function fdet_eval(f_true, knoise_dB, numFrames)

fs = 48000;
N = 1024;
K = numFrames;
phi_true = 1;

% Adaption rate
mu = 20.0;

% Generate signal
x_true = exp(j*(2*pi*f_true/fs*(0:K*N-1)'+phi_true)) + sqrt(10^(knoise_dB/10))*randn(K*N,1);

% Initial guess
X = fft(x_true, N);
[v maxk] = max(abs(X));
f_est = (maxk-1)*fs/N; % * (1+angle(X(maxk)))

for k=1:K
    xp = x_true((k-1)*N+1:k*N);
    phi_est = angle(xp(1));
    x_est = exp(-j*(2*pi*f_est/fs*(0:N-1)'+phi_est));
    dmod(k) = sum(xp.*x_est);
    err(k) = sign(imag(dmod(k)))*(1-real(dmod(k)/abs(dmod(k))))^.5;
    f_est = f_est + mu*err(k);
	f(k) = f_est;
    phi(k) = phi_est;
end;

f_true = f_true
f_est = f_est
mean_fest = mean(f(K/2+1:K))
error_ppm = 1E6*(1-mean_fest/f_true)

close all;
subplot(4,1,1)
plot(1:lge(f), 1-real(dmod)./abs(dmod), 1:lge(f), imag(dmod)./abs(dmod), 1:lge(f), real(dmod)./abs(dmod)); grid;

subplot(4,1,2)
plot(1:lge(f), err, '-*'); grid;

subplot(4,1,3)
plot(1:lge(f), f); grid;

subplot(4,1,4)
plot(1:lge(f), angle(dmod)); grid;