signal.py 文件源码

def ispec(spec, frame_length, step_length=None, window="hann",
          nb_iter=48, normalize=True, db=False, padding=False):
  """ Invert power spectrogram back to raw waveform

  Parameters
  ----------
  spec : np.ndarray [shape=(t, n_fft / 2 + 1)]
      magnitude, power, or DB spectrogram of STFT
  frame_length: int
      number of samples point for 1 frame
  step_length: int
      number of samples point for 1 step (when shifting the frames)
      If unspecified, defaults `frame_length / 4`.
  window : string, tuple, number, function, or np.ndarray [shape=(n_fft,)]
      - a window specification (string, tuple, or number);
        see `scipy.signal.get_window`
      - a window function, such as `scipy.signal.hanning`
      - a vector or array of length `n_fft`
  nb_iter: int
      number of iteration, the higher the better audio quality
  db: bool
      if the given spectrogram is in decibel (dB) units (used logarithm)
  normalize: bool
      normalize output raw signal to have mean=0., and std=1.

  """
  spec = spec.astype('float64')
  # ====== check arguments ====== #
  frame_length = int(frame_length)
  if step_length is None:
    step_length = frame_length // 4
  else:
    step_length = int(step_length)
  # ====== convert to power spectrogram ====== #
  if db:
    spec = db2power(spec)
  # ====== iterative estmate best phase ====== #
  X_best = copy.deepcopy(spec)
  nfft = (spec.shape[1] - 1) * 2
  for i in range(nb_iter):
    X_t = istft(X_best, frame_length=frame_length, step_length=step_length,
                window=window, padding=padding)
    est = stft(X_t, frame_length=frame_length, step_length=step_length,
               nfft=nfft, window=window, padding=padding, energy=False)
    phase = est / np.maximum(1e-8, np.abs(est))
    X_best = spec * phase
  X_t = istft(X_best, frame_length=frame_length, step_length=step_length,
              window=window, padding=padding)
  y = np.real(X_t)
  if normalize:
    y = (y - y.mean()) / y.std()
  return y


# ===========================================================================
# F0 analysis
# ===========================================================================