| Description |
1 online resource (xiii, 200 pages) : illustrations |
| Series |
ISTE |
|
ISTE publications.
|
| Note |
"Adapted and updated from Outils pour la compression des signaux : applications aux signaux audioechnologies du stockage d'energie." |
| Bibliography |
Includes bibliographical references (pages 195-198) and index. |
| Contents |
Machine generated contents note: pt. 1 TOOLS FOR SIGNAL COMPRESSION -- ch. 1 Scalar Quantization -- 1.1. Introduction -- 1.2. Optimum scalar quantization -- 1.2.1. Necessary conditions for optimization -- 1.2.2. Quantization error power -- 1.2.3. Further information -- 1.2.3.1. Lloyd-Max algorithm -- 1.2.3.2. Non-linear transformation -- 1.2.3.3. Scale factor -- 1.3. Predictive scalar quantization -- 1.3.1. Principle -- 1.3.2. Reminders on the theory of linear prediction -- 1.3.2.1. Introduction: least squares minimization -- 1.3.2.2. Theoretical approach -- 1.3.2.3. Comparing the two approaches -- 1.3.2.4. Whitening filter -- 1.3.2.5. Levinson algorithm -- 1.3.3. Prediction gain -- 1.3.3.1. Definition -- 1.3.4. Asymptotic value of the prediction gain -- 1.3.5. Closed-loop predictive scalar quantization -- ch. 2 Vector Quantization -- 2.1. Introduction. |
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2.2. Rationale -- 2.3. Optimum codebook generation -- 2.4. Optimum quantizer performance -- 2.5. Using the quantizer -- 2.5.1. Tree-structured vector quantization -- 2.5.2. Cartesian product vector quantization -- 2.5.3. Gain-shape vector quantization -- 2.5.4. Multistage vector quantization -- 2.5.5. Vector quantization by transform -- 2.5.6. Algebraic vector quantization -- 2.6. Gain-shape vector quantization -- 2.6.1. Nearest neighbor rule -- 2.6.2. Lloyd-Max algorithm -- ch. 3 Sub-band Transform Coding -- 3.1. Introduction -- 3.2. Equivalence of filter banks and transforms -- 3.3. Bit allocation -- 3.3.1. Defining the problem -- 3.3.2. Optimum bit allocation -- 3.3.3. Practical algorithm -- 3.3.4. Further information -- 3.4. Optimum transform -- 3.5. Performance -- 3.5.1. Transform gain -- 3.5.2. Simulation results -- ch. 4 Entropy Coding -- 4.1. Introduction -- 4.2. Noiseless coding of discrete, memoryless sources. |
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4.2.1. Entropy of a source -- 4.2.2. Coding a source -- 4.2.2.1. Definitions -- 4.2.2.2. Uniquely decodable instantaneous code -- 4.2.2.3. Kraft inequality -- 4.2.2.4. Optimal code -- 4.2.3. Theorem of noiseless coding of a memoryless discrete source -- 4.2.3.1. Proposition 1 -- 4.2.3.2. Proposition 2 -- 4.2.3.3. Proposition 3 -- 4.2.3.4. Theorem -- 4.2.4. Constructing a code -- 4.2.4.1. Shannon code -- 4.2.4.2. Huffman algorithm -- 4.2.4.3. Example 1 -- 4.2.5. Generalization -- 4.2.5.1. Theorem -- 4.2.5.2. Example 2 -- 4.2.6. Arithmetic coding -- 4.3. Noiseless coding of a discrete source with memory -- 4.3.1. New definitions -- 4.3.2. Theorem of noiseless coding of a discrete source with memory -- 4.3.3. Example of a Markov source -- 4.3.3.1. General details -- 4.3.3.2. Example of transmitting documents by fax -- 4.4. Scalar quantizer with entropy constraint -- 4.4.1. Introduction -- 4.4.2. Lloyd-Max quantizer -- 4.4.3. Quantizer with entropy constraint. |
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4.4.3.1. Expression for the entropy -- 4.4.3.2. Jensen inequality -- 4.4.3.3. Optimum quantizer -- 4.4.3.4. Gaussian source -- 4.5. Capacity of a discrete memoryless channel -- 4.5.1. Introduction -- 4.5.2. Mutual information -- 4.5.3. Noisy-channel coding theorem -- 4.5.4. Example: symmetrical binary channel -- 4.6. Coding a discrete source with a fidelity criterion -- 4.6.1. Problem -- 4.6.2. Rate-distortion function -- 4.6.3. Theorems -- 4.6.3.1. Source coding theorem -- 4.6.3.2. Combined source-channel coding -- 4.6.4. Special case: quadratic distortion measure -- 4.6.4.1. Shannon's lower bound for a memoryless source -- 4.6.4.2. Source with memory -- 4.6.5. Generalization -- pt. 2 AUDIO SIGNAL APPLICATIONS -- ch. 5 Introduction to Audio Signals -- 5.1. Speech signal characteristics -- 5.2. Characteristics of music signals -- 5.3. Standards and recommendations -- 5.3.1. Telephone-band speech signals -- 5.3.1.1. Public telephone network. |
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5.3.1.2. Mobile communication -- 5.3.1.3. Other applications -- 5.3.2. Wideband speech signals -- 5.3.3. High-fidelity audio signals -- 5.3.3.1. MPEG-1 -- 5.3.3.2. MPEG-2 -- 5.3.3.3. MPEG-4 -- 5.3.3.4. MPEG-7 and MPEG-21 -- 5.3.4. Evaluating the quality -- ch. 6 Speech Coding -- 6.1. PCM and ADPCM coders -- 6.2. The 2.4 bit/s LPC-10 coder -- 6.2.1. Determining the filter coefficients -- 6.2.2. Unvoiced sounds -- 6.2.3. Voiced sounds -- 6.2.4. Determining voiced and unvoiced sounds -- 6.2.5. Bit rate constraint -- 6.3. The CELP coder -- 6.3.1. Introduction -- 6.3.2. Determining the synthesis filter coefficients -- 6.3.3. Modeling the excitation -- 6.3.3.1. Introducing a perceptual factor -- 6.3.3.2. Selecting the excitation model -- 6.3.3.3. Filtered codebook -- 6.3.3.4. Least squares minimization -- 6.3.3.5. Standard iterative algorithm -- 6.3.3.6. Choosing the excitation codebook -- 6.3.3.7. Introducing an adaptive codebook. |
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6.3.4. Conclusion -- ch. 7 Audio Coding -- 7.1. Principles of "perceptual coders" -- 7.2. MPEG-1 layer 1 coder -- 7.2.1. Time/frequency transform -- 7.2.2. Psychoacoustic modeling and bit allocation -- 7.2.3. Quantization -- 7.3. MPEG-2 AAC coder -- 7.4. Dolby AC-3 coder -- 7.5. Psychoacoustic model: calculating a masking threshold -- 7.5.1. Introduction -- 7.5.2. The ear -- 7.5.3. Critical bands -- 7.5.4. Masking curves -- 7.5.5. Masking threshold -- ch. 8 Audio Coding: Additional Information -- 8.1. Low bit rate/acceptable quality coders -- 8.1.1. Tool one: SBR -- 8.1.2. Tool two: PS -- 8.1.2.1. Historical overview -- 8.1.2.2. Principle of PS audio coding -- 8.1.2.3. Results -- 8.1.3. Sound space perception -- 8.2. High bit rate lossless or almost lossless coders -- 8.2.1. Introduction -- 8.2.2. ISO/IEC MPEG-4 standardization -- 8.2.2.1. Principle -- 8.2.2.2. Some details -- ch. 9 Stereo Coding: A Synthetic Presentation. |
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9.1. Basic hypothesis and notation -- 9.2. Determining the inter-channel indices -- 9.2.1. Estimating the power and the intercovariance -- 9.2.2. Calculating the inter-channel indices -- 9.2.3. Conclusion -- 9.3. Downmixing procedure -- 9.3.1. Development in the time domain -- 9.3.2. In the frequency domain -- 9.4. At the receiver -- 9.4.1. Stereo signal reconstruction -- 9.4.2. Power adjustment -- 9.4.3. Phase alignment -- 9.4.4. Information transmitted via the channel -- 9.5. Draft International Standard -- pt. 3 MATLAB® PROGRAMS -- ch. 10 A Speech Coder -- 10.1. Introduction -- 10.2. Script for the calling function -- 10.3. Script for called functions -- ch. 11 A Music Coder -- 11.1. Introduction -- 11.2. Script for the calling function -- 11.3. Script for called functions. |
| Summary |
This book presents tools and algorithms required to compress/uncompress signals such as speech and music. These algorithms are largely used in mobile phones, DVD players, HDTV sets, etc. In a first rather theoretical part, this book presents the standard tools used in compression systems: scalar and vector quantization, predictive quantization, transform quantization, entropy coding. In particular we show the consistency between these different tools. The second part explains how these tools are used in the latest speech and audio coders. The third part gives Matlab programs simulating t. |
| Language |
English. |
| Subject |
Sound -- Recording and reproducing -- Digital techniques.
|
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Data compression (Telecommunication)
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Speech processing systems.
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Données -- Compression (Télécommunications) |
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Traitement automatique de la parole. |
|
Data compression (Telecommunication) |
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Sound -- Recording and reproducing -- Digital techniques |
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Speech processing systems |
| Other Form: |
Print version: Moreau, Nicolas, 1945- Outils pour la compression des signaux. English. Tools for signal compression. London : ISTE ; Hoboken, N.J. : Wiley, 2011 9781848212558 (DLC) 2011003206 (OCoLC)679941445 |
| ISBN |
9781118616550 (electronic bk.) |
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1118616553 (electronic bk.) |
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1299315224 (ebk) |
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9781299315228 (ebk) |
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9781118616628 |
|
1118616626 |
|
1118616618 |
|
9781118616611 |
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