Comparative analysis of speech coders | Zendy

Draganov Ivo R | Zendy; Snejana Pleshkova | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Comparative analysis of speech coders

Author(s) -

Draganov Ivo R,

Snejana Pleshkova

Publication year - 2020

Publication title -

trends in computer science and information technology

Language(s) - English

Resource type - Journals

ISSN - 2641-3086

DOI - 10.17352/tcsit.000010

Subject(s) - speech recognition , speech coding , linear predictive coding , covariance , computer science , coding (social sciences) , linear prediction , correlation , mathematics , statistics , geometry

In this paper a comparative analysis of some of the most popular speech coders is presented. Qualitatively and quantitatively are tested Linear Prediction Coding in its implementation LPC-10e and with the use of auto-correlation and covariance, companding coding including A-law and µ-Law, ADPCM IMA, G.726 A- and µ-Law, and a fully featured MELP coder. All of them proved their efficiency for the typical applications they have been assign to providing the necessary quality vs. output data rate. The methodology for evaluation along with the codecs’ descriptions are considered useful for new coming specialists in the field of audio compression as one possible starting point for them.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research