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saf_sr_kalman.m
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saf_sr_kalman.m
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function out = saf_sr_kalman(mic, spk, frame_size)
out_len = min(length(mic),length(spk));
out = zeros(out_len,1);
out_num = floor(out_len/frame_size);
st = init(frame_size);
for i = 1 : out_num
mic_frame = mic((i - 1) * frame_size + 1 : i * frame_size);
spk_frame = spk((i - 1) * frame_size + 1 : i * frame_size);
[st, out_frame] = saf_process(st, mic_frame, spk_frame);
out(1+(i-1)*frame_size:i*frame_size) = out_frame;
end
function st = init(frame_size)
% static para
st.frame_len = frame_size;
st.K = frame_size*2;
st.half_bin = st.K / 2 + 1;
st.win_len = st.K * 2;
st.notch_radius = .982;
st.notch_mem = zeros(2,1);
st.memX = 0;
st.memD = 0;
st.memE = 0;
st.preemph = .98;
% lp win
win_st = load('win_para_update.mat');
st.win_global = win_st.win;
% subband para
st.ana_win_echo = zeros(1, st.win_len);
st.ana_win_far = zeros(1, st.win_len);
st.sys_win = zeros(1, st.win_len);
st.tap = 15;
st.subband_in = zeros(st.half_bin, st.tap);
st.subband_adf = zeros(st.half_bin, st.tap);
%sr-kalman para
st.Ryu = ones(st.half_bin,st.tap,st.tap)*5;
st.w_cov = ones(st.half_bin, 1)*0.1;
st.v_cov = ones(st.half_bin, 1)*0.001;
st.gain = zeros(st.half_bin,st.tap);
% nlp
st.Eh = zeros(st.half_bin,1);
st.Yh = zeros(st.half_bin,1);
st.est_ps = zeros(st.half_bin,1);
st.spec_ave = 0.01;
st.Pey = 0;
st.Pyy = 0;
st.beta0 = 0.016;
st.beta_max = st.beta0/4;
st.min_leak = 0.005;
st.echo_noise_ps = 0;
st.adapt_cnt=0;
st.res_old_ps = 0;
st.suppress_gain = 20;
st.wiener_gain = zeros(st.half_bin,1);
st.gain_floor = ones(st.half_bin,1).*0.01;
end
function [out,mem] = filter_dc_notch16(in, radius, len, mem)
out = zeros(size(in));
den2 = radius*radius + .7*(1-radius)*(1-radius);
for ii=1:len
vin = in(ii);
vout = mem(1) + vin;
mem(1) = mem(2) + 2*(-vin + radius*vout);
mem(2) = vin - (den2*vout);
out(ii) = radius*vout;
end
end
function [st, out] = saf_process(st, mic_frame, spk_frame)
N = st.frame_len;
K = st.K;
M = st.tap;
[mic_in, st.notch_mem] = filter_dc_notch16(mic_frame, st.notch_radius, N, st.notch_mem);
st.ana_win_echo = [st.ana_win_echo(N+1:end), mic_in'];
ana_win_echo_windowed = st.win_global .* st.ana_win_echo;
ana_wined_echo = ana_win_echo_windowed(1:K)+ana_win_echo_windowed(K+1:2*K);
fft_out_echo = fft(ana_wined_echo);
st.ana_win_far = [st.ana_win_far(N + 1:end), spk_frame'];
ana_win_far_windowed = st.win_global .* st.ana_win_far;
ana_wined_far = ana_win_far_windowed(1:K)+ana_win_far_windowed(K+1:2*K);
fft_out_far = fft(ana_wined_far, K);
st.subband_in = [fft_out_far(1:st.half_bin)', st.subband_in(:,1:M-1)];
subband_adf_out = sum(st.subband_adf .* st.subband_in,2);
subband_adf_err = fft_out_echo(1:st.half_bin)' - subband_adf_out;
% qr-kalman update
for j = 1:st.half_bin
%update sigmal v
st.v_cov(j) = 0.9*st.v_cov(j) + 0.1*(subband_adf_err(j)*subband_adf_err(j)');
% predict
A1 = [(eye(M).*sqrt(st.w_cov(j))), squeeze(st.Ryu(j,:,:))]';
[~, B1] = qr(A1);
Rmu = B1(1:M,1:M)';
% update
A2 =[sqrt(st.v_cov(j)), zeros(1,M);(st.subband_in(j,:) * Rmu)', Rmu'];
[~,B2] = qr(A2);
r12 = B2(1,1);
g_r12 = B2(1, 2:end)';
gain = g_r12 ./ ((r12)+1e-14);
phi = gain .* subband_adf_err(j);
st.subband_adf(j,:) = st.subband_adf(j,:) + phi.';
st.Ryu(j,:,:) = B2(2:end,2:end)';
%update sigmal w
st.w_cov(j) = 0.9* st.w_cov(j) + 0.1 * (sqrt(phi' * phi)/M);
end
% compose subband
if(1)
% nlp
[st,nlpout] = nlpProcess(st,subband_adf_err, subband_adf_out);
ifft_in = [nlpout', fliplr(conj(nlpout(2:end-1)'))];
else
ifft_in = [subband_adf_err', fliplr(conj(subband_adf_err(2:end-1)'))];
end
fft_out = real(ifft(ifft_in));
win_in = [fft_out, fft_out];
comp_out = win_in .* st.win_global;
st.sys_win = st.sys_win + comp_out;
out = st.sys_win(1 : N);
st.sys_win = [st.sys_win(N + 1 : end), zeros(1, N)];
st.adapt_cnt = st.adapt_cnt + 1;
end
function [st,nlp_out] = nlpProcess(st, error, est_echo)
st.est_ps = abs(est_echo).^2;
res_ps = abs(error).^2;
Eh_curr = res_ps - st.Eh;
Yh_curr = st.est_ps - st.Yh;
Pey = sum(Eh_curr.*Yh_curr);
Pyy = sum(Yh_curr.*Yh_curr);
st.Eh = (1-st.spec_ave)*st.Eh + st.spec_ave*(res_ps);
st.Yh = (1-st.spec_ave)*st.Yh + st.spec_ave*(st.est_ps);
Syy = sum(st.est_ps);
See = sum(res_ps);
Pyy = sqrt(Pyy);
Pey = Pey/(Pyy+1e-10);
tmp32 = st.beta0*Syy/See;
alpha = min(tmp32, st.beta_max);
st.Pyy = (1-alpha)*st.Pyy + alpha*Pyy;
st.Pey = (1-alpha)*st.Pey + alpha*Pey;
st.Pyy = max(st.Pyy,1);
st.Pey = max(st.Pey, st.Pyy*st.min_leak);
st.Pey = min(st.Pey, st.Pyy);
leak = st.Pey/st.Pyy;
if(leak>0.5)
leak=1;
end
residual_ps = leak*st.est_ps*st.suppress_gain;
if(st.adapt_cnt==0)
st.echo_noise_ps = residual_ps;
else
st.echo_noise_ps = max(0.85*st.echo_noise_ps, residual_ps);
end
st.echo_noise_ps = max(st.echo_noise_ps, 1e-10);
postser = res_ps./st.echo_noise_ps -1;
postser = min(postser,100);
if(st.adapt_cnt==0)
st.res_old_ps = res_ps;
end
prioriser = 0.5.*max(0, postser)+0.5.*(st.res_old_ps./st.echo_noise_ps);
prioriser = min(prioriser,100);
st.wiener_gain = prioriser./(prioriser+1);
st.wiener_gain = max(st.wiener_gain,st.gain_floor);
st.res_old_ps = 0.8*st.res_old_ps + 0.2*st.wiener_gain.*res_ps;
nlp_out = st.wiener_gain.*error;
end
end