A User's Guide to Compressed Sensing for Communications Systems

Hayashi, Kazunori; Nagahara, Masaaki; Tanaka, Toshiyuki

doi:10.1587/transcom.e96.b.685

Cited by 200 publications

(112 citation statements)

References 195 publications

(220 reference statements)

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“…Literature suggests that the deterministic measurement matrix for specific applications is almost impossible to construct, therefore randomly generated matrices are used in practical scenarios [5], [6], [7]. The 1 Matrix sparsity indicates the ratio of non-zero matrix elements over the zero ones.…”

Section: Compressed Sensing Methods (With Periodic Sampling)mentioning

confidence: 99%

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

Kochláň

Hodou

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Compressed sensing represents an interesting approach in signal processing and reconstruction. The theory involves a surprising number of branches of mathematics: linear algebra, functional analysis, convex and non-convex optimization, nonlinear approximation theory and probability. In general, the applications of compressed sensing can be found (or searched) wherever it is possible to express the signal in sparse representation in a "standard" base or in a base that was adjusted for particular signal. Core applications of compressed sensing today include image processing, signal denoising, image deblurring and inpainting. This paper addresses analysis the influence of external phenomena on the signal reconstruction using compressed sensing in wireless sensor networks. Such external phenomena include, for instance, additive white Gaussian noise (AWGN), attenuation or time shift. Three acoustic input signals sparse in frequency domain are used in experiments. The first one with significant frequency band from 500Hz up to 700Hz. The second signal with one significant frequency band from 2400Hz up to 3100Hz with considerable frequency bands between 0Hz to 1000Hz and 5000Hz to 6000Hz. The third signal used is a synthesized artificial sound invented for the experiment purposes only. It is strictly sparse in the frequency domain and has exactly three frequency bands between 400Hz and 500Hz, 2000Hz and 2100Hz, 9000Hz and 9100Hz. The results show that additive noise as well as attenuation have significant effect on the reconstruction accuracy using the selected distribution scenario and reconstruction method. On the other side, the time shift has no significant effect on the reconstruction.

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Section: Compressed Sensing Methods (With Periodic Sampling)mentioning

confidence: 99%

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

Kochláň

Hodou

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…In 2013, Hayashi et al [12] have presented a survey paper with focus on design and development of sensing matrix and sparsity aspects in compressed sensing. In same year, Kaur et.…”

Section: Table1 Papers On Compressive Sensing Techniquesmentioning

confidence: 99%

Scaling the Effectiveness of Existing Compressive Sensing in Multimedia Contents

Lakshminarayana¹,

Sarvagya²

2015

IJCA

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Compression has always played a crucial role in storage and transmission of heavier multimedia files. The existences of compression algorithms are more than two decade old. The normal compression algorithms are sometimes not required to process a signal in many cases where the signals are sparse. In such cases, compressive sensing highly contributes and compensates the issues of conventional compression algorithms as it performs sampling as well as compression at a same time. The concept of compressive sensing is quite new and is not much in matured stage. Our findings reported in this paper is a result of observation being carried out on all major research journals, which states that there are little amount of studies being done on compressive sensing and reconstruction of multimedia contents. The paper also discusses about the significant research gap and evaluates teh effectiveness of existing techniques.

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“…In this paper, we try to propose an SE method to achieve not only comparable to but also more stable performance than traditional SE algorithms in different environments. Compressive sensing, proposed by Donoho [8], Candes and Tao [9], has been established on solving an l 0 -norm minimization problem to recover a sparse or compressible signal from its downsamples at a low rate [10]. Though CS has been used in digital image processing [11] for many years, it also raises researchers' concern in speech and audio signal processing recently.…”

Section: Introductionmentioning

confidence: 99%

A Speech Enhancement Method Based on Multi-Task Bayesian Compressive Sensing

You

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYTraditional speech enhancement (SE) algorithms usually have fluctuant performance when they deal with different types of noisy speech signals. In this paper, we propose multi-task Bayesian compressive sensing based speech enhancement (MT-BCS-SE) algorithm to achieve not only comparable performance to but also more stable performance than traditional SE algorithms. MT-BCS-SE algorithm utilizes the dependence information among compressive sensing (CS) measurements and the sparsity of speech signals to perform SE. To obtain sufficient sparsity of speech signals, we adopt overcomplete dictionary to transform speech signals into sparse representations. K-SVD algorithm is employed to learn various overcomplete dictionaries. The influence of the overcomplete dictionary on MT-BCS-SE algorithm is evaluated through large numbers of experiments, so that the most suitable dictionary could be adopted by MT-BCS-SE algorithm for obtaining the best performance. Experiments were conducted on well-known NOIZEUS corpus to evaluate the performance of the proposed algorithm. In these cases of NOIZEUS corpus, MT-BCS-SE is shown that to be competitive or even superior to traditional SE algorithms, such as optimally-modified log-spectral amplitude (OMLSA), multi-band spectral subtraction (SSMul), and minimum mean square error (MMSE), in terms of signal-noise ratio (SNR), speech enhancement gain (SEG) and perceptual evaluation of speech quality (PESQ) and to have better stability than traditional SE algorithms.

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A User's Guide to Compressed Sensing for Communications Systems

Cited by 200 publications

References 195 publications

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

Scaling the Effectiveness of Existing Compressive Sensing in Multimedia Contents

A Speech Enhancement Method Based on Multi-Task Bayesian Compressive Sensing

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