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

        Returns
        -------
        pcp: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
        """
        audio_harmonic, _ = self.compute_HPSS()
        pcp_cqt = np.abs(librosa.hybrid_cqt(audio_harmonic,
                                            sr=self.sr,
                                            hop_length=self.hop_length,
                                            n_bins=self.n_bins,
                                            norm=self.norm,
                                            fmin=self.f_min)) ** 2
        pcp = librosa.feature.chroma_cqt(C=pcp_cqt,
                                         sr=self.sr,
                                         hop_length=self.hop_length,
                                         n_octaves=self.n_octaves,
                                         fmin=self.f_min).T
        return pcp
def compute_features(self):
        """Actual implementation of the features.

        Returns
        -------
        pcp: np.array(N, F)
            The features, each row representing a feature vector for a give
            time frame/beat.
        """
        audio_harmonic, _ = self.compute_HPSS()
        pcp_cqt = np.abs(librosa.hybrid_cqt(audio_harmonic,
                                            sr=self.sr,
                                            hop_length=self.hop_length,
                                            n_bins=self.n_bins,
                                            norm=self.norm,
                                            fmin=self.f_min)) ** 2
        pcp = librosa.feature.chroma_cqt(C=pcp_cqt,
                                         sr=self.sr,
                                         hop_length=self.hop_length,
                                         n_octaves=self.n_octaves,
                                         fmin=self.f_min).T
        return pcp
Actual implementation of the features.

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