librosa/core/time_frequency.py
def A_weighting(frequencies, min_db=-80.0):     # pylint: disable=invalid-name
    '''Compute the A-weighting of a set of frequencies.

    Parameters
    ----------
    frequencies : scalar or np.ndarray [shape=(n,)]
        One or more frequencies (in Hz)

    min_db : float [scalar] or None
        Clip weights below this threshold.
        If `None`, no clipping is performed.

    Returns
    -------
    A_weighting : scalar or np.ndarray [shape=(n,)]
        `A_weighting[i]` is the A-weighting of `frequencies[i]`

    See Also
    --------
    perceptual_weighting


    Examples
    --------

    Get the A-weighting for CQT frequencies

    >>> import matplotlib.pyplot as plt
    >>> freqs = librosa.cqt_frequencies(108, librosa.note_to_hz('C1'))
    >>> aw = librosa.A_weighting(freqs)
    >>> plt.plot(freqs, aw)
    >>> plt.xlabel('Frequency (Hz)')
    >>> plt.ylabel('Weighting (log10)')
    >>> plt.title('A-Weighting of CQT frequencies')

    '''

    # Vectorize to make our lives easier
    frequencies = np.asanyarray(frequencies)

    # Pre-compute squared frequency
    f_sq = frequencies**2.0

    const = np.array([12200, 20.6, 107.7, 737.9])**2.0

    weights = 2.0 + 20.0 * (np.log10(const[0]) + 4 * np.log10(frequencies)
                            - np.log10(f_sq + const[0])
                            - np.log10(f_sq + const[1])
                            - 0.5 * np.log10(f_sq + const[2])
                            - 0.5 * np.log10(f_sq + const[3]))

    if min_db is not None:
        weights = np.maximum(min_db, weights)

    return weights
def A_weighting(frequencies, min_db=-80.0):     # pylint: disable=invalid-name
    '''Compute the A-weighting of a set of frequencies.

    Parameters
    ----------
    frequencies : scalar or np.ndarray [shape=(n,)]
        One or more frequencies (in Hz)

    min_db : float [scalar] or None
        Clip weights below this threshold.
        If `None`, no clipping is performed.

    Returns
    -------
    A_weighting : scalar or np.ndarray [shape=(n,)]
        `A_weighting[i]` is the A-weighting of `frequencies[i]`

    See Also
    --------
    perceptual_weighting


    Examples
    --------

    Get the A-weighting for CQT frequencies

    >>> import matplotlib.pyplot as plt
    >>> freqs = librosa.cqt_frequencies(108, librosa.note_to_hz('C1'))
    >>> aw = librosa.A_weighting(freqs)
    >>> plt.plot(freqs, aw)
    >>> plt.xlabel('Frequency (Hz)')
    >>> plt.ylabel('Weighting (log10)')
    >>> plt.title('A-Weighting of CQT frequencies')

    '''

    # Vectorize to make our lives easier
    frequencies = np.asanyarray(frequencies)

    # Pre-compute squared frequency
    f_sq = frequencies**2.0

    const = np.array([12200, 20.6, 107.7, 737.9])**2.0

    weights = 2.0 + 20.0 * (np.log10(const[0]) + 4 * np.log10(frequencies)
                            - np.log10(f_sq + const[0])
                            - np.log10(f_sq + const[1])
                            - 0.5 * np.log10(f_sq + const[2])
                            - 0.5 * np.log10(f_sq + const[3]))

    if min_db is not None:
        weights = np.maximum(min_db, weights)

    return weights
def A_weighting ( frequencies , min_db = - 80.0 ) : # pylint: disable=invalid-name # Vectorize to make our lives easier frequencies = np . asanyarray ( frequencies ) # Pre-compute squared frequency f_sq = frequencies ** 2.0 const = np . array ( [ 12200 , 20.6 , 107.7 , 737.9 ] ) ** 2.0 weights = 2.0 + 20.0 * ( np . log10 ( const [ 0 ] ) + 4 * np . log10 ( frequencies ) - np . log10 ( f_sq + const [ 0 ] ) - np . log10 ( f_sq + const [ 1 ] ) - 0.5 * np . log10 ( f_sq + const [ 2 ] ) - 0.5 * np . log10 ( f_sq + const [ 3 ] ) ) if min_db is not None : weights = np . maximum ( min_db , weights ) return weights
Compute the A-weighting of a set of frequencies.

    Parameters
    ----------
    frequencies : scalar or np.ndarray [shape=(n,)]
        One or more frequencies (in Hz)

    min_db : float [scalar] or None
        Clip weights below this threshold.
        If `None`, no clipping is performed.

    Returns
    -------
    A_weighting : scalar or np.ndarray [shape=(n,)]
        `A_weighting[i]` is the A-weighting of `frequencies[i]`

    See Also
    --------
    perceptual_weighting


    Examples
    --------

    Get the A-weighting for CQT frequencies

    >>> import matplotlib.pyplot as plt
    >>> freqs = librosa.cqt_frequencies(108, librosa.note_to_hz('C1'))
    >>> aw = librosa.A_weighting(freqs)
    >>> plt.plot(freqs, aw)
    >>> plt.xlabel('Frequency (Hz)')
    >>> plt.ylabel('Weighting (log10)')
    >>> plt.title('A-Weighting of CQT frequencies')
Compute the A - weighting of a set of frequencies .