Filter Banks and Audio Coding : Compressing Audio Signals Using Python / by Gerald Schuller.

Format:

Book

Author/Creator:

Schuller, Gerald, 1968- author.

Contributor:

SpringerLink (Online service)

Series:

Physics and Astronomy (SpringerNature-11651)

Language:

English

Subjects (All):

Signal processing.

Image processing.

Speech processing systems.

Acoustical engineering.

Acoustics.

User interfaces (Computer systems).

Application software.

Signal, Image and Speech Processing.

Engineering Acoustics.

User Interfaces and Human Computer Interaction.

Computer Appl. in Social and Behavioral Sciences.

Local Subjects:

Signal, Image and Speech Processing.

Engineering Acoustics.

Acoustics.

User Interfaces and Human Computer Interaction.

Computer Appl. in Social and Behavioral Sciences.

Physical Description:

1 online resource (XI, 197 pages) : 72 illustrations, 49 illustrations in color

Edition:

First edition 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

This textbook presents the fundamentals of audio coding, used to compress audio and music signals, using Python programs both as examples to illustrate the principles and for experiments for the reader. Together, these programs then form complete audio coders. The author starts with basic knowledge of digital signal processing (sampling, filtering) to give a thorough introduction to filter banks as used in audio coding, and their design methods. He then continues with the next core component, which are psycho-acoustic models. The author finally shows how to design and implement them. Lastly, the author goes on to describe components for more specialized coders, like the Integer-to-Integer MDCT filter bank, and predictive coding for lossless and low delay coding. Included are Python program examples for each section, which illustrate the principles and provide the tools for experiments. Comprehensively explains the fundamentals of filter banks and audio coding; Provides Python examples for each principle so that completed audio coders are obtained in the language; Includes a suite of classroom materials including exercises, experiments, and examples.

Contents:

Introduction

Filter Banks

With a Changing Number of Subbands

Predictive Coding

Psychoacoustic Models

Psychoacoustic Models and Quantization

Entropy Coding

The Python Perceptual Audio Coder

Predictive Lossless Audio Coding

Scalable Lossless Audio Coding

Psycho-Acoustic Pre-Filter

Conclusion.

Other Format:

Printed edition:

ISBN:

978-3-030-51249-1

9783030512491

Access Restriction:

Restricted for use by site license.