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Tools for Signal Compression: Applications to Speech and Audio Coding [Kõva köide]

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(Technical University of Berlin, Germany)
  • Formaat: Hardback, 224 pages, kõrgus x laius x paksus: 236x160x28 mm, kaal: 522 g
  • Ilmumisaeg: 08-Apr-2011
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848212550
  • ISBN-13: 9781848212558
Teised raamatud teemal:
Tools for Signal Compression: Applications to Speech and Audio CodingSuurem pilt
  • Formaat: Hardback, 224 pages, kõrgus x laius x paksus: 236x160x28 mm, kaal: 522 g
  • Ilmumisaeg: 08-Apr-2011
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848212550
  • ISBN-13: 9781848212558
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781848212558.html
Märksõnad:
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 these coders.
Introduction xi
PART 1 TOOLS FOR SIGNAL COMPRESSION
1(88)
Chapter 1 Scalar Quantization
3(20)
1.1 Introduction
3(1)
1.2 Optimum scalar quantization
4(6)
1.2.1 Necessary conditions for optimization
5(2)
1.2.2 Quantization error power
7(3)
1.2.3 Further information
10(1)
1.2.3.1 Lloyd-Max algorithm
10(1)
1.2.3.2 Non-linear transformation
10(1)
1.2.3.3 Scale factor
10(1)
1.3 Predictive scalar quantization
10(13)
1.3.1 Principle
10(2)
1.3.2 Reminders on the theory of linear prediction
12(1)
1.3.2.1 Introduction: least squares minimization
12(1)
1.3.2.2 Theoretical approach
13(1)
1.3.2.3 Comparing the two approaches
14(1)
1.3.2.4 Whitening filter
15(1)
1.3.2.5 Levinson algorithm
16(1)
1.3.3 Prediction gain
17(1)
1.3.3.1 Definition
17(1)
1.3.4 Asymptotic value of the prediction gain
17(3)
1.3.5 Closed-loop predictive scalar quantization
20(3)
Chapter 2 Vector Quantization
23(14)
2.1 Introduction
23(1)
2.2 Rationale
23(3)
2.3 Optimum codebook generation
26(2)
2.4 Optimum quantizer performance
28(2)
2.5 Using the quantizer
30(2)
2.5.1 Tree-structured vector quantization
31(1)
2.5.2 Cartesian product vector quantization
31(1)
2.5.3 Gain-shape vector quantization
31(1)
2.5.4 Multistage vector quantization
31(1)
2.5.5 Vector quantization by transform
31(1)
2.5.6 Algebraic vector quantization
32(1)
2.6 Gain-shape vector quantization
32(5)
2.6.1 Nearest neighbor rule
33(1)
2.6.2 Lloyd-Max algorithm
34(3)
Chapter 3 Sub-band Transform Coding
37(16)
3.1 Introduction
37(1)
3.2 Equivalence of filter banks and transforms
38(2)
3.3 Bit allocation
40(6)
3.3.1 Defining the problem
40(1)
3.3.2 Optimum bit allocation
41(2)
3.3.3 Practical algorithm
43(1)
3.3.4 Further information
43(3)
3.4 Optimum transform
46(2)
3.5 Performance
48(5)
3.5.1 Transform gain
48(3)
3.5.2 Simulation results
51(2)
Chapter 4 Entropy Coding
53(36)
4.1 Introduction
53(1)
4.2 Noiseless coding of discrete, memoryless sources
54(12)
4.2.1 Entropy of a source
54(2)
4.2.2 Coding a source
56(1)
4.2.2.1 Definitions
56(1)
4.2.2.2 Uniquely decodable instantaneous code
57(1)
4.2.2.3 Kraft inequality
58(1)
4.2.2.4 Optimal code
58(2)
4.2.3 Theorem of noiseless coding of a memoryless discrete source
60(1)
4.2.3.1 Proposition 1
60(1)
4.2.3.2 Proposition 2
61(1)
4.2.3.3 Proposition 3
61(1)
4.2.3.4 Theorem
62(1)
4.2.4 Constructing a code
62(1)
4.2.4.1 Shannon code
62(1)
4.2.4.2 Huffman algorithm
63(1)
4.2.4.3 Example 1
63(1)
4.2.5 Generalization
64(1)
4.2.5.1 Theorem
64(1)
4.2.5.2 Example 2
65(1)
4.2.6 Arithmetic coding
65(1)
4.3 Noiseless coding of a discrete source with memory
66(7)
4.3.1 New definitions
67(1)
4.3.2 Theorem of noiseless coding of a discrete source with memory
68(1)
4.3.3 Example of a Markov source
69(1)
4.3.3.1 General details
69(1)
4.3.3.2 Example of transmitting documents by fax
70(3)
4.4 Scalar quantizer with entropy constraint
73(6)
4.4.1 Introduction
73(1)
4.4.2 Lloyd-Max quantizer
74(1)
4.4.3 Quantizer with entropy constraint
75(1)
4.4.3.1 Expression for the entropy
76(1)
4.4.3.2 Jensen inequality
77(1)
4.4.3.3 Optimum quantizer
78(1)
4.4.3.4 Gaussian source
78(1)
4.5 Capacity of a discrete memoryless channel
79(4)
4.5.1 Introduction
79(1)
4.5.2 Mutual information
80(2)
4.5.3 Noisy-channel coding theorem
82(1)
4.5.4 Example: symmetrical binary channel
82(1)
4.6 Coding a discrete source with a fidelity criterion
83(6)
4.6.1 Problem
83(1)
4.6.2 Rate-distortion function
84(1)
4.6.3 Theorems
85(1)
4.6.3.1 Source coding theorem
85(1)
4.6.3.2 Combined source-channel coding
85(1)
4.6.4 Special case: quadratic distortion measure
85(1)
4.6.4.1 Shannon's lower bound for a memoryless source
85(1)
4.6.4.2 Source with memory
86(1)
4.6.5 Generalization
87(2)
PART 2 AUDIO SIGNAL APPLICATIONS
89(74)
Chapter 5 Introduction to Audio Signals
91(10)
5.1 Speech signal characteristics
91(1)
5.2 Characteristics of music signals
92(1)
5.3 Standards and recommendations
93(8)
5.3.1 Telephone-band speech signals
93(1)
5.3.1.1 Public telephone network
93(1)
5.3.1.2 Mobile communication
94(1)
5.3.1.3 Other applications
95(1)
5.3.2 Wideband speech signals
95(1)
5.3.3 High-fidelity audio signals
95(1)
5.3.3.1 MPEG-1
96(1)
5.3.3.2 MPEG-2
96(1)
5.3.3.3 MPEG-4
96(3)
5.3.3.4 MPEG-7 and MPEG-21
99(1)
5.3.4 Evaluating the quality
99(2)
Chapter 6 Speech Coding
101(22)
6.1 PCM and ADPCM coders
101(1)
6.2 The 2.4 bit/s LPC-10 coder
102(5)
6.2.1 Determining the filter coefficients
102(1)
6.2.2 Unvoiced sounds
103(1)
6.2.3 Voiced sounds
104(2)
6.2.4 Determining voiced and unvoiced sounds
106(1)
6.2.5 Bit rate constraint
107(1)
6.3 The CELP coder
107(16)
6.3.1 Introduction
107(2)
6.3.2 Determining the synthesis filter coefficients
109(2)
6.3.3 Modeling the excitation
111(1)
6.3.3.1 Introducing a perceptual factor
111(2)
6.3.3.2 Selecting the excitation model
113(1)
6.3.3.3 Filtered codebook
113(2)
6.3.3.4 Least squares minimization
115(1)
6.3.3.5 Standard iterative algorithm
116(1)
6.3.3.6 Choosing the excitation codebook
117(1)
6.3.3.7 Introducing an adaptive codebook
118(3)
6.3.4 Conclusion
121(2)
Chapter 7 Audio Coding
123(18)
7.1 Principles of "perceptual coders"
123(3)
7.2 MPEG-1 layer 1 coder
126(4)
7.2.1 Time/frequency transform
127(1)
7.2.2 Psychoacoustic modeling and bit allocation
128(1)
7.2.3 Quantization
128(2)
7.3 MPEG-2 AAC coder
130(4)
7.4 Dolby AC-3 coder
134(1)
7.5 Psychoacoustic model: calculating a masking threshold
135(6)
7.5.1 Introduction
135(1)
7.5.2 The ear
135(1)
7.5.3 Critical bands
136(1)
7.5.4 Masking curves
137(2)
7.5.5 Masking threshold
139(2)
Chapter 8 Audio Coding: Additional Information
141(8)
8.1 Low bit rate/acceptable quality coders
141(5)
8.1.1 Tool one: SBR
142(1)
8.1.2 Tool two: PS
143(1)
8.1.2.1 Historical overview
143(1)
8.1.2.2 Principle of PS audio coding
143(1)
8.1.2.3 Results
144(1)
8.1.3 Sound space perception
145(1)
8.2 High bit rate lossless or almost lossless coders
146(3)
8.2.1 Introduction
146(1)
8.2.2 ISO/IEC MPEG-4 standardization
147(1)
8.2.2.1 Principle
147(1)
8.2.2.2 Some details
147(2)
Chapter 9 Stereo Coding: A Synthetic Presentation
149(14)
9.1 Basic hypothesis and notation
149(2)
9.2 Determining the inter-channel indices
151(3)
9.2.1 Estimating the power and the intercovariance
151(1)
9.2.2 Calculating the inter-channel indices
152(2)
9.2.3 Conclusion
154(1)
9.3 Downmixing procedure
154(4)
9.3.1 Development in the time domain
155(2)
9.3.2 In the frequency domain
157(1)
9.4 At the receiver
158(3)
9.4.1 Stereo signal reconstruction
158(1)
9.4.2 Power adjustment
159(1)
9.4.3 Phase alignment
160(1)
9.4.4 Information transmitted via the channel
161(1)
9.5 Draft International Standard
161(2)
PART 3 MATLAB® PROGRAMS
163(32)
Chapter 10 A Speech Coder
165(8)
10.1 Introduction
165(1)
10.2 Script for the calling function
165(5)
10.3 Script for called functions
170(3)
Chapter 11 A Music Coder
173(22)
11.1 Introduction
173(1)
11.2 Script for the calling function
173(3)
11.3 Script for called functions
176(19)
Bibliography 195(4)
Index 199
Nicolas Moreau, Retired Professor.