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Filter banks and audio codingcompres...

Schuller, Gerald.

Filter banks and audio codingcompressing audio signals using Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Filter banks and audio codingby Gerald Schuller.
Reminder of title:	compressing audio signals using Python /
Author:	Schuller, Gerald.
Published:	Cham :Springer International Publishing :2020.
Description:	xi, 197 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	SoundRecording and reproducing
Online resource:	https://doi.org/10.1007/978-3-030-51249-1
ISBN:	9783030512491$q(electronic bk.)

Filter banks and audio codingcompressing audio signals using Python /
Schuller, Gerald.

Filter banks and audio codingcompressing audio signals using Python /[electronic resource] :by Gerald Schuller. - Cham :Springer International Publishing :2020. - xi, 197 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030512491$q(electronic bk.)

Standard No.: 10.1007/978-3-030-51249-1doiSubjects--Topical Terms:

182344
Sound
--Recording and reproducing

LC Class. No.: TK7881.4 / .S38 2020

Dewey Class. No.: 621.3893

Filter banks and audio codingcompressing audio signals using Python /
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245 1 0 $a Filter banks and audio coding $h [electronic resource] : $b compressing audio signals using Python / $c by Gerald Schuller.
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300 $a xi, 197 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
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650 0 $a Python (Computer program language) $3 215247
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650 2 4 $a Engineering Acoustics. $3 357294
650 2 4 $a Acoustics. $3 242702
650 2 4 $a User Interfaces and Human Computer Interaction. $3 274517
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 274376
710 2 $a SpringerLink (Online service) $3 273601
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856 4 0 $u https://doi.org/10.1007/978-3-030-51249-1
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