signal_processing.py 文件源码

def istft(stft_signal, signal_len, size=1024, shift=256,
          window=signal.blackman, fading=True, window_length=None):
    """
    Calculated the inverse short time Fourier transform to exactly reconstruct
    the time signal.

    :param stft_signal: Single channel complex STFT signal
        with dimensions frames times size/2+1.
    :param size: Scalar FFT-size.
    :param shift: Scalar FFT-shift. Typically shift is a fraction of size.
    :param window: Window function handle.
    :param fading: Removes the additional padding, if done during STFT.
    :param window_length: Sometimes one desires to use a shorter window than
        the fft size. In that case, the window is padded with zeros.
        The default is to use the fft-size as a window size.
    :return: Single channel complex STFT signal
    :return: Single channel time signal.
    """
    assert stft_signal.shape[1] == size // 2 + 1

    if window_length is None:
        window = window(size)
    else:
        window = window(window_length)
        window = np.pad(window, (0, size - window_length), mode='constant')

    window = _biorthogonal_window_loopy(window, shift)

    # Why? Line created by Hai, Lukas does not know, why it exists.
    window *= size

    time_signal = scipy.zeros(stft_signal.shape[0] * shift + size - shift)

    for j, i in enumerate(range(0, len(time_signal) - size + shift, shift)):
        time_signal[i:i + size] += window * np.real(irfft(stft_signal[j]))
    time_signal = time_signal[0:signal_len]
    # Compensate fade-in and fade-out
    #if fading:
    #    time_signal = time_signal[
    #                  size - shift:len(time_signal) - (size - shift)]

    return time_signal