# Spectral factorization using Kolmogorov 1939 approach

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    % (code follows pp. 232-233, Signal Analysis, by A. Papoulis)
    %
    % Computes the minimum-phase impulse response which satisfies
    % given auto-correlation.
    %
    % Input:
    %   r: top-half of the auto-correlation coefficients
    %      starts from 0th element to end of the auto-corelation
    %      should be passed in as a column vector
    % Output
    %   h: impulse response that gives the desired auto-correlation

    function h = spectral_fact(r)

    % length of the impulse response sequence
    nr = length(r);
    n  = (nr+1)/2;

    % over-sampling factor
    mult_factor = 30;        % should have mult_factor*(n) >> n
    m = mult_factor*n;

    % computation method:
    % H(exp(jTw)) = alpha(w) + j*phi(w)
    % where alpha(w) = 1/2*ln(R(w)) and phi(w) = Hilbert_trans(alpha(w))

    % compute 1/2*ln(R(w))
    w = 2*pi*[0:m-1]/m;
    R = exp( -j*kron(w',[-(n-1):n-1]) )*r;
    R = abs(real(R)); % remove numerical noise from the imaginary part
    figure; plot(20*log10(R));
    alpha = 1/2*log(R);

    % find the Hilbert transform
    alphatmp = fft(alpha);
    alphatmp(floor(m/2)+1:m) = -alphatmp(floor(m/2)+1:m);
    alphatmp(1) = 0;
    alphatmp(floor(m/2)+1) = 0;
    phi = real(ifft(j*alphatmp));

    % now retrieve the original sampling
    index  = find(rem([0:m-1],mult_factor)==0);
    alpha1 = alpha(index);
    phi1   = phi(index);

    % compute the impulse response (inverse Fourier transform)
    h = ifft(exp(alpha1+j*phi1),n);