Title

Statistical model based SNR estimation method for speech signals

Document Type

Article

Publication Date

6-7-2012

Publication Title

Electronics Letters

Volume

48

First page number:

727

Last page number:

729

Abstract

The performance of speech enhancement algorithms to a large extent is related to the employed signal-to-noise ratio (SNR) estimation techniques. Many of the existing SNR estimation techniques are based on approaches that require either an experimentally pre-specified weighting factor or prior assumptions of the parameters in the signal model. In this reported work, a closed form SNR estimator is derived by modelling the noisy speech signal as a generalised normal-Laplace distribution and estimating the variance of the signal and variance of the noise using high-order sample moments. The performance of the proposed technique is tested using real speech signals and compared with the well-known eigenvalue method.

Keywords

Laplace equations; Normal distribution; Speech enhancement; Statistical analysis

Disciplines

Electrical and Computer Engineering | Electrical and Electronics | Electromagnetics and Photonics | Electronic Devices and Semiconductor Manufacturing | Power and Energy | Signal Processing | Systems and Communications

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

Identifier

DOI: http://dx.doi.org/10.1049/el.2012.0799

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