correlate.py 文件源码

def spectral_whitening(data, sr=None, smooth=None, filter=None,
                       waterlevel=1e-8):
    """
    Apply spectral whitening to data

    Data is divided by its smoothed (Default: None) amplitude spectrum.

    :param data: numpy array with data to manipulate
    :param sr: sampling rate (only needed for smoothing)
    :param smooth: length of smoothing window in Hz
        (default None -> no smoothing)
    :param filter: filter spectrum with bandpass after whitening
        (tuple with min and max frequency)
    :param waterlevel: waterlevel relative to mean of spectrum

    :return: whitened data
    """
    mask = np.ma.getmask(data)
    N = len(data)
    nfft = next_fast_len(N)
    spec = fft(data, nfft)
    spec_ampl = np.sqrt(np.abs(np.multiply(spec, np.conjugate(spec))))
    if smooth:
        smooth = int(smooth * N / sr)
        spec_ampl = ifftshift(smooth_func(fftshift(spec_ampl), smooth))
    # save guard against division by 0
    wl = waterlevel * np.mean(spec_ampl)
    spec_ampl[spec_ampl < wl] = wl
    spec /= spec_ampl
    if filter is not None:
        spec *= _filter_resp(*filter, sr=sr, N=len(spec), whole=True)[1]
    ret = np.real(ifft(spec, nfft)[:N])
    return _fill_array(ret, mask=mask, fill_value=0.)