Dynamic Time Warping Python Module

Build Status

Dynamic time warping is used as a similarity measured between temporal sequences. This package provides two implementations:


import numpy as np

# We define two sequences x, y as numpy array
# where y is actually a sub-sequence from x
x = np.array([2, 0, 1, 1, 2, 4, 2, 1, 2, 0]).reshape(-1, 1)
y = np.array([1, 1, 2, 4, 2, 1, 2, 0]).reshape(-1, 1)

from dtw import dtw

manhattan_distance = lambda x, y: np.abs(x - y)

d, cost_matrix, acc_cost_matrix, path = dtw(x, y, dist=manhattan_distance)

print(d)
>>> 2.0 # Only the cost for the insertions is kept

# You can also visualise the accumulated cost and the shortest path
import matplotlib.pyplot as plt

plt.imshow(acc_cost_matrix.T, origin='lower', cmap='gray', interpolation='nearest')
plt.plot(path[0], path[1], 'w')
plt.show()

Result of the accumulated cost matrix and the shortest path (in white) found: Acc cost matrix and shortest path

Other examples are available as notebook

  • the code above as a notebook
  • a sound comparison based on DTW + MFCC
  • simple speech recognition

Installation

python -m pip install dtw

It is tested on Python 2.7, 3.4, 3.5 and 3.6. It requires numpy and scipy.

Dtw

DTW (Dynamic Time Warping) python module

Dtw Info

⭐ Stars 893
🔗 Source Code github.com
🕒 Last Update 9 days ago
🕒 Created 7 years ago
🐞 Open Issues 9
➗ Star-Issue Ratio 99
😎 Author pierre-rouanet