Wide-band speech coding based on bandwidth extension and sparse linear prediction

Alipoor, Ghasem; Savoji, M.H.

doi:10.1109/tsp.2012.6256335

Cited by 11 publications

(9 citation statements)

References 12 publications

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“…In this approach, however, the sparsity is not preserved and this approximation only solves (5) for γ = 0. The work in [20], to the authors' knowledge, is the first to introduce a LP formulation for the sparse LPC problem (5) to reduce the complexity of its solution. Also in this approach, the solution is only defined for γ = 0.…”

Section: Discussionmentioning

confidence: 99%

“…The purpose is twofold. Firstly, these are well known efficient methods for convex optimization used in real-time convex optimization [19,20]; Secondly, by comparing the proposed solver with general-purpose software based on interior-point methods [11,21], we can measure the improvement of hand-tailoring the algorithms for the same class of methods. Specialized software to solve real-time convex optimization exists but it has a few limitations.…”

Section: Methodsmentioning

confidence: 99%

“…In the numerical experiments, we will consider this method only for a small problem instance. The work in [20] presents a primal-dual algorithm for the sparse LPC problem (γ = 0). We extend this work to the sparse coefficients problem (γ > 0), and also consider compiled implementations, and timing based benchmarking among state-of-the-art methods.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Real-time implementations of sparse linear prediction for speech processing

Jensen

Giacobello

Christensen

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Real-time implementations of sparse linear prediction for speech processing

Jensen

Giacobello

Christensen

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The former, has a closed-form solution that, e.g., can be calculated via the Levinson-Durbin algorithm with time complexity O(N 2 ), with N being the prediction order. The latter, when solved with, e.g., interiorpoint methods [39,37], has a time complexity of O(…”

Section: Introductionmentioning

confidence: 99%

Fast algorithms for high-order sparse linear prediction with applications to speech processing

Jensen

Giacobello

Waterschoot

et al. 2016

Speech Communication

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In speech processing applications, imposing sparsity constraints on high-order linear prediction coefficients and prediction residuals has proven successful in overcoming some of the limitation of conventional linear predictive modeling. However, this modeling scheme, named sparse linear prediction, is generally formulated as a linear programming problem that comes at the expenses of a much higher computational burden compared to the conventional approach. In this paper, we propose to solve the optimization problem by combining splitting methods with two approaches: the Douglas-Rachford method and the alternating direction method of multipliers. These methods allow to obtain solutions with a higher computational efficiency, orders of magnitude faster than with general purpose software based on interior-point methods. Furthermore, computational savings are achieved by solving the sparse linear prediction problem with lower accuracy than in previous work. In the experimental analysis, we clearly show that a solution with lower accuracy can achieve approximately the same performance as a high accuracy solution both objectively, in terms of prediction gain, as well as with perceptually relevant measures, when evaluated in a speech reconstruction application.

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“…Such diagonal matrices often destroys the possibility of faster direct methods for solving a linear system (see, e.g., the discussion in [28]). Specifically, IP methods for the SLP problem (5) would have cubic per iteration time complexity [29,30] and quadratic space complexity. In particular, the introduction of the diagonal scaling matrix for the SLP problem prohibits the exploration of Toeplitz structure since such matrices do not have a low displacement rank.…”

Section: Introductionmentioning

confidence: 99%

Computational analysis of a fast algorithm for high-order sparse linear prediction

Jensen

Giacobello²,

Waterschoot

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Using a sparsity promoting convex penalty function on high-order linear prediction coefficients and residuals has shown to result in improved modeling of speech and other signals as this addresses the inherent limitations of standard linear prediction methods. However, this new formulation is computationally more demanding which may limit its use, in particular for embedded signal processing. This paper analyzes the algorithmic and computational aspects of the matrix structures associated with an alternating direction method of multipliers algorithm for solving the convex high-order sparse linear prediction problem. The paper also analyzes the inherent trade-off between accuracy and the objective measure of prediction gain and shows that a few iterations are sufficient to achieve similar results as computationally more expensive interior-point methods.

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Wide-band speech coding based on bandwidth extension and sparse linear prediction

Cited by 11 publications

References 12 publications

Real-time implementations of sparse linear prediction for speech processing

Real-time implementations of sparse linear prediction for speech processing

Fast algorithms for high-order sparse linear prediction with applications to speech processing

Computational analysis of a fast algorithm for high-order sparse linear prediction

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