function fdet_eval(f_true, phi_true, knoise_dB, p)
%
% Example: fdet_eval(1200, pi/4, -10, [1100 1350 1])

fs = 48000;
N = 1024;
K = 20;

f_min = p(1);
f_max = p(2);
f_step = p(3);

% Adaption rate
mu = 0.01;

% 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);

last_mag = 0;

% Initial guess
f_est = fdet_coarse(x_true, N, fs, f_min, f_step, f_max)

Xm = zeros(N, 1);
for k=1:K
    xp = x_true((k-1)*N+1:k*N);
    x_est = exp(-j*(2*pi*f_est/fs*(0:N-1)' + 0));
    dmod(k) = sum(xp.*x_est);
    mag = abs(dmod(k))/N;
    err(k) = mag-last_mag;
    err(k) = sin(1*err(k));
    last_mag = mag;
    phi(k) = angle(dmod(k));
    f_est = f_est + mu*err(k);
	f(k) = f_est;
end;

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), real(dmod)/N, 1:lge(f), imag(dmod)/N, 1:lge(f), abs(dmod)/N); grid; legend('Re', 'Im', 'abs()');

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

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

subplot(4,1,4)
plot(1:lge(f), phi, '-*'); grid; legend('phi');