msaf/features.py
def compute_features(self):
        """Actual implementation of the features.

        Returns
        -------
        tonnetz: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
        """
        pcp = PCP(self.file_struct, self.feat_type, self.sr, self.hop_length,
                  self.n_bins, self.norm, self.f_min, self.n_octaves).features
        tonnetz = librosa.feature.tonnetz(chroma=pcp.T).T
        return tonnetz
def compute_features(self):
        """Actual implementation of the features.

        Returns
        -------
        tonnetz: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
        """
        pcp = PCP(self.file_struct, self.feat_type, self.sr, self.hop_length,
                  self.n_bins, self.norm, self.f_min, self.n_octaves).features
        tonnetz = librosa.feature.tonnetz(chroma=pcp.T).T
        return tonnetz
def compute_features ( self ) : pcp = PCP ( self . file_struct , self . feat_type , self . sr , self . hop_length , self . n_bins , self . norm , self . f_min , self . n_octaves ) . features tonnetz = librosa . feature . tonnetz ( chroma = pcp . T ) . T return tonnetz
Actual implementation of the features.

        Returns
        -------
        tonnetz: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
Actual implementation of the features .