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Publication Type : Conference Paper
Publisher : SAPA-SCALE 2012
Source : Proc. SAPA–SCALE 2012, Portland, Oregon, USA, pp. 28–33
Url : https://www.sapaworkshops.org/2012/papers/sapa2012-132.pdf
Campus : Amritapuri
School : School of Computing
Department : Computer Science and Engineering
Year : 2012
Abstract : We address the problem of speech enhancement using a riskestimation approach. In particular, we propose the use the Stein’s unbiased risk estimator (SURE) for solving the problem. The need for a suitable finite-sample risk estimator arises because the actual risks invariably depend on the unknown ground truth. We consider the popular mean-squared error (MSE) criterion first, and then compare it against the perceptually-motivated Itakura-Saito (IS) distortion, by deriving unbiased estimators of the corresponding risks. We use a generalized SURE (GSURE) development, recently proposed by Eldar for MSE. We consider dependent observation models from the exponential family with an additive noise model, and derive an unbiased estimator for the risk corresponding to the IS distortion, which is non-quadratic. This serves to address the speech enhancement problem in a more general setting. Experimental results illustrate that the IS metric is efficient in suppressing musical noise, which affects the MSE-enhanced speech. However, in terms of global signal-to-noise ratio (SNR), the minimum MSE solution gives better results.
Cite this Research Publication : S. R. Krishnan and C. S. Seelamantula “A generalized Stein’s estimation approach for speech enhancement based on perceptual criteria,” in Proc. SAPA–SCALE 2012, Portland, Oregon, USA, pp. 28–33.