Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing

Griffin, Anthony; Hirvonen, Toni; Tzagkarakis, Christos; Mouchtaris, Athanasios; Tsakalides, Panagiotis

doi:10.1109/tasl.2010.2090656

Cited by 32 publications

(13 citation statements)

References 34 publications

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“…Compressed Sensing (CS) approaches for signal compression/reconstruction offers an affordable solution for audio compression in wireless sensor networks [5], by allowing the reconstructing of audio signals from a small number of random linear observations. To the best of our knowledge, this is the first work that demonstrates the benefits of CS based compression/reconstruction schemes for the efficient telemonitoring of breath sounds in wireless body ares networks (WBANs).…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, this is the first work that demonstrates the benefits of CS based compression/reconstruction schemes for the efficient telemonitoring of breath sounds in wireless body ares networks (WBANs). More specifically, we enhance the benefits of the conventional CS schemes proposed in [5], by taking into account specific characteristics (e.g., block sparsity, sample correlation) of the breath sounds in the eigen spectrum domain. The proposed novel recovery algorithm, named PCA based Group LASSO, increases the mhealth system energy efficiency by a factor of 1.8 as compared to traditional CS recovery approaches.…”

Section: Introductionmentioning

confidence: 99%

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Energy efficient telemonitoring of wheezes

Lalos

Μουστάκας

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Wheezes are abnormal continuous adventitious lung sounds that are strongly related to patients with obstructive airways diseases. Wireless telemonitoring of these sounds facilitate early diagnosis (short, long term) and management of chronic inflammatory disease of the airways (e.g., asthma) through the use of an accurate and energy efficient mhealth system. Therefore, low complexity breath compression schemes with high compression ratio are required. To this end, we propose a compressed sensing based compression/reconstruction solution that enables wheeze detection from a small number of linearly encoded samples, by exploiting the block sparsity of the breath eigenspectrum during reconstruction at the receiver. Simulation studies, carried out with publicly available breath sounds, show the energy efficiency benefits of the proposed CS scheme, compared to traditional CS recovery approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy efficient telemonitoring of wheezes

Lalos

Μουστάκας

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…Therefore, it can be said that sinusoid has sparse representation in the frequency domain. When talking about musical signals, we can say that they consist of or can be modeled using small number of timevarying sinusoidal signals [5], [6]. For that reason, musical signals exhibit sparsity property in the frequency domain, and consequently, they can be good candidates for the applications of CS approach.…”

Section: Introductionmentioning

confidence: 99%

CS performance analysis for the musical signals reconstruction

Scekic

Mihajlovic

Orović

et al. 2014

2014 3rd Mediterranean Conference on Embedded Computing (MECO)

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The Compressive Sensing (CS) method for reconstruction of musical signals is analyzed in this paper. CS is a new method for signal acquisition which has been developed in recent years. In the CS scenarios, it is possible to reconstruct the entire signal information from just a small set of randomly chosen measurements, using different minimization algorithms. Consequently, this method founds application in a large number of signal processing areas. The analyzed musical signals and the applied acquisition procedure, satisfy two important CS requirements. Namely, the observed signals have sparse representation in frequency domain, and the measurement procedure provides conservation of the main information about the signal, despite the reduction of the number of analyzed samples. Musical signals of different nature and complexity are observed in the paper. The efficiency of the CS reconstruction is analyzed for different number of available measurements. It will be shown that the minimal number of measurements required for successful signal reconstruction depends on the complexity of musical tones. Based on reconstruction error, the simple CS procedure for classification of two types of musical signals is presented. The reconstruction accuracy is measured by mean relative error between original and reconstructed signal, as well as perceptually -by listening both original and reconstructed signal.

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“…With respect to the CS for audio signals [4], [5], finding a dictionary, on which the audio signals can be well sparsely represented, is usually the primary task. As the audio signals are time-varying and consequently can hardly be well sparsely decomposed within a single orthogonal dictionary [6], sub-optimal methods [7], [8], which achieve the best sparse approximation of the audio signal, are proposed in reThis work was partially supported by the National Natural Science Foundation of China under grant number 61171171 and 61102169. cent years.…”

Section: Introductionmentioning

confidence: 99%

Lossy audio signal compression via structured sparse decomposition and compressed sensing

Jiang

Ying

et al. 2014

2014 IEEE International Conference on Multimedia and Expo (ICME)

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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, which takes advantage of the structure information of transform coefficients, the LASSO is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two resulting layers, thus improving the performance of CS. Experimental results showed that the new method provided a better compression performance than conventional methods did.

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Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing

Cited by 32 publications

References 34 publications

Energy efficient telemonitoring of wheezes

Energy efficient telemonitoring of wheezes

CS performance analysis for the musical signals reconstruction

Lossy audio signal compression via structured sparse decomposition and compressed sensing

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