A frequency-based BSS technique for speech source separation.

• Other Authors: Ngan, Lai Yin.
• Format: Others
• Language: English; Chinese
• Published: 2003
• Subjects: Adaptive signal processing; Automatic speech recognition; Speech processing systems
• Online Access: http://library.cuhk.edu.hk/record=b5891580; http://repository.lib.cuhk.edu.hk/en/item/cuhk-324228

Ngan Lai Yin. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. === Includes bibliographical references (leaves 95-100). === Abstracts in English and Chinese. === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Blind Signal Separation (BSS) Methods --- p.4 === Chapter 1.2 --- Objectives of the Thesis --- p.6 === Chapter 1.3 --- Thesis Outline --- p.8 === Chapter 2 --- Blind Adaptive Frequency-Shift (BA-FRESH) Filter --- p.9 === Chapter 2.1 --- Cyclostationarity Properties --- p.10 === Chapter 2.2 --- Frequency-Shift (FRESH) Filter --- p.11 === Chapter 2.3 --- Blind Adaptive FRESH Filter --- p.12 === Chapter 2.4 --- Reduced-Rank BA-FRESH Filter --- p.14 === Chapter 2.4.1 --- CSP Method --- p.14 === Chapter 2.4.2 --- PCA Method --- p.14 === Chapter 2.4.3 --- Appropriate Choice of Rank --- p.14 === Chapter 2.5 --- Signal Extraction of Spectrally Overlapped Signals --- p.16 === Chapter 2.5.1 --- Simulation 1: A Fixed Rank --- p.17 === Chapter 2.5.2 --- Simulation 2: A Variable Rank --- p.18 === Chapter 2.6 --- Signal Separation of Speech Signals --- p.20 === Chapter 2.7 --- Chapter Summary --- p.22 === Chapter 3 --- Reverberant Environment --- p.23 === Chapter 3.1 --- Small Room Acoustics Model --- p.23 === Chapter 3.2 --- Effects of Reverberation to Speech Recognition --- p.27 === Chapter 3.2.1 --- Short Impulse Response --- p.27 === Chapter 3.2.2 --- Small Room Impulse Response Modelled by Image Method --- p.32 === Chapter 3.3 --- Chapter Summary --- p.34 === Chapter 4 --- Information Theoretic Approach for Signal Separation --- p.35 === Chapter 4.1 --- Independent Component Analysis (ICA) --- p.35 === Chapter 4.1.1 --- Kullback-Leibler (K-L) Divergence --- p.37 === Chapter 4.2 --- Information Maximization (Infomax) --- p.39 === Chapter 4.2.1 --- Stochastic Gradient Descent and Stability Problem --- p.41 === Chapter 4.2.2 --- Infomax and ICA --- p.41 === Chapter 4.2.3 --- Infomax and Maximum Likelihood --- p.42 === Chapter 4.3 --- Signal Separation by Infomax --- p.43 === Chapter 4.4 --- Chapter Summary --- p.45 === Chapter 5 --- Blind Signal Separation (BSS) in Frequency Domain --- p.47 === Chapter 5.1 --- Convolutive Mixing System --- p.48 === Chapter 5.2 --- Infomax in Frequency Domain --- p.52 === Chapter 5.3 --- Adaptation Algorithms --- p.54 === Chapter 5.3.1 --- Standard Gradient Method --- p.54 === Chapter 5.3.2 --- Natural Gradient Method --- p.55 === Chapter 5.3.3 --- Convergence Performance --- p.56 === Chapter 5.4 --- Subband Adaptation --- p.57 === Chapter 5.5 --- Energy Weighting --- p.59 === Chapter 5.6 --- The Permutation Problem --- p.61 === Chapter 5.7 --- Performance Evaluation --- p.63 === Chapter 5.7.1 --- De-reverberation Performance Factor --- p.63 === Chapter 5.7.2 --- De-Noise Performance Factor --- p.63 === Chapter 5.7.3 --- Spectral Signal-to-noise Ratio (SNR) --- p.65 === Chapter 5.8 --- Chapter Summary --- p.65 === Chapter 6 --- Simulation Results and Performance Analysis --- p.67 === Chapter 6.1 --- Small Room Acoustics Modelled by Image Method --- p.67 === Chapter 6.2 --- Signal Sources --- p.68 === Chapter 6.2.1 --- Cantonese Speech --- p.69 === Chapter 6.2.2 --- Noise --- p.69 === Chapter 6.3 --- De-Noise and De-Reverberation Performance Analysis --- p.69 === Chapter 6.3.1 --- Speech and White Noise --- p.73 === Chapter 6.3.2 --- Speech and Voice Babble Noise --- p.76 === Chapter 6.3.3 --- Two Female Speeches --- p.79 === Chapter 6.4 --- Recognition Accuracy Performance Analysis --- p.83 === Chapter 6.4.1 --- Speech and White Noise --- p.83 === Chapter 6.4.2 --- Speech and Voice Babble Noise --- p.84 === Chapter 6.4.3 --- Two Cantonese Speeches --- p.85 === Chapter 6.5 --- Chapter Summary --- p.87 === Chapter 7 --- Conclusions and Suggestions for Future Research --- p.88 === Chapter 7.1 --- Conclusions --- p.88 === Chapter 7.2 --- Suggestions for Future Research --- p.91 === Appendices --- p.92 === A The Proof of Stability Conditions for Stochastic Gradient De- scent Algorithm (Ref. (4.15)) --- p.92 === Bibliography --- p.95