Acoustic Signal Processing for Telecommunication by Steven L. Gay, Jacob Benesty

By Steven L. Gay, Jacob Benesty

158 2. Wiener Filtering 159 three. Speech Enhancement through Short-Time Spectral amendment three. 1 Short-Time Fourier research and Synthesis 159 a hundred and sixty three. 2 Short-Time Wiener clear out 161 three. three strength Subtraction three. four importance Subtraction 162 three. five Parametric Wiener Filtering 163 164 three. 6 evaluate and dialogue Averaging strategies for Envelope Estimation 169 four. 169 four. 1 relocating commonplace a hundred and seventy four. 2 Single-Pole Recursion a hundred and seventy four. three Two-Sided Single-Pole Recursion four. four Nonlinear facts Processing 171 five. instance Implementation 172 five. 1 Subband clear out financial institution structure 172 173 five. 2 A-Posteriori-SNR Voice job Detector five. three instance one hundred seventy five 6. end a hundred seventy five half IV Microphone Arrays 10 Superdirectional Microphone Arrays 181 Gary W. Elko 1. advent 181 2. Differential Microphone Arrays 182 three. Array Directional achieve 192 four. optimum Arrays for Spherically Isotropic Fields 193 four. 1 greatest achieve for Omnidirectional Microphones 193 four. 2 greatest Directivity Index for Differential Microphones 195 four. three Maximimum Front-to-Back Ratio 197 four. four minimal height Directional reaction two hundred four. five Beamwidth 201 five. layout Examples 201 five. 1 First-Order Designs 202 five. 2 Second-Order Designs 207 five. three Third-Order Designs 216 five. four Higher-Order designs 221 6. optimum Arrays for Cylindrically Isotropic Fields 222 6. 1 greatest achieve for Omnidirectional Microphones 222 6. 2 optimum Weights for max Directional achieve 224 6. three answer for optimum Weights for max Front-to-Back Ratio for Cylindrical Noise 225 7. Sensitivity to Microphone Mismatch and Noise 230 8.

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The sliding windowed method assumes that Xn = 0 for n < 0, fixes N to some value greater than L, and Wn-i = 1 for 0 :::: i :::: N - 1. 26) is undefined since the rank of XnX~ is at most n + 1. There are a couple of ways around this problem. One is to set L n + 1 while in this initial transient phase and only fix L when it reaches the desired value. This is called exact initialization. Another approach is to use the method of soft constraints. , ... L-l}. 32) leads to the solution t h opt = [X nWXn + 8Wl -1 [X nWS n + 8Who].

The price for this forgetfulness is a decrease in fidelity in the estimation of hLS. 33) has 0 (L 3) complexity, making it an impractical method for echo path lengths of any reasonable size. This is particularly true for adaptive filters where the coefficients are updated each sample period. In recursive least squares, RLS, the complexity is reduced to 0(L2) operations per sample period. RLS exploits the following observations: 1. 35) 2. 37) Depending on the choice of N and A the updates of Rn and rn can satisfy those required for any of the above methods.

Lim and A. V. Oppenheim, "Enhancement and bandwidth compression of noisy speech," Proc. of the IEEE, vol. 67, pp. 1586-1604, Dec. 1979. [17] R. Martin, "Spectral subtraction based on minimum statistics," in Proc. EUSIPCO, 1994, pp. 1182-1185. I MONO· CHANNEL ACOUSTIC ECHO CANCELLATION Chapter 2 THE FAST AFFINE PROJECTION ALGORITHM Steven L. com Abstract This chapter discusses an adaptive filtering algorithm called fast affine projections (FAP). FAP's key features include LMS like complexity and memory requirements (low), and RLS like convergence (fast) for the important case where the excitation signal is speech.

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