Fast Algorithms for High-Order Sparse Linear Prediction with Applications to Speech Processing

Tobias Lindstrøm Jensen, Daniele Giacobello, Toon van Waterschoot, Mads Græsbøll Christensen

Research output: Contribution to journalJournal articleResearchpeer-review

10 Citations (Scopus)
140 Downloads (Pure)

Abstract

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 perceptual relevant measures, when evaluated in a speech reconstruction application.
Original languageEnglish
JournalSpeech Communication
Volume76
Pages (from-to)143–156
ISSN0167-6393
DOIs
Publication statusPublished - Feb 2016

Fingerprint

Speech Processing
Speech processing
Linear Prediction
Fast Algorithm
Higher Order
Method of multipliers
Predictive Modeling
Alternating Direction Method
Prediction
Splitting Method
Interior Point Method
Experimental Analysis
Sparsity
Computational Efficiency
High Efficiency
Linear programming
High Accuracy
Optimization Problem
multiplier
Computational efficiency

Cite this

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title = "Fast Algorithms for High-Order Sparse Linear Prediction with Applications to Speech Processing",
abstract = "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 generallyformulated as a linear programming problem that comes at the expenses of amuch 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 perceptual relevant measures, when evaluated in a speech reconstruction application.",
author = "Jensen, {Tobias Lindstr{\o}m} and Daniele Giacobello and {van Waterschoot}, Toon and Christensen, {Mads Gr{\ae}sb{\o}ll}",
year = "2016",
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Fast Algorithms for High-Order Sparse Linear Prediction with Applications to Speech Processing. / Jensen, Tobias Lindstrøm; Giacobello, Daniele; van Waterschoot, Toon; Christensen, Mads Græsbøll.

In: Speech Communication, Vol. 76, 02.2016, p. 143–156.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Fast Algorithms for High-Order Sparse Linear Prediction with Applications to Speech Processing

AU - Jensen, Tobias Lindstrøm

AU - Giacobello, Daniele

AU - van Waterschoot, Toon

AU - Christensen, Mads Græsbøll

PY - 2016/2

Y1 - 2016/2

N2 - 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 generallyformulated as a linear programming problem that comes at the expenses of amuch 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 perceptual relevant measures, when evaluated in a speech reconstruction application.

AB - 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 generallyformulated as a linear programming problem that comes at the expenses of amuch 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 perceptual relevant measures, when evaluated in a speech reconstruction application.

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ER -