By Steven L. Gay, Jacob Benesty
158 2. Wiener Filtering 159 three. Speech Enhancement by means of 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 energy Subtraction three. four importance Subtraction 162 three. five Parametric Wiener Filtering 163 164 three. 6 evaluate and dialogue Averaging options for Envelope Estimation 169 four. 169 four. 1 relocating normal 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 a hundred seventy five 6. end a hundred seventy five half IV Microphone Arrays 10 Superdirectional Microphone Arrays 181 Gary W. Elko 1. creation 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 2 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 resolution 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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Extra resources for Acoustic Signal Processing for Telecommunication
15] S. Haykin, Adaptive Filter Theory. , 1996.  J. S. Lim and A. V. Oppenheim, "Enhancement and bandwidth compression of noisy speech," Proc. of the IEEE, vol. 67, pp. 1586-1604, Dec. 1979.  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).
The down-date vectors are defined analogously. Using the above definitions, the sliding windowed Fast Kalman algorithm is: 1. [ eO,n,a -eO,n-L,a [ el,n,a ] -el,n-L,a - 4. En,a . l,n,l = En-l,a = el,n,a En,a' 0 kl,n 3. t - O:n_L ~ 2. an = an-l - [ 5 k o:~ = [ ] kl,n-L o:t n t - O:n-L [ + eO,n,ael,n,a - k l,n-L,l 6. [kl,n, kl,n-d = [ 0 = ] an-I, ] [ el,n,a ] -el,n-L,a ' ] an, eO,n-L,ael,n-L,a, eJ,n-L,a En,a' 0 kl,n kl,n-L ficients, kl,n,N and kl,n-L,N, ] an [kl,n, I , kl,n-L,I] extract lastcoef- 44 Acoustic Signal Processing ~~~:-L,b ] = [ ~~~-L ] bn-l, 7.
4, we show the convergence ofNLMS and FAP with various orders of projections. Once again, speech was the excitation, the length of the filter Was 1000 samples, and the signal to noise ratio was 30 dB. We see that quite a bit of improvement is gained with just N = 2 and that increasing N to 10 does not improve the speed of convergence significantly. However, if N is further increased to 50, there is again a significant gain in the speed of convergence. Note 0-; 40 Acoustic Signal Processing that for FAP, the increase from N = 2 to N = 50 does not significantly increase the computational complexity.
Acoustic Signal Processing for Telecommunication by Steven L. Gay, Jacob Benesty