librosa.segment.recurrence_matrix

librosa.segment.recurrence_matrix(data, k=None, width=1, metric='sqeuclidean', sym=False, axis=-1)[source]

Compute the binary recurrence matrix from a time-series.

rec[i,j] == True if (and only if) (data[:,i], data[:,j]) are k-nearest-neighbors and |i-j| >= width

Parameters:

data : np.ndarray

A feature matrix

k : int > 0 [scalar] or None

the number of nearest-neighbors for each sample

Default: k = 2 * ceil(sqrt(t - 2 * width + 1)), or k = 2 if t <= 2 * width + 1

width : int >= 1 [scalar]

only link neighbors (data[:, i], data[:, j]) if |i-j| >= width

metric : str

Distance metric to use for nearest-neighbor calculation.

See scipy.spatial.distance.cdist for details.

sym : bool [scalar]

set sym=True to only link mutual nearest-neighbors

axis : int

The axis along which to compute recurrence. By default, the last index (-1) is taken.

Returns:

rec : np.ndarray [shape=(t,t), dtype=bool]

Binary recurrence matrix

See also

scipy.spatial.distance.cdist, librosa.feature.stack_memory, structure_feature

Examples

Find nearest neighbors in MFCC space

>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> mfcc = librosa.feature.mfcc(y=y, sr=sr)
>>> R = librosa.segment.recurrence_matrix(mfcc)

Or fix the number of nearest neighbors to 5

>>> R = librosa.segment.recurrence_matrix(mfcc, k=5)

Suppress neighbors within +- 7 samples

>>> R = librosa.segment.recurrence_matrix(mfcc, width=7)

Use cosine similarity instead of Euclidean distance

>>> R = librosa.segment.recurrence_matrix(mfcc, metric='cosine')

Require mutual nearest neighbors

>>> R = librosa.segment.recurrence_matrix(mfcc, sym=True)

Plot the feature and recurrence matrices

>>> import matplotlib.pyplot as plt
>>> plt.figure(figsize=(10, 6))
>>> plt.subplot(1, 2, 1)
>>> librosa.display.specshow(mfcc, x_axis='time')
>>> plt.title('MFCC')
>>> plt.subplot(1, 2, 2)
>>> librosa.display.specshow(R, x_axis='time', y_axis='time',
...                          aspect='equal')
>>> plt.title('MFCC recurrence (symmetric)')
>>> plt.tight_layout()

(Source code)

../_images/librosa-segment-recurrence_matrix-1.png