librosa/effects.py
def harmonic(y, **kwargs):
    '''Extract harmonic elements from an audio time-series.

    Parameters
    ----------
    y : np.ndarray [shape=(n,)]
        audio time series
    kwargs : additional keyword arguments.
        See `librosa.decompose.hpss` for details.

    Returns
    -------
    y_harmonic : np.ndarray [shape=(n,)]
        audio time series of just the harmonic portion

    See Also
    --------
    hpss : Separate harmonic and percussive components
    percussive : Extract only the percussive component
    librosa.decompose.hpss : HPSS for spectrograms

    Examples
    --------
    >>> # Extract harmonic component
    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> y_harmonic = librosa.effects.harmonic(y)

    >>> # Use a margin > 1.0 for greater harmonic separation
    >>> y_harmonic = librosa.effects.harmonic(y, margin=3.0)

    '''

    # Compute the STFT matrix
    stft = core.stft(y)

    # Remove percussives
    stft_harm = decompose.hpss(stft, **kwargs)[0]

    # Invert the STFTs
    y_harm = util.fix_length(core.istft(stft_harm, dtype=y.dtype), len(y))

    return y_harm
def harmonic(y, **kwargs):
    '''Extract harmonic elements from an audio time-series.

    Parameters
    ----------
    y : np.ndarray [shape=(n,)]
        audio time series
    kwargs : additional keyword arguments.
        See `librosa.decompose.hpss` for details.

    Returns
    -------
    y_harmonic : np.ndarray [shape=(n,)]
        audio time series of just the harmonic portion

    See Also
    --------
    hpss : Separate harmonic and percussive components
    percussive : Extract only the percussive component
    librosa.decompose.hpss : HPSS for spectrograms

    Examples
    --------
    >>> # Extract harmonic component
    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> y_harmonic = librosa.effects.harmonic(y)

    >>> # Use a margin > 1.0 for greater harmonic separation
    >>> y_harmonic = librosa.effects.harmonic(y, margin=3.0)

    '''

    # Compute the STFT matrix
    stft = core.stft(y)

    # Remove percussives
    stft_harm = decompose.hpss(stft, **kwargs)[0]

    # Invert the STFTs
    y_harm = util.fix_length(core.istft(stft_harm, dtype=y.dtype), len(y))

    return y_harm
def harmonic ( y , * * kwargs ) : # Compute the STFT matrix stft = core . stft ( y ) # Remove percussives stft_harm = decompose . hpss ( stft , * * kwargs ) [ 0 ] # Invert the STFTs y_harm = util . fix_length ( core . istft ( stft_harm , dtype = y . dtype ) , len ( y ) ) return y_harm
Extract harmonic elements from an audio time-series.

    Parameters
    ----------
    y : np.ndarray [shape=(n,)]
        audio time series
    kwargs : additional keyword arguments.
        See `librosa.decompose.hpss` for details.

    Returns
    -------
    y_harmonic : np.ndarray [shape=(n,)]
        audio time series of just the harmonic portion

    See Also
    --------
    hpss : Separate harmonic and percussive components
    percussive : Extract only the percussive component
    librosa.decompose.hpss : HPSS for spectrograms

    Examples
    --------
    >>> # Extract harmonic component
    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> y_harmonic = librosa.effects.harmonic(y)

    >>> # Use a margin > 1.0 for greater harmonic separation
    >>> y_harmonic = librosa.effects.harmonic(y, margin=3.0)
Extract harmonic elements from an audio time - series .