nussl/separation/primitive/repet_sim.py
def _find_similarity_indices(self):
        """Finds the similarity indices for all time frames from the similarity matrix

        Returns:
            similarity_indices (list of lists): similarity indices for all time frames
        """
        similarity_indices = []
        for i in range(self.audio_signal.stft_length):
            cur_indices = utils.find_peak_indices(
                self.similarity_matrix[i, :], self.max_repeating_frames,
                min_dist=self.min_distance_between_frames,
                threshold=self.similarity_threshold)

            # the first peak is always itself so we throw it out
            # we also want only self.max_repeating_frames peaks
            # so +1 for 0-based, and +1 for the first peak we threw out
            cur_indices = cur_indices[1:self.max_repeating_frames + 2]
            similarity_indices.append(cur_indices)

        return similarity_indices
Finds the similarity indices for all time frames from the similarity matrix

        Returns:
            similarity_indices (list of lists): similarity indices for all time frames
        
Finds the similarity indices for all time frames from the similarity matrix
Finds the similarity indices for all time frames from the similarity matrix
def _find_similarity_indices(self):
        similarity_indices = []
        for i in range(self.audio_signal.stft_length):
            cur_indices = utils.find_peak_indices(
                self.similarity_matrix[i, :], self.max_repeating_frames,
                min_dist=self.min_distance_between_frames,
                threshold=self.similarity_threshold)
            cur_indices = cur_indices[1:self.max_repeating_frames + 2]
            similarity_indices.append(cur_indices)
        return similarity_indices
def _find_similarity_indices ( self ) : similarity_indices = [ ] for i in range ( self . audio_signal . stft_length ) : cur_indices = utils . find_peak_indices ( self . similarity_matrix [ i , : ] , self . max_repeating_frames , min_dist = self . min_distance_between_frames , threshold = self . similarity_threshold ) cur_indices = cur_indices [ 1 : self . max_repeating_frames + 2 ] similarity_indices . append ( cur_indices ) return similarity_indices
Finds the similarity indices for all time frames from the similarity matrix
Finds the similarity indices for all time frames from the similarity matrix
"""Finds the similarity indices for all time frames from the similarity matrix

        Returns:
            similarity_indices (list of lists): similarity indices for all time frames
        """ # the first peak is always itself so we throw it out # we also want only self.max_repeating_frames peaks # so +1 for 0-based, and +1 for the first peak we threw out
similarity_indices (list of lists): similarity indices for all time frames
similarity_indices ( list of lists ) : similarity indices for all time frames
self