librosa/core/time_frequency.py
def tempo_frequencies(n_bins, hop_length=512, sr=22050):
    '''Compute the frequencies (in beats-per-minute) corresponding
    to an onset auto-correlation or tempogram matrix.

    Parameters
    ----------
    n_bins : int > 0
        The number of lag bins

    hop_length : int > 0
        The number of samples between each bin

    sr : number > 0
        The audio sampling rate

    Returns
    -------
    bin_frequencies : ndarray [shape=(n_bins,)]
        vector of bin frequencies measured in BPM.

        .. note:: `bin_frequencies[0] = +np.inf` corresponds to 0-lag

    Examples
    --------
    Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram

    >>> librosa.tempo_frequencies(384)
    array([      inf,  2583.984,  1291.992, ...,     6.782,
               6.764,     6.747])
    '''

    bin_frequencies = np.zeros(int(n_bins), dtype=np.float)

    bin_frequencies[0] = np.inf
    bin_frequencies[1:] = 60.0 * sr / (hop_length * np.arange(1.0, n_bins))

    return bin_frequencies
def tempo_frequencies(n_bins, hop_length=512, sr=22050):
    '''Compute the frequencies (in beats-per-minute) corresponding
    to an onset auto-correlation or tempogram matrix.

    Parameters
    ----------
    n_bins : int > 0
        The number of lag bins

    hop_length : int > 0
        The number of samples between each bin

    sr : number > 0
        The audio sampling rate

    Returns
    -------
    bin_frequencies : ndarray [shape=(n_bins,)]
        vector of bin frequencies measured in BPM.

        .. note:: `bin_frequencies[0] = +np.inf` corresponds to 0-lag

    Examples
    --------
    Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram

    >>> librosa.tempo_frequencies(384)
    array([      inf,  2583.984,  1291.992, ...,     6.782,
               6.764,     6.747])
    '''

    bin_frequencies = np.zeros(int(n_bins), dtype=np.float)

    bin_frequencies[0] = np.inf
    bin_frequencies[1:] = 60.0 * sr / (hop_length * np.arange(1.0, n_bins))

    return bin_frequencies
def tempo_frequencies ( n_bins , hop_length = 512 , sr = 22050 ) : bin_frequencies = np . zeros ( int ( n_bins ) , dtype = np . float ) bin_frequencies [ 0 ] = np . inf bin_frequencies [ 1 : ] = 60.0 * sr / ( hop_length * np . arange ( 1.0 , n_bins ) ) return bin_frequencies
Compute the frequencies (in beats-per-minute) corresponding
    to an onset auto-correlation or tempogram matrix.

    Parameters
    ----------
    n_bins : int > 0
        The number of lag bins

    hop_length : int > 0
        The number of samples between each bin

    sr : number > 0
        The audio sampling rate

    Returns
    -------
    bin_frequencies : ndarray [shape=(n_bins,)]
        vector of bin frequencies measured in BPM.

        .. note:: `bin_frequencies[0] = +np.inf` corresponds to 0-lag

    Examples
    --------
    Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram

    >>> librosa.tempo_frequencies(384)
    array([      inf,  2583.984,  1291.992, ...,     6.782,
               6.764,     6.747])
Compute the frequencies ( in beats - per - minute ) corresponding to an onset auto - correlation or tempogram matrix .