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Filter Banks and Audio Coding = Compressing Audio Signals Using Python /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Filter Banks and Audio Coding/ by Gerald Schuller.
Reminder of title:	Compressing Audio Signals Using Python /
Author:	Schuller, Gerald.
Description:	XI, 197 p. 72 illus., 49 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Signal processing. -
Online resource:	https://doi.org/10.1007/978-3-030-51249-1
ISBN:	9783030512491

Filter Banks and Audio Coding = Compressing Audio Signals Using Python /
Schuller, Gerald.

Filter Banks and Audio CodingCompressing Audio Signals Using Python /[electronic resource] :by Gerald Schuller. - 1st ed. 2020. - XI, 197 p. 72 illus., 49 illus. in color.online resource.

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

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

561459
Signal processing.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Filter Banks and Audio Coding = Compressing Audio Signals Using Python /
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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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