librosa.segment.timelag_filter

librosa.segment.timelag_filter(function, pad=True, index=0)

Apply a filter in the time-lag domain.

This is primarily useful for adapting image filters to operate on recurrence_to_lag output.

Using timelag_filter is equivalent to the following sequence of operations:

>>> data_tl = librosa.segment.recurrence_to_lag(data)
>>> data_filtered_tl = function(data_tl)
>>> data_filtered = librosa.segment.lag_to_recurrence(data_filtered_tl)

Parameters:

functioncallable

The filtering function to wrap, e.g., scipy.ndimage.median_filter

padbool

Whether to zero-pad the structure feature matrix

indexint >= 0

If function accepts input data as a positional argument, it should be indexed by index

Returns:

wrapped_functioncallable

A new filter function which applies in time-lag space rather than time-time space.

Examples

Apply a 31-bin median filter to the diagonal of a recurrence matrix. With default, parameters, this corresponds to a time window of about 0.72 seconds.

>>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=30)
>>> chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
>>> chroma_stack = librosa.feature.stack_memory(chroma, n_steps=3, delay=3)
>>> rec = librosa.segment.recurrence_matrix(chroma_stack)
>>> from scipy.ndimage import median_filter
>>> diagonal_median = librosa.segment.timelag_filter(median_filter)
>>> rec_filtered = diagonal_median(rec, size=(1, 31), mode='mirror')

Or with affinity weights

>>> rec_aff = librosa.segment.recurrence_matrix(chroma_stack, mode='affinity')
>>> rec_aff_fil = diagonal_median(rec_aff, size=(1, 31), mode='mirror')

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
>>> librosa.display.specshow(rec, y_axis='s', x_axis='s', ax=ax[0, 0])
>>> ax[0, 0].set(title='Raw recurrence matrix')
>>> ax[0, 0].label_outer()
>>> librosa.display.specshow(rec_filtered, y_axis='s', x_axis='s', ax=ax[0, 1])
>>> ax[0, 1].set(title='Filtered recurrence matrix')
>>> ax[0, 1].label_outer()
>>> librosa.display.specshow(rec_aff, x_axis='s', y_axis='s',
...                          cmap='magma_r', ax=ax[1, 0])
>>> ax[1, 0].set(title='Raw affinity matrix')
>>> librosa.display.specshow(rec_aff_fil, x_axis='s', y_axis='s',
...                          cmap='magma_r', ax=ax[1, 1])
>>> ax[1, 1].set(title='Filtered affinity matrix')
>>> ax[1, 1].label_outer()