librosa.filters.cq_to_chroma

librosa.filters.cq_to_chroma(n_input, *, bins_per_octave=12, n_chroma=12, fmin=None, window=None, base_c=True, dtype=<class 'numpy.float32'>)

Construct a linear transformation matrix to map Constant-Q bins onto chroma bins (i.e., pitch classes).

Parameters

n_inputint > 0 [scalar]

Number of input components (CQT bins)

bins_per_octaveint > 0 [scalar]

How many bins per octave in the CQT

n_chromaint > 0 [scalar]

Number of output bins (per octave) in the chroma

fminNone or float > 0

Center frequency of the first constant-Q channel. Default: ‘C1’ ~= 32.7 Hz

windowNone or np.ndarray

If provided, the cq_to_chroma filter bank will be convolved with window.

base_cbool

If True, the first chroma bin will start at ‘C’ If False, the first chroma bin will start at ‘A’

dtypenp.dtype

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

Returns

cq_to_chromanp.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.ex('trumpet'))
>>> 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
>>> fig, ax = plt.subplots(nrows=3, sharex=True)
>>> imgcq = librosa.display.specshow(librosa.amplitude_to_db(CQT,
...                                                         ref=np.max),
...                                  y_axis='cqt_note', x_axis='time',
...                                  ax=ax[0])
>>> ax[0].set(title='CQT Power')
>>> ax[0].label_outer()
>>> librosa.display.specshow(chromagram, y_axis='chroma', x_axis='time',
...                          ax=ax[1])
>>> ax[1].set(title='Chroma (wrapped CQT)')
>>> ax[1].label_outer()
>>> chroma = librosa.feature.chroma_stft(y=y, sr=sr)
>>> imgchroma = librosa.display.specshow(chroma, y_axis='chroma', x_axis='time', ax=ax[2])
>>> ax[2].set(title='librosa.feature.chroma_stft')