Retrieving Sparse Patterns Using a Compressed Sensing Framework: Applications to Speech Coding Based on Sparse Linear Prediction

Giacobello, Daniele; Christensen, Mads Græsbøll; Murthi, Manohar N.; Jensen, Søren Holdt; Moonen, Marc

doi:10.1109/lsp.2009.2034560

Cited by 65 publications

(47 citation statements)

References 14 publications

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“…However, it is not straightforward in such approaches to control the degree of sparsity ||g|| 0 . For example, the solution of the problem [9,27] …”

Section: Methods Based On the Minimization Of The L 1 Normmentioning

confidence: 99%

Greedy sparse decompositions: a comparative study

Dymarski

Moreau

Richard

2011

EURASIP J. Adv. Signal Process.

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The purpose of this article is to present a comparative study of sparse greedy algorithms that were separately introduced in speech and audio research communities. It is particularly shown that the Matching Pursuit (MP) family of algorithms (MP, OMP, and OOMP) are equivalent to multi-stage gain-shape vector quantization algorithms previously designed for speech signals coding. These algorithms are comparatively evaluated and their merits in terms of trade-off between complexity and performances are discussed. This article is completed by the introduction of the novel methods that take their inspiration from this unified view and recent study in audio sparse decomposition.

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“…However, it is not straightforward in such approaches to control the degree of sparsity ||g|| 0 . For example, the solution of the problem [9,27] …”

Section: Methods Based On the Minimization Of The L 1 Normmentioning

confidence: 99%

Greedy sparse decompositions: a comparative study

Dymarski

Moreau

Richard

2011

EURASIP J. Adv. Signal Process.

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“…Where, r= residue vector and. H is the inverse matrix of A (Matrix that performs the whitening of the signal, constructed from the coefficients of the predictor a of order P) and it is commonly referred to as the synthesis matrix that maps the residual representation to the original speech domain and H   [4] . Hence, various transforms, such as Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), and Linear Prediction coding can be used to sparsify the speech signal.…”

Section: Compressive Sensing Basicsmentioning

confidence: 99%

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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“…We have tried to follow the same experimental protocol as in [22]. That is, we evaluate our method using about 1 h of clean speech signal randomly chosen from the TIMIT database (re-sampled to 8 kHz) uttered by speakers of different genders, accents and ages which provides enough diversity in the characteristics of the analyzed signals.…”

Section: Figurementioning

confidence: 99%

An efficient solution to sparse linear prediction analysis of speech

Khanagha

Daoudi

2013

J AUDIO SPEECH MUSIC PROC.

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We propose an efficient solution to the problem of sparse linear prediction analysis of the speech signal. Our method is based on minimization of a weighted l 2 -norm of the prediction error. The weighting function is constructed such that less emphasis is given to the error around the points where we expect the largest prediction errors to occur (the glottal closure instants) and hence the resulting cost function approaches the ideal l 0 -norm cost function for sparse residual recovery. We show that the efficient minimization of this objective function (by solving normal equations of linear least squares problem) provides enhanced sparsity level of residuals compared to the l 1 -norm minimization approach which uses the computationally demanding convex optimization methods. Indeed, the computational complexity of the proposed method is roughly the same as the classic minimum variance linear prediction analysis approach. Moreover, to show a potential application of such sparse representation, we use the resulting linear prediction coefficients inside a multi-pulse synthesizer and show that the corresponding multi-pulse estimate of the excitation source results in slightly better synthesis quality when compared to the classical technique which uses the traditional non-sparse minimum variance synthesizer.

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Retrieving Sparse Patterns Using a Compressed Sensing Framework: Applications to Speech Coding Based on Sparse Linear Prediction

Cited by 65 publications

References 14 publications

Greedy sparse decompositions: a comparative study

Greedy sparse decompositions: a comparative study

Evaluating Performance of Compressive Sensing for Speech Signal with various Basis

An efficient solution to sparse linear prediction analysis of speech

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