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getweights.m
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function [G, SNR] = getweights(Fvv, k, d, Gmin, method)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% get dual-mic coherence-based weight
% example Usage:
% Y = getweights(Fvv,16,0.032,0.1)
%
% Inputs:
% Fvv coherence function at k frequency bin
% k frequency bin
% d dual-min distance
% Gmin gain floor
%
%
% Outputs:
% G filter gain
% SNR estimate SNR
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~exist('Gmin', 'var')
Gmin = 0.1;
end
if ~exist('method', 'var')
method = 2;
end
fs = 16000;
N_FFT = (size(Fvv,3)-1)*2;
c = 340;
dij = d;
Fy_real = real(Fvv(1, 2, k));
Fy_imag = imag(Fvv(1, 2, k));
switch method
case 1
% endfire
% refer to
% "A Dual-Microphone Speech Enhancement Algorithm
% Based on the Coherence Function"
alpha_low = 16;
alpha_hi = 2;
beta_low = -0.1;
beta_hi = -0.3;
mu = 0.05;
gamma = 0.6;
SNR_est = (1 - (real(Fvv(1, 2, k)))^2 - imag(Fvv(1, 2, k))^2) / ...
((1 - real(Fvv(1, 2, k)))^2 + imag(Fvv(1, 2, k))^2);
if SNR_est < 0.01
L = 1;
elseif SNR_est > 100
L = 512;
else
L = 2^(SNR_est / 5 + 5);
end
G = 1 - abs(real(Fvv(1, 2, k)))^L;
if (k <= 16)
G1 = 1 - abs(real(Fvv(1, 2, k)))^alpha_low;
Q = beta_low;
else
G1 = 1 - abs(real(Fvv(1, 2, k)))^alpha_hi;
Q = beta_hi;
end
if (imag(Fvv(1, 2, k)) < Q)
G2 = mu;
else
G2 = 1;
end
G = G1 * G2; % endfire
case 2
% refer to
% [1] "A Dual-Microphone Algorithm That Can
% Cope With Competing-Talker Scenarios"
% endfire ,coherent
%theta = 0 * pi / 180; % 90,interference broadside
ata = 0 * pi / 180; % 0,target endfire
omega = 2 * pi * (k - 1) / N_FFT;
tao = fs * d / c;
omega_ = omega * tao;
beta = omega_ * cos(ata);
%alpha = omega_ * cos(theta);
A = Fy_imag - sin(omega_);
B = cos(omega_) - Fy_real;
C = Fy_real * sin(omega_) - Fy_imag * cos(omega_); % eq.13
T = 1 - Fy_real * cos(omega_) - Fy_imag * sin(omega_); % eq.18
sin_alpha = (-1 * B * C + A * T) / ...
(A^2 + B^2); % eq.17/18
SNR = (sin_alpha - Fy_imag) / ...
(Fy_imag - sin(beta)); % eq.11 in [1]
% square-root wiener filter
G = sqrt(SNR / ...
(SNR + 1));
case 5
% refer to
% [1] "A coherence-based noise reduction algorithm for binaural
% hearing aids"
% broadside ,coherent
% [2] "A Joint Speech Enhancement Algorithm Based on the
% Tri-microphone"
sin_alpha = (2*(1-Fy_real)*Fy_imag) / ...
((1-Fy_real)^2+Fy_imag^2); % eq.19 in [1]
G = (1-Fy_real^2-Fy_imag^2)/...
(2*(1-Fy_real)); % eq.20 in [1]
SNR = (1-Fy_real^2-Fy_imag^2)/...
((1-Fy_real)^2+Fy_imag^2); % eq.23 in [1]
% is
% equal
SNR = (sin_alpha - Fy_imag) / ... % to
(Fy_imag); % eq.7 in [2]
G = sqrt(SNR / ...
(SNR + 1));
case 3
%
% refer to
% "Hybrid Coherence Model for Noise Reduction in Reverberant
% Environments"
%
k_optimal = 1;
abs_Fvv2 = sqrt(Fy_real^2 + Fy_imag^2);
Fn = sin(2 * pi * k * fs * dij * k_optimal / c / N_FFT) ./ (2 * pi * k * fs * dij * k_optimal / c / N_FFT);
% Fn = sinc(2*pi*k*fs*d/(N_FFT*c));
DDR = (abs(Fn)^2 - abs_Fvv2^2) / ...
(abs_Fvv2^2 - 1);
K = DDR / (DDR + 1);
%theta = 0 * pi / 180; % 90,interference broadside
ata = 0 * pi / 180; % 0,target endfire
% omega = 2*pi*k/N_FFT;
omega = 2 * (k - 1) / N_FFT;
tao = fs * d / c;
omega_ = omega * tao;
beta = pi * omega_ * cos(ata);
%alpha = omega_ * cos(theta);
constant = 2 * pi * k * fs * d / ((N_FFT * c));
% if we set K = 1,then this method is same as method2
A = K * (Fy_imag - sin(beta));
B = (1 - K) * sinc(omega_) + K * cos(beta) - Fy_real;
C = Fy_real * sin(beta) - Fy_imag * K * cos(beta) - (1 - K) * sinc(omega_) * sin(beta);
T = K - Fy_real * cos(beta) - K * Fy_imag * sin(beta) + (1 - K) * sinc(omega_) * cos(beta);
sin_alpha = (-1 * B * C + A * T) / ...
(A^2 + B^2); % eq.14
SNR = (sin_alpha - Fy_imag) / ...
(Fy_imag - sin(beta)); % eq.10
if (SNR <- 0.99 || isnan(SNR))
SNR = -0.99;
end
G = SNR / ...
SNR + 1;
G = G^2;
% G = sqrt(SNR / ...
% (SNR + 1));
case 4
% refer to "Coherence-based dual-channel noise reduction algorithm in a complex noisy
% environment" method_1
% endfire ,coherent+diffuse
k_optimal = 1;
Fy_real = real(Fvv(1, 2, k));
Fy_imag = imag(Fvv(1, 2, k));
Fn = sin(2 * pi * k * fs * dij * k_optimal / c / N_FFT) ./ (2 * pi * k * fs * dij * k_optimal / c / N_FFT);
% Fn = sinc(2*pi*k*fs*d/(N_FFT*c));
abs_Fvv2 = sqrt(Fy_real^2 + Fy_imag^2);
DDR = (abs(Fn)^2 - abs_Fvv2^2) / ...
(abs_Fvv2^2 - 1+1e-6);
% DDR = max(0,DDR);
K = DDR / (DDR + 1);
% K = 1;
theta_s = 90 * pi / 180; % 90,target,endfire
%theta_i = 0 * pi / 180; % 0,interference ,broadside
constant = 2 * pi * (k - 1) * fs * d / ((N_FFT * c));
sin_alpha = sin(constant * sin(theta_s));
cos_alpha = cos(constant * sin(theta_s));
A = sin_alpha * K - Fy_imag;
B = cos_alpha * K - Fy_real + Fn * (1 - K);
C = (Fy_real - Fn * (1 - K)) * sin_alpha - Fy_imag * cos_alpha;
T = K - cos_alpha * (Fy_real - Fn * (1 - K)) - Fy_imag * sin_alpha;
sin_beta = (-1 * B * C - A * T) / ...
(A^2 + B^2);
G = ((Fy_imag - sin_beta * K) / ...
(sin_alpha - sin_beta));
SNR = G / (1 - G);
% G = G*K;
case 6
% refer to "Coherence-based dual-channel noise reduction algorithm in a complex noisy
% environment" method_3
% endfire ,coherent+diffuse
k_optimal = 1;
SPECTRAL_FLOOR = 0.4;
Fvv_UPPER = 0.98;
Fy_real = real(Fvv(1, 2, k));
Fy_imag = imag(Fvv(1, 2, k));
Fn = sin(2 * pi * k * fs * dij * k_optimal / c / N_FFT) ./ (2 * pi * k * fs * dij * k_optimal / c / N_FFT);
% Fn = sinc(2*pi*k*fs*d/(N_FFT*c));
if (Fy_real > Fvv_UPPER)
Fy_real = Fvv_UPPER;
end
abs_Fvv2 = sqrt(Fy_real^2 + Fy_imag^2);
if (abs_Fvv2 > Fvv_UPPER)
abs_Fvv2 = Fvv_UPPER;
end
DDR = (abs(Fn)^2 - abs_Fvv2^2) / ...
(abs_Fvv2^2 - 1);
K = DDR / (DDR + 1);
% K = 1;
theta_s = 90 * pi / 180; % 90,target,endfire
%theta_i = 0 * pi / 180; % interference ,broadside
constant = 2 * pi * (k - 1) * fs * d / ((N_FFT * c));
sin_alpha = sin(constant * sin(theta_s));
cos_alpha = cos(constant * sin(theta_s));
A = sin_alpha * K - Fy_imag;
B = cos_alpha * K - Fy_real + Fn * (1 - K);
C = (Fy_real - Fn * (1 - K)) * sin_alpha - Fy_imag * cos_alpha;
T = K - cos_alpha * (Fy_real - Fn * (1 - K)) - Fy_imag * sin_alpha;
sin_beta = (-1 * B * C - A * T) / ...
(A^2 + B^2); % eq.21
cos_beta = (A*C-B*T)/(A^2+B^2); % eq.22
A_ = cos_alpha - cos_beta;
B_ = cos_beta + Fn*(1-K);
C_ = sin_alpha - sin_beta;
D_ = sin_beta*K; % eq.16
if(abs(Fy_imag-sin_alpha)<abs(Fy_imag-sin_beta))
gamma = 1;
else
gamma = -1;
end % eq.18
T_ = abs_Fvv2^2*(A_^2+C_^2)-(A_*D_-B_*C_)^2;
G = (-1*(A_*B_+C_*D_)+gamma*sqrt(T_))/(A_^2+C_^2); % eq.17
SNR = G / (1 - G);
case 7
% refer to "Roubust Recognition of Reverberant and noisy speech using
% coherence-based processing"
% braodside ,coherent+diffuse
k_optimal = 1;
Fn = sin(2 * pi * k * fs * dij * k_optimal / c / N_FFT) ./ (2 * pi * k * fs * dij * k_optimal / c / N_FFT);
% Fn = sinc(2*pi*k*fs*d/(N_FFT*c));
DDR = (Fn - Fy_real) / ...
(Fy_real - 1+1e-6);
DDR = max(0,DDR);
K = DDR / (DDR + 1);
SNR = K;
G = K;
end
G = max(G, Gmin);
G = min(G, 1);
end