def cq_to_chroma(n_input, bins_per_octave=12, n_chroma=12,
                 fmin=None, window=None, base_c=True, dtype=np.float32):
    '''Convert a Constant-Q basis to Chroma.


    Parameters
    ----------
    n_input : int > 0 [scalar]
        Number of input components (CQT bins)

    bins_per_octave : int > 0 [scalar]
        How many bins per octave in the CQT

    n_chroma : int > 0 [scalar]
        Number of output bins (per octave) in the chroma

    fmin : None or float > 0
        Center frequency of the first constant-Q channel.
        Default: 'C1' ~= 32.7 Hz

    window : None or np.ndarray
        If provided, the cq_to_chroma filter bank will be
        convolved with `window`.

    base_c : bool
        If True, the first chroma bin will start at 'C'
        If False, the first chroma bin will start at 'A'

    dtype : np.dtype
        The data type of the output basis.
        By default, uses 32-bit (single-precision) floating point.


    Returns
    -------
    cq_to_chroma : np.ndarray [shape=(n_chroma, n_input)]
        Transformation matrix: `Chroma = np.dot(cq_to_chroma, CQT)`

    Raises
    ------
    ParameterError
        If `n_input` is not an integer multiple of `n_chroma`

    Notes
    -----
    This function caches at level 10.

    Examples
    --------
    Get a CQT, and wrap bins to chroma

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> CQT = np.abs(librosa.cqt(y, sr=sr))
    >>> chroma_map = librosa.filters.cq_to_chroma(CQT.shape[0])
    >>> chromagram = chroma_map.dot(CQT)
    >>> # Max-normalize each time step
    >>> chromagram = librosa.util.normalize(chromagram, axis=0)

    >>> import matplotlib.pyplot as plt
    >>> plt.subplot(3, 1, 1)
    >>> librosa.display.specshow(librosa.amplitude_to_db(CQT,
    ...                                                  ref=np.max),
    ...                          y_axis='cqt_note')
    >>> plt.title('CQT Power')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 2)
    >>> librosa.display.specshow(chromagram, y_axis='chroma')
    >>> plt.title('Chroma (wrapped CQT)')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 3)
    >>> chroma = librosa.feature.chroma_stft(y=y, sr=sr)
    >>> librosa.display.specshow(chroma, y_axis='chroma', x_axis='time')
    >>> plt.title('librosa.feature.chroma_stft')
    >>> plt.colorbar()
    >>> plt.tight_layout()

    '''

    # How many fractional bins are we merging?
    n_merge = float(bins_per_octave) / n_chroma

    if fmin is None:
        fmin = note_to_hz('C1')

    if np.mod(n_merge, 1) != 0:
        raise ParameterError('Incompatible CQ merge: '
                             'input bins must be an '
                             'integer multiple of output bins.')

    # Tile the identity to merge fractional bins
    cq_to_ch = np.repeat(np.eye(n_chroma), n_merge, axis=1)

    # Roll it left to center on the target bin
    cq_to_ch = np.roll(cq_to_ch, - int(n_merge // 2), axis=1)

    # How many octaves are we repeating?
    n_octaves = np.ceil(np.float(n_input) / bins_per_octave)

    # Repeat and trim
    cq_to_ch = np.tile(cq_to_ch, int(n_octaves))[:, :n_input]

    # What's the note number of the first bin in the CQT?
    # midi uses 12 bins per octave here
    midi_0 = np.mod(hz_to_midi(fmin), 12)

    if base_c:
        # rotate to C
        roll = midi_0
    else:
        # rotate to A
        roll = midi_0 - 9

    # Adjust the roll in terms of how many chroma we want out
    # We need to be careful with rounding here
    roll = int(np.round(roll * (n_chroma / 12.)))

    # Apply the roll
    cq_to_ch = np.roll(cq_to_ch, roll, axis=0).astype(dtype)

    if window is not None:
        cq_to_ch = scipy.signal.convolve(cq_to_ch,
                                         np.atleast_2d(window),
                                         mode='same')

    return cq_to_ch
Convert a Constant-Q basis to Chroma.


    Parameters
    ----------
    n_input : int > 0 [scalar]
        Number of input components (CQT bins)

    bins_per_octave : int > 0 [scalar]
        How many bins per octave in the CQT

    n_chroma : int > 0 [scalar]
        Number of output bins (per octave) in the chroma

    fmin : None or float > 0
        Center frequency of the first constant-Q channel.
        Default: 'C1' ~= 32.7 Hz

    window : None or np.ndarray
        If provided, the cq_to_chroma filter bank will be
        convolved with `window`.

    base_c : bool
        If True, the first chroma bin will start at 'C'
        If False, the first chroma bin will start at 'A'

    dtype : np.dtype
        The data type of the output basis.
        By default, uses 32-bit (single-precision) floating point.


    Returns
    -------
    cq_to_chroma : np.ndarray [shape=(n_chroma, n_input)]
        Transformation matrix: `Chroma = np.dot(cq_to_chroma, CQT)`

    Raises
    ------
    ParameterError
        If `n_input` is not an integer multiple of `n_chroma`

    Notes
    -----
    This function caches at level 10.

    Examples
    --------
    Get a CQT, and wrap bins to chroma

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> CQT = np.abs(librosa.cqt(y, sr=sr))
    >>> chroma_map = librosa.filters.cq_to_chroma(CQT.shape[0])
    >>> chromagram = chroma_map.dot(CQT)
    >>> # Max-normalize each time step
    >>> chromagram = librosa.util.normalize(chromagram, axis=0)

    >>> import matplotlib.pyplot as plt
    >>> plt.subplot(3, 1, 1)
    >>> librosa.display.specshow(librosa.amplitude_to_db(CQT,
    ...                                                  ref=np.max),
    ...                          y_axis='cqt_note')
    >>> plt.title('CQT Power')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 2)
    >>> librosa.display.specshow(chromagram, y_axis='chroma')
    >>> plt.title('Chroma (wrapped CQT)')
    >>> plt.colorbar()
    >>> plt.subplot(3, 1, 3)
    >>> chroma = librosa.feature.chroma_stft(y=y, sr=sr)
    >>> librosa.display.specshow(chroma, y_axis='chroma', x_axis='time')
    >>> plt.title('librosa.feature.chroma_stft')
    >>> plt.colorbar()
    >>> plt.tight_layout()