Speech Coding

with Code-Excited Linear Prediction

Автор: Tom Backstroem

Език:

Корици: С меки корици

Издател: Springer International Publishing AG

Наличност: Външен склад

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This book provides scientific understanding of the most central techniques used in speech coding bot...

Информация за книгата

Автор

Tom Backstroem

Език

Английски език

Корици

Книга - С меки корици

Издадена

2018

страници

240

EAN

9783319843445

Enbook ID

20093623

Издател

Springer International Publishing AG

Теглоt

4044

Размери

155 x 235 x 15

Пълно описание

This book provides scientific understanding of the most central techniques used in speech coding both for advanced students as well as professionals with a background in speech audio and or digital signal processing. It provides a clear connection between the Why's?, How's?, and What's, such that the necessity, purpose and solutions provided by tools should be always within sight, as well as their strengths and weaknesses in each respect. Equivalently, this book sheds light on the following perspectives for each technology presented: Objective: What do we want to achieve and especially why is this goal important? Resource / Information: What information is available and how can it be useful? Resource / Platform: What kind of platforms are we working with and what are the capabilities/restrictions of those platforms? This includes properties such as computational, memory, acoustic and transmission capacity of devices used. Solutions: Which solutions have been proposed and how can they be used to reach the stated goals? Strengths and weaknesses: In which ways do the solutions fulfill the objectives and where are they insufficient? Are resources used efficiently? This book concentrates solely on code excited linear prediction and its derivatives since mainstream speech codecs are based on linear prediction It also concentrates exclusively on time domain techniques because frequency domain tools are to a large extent common with audio codecs.

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Speech Coding

with Code-Excited Linear Prediction

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