] showed how CS can be used to analyse speech signals to preserve their quality. Figure 6 illustrates the reconstruction performance of various speech signals.…”

Section: B Random and Deterministic Samplingmentioning

confidence: 99%

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

et al. 2022

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Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.

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“…Another paper [28] showed how CS can be used to analyse speech signals to preserve their quality. Figure 6 illustrates the reconstruction performance of various speech signals.…”

Section: B Random and Deterministic Samplingmentioning

confidence: 99%

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

et al. 2022

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“…Sparse representation is one of the prerequisites of compressed sensing (CS) theory [2], [13]- [16]. Over the past decade, it has been widely used in many applications such as signal processing [17]- [21], image processing [22]- [26], pattern recognition [27]- [34], machine learning [35]- [39][40], etc.…”

Section: Sparse Representationmentioning

confidence: 99%

“…13 shows the exemplar images of 2 subjects from the Extended Yale B face database. The represents the subject number.…”

mentioning

confidence: 99%

Novel techniques for sparse representation problems

Chai¹

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This thesis contains material from papers published in the following peer-reviewed conferences and publication drafts under preparation for the publications in the following peer-reviewed journals where I was the first and corresponding author. Chapter 3 is prepared as W.H. Chai, and C. Quek. A novel Quasi-Newton Technique for Composite Convex Minimization. Pattern Recognition (Under revision) The contributions of the co-authors are as follows:  I defined the initial project direction  Assoc Prof Quek edited the manuscript drafts.  The manuscript was revised by Assoc Prof Quek.  I generated the idea, derived the theory, designed the study and the algorithm, performed all the experimental works, and analyzed the results at the School of Computer Science and Rolls-Royce@NTU Corporate Lab. Chapter 4 is prepared as W.H. Chai, and C. Quek. Representation Recovery via -norm Minimization with Corrupted Data. Information Sciences (Submitted)The contributions of the co-authors are as follows: Assoc Prof Quek provided the initial project direction and edited the manuscript drafts. The manuscript was revised by Assoc Prof Quek. I generated the idea, derived the theory, designed the study and the algorithm, performed all the experimental works, and analyzed the results at the School of Computer Science and Rolls-Royce@NTU Corporate Lab.Chapter 5 is prepared as W.H. Chai, and C. Quek. Feature Selection for Dictionary Transfer. Pattern Recognition Letters (Under preparation for publication)The contributions of the co-authors are as follows:x  I defined the initial project direction  Assoc Prof Quek edited the manuscript drafts. The manuscript was revised by Assoc Prof Quek. I generated the idea, derived the theory, designed the study and the algorithm, performed all the experimental works, and analyzed the results at the School of Computer Science and Rolls-Royce@NTU Corporate Lab.

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Efficient measurement matrix for speech compressive sampling

Ahmed

Khan

et al. 2021

Multimed Tools Appl

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Application of compressed sensing theory in the sampling and reconstruction of speech signals

Cited by 3 publications

References 1 publication

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

Novel techniques for sparse representation problems

Efficient measurement matrix for speech compressive sampling

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