Speech compression using compressive sensing on a multicore system

Gunawan, Teddy Surya; Khalifa, Othman O.; Shafie, Amir Akramin; Ambikairajah, Eliathamby

doi:10.1109/icom.2011.5937130

Cited by 19 publications

(20 citation statements)

References 13 publications

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“…It is known that the perceptually significant features of spectral resonances and the harmonicity due to periodic excitation, are the most important and basic parameters in speech and audio [6] . Therefore, to explore sparsity of the speech signal, several alternative representation of a speech frame can be considered, such as…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

“…(5) gives representation of x in terms of DCT where C is the real valued transform matrix and 1  is the DCT coefficients [6] . Similarly, in Eq.…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

“…The algorithm above is known as "Basis Pursuit" (BP) since a subset of the column vector of  is being determined. Another efficient algorithm to solve CS is "orthogonal matching pursuit" (OMP) which can be formulated as follows [6]:…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

“…At Receiver side, for reconstruction of signal, convex optimization techniques are used [6]. Convex optimization then can be utilized as follows [6]:…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

“…It has been used in noise reduction, speech denoising and speech coding [6]. However, as it is still a new technology, not much research has been done on the use of CS for speech compression with some rigorous evaluation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluating Performance of Compressive Sensing for Speech Signal with various Basis

Siddhi¹,

Naitik²

2014

IJCA

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Compressive sensing is a promising focus in signal processing field, which offers a novel approach of simultaneous compression and sampling. In this technology, a sparse approximated signal is obtained with samples much less than that required by the Nyquist sampling theorem if the signal is sparse on one basis. Encouraged by its exciting potential application in signal compression, Compressive sensing framework has been used for speech Compression. This paper shows detailed comparison of compressive sensing theory applied with different sparsity basis on 8 KHz sampled speech signal. Performance of various basis has been compared with Mean square error, Signal to noise ratio and Perceptual Evaluation of Speech Quality parameters.

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Section: Compressive Sensing Basicsmentioning

confidence: 99%

“…(5) gives representation of x in terms of DCT where C is the real valued transform matrix and 1  is the DCT coefficients [6] . Similarly, in Eq.…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

Section: Compressive Sensing Basicsmentioning

confidence: 99%

“…At Receiver side, for reconstruction of signal, convex optimization techniques are used [6]. Convex optimization then can be utilized as follows [6]:…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating Performance of Compressive Sensing for Speech Signal with various Basis

Siddhi¹,

Naitik²

2014

IJCA

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A Novel Incremental Dictionary Learning Method for Low Bit Rate Speech Streaming

Teng

Huo

Song

et al. 2018

Web Information Systems Engineering – WISE 2018

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Simultaneous speech coding and de-noising in a dictionary based quantized CS framework

Ramdas

Gorthi

Mishra

2016

Int J Speech Technol

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Speech compression or speech coding is inevitable for effective communication of speech signals in resource limited scenarios and researcher's have been working on achieving lower and lower transmission bit rates (BR) without much compromise on the quality of speech. Medium BR hybrid speech coding schemes have gained much interest in the recent years with most of them based on CELP, the basic medium bit-rate coding scheme. In this work, we provide an insight to the capabilities of compressive sensing (CS) in speech processing and propose a novel idea in the quantized framework. Three major aspects demonstrated in this paper are (1) Inherent denoising of noisy speech by the CS based coder along with compression (2) Quantization of CS measurements to achieve medium transmission bit-rates and (3) Enhancement of quality and compression performance of the coder with better sparse representations of speech using dictionaries. The results indicate that the proposed scheme offers better compression in comparison with basic Gaussian codebook CELP. The CS scheme has the added advantage of inherent noise suppression and provides more robustness to background noise in comparison with parameter extraction based medium bit-rate speech coding systems.

show abstract

Speech compression using compressive sensing on a multicore system

Cited by 19 publications

References 13 publications

Evaluating Performance of Compressive Sensing for Speech Signal with various Basis

Evaluating Performance of Compressive Sensing for Speech Signal with various Basis

A Novel Incremental Dictionary Learning Method for Low Bit Rate Speech Streaming

Simultaneous speech coding and de-noising in a dictionary based quantized CS framework

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