You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.10.2.


librosa.sequence.dtw(X=None, Y=None, C=None, metric='euclidean', step_sizes_sigma=None, weights_add=None, weights_mul=None, subseq=False, backtrack=True, global_constraints=False, band_rad=0.25, return_steps=False)[source]

Dynamic time warping (DTW).

This function performs a DTW and path backtracking on two sequences. We follow the nomenclature and algorithmic approach as described in [1].

Xnp.ndarray [shape=(K, N)]

audio feature matrix (e.g., chroma features)

Ynp.ndarray [shape=(K, M)]

audio feature matrix (e.g., chroma features)

Cnp.ndarray [shape=(N, M)]

Precomputed distance matrix. If supplied, X and Y must not be supplied and metric will be ignored.


Identifier for the cost-function as documented in scipy.spatial.distance.cdist()

step_sizes_sigmanp.ndarray [shape=[n, 2]]

Specifies allowed step sizes as used by the dtw.

weights_addnp.ndarray [shape=[n, ]]

Additive weights to penalize certain step sizes.

weights_mulnp.ndarray [shape=[n, ]]

Multiplicative weights to penalize certain step sizes.


Enable subsequence DTW, e.g., for retrieval tasks.


Enable backtracking in accumulated cost matrix.


Applies global constraints to the cost matrix C (Sakoe-Chiba band).


The Sakoe-Chiba band radius (1/2 of the width) will be int(radius*min(C.shape)).


If true, the function returns steps, the step matrix, containing the indices of the used steps from the cost accumulation step.

Dnp.ndarray [shape=(N, M)]

accumulated cost matrix. D[N, M] is the total alignment cost. When doing subsequence DTW, D[N,:] indicates a matching function.

wpnp.ndarray [shape=(N, 2)]

Warping path with index pairs. Each row of the array contains an index pair (n, m). Only returned when backtrack is True.

stepsnp.ndarray [shape=(N, M)]

Step matrix, containing the indices of the used steps from the cost accumulation step. Only returned when return_steps is True.


If you are doing diagonal matching and Y is shorter than X or if an incompatible combination of X, Y, and C are supplied.

If your input dimensions are incompatible.

If the cost matrix has NaN values.


>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.ex('brahms'), offset=10, duration=15)
>>> X = librosa.feature.chroma_cens(y=y, sr=sr)
>>> noise = np.random.rand(X.shape[0], 200)
>>> Y = np.concatenate((noise, noise, X, noise), axis=1)
>>> D, wp = librosa.sequence.dtw(X, Y, subseq=True)
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> img = librosa.display.specshow(D, x_axis='frames', y_axis='frames',
...                                ax=ax[0])
>>> ax[0].set(title='DTW cost', xlabel='Noisy sequence', ylabel='Target')
>>> ax[0].plot(wp[:, 1], wp[:, 0], label='Optimal path', color='y')
>>> ax[0].legend()
>>> fig.colorbar(img, ax=ax[0])
>>> ax[1].plot(D[-1, :] / wp.shape[0])
>>> ax[1].set(xlim=[0, Y.shape[1]], ylim=[0, 2],
...           title='Matching cost function')