code: def correlation_diff(spec, diff_frames=1, pos=False, diff_bins=1):
      """
      Calculates the difference of the magnitude spectrogram relative to the
      N-th previous frame shifted in frequency to achieve the highest
      correlation between these two frames.
  
      Parameters
      ----------
      spec : numpy array
          Magnitude spectrogram.
      diff_frames : int, optional
          Calculate the difference to the `diff_frames`-th previous frame.
      pos : bool, optional
          Keep only positive values.
      diff_bins : int, optional
          Maximum number of bins shifted for correlation calculation.
  
      Returns
      -------
      correlation_diff : numpy array
          (Positive) magnitude spectrogram differences.
  
      Notes
      -----
      This function is only because of completeness, it is not intended to be
      actually used, since it is extremely slow. Please consider the superflux()
      function, since if performs equally well but much faster.
  
      """
      # init diff matrix
      diff_spec = np.zeros_like(spec)
      if diff_frames < 1:
          raise ValueError("number of `diff_frames` must be >= 1")
      # calculate the diff
      frames, bins = diff_spec.shape
      corr = np.zeros((frames, diff_bins * 2 + 1))
      for f in range(diff_frames, frames):
          # correlate the frame with the previous one
          # resulting size = bins * 2 - 1
          c = np.correlate(spec[f], spec[f - diff_frames], mode="full")
          # save the middle part
          centre = len(c) / 2
          corr[f] = c[centre - diff_bins : centre + diff_bins + 1]
          # shift the frame for difference calculation according to the
          # highest peak in correlation
          bin_offset = diff_bins - np.argmax(corr[f])
          bin_start = diff_bins + bin_offset
          bin_stop = bins - 2 * diff_bins + bin_start
          diff_spec[f, diff_bins:-diff_bins] = (
              spec[f, diff_bins:-diff_bins]
              - spec[f - diff_frames, bin_start:bin_stop]
          )
      # keep only positive values
      if pos:
          np.maximum(diff_spec, 0, diff_spec)
      return np.asarray(diff_spec)
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  from scipy.ndimage import maximum_filter, minimum_filter, uniform_filter
  from ..audio.signal import smooth
  from ..processors import BufferProcessor, OnlineProcessor, ParallelProcessor, SequentialProcessor
  from ..utils import combine_events
label_desc: Use image node_id 2 for calling a global function with example usage: correlation_diff(spec, diff_frames, pos, diff_bins) and returns: np