NDLI: Sparse regressions for joint segmentation and linear prediction

Content Provider	IEEE Xplore Digital Library
Author	Angelosante, D.
Copyright Year	2014
Description	Author affiliation: ABB Corp. Res. Center, Baden-Daettwil, Switzerland (Angelosante, D.)
Abstract	Regularizing the least-squares criterion with the total number of coefficient changes, it is possible to estimate time-varying (TV) autoregressive (AR) models with piecewise-constant coefficients. Such models emerge in various applications including speech segmentation using linear predictors. To cope with the large-size optimization task, the problem is cast as a sparse regression one, and is solved by resorting to an efficient block-coordinate descent algorithm. This enables joint segmentation and linear predictor coefficients identification with linear computational complexity per iteration. Modern trends in linear prediction for speech processing also envision sparsity in the model residuals. Indeed, sparse residuals allow for an improved representation of voiced speech. So far, sparse linear coding was proposed in a stationary scenario, i.e, after speech segmentation. This paper extends joint segmentation and linear prediction coefficients identification to sparse linear coding. Fortunately, coordinate descent approaches are still applicable to carry out the optimization tasks. Numerical tests have shown the benefits of the proposed algorithm.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	335
Ending Page	339
File Size	150186
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6853613
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Speech processing Joints Cost function Computational modeling Coordinate descent Sparse regression Linear prediction Convex optimization
Content Type	Text
Resource Type	Article

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