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by Nicolas Moreau
Tools for Signal Compression: Applications to Speech and Audio Coding
Copyright
Introduction
Part 1: Tools for Signal Compression
Chapter 1: Scalar Quantization
1.1. Introduction
1.2. Optimum scalar quantization
1.3. Predictive scalar quantization
Chapter 2: Vector Quantization
2.1. Introduction
2.2. Rationale
2.3. Optimum codebook generation
2.4. Optimum quantizer performance
2.5. Using the quantizer
2.6. Gain-shape vector quantization
Chapter 3: Sub-band Transform Coding
3.1. Introduction
3.2. Equivalence of filter banks and transforms
3.3. Bit allocation
3.4. Optimum transform
3.5. Performance
Chapter 4: Entropy Coding
4.1. Introduction
4.2. Noiseless coding of discrete, memoryless sources
4.3. Noiseless coding of a discrete source with memory
4.4. Scalar quantizer with entropy constraint
4.5. Capacity of a discrete memoryless channel
4.6. Coding a discrete source with a fidelity criterion
Part 2: Audio Signal Applications
Chapter 5: Introduction to Audio Signals
5.1. Speech signal characteristics
5.2. Characteristics of music signals
5.3. Standards and recommendations
Chapter 6: Speech Coding
6.1. PCM and ADPCM coders
6.2. The 2.4 bit/s LPC-10 coder
6.3. The CELP coder
Chapter 7: Audio Coding
7.1. Principles of “perceptual coders”
7.2. MPEG-1 layer 1 coder
7.3. MPEG-2 AAC coder
7.4. Dolby AC-3 coder
7.5. Psychoacoustic model: calculating a masking threshold
Chapter 8: Audio Coding: Additional Information
8.1. Low bit rate/acceptable quality coders
8.2. High bit rate lossless or almost lossless coders
Part 3: MATLAB® Programs
Chapter 10: A Speech Coder
10.1. Introduction
10.2. Script for the calling function
10.3. Script for called functions
Chapter 11: A Music Coder
11.1. Introduction
11.2. Script for the calling function
11.3. Script for called functions
Bibliography
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