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

        Returns
        -------
        cqt: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
        """
        linear_cqt = np.abs(librosa.cqt(
            self._audio, sr=self.sr, hop_length=self.hop_length,
            n_bins=self.n_bins, norm=self.norm, filter_scale=self.filter_scale)
                            ) ** 2
        cqt = librosa.amplitude_to_db(linear_cqt, ref=self.ref_power).T
        return cqt
def compute_features(self):
        """Actual implementation of the features.

        Returns
        -------
        cqt: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
        """
        linear_cqt = np.abs(librosa.cqt(
            self._audio, sr=self.sr, hop_length=self.hop_length,
            n_bins=self.n_bins, norm=self.norm, filter_scale=self.filter_scale)
                            ) ** 2
        cqt = librosa.amplitude_to_db(linear_cqt, ref=self.ref_power).T
        return cqt
def compute_features ( self ) : linear_cqt = np . abs ( librosa . cqt ( self . _audio , sr = self . sr , hop_length = self . hop_length , n_bins = self . n_bins , norm = self . norm , filter_scale = self . filter_scale ) ) ** 2 cqt = librosa . amplitude_to_db ( linear_cqt , ref = self . ref_power ) . T return cqt
Actual implementation of the features.

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