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TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Chetupalli, Srikanth Raj and Sreenivas, TV (2014) TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 04-09, 2014, Florence, ITALY.

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Official URL: http://dx.doi.org/ 10.1109/ICASSP.2014.6854814

Abstract

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.

Item Type: Conference Proceedings
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Additional Information: Copyright for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Linear prediction; sparse representation; 1-norm minimization; speech analysis; non-stationary signals; time-varying systems
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 12 Jan 2015 06:50
Last Modified: 12 Jan 2015 06:50
URI: http://eprints.iisc.ernet.in/id/eprint/50605

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