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Mass transfer coefficients of extracting Mo (VI) and W (VI) in a stirred tank by solvent extraction using mixture of Cyanex272 and D2EHPA
Shakib, B

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Mass transfer coefficients of extracting Mo (VI) and W (VI) in a stirred tank by solvent extraction using mixture of Cyanex272 and D2EHPA
Author :   Shakib, B
Publisher :   Taylor and Francis Inc
Pub. Year  :   2019
Subjects :   Mass transfer coefficient Mean drop diameter Molybdenum Solvent extraction Ammonium...
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  • Chapter 1 (14)
  • Introduction (14)
  • Chapter 2 (17)
  • Speech Production and Modeling (17)
    • 2.1 Introduction to Speech (17)
    • 2.2 Human Anatomy of Speech Production (18)
      • 1. (20)
      • 2. (20)
      • 2.1 (20)
      • 2.2 (20)
      • 2.2.1 Phonemes (20)
    • 2.3 Human Anatomy of Speech Perception (22)
    • 2.4 Modeling Speech Signals (23)
      • 2.3 (23)
      • 2.4 (23)
      • Chapter 2 (23)
      • 2.1 (23)
      • 2.2 (23)
      • 2.3 (23)
      • 2.4.1 Source -Filter model (23)
      • 2.4.2 Linear Prediction Model (25)
    • 2.5 Summary (26)
  • Chapter 3 (27)
  • Related Works (27)
    • 3.1 Speech Enhancement (27)
    • 3.2 Related Works in Speech Enhancement (29)
    • 3.3 Noise Reduction (30)
    • 3.4 Spectral Subtraction Method (31)
    • 3.5 Wiener Filter (32)
      • 3.5.1 Solving the Least Square Error Equation (34)
      • 3.5.2 QR Decomposition Method (36)
    • 3.6 Introduction and Related Work for Frequency Warping (37)
      • 3.6.1 FIR Filter (39)
    • 3.7 Related Works for Warped Wiener Filter (42)
    • 3.8 Single Channel Wiener Filter (43)
    • 3.9 Multichannel Wiener Filter (44)
    • 3.10 Summary (48)
  • Chapter 4 (50)
  • Proposed Method and Results (50)
    • 4.1 Single Channel Wiener Filter (50)
      • 4.1.1 Windowing (52)
    • 4.2 All-pole FIR Warped Filter (57)
    • 4.3 FIR Warped Wiener Filter (61)
    • 4.4 Formant (66)
    • Resonances of vocal tract called formant and represents the sound differences. (66)
    • 4.5 Multichannel Wiener Filter (69)
    • Another experiment is for the filter with 4 channels and third order, The waveform of the clean speech signal, and the noisy speech signals for all 4 channels (SNR=10 dB) and finally the estimated signal can be seen in Figure 4.23. (74)
    • 4.6 Algorithm Outline (76)
    • 4.7 Summary (76)
  • Chapter 5 (77)
  • Conclusion and Future Works (77)
    • 5.1 Conclusion (77)
    • 5.2 Future Works (78)
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