def processHierarchical(self):
        """Main process for hierarchical segmentation.
        Returns
        -------
        est_idxs : list
            List containing estimated times for each layer in the hierarchy
            as np.arrays
        est_labels : list
            List containing estimated labels for each layer in the hierarchy
            as np.arrays
        """
        # Preprocess to obtain features, times, and input boundary indeces
        F, dur = features(self.file_struct, self.annot_beats, self.framesync)

        try:
            # Load and apply transform
            W = load_transform(self.config["transform"])
            F = W.dot(F)

            # Get Segments
            kmin, kmax = get_num_segs(dur)

            # Run algorithm layer by layer
            est_idxs = []
            est_labels = []
            for k in range(kmin, kmax):
                S, cost = get_k_segments(F, k)
                est_idxs.append(S)
                est_labels.append(np.ones(len(S) - 1) * -1)

                # Make sure that the first and last boundaries are included
                assert est_idxs[-1][0] == 0 and \
                    est_idxs[-1][-1] == F.shape[1] - 1, "Layer %d does not " \
                    "start or end in the right frame(s)." % k

                # Post process layer
                est_idxs[-1], est_labels[-1] = \
                        self._postprocess(est_idxs[-1], est_labels[-1])
        except:
            # The audio file is too short, only beginning and end
            logging.warning("Audio file too short! "
                            "Only start and end boundaries.")
            est_idxs = [np.array([0, F.shape[1] - 1])]
            est_labels = [np.ones(1) * -1]

        return est_idxs, est_labels
Main process for hierarchical segmentation.
        Returns
        -------
        est_idxs : list
            List containing estimated times for each layer in the hierarchy
            as np.arrays
        est_labels : list
            List containing estimated labels for each layer in the hierarchy
            as np.arrays