diff --git a/doc/tutorial/statistical_inference/unsupervised_learning.rst b/doc/tutorial/statistical_inference/unsupervised_learning.rst
index be32fabd96cb8..afe51320414c6 100644
--- a/doc/tutorial/statistical_inference/unsupervised_learning.rst
+++ b/doc/tutorial/statistical_inference/unsupervised_learning.rst
@@ -305,14 +305,17 @@ a maximum amount of independent information. It is able to recover
 ::
 
     >>> # Generate sample data
+    >>> import numpy as np
+    >>> from scipy import signal
     >>> time = np.linspace(0, 10, 2000)
     >>> s1 = np.sin(2 * time)  # Signal 1 : sinusoidal signal
     >>> s2 = np.sign(np.sin(3 * time))  # Signal 2 : square signal
-    >>> S = np.c_[s1, s2]
+    >>> s3 = signal.sawtooth(2 * np.pi * time)  # Signal 3: saw tooth signal
+    >>> S = np.c_[s1, s2, s3]
     >>> S += 0.2 * np.random.normal(size=S.shape)  # Add noise
     >>> S /= S.std(axis=0)  # Standardize data
     >>> # Mix data
-    >>> A = np.array([[1, 1], [0.5, 2]])  # Mixing matrix
+    >>> A = np.array([[1, 1, 1], [0.5, 2, 1], [1.5, 1, 2]])  # Mixing matrix
     >>> X = np.dot(S, A.T)  # Generate observations
 
     >>> # Compute ICA