| Description |
1 online resource |
| Contents |
Intro -- Preface -- Contents -- About the Author -- 1 Filter Banks -- 1.1 Introduction -- 1.2 Frequency Domain Transforms and Notation -- 1.3 Linear Filters -- 1.4 Sampling an Analog Signal -- 1.5 Downsampling a Time-Discrete Signal -- 1.5.1 Python Example Sampling with Unit Impulse Train -- 1.5.2 Removal of the Zeros -- 1.5.3 Python Example -- 1.6 Upsampling -- 1.7 The z-Transform and Effects in the z-Domain -- 1.8 Non-Ideal Filters -- 1.9 Filter Bank Design for Perfect Reconstruction -- 1.9.1 Analysis Filter Bank -- 1.9.2 Synthesis Filter Bank -- 1.9.3 Block Transforms |
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1.10 Connection of Block Transforms to Filter Banks -- 1.10.1 DFT Analysis Transform -- 1.10.2 DFT Synthesis Transform -- 1.10.3 Discrete Cosine Transform -- 1.10.4 Short Time Fourier Transform (STFT) -- 1.10.5 Block Processing for Longer Filters -- Analysis Filter Bank -- Synthesis Filter Bank -- Visualization of Polyphase Elements and Blocks -- 1.11 Polyphase Matrices -- 1.11.1 Converting Polyphase Representations -- 1.11.2 Time-Reversal in Polyphase Components -- 1.11.3 Polyphase Representation for Correlation Coefficients -- 1.11.4 Parseval's Theorem for the Polyphase Representation |
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1.12 Perfect Reconstruction -- 1.12.1 MDCT -- 1.12.2 Python Example -- Optimization Example -- MDCT Filter Bank Implementation -- 1.13 Further Extending the Impulse Response Length -- 1.13.1 Low Delay Filter Banks -- 1.13.2 Python Example for Low Delay Filter Banks -- Low Delay Filter Bank Implementation -- 1.14 Pseudo Quadrature Mirror Filter Banks (PQMF) -- Reconstruction Error of a PQMF Design -- Python Example for PQMF Filter Banks -- 1.15 Time-Varying and Switchable Filter Banks -- 1.15.1 With a Constant Number of Subbands -- 1.15.2 Example: MDCT Window Switching |
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1.15.3 With a Changing Number of Subbands -- 1.15.4 Example: MDCT and Low Delay Filter Bank Switching -- 2 Quantization -- 3 Predictive Coding -- 3.1 Introduction -- 3.2 The Mean Squared Error Solution -- 3.3 Online Adaptation -- 3.3.1 LPC Coder -- Python Example -- 3.3.2 Least Mean Squares (LMS) Adaptation -- 3.3.3 Quantization, Decoder in Encoder -- 3.3.4 Python Example LMS -- 3.3.5 Prediction for Lossless Coding -- 3.3.6 Python Example for Predictive Lossless Coding -- 3.3.7 Weighted Cascaded Least Means Squares (WCLMS) Prediction -- 4 Psycho-Acoustic Models -- 4.1 Introduction |
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4.2 The Bark Scale -- 4.2.1 The Schroeder Approximation -- 4.2.2 Bark Scale Mapping -- 4.2.3 Mapping from Bark Scale Back to Uniform -- 4.3 Hearing Threshold in Quiet -- 4.4 The Spreading Function -- 4.5 Non-linear Superposition -- 4.6 The Complete Psycho-Acoustic Model -- 4.7 Perceptual Evaluation -- 4.8 Psycho-Acoustic Models and Quantization -- 5 Entropy Coding -- 5.1 Introduction -- 5.2 Huffman Coding -- 5.3 Golomb-Rice Coding -- 6 The Python Perceptual Audio Coder -- 6.1 Introduction -- 6.2 The Encoder -- 6.3 The Decoder -- 7 Predictive Lossless Audio Coding -- 7.1 Introduction |
| 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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on November 18, 2020) |
| Subject |
Sound -- Recording and reproducing -- Digital techniques.
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Python (Computer program language)
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Acoustic & sound engineering.
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Wave mechanics (vibration & acoustics)
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User interface design & usability.
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Society & social sciences.
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Imaging systems & technology.
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Technology & Engineering -- Acoustics & Sound.
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Science -- Acoustics & Sound.
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Computers -- User Interfaces.
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Computers -- Data Processing.
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Technology & Engineering -- Electronics -- General.
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Python (Computer program language)
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Sound -- Recording and reproducing -- Digital techniques
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| ISBN |
9783030512491 |
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3030512495 |
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