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An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

Adiga, Aniruddha and Seelamantula, Chandra Sekhar (2014) An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints. In: 19th International Conference on Digital Signal Processing (DSP), AUG 20-23, 2014, Hong Kong, PEOPLES R CHINA, pp. 291-296.

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Abstract

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

Item Type: Conference Proceedings
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Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Linear prediction; Deconvolution; Sparsity constraints; Iteratively reweighted least-squares (IRLS) technique
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 09 Oct 2015 05:46
Last Modified: 09 Oct 2015 05:46
URI: http://eprints.iisc.ernet.in/id/eprint/52525

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