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librosa.util.sync

librosa.util.sync(data, idx, *, aggregate=None, pad=True, axis=-1)[source]

Synchronous aggregation of a multi-dimensional array between boundaries

Note

In order to ensure total coverage, boundary points may be added to idx.

If synchronizing a feature matrix against beat tracker output, ensure that frame index numbers are properly aligned and use the same hop length.

Parameters:

datanp.ndarray: multi-dimensional array of features
idxsequence of ints or slices: Either an ordered array of boundary indices, or an iterable collection of slice objects.
aggregatefunction: aggregation function (default: np.mean)
padboolean: If True, idx is padded to span the full range [0, data.shape[axis]]
axisint: The axis along which to aggregate data

Returns:

data_syncndarray

data_sync will have the same dimension as data, except that the axis coordinate will be reduced according to idx.

For example, a 2-dimensional data with axis=-1 should satisfy:

data_sync[:, i] = aggregate(data[:, idx[i-1]:idx[i]], axis=-1)

Raises:

ParameterError: If the index set is not of consistent type (all slices or all integers)

Notes

This function caches at level 40.

Examples

Beat-synchronous CQT spectra

>>> y, sr = librosa.load(librosa.ex('choice'))
>>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr, trim=False)
>>> C = np.abs(librosa.cqt(y=y, sr=sr))
>>> beats = librosa.util.fix_frames(beats)

By default, use mean aggregation

>>> C_avg = librosa.util.sync(C, beats)

Use median-aggregation instead of mean

>>> C_med = librosa.util.sync(C, beats,
...                              aggregate=np.median)

Or sub-beat synchronization

>>> sub_beats = librosa.segment.subsegment(C, beats)
>>> sub_beats = librosa.util.fix_frames(sub_beats)
>>> C_med_sub = librosa.util.sync(C, sub_beats, aggregate=np.median)

Plot the results

>>> import matplotlib.pyplot as plt
>>> beat_t = librosa.frames_to_time(beats, sr=sr)
>>> subbeat_t = librosa.frames_to_time(sub_beats, sr=sr)
>>> fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True)
>>> librosa.display.specshow(librosa.amplitude_to_db(C,
...                                                  ref=np.max),
...                          x_axis='time', ax=ax[0])
>>> ax[0].set(title='CQT power, shape={}'.format(C.shape))
>>> ax[0].label_outer()
>>> librosa.display.specshow(librosa.amplitude_to_db(C_med,
...                                                  ref=np.max),
...                          x_coords=beat_t, x_axis='time', ax=ax[1])
>>> ax[1].set(title='Beat synchronous CQT power, '
...                 'shape={}'.format(C_med.shape))
>>> ax[1].label_outer()
>>> librosa.display.specshow(librosa.amplitude_to_db(C_med_sub,
...                                                  ref=np.max),
...                          x_coords=subbeat_t, x_axis='time', ax=ax[2])
>>> ax[2].set(title='Sub-beat synchronous CQT power, '
...                 'shape={}'.format(C_med_sub.shape))