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E-raamat: Metaheuristic Applications to Speech Enhancement

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Metaheuristic Applications to Speech EnhancementSuurem pilt
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This book serves as a basic reference for those interested in the application of metaheuristics to speech enhancement. The major goal of the book is to explain the basic concepts of optimization methods and their use in heuristic optimization in speech enhancement to scientists, practicing engineers, and academic researchers in speech processing. The authors discuss why it has been a challenging problem for researchers to develop new enhancement algorithms that aid in the quality and intelligibility of degraded speech. They present powerful optimization methods to speech enhancement that can help to solve the noise reduction problems. Readers will be able to understand the fundamentals of speech processing as well as the optimization techniques, how the speech enhancement algorithms are implemented by utilizing optimization methods, and will be given the tools to develop new algorithms. The authors also provide a comprehensive literature survey regarding the topic.
1 Introduction
1(6)
1.1 Speech Enhancement and Its Applications
2(1)
1.2 Sources of Noise that Degrade Speech
2(1)
1.3 Classification of Speech Enhancement Methods
3(2)
1.3.1 Single-Channel Enhancement Systems
4(1)
1.3.2 Multichannel Enhancement Systems
4(1)
1.4 Organization of the Book
5(2)
References
6(1)
2 Adaptive Noise Cancellation to Speech Enhancement
7(10)
2.1 Concepts of Adaptive Noise Cancellation
7(4)
2.1.1 Adaptive Filters
8(1)
2.1.2 IIR Filter
9(1)
2.1.3 Filter Modelling
10(1)
2.2 Gradient-Based Algorithms to Speech Enhancement
11(2)
2.2.1 LMS Algorithm
11(1)
2.2.2 Normalized LMS Algorithm
12(1)
2.2.3 RLS Algorithm
12(1)
2.3 Gradient-Based Algorithms Versus Stochastic Optimization Techniques
13(1)
2.4 Conclusions
14(3)
References
14(3)
3 Heuristic and Meta-Heuristic Optimization
17(8)
3.1 General Introduction to Optimization
17(1)
3.2 Stochastic Optimization
18(1)
3.3 Heuristic and Meta-Heuristic Optimization Techniques
19(2)
3.4 Intensification and Diversification
21(1)
3.5 Swarm Intelligence
21(2)
3.5.1 Applications of Swarm Intelligence
22(1)
3.6 Conclusion
23(2)
References
23(2)
4 Application of Meta-Heuristics to Speech Enhancement
25(14)
4.1 Implementation of Speech Enhancement Via Meta-Heuristic Optimization
25(1)
4.2 Objective Function and Its Selection
26(1)
4.3 Proposed Meta-Heuristics to Speech Enhancement
27(9)
4.3.1 PSO
28(1)
4.3.2 MPSO
29(1)
4.3.3 LPSO
29(3)
4.3.4 IPSO
32(1)
4.3.5 Asexual Reproduction-based Adaptive Quantum PSO
33(1)
4.3.6 HPSO
34(2)
4.4 Conclusion
36(3)
References
37(2)
5 Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)
39(22)
5.1 Biological Background of PSO
39(1)
5.2 PSO Algorithm
40(2)
5.3 PSO Parameters
42(1)
5.3.1 Population Size (The Number of Particles)
42(1)
5.3.2 Acceleration Coefficients (Learning Factors)
42(1)
5.3.3 Inertia Weight
43(1)
5.3.4 The Stop Condition
43(1)
5.4 PSO-Based Adaptive Noise Cancellation to Speech Enhancement
43(2)
5.5 APSO
45(1)
5.6 Application of APSO to Speech Enhancement
46(1)
5.7 Implementation
47(3)
5.7.1 Parameter Selection for APSO
49(1)
5.8 Objective Measures
50(2)
5.8.1 SNR
50(1)
5.8.2 PESQ
50(1)
5.8.3 FAI
51(1)
5.8.4 WSS
51(1)
5.9 Results and Discussion
52(7)
5.10 Conclusions
59(2)
References
59(2)
6 Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)
61(16)
6.1 Gravitational Search Algorithm (GSA)
62(3)
6.2 Advantages of GSA
65(1)
6.3 GSA Versus PSO
66(1)
6.4 Proposed Speech Enhancement Algorithm with GSA
66(2)
6.5 Results and Discussion
68(6)
6.6 Observations on the Application of GSA to Speech Enhancement
74(1)
6.7 Conclusions
75(2)
References
75(2)
7 Speech Enhancement Based on Hybrid PSOGSA
77(14)
7.1 Hybrid PSOGSA
77(1)
7.2 Advantages of PSOGSA
78(1)
7.3 Implementation of PSOGSA in Speech Enhancement
79(2)
7.3.1 Parameter Selection
81(1)
7.4 Results and Discussion
81(6)
7.5 Observations on the Application of Hybrid PSOGSA to Speech Enhancement
87(2)
7.6 Conclusions
89(2)
Reference
89(2)
8 Speech Enhancement Based on Bat Algorithm (BA)
91(20)
8.1 Biological Background of Bat Algorithm
91(1)
8.2 Bat Algorithm
92(1)
8.3 Movement of Virtual Bats
93(1)
8.4 Loudness and Pulse Emission
94(2)
8.5 Advantages of Bat Algorithm
96(1)
8.6 BA in Speech Enhancement
97(1)
8.7 Results and Discussion
98(12)
8.8 Conclusions
110(1)
References
110(1)
9 Conclusions and Future Scope
111(4)
9.1 Summary of the Present Work
111(2)
9.2 Directions for Future Research
113(2)
Bibliography 115(4)
Index 119
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