diff --git a/mysignal/__init__.py b/mysignal/__init__.py
index 692b2b8..13b519b 100644
--- a/mysignal/__init__.py
+++ b/mysignal/__init__.py
@@ -1 +1 @@
-__version__ = '0.1.27'
+__version__ = '0.1.28'
diff --git a/mysignal/phasefreq.py b/mysignal/phasefreq.py
index c4cd9c9..fd7f2d8 100644
--- a/mysignal/phasefreq.py
+++ b/mysignal/phasefreq.py
@@ -4,7 +4,7 @@ import pandas as pd
 import pywt
 
 import matplotlib.pyplot as plt
-
+from joblib import Parallel, delayed
 
 from scipy.fft import fft, fftfreq
 from scipy.signal import find_peaks, hilbert, butter, filtfilt, correlate, sosfiltfilt
@@ -578,20 +578,38 @@ def analyze_signal_wavelet(time, e_signal, s_signal, freq_rtol=0.01):
     return res
 
 
-def get_phases(e_time, e_filtered, e_frequencies, s_filtered, n_modes, callback=analyze_signal_wavelet):
+def get_phases(e_time, e_filtered, s_filtered, n_modes, callback=analyze_signal_wavelet, n_jobs=-2):
     """
     """
-    res = []
 
-    for mod in range(n_modes):
+    def process_mode(mod, callback, e_time, e_filtered, s_filtered):
+        """Fonction à exécuter en parallèle pour chaque mode"""
         output = callback(e_time, e_filtered[mod], s_filtered[mod], freq_rtol=0.3)
-        res.append({
-                    'mode': mod,
-                    'freq': output['freq_e'],
-                    'phase': output['phase'],
-                    'delay': output['delay']
-            })
-    return pd.DataFrame(res)
+        return {
+            'mode': mod,
+            'freq': output['freq_e'],
+            'phase': output['phase'],
+            'delay': output['delay']
+        }
+
+    results = Parallel(n_jobs=n_jobs)(
+        delayed(process_mode)(mod, callback, e_time, e_filtered, s_filtered)
+        for mod in range(n_modes)
+    )
+
+    return pd.DataFrame(results)
+
+#    res = []
+#
+#    for mod in range(n_modes):
+#        output = callback(e_time, e_filtered[mod], s_filtered[mod], freq_rtol=0.3)
+#        res.append({
+#                    'mode': mod,
+#                    'freq': output['freq_e'],
+#                    'phase': output['phase'],
+#                    'delay': output['delay']
+#            })
+#    return pd.DataFrame(res)
 
 
 def plot_phases(e_time, e_filtered, e_frequencies, s_filtered, n_modes, df, output=None):
@@ -601,8 +619,6 @@ def plot_phases(e_time, e_filtered, e_frequencies, s_filtered, n_modes, df, outp
     fig, ax = plt.subplots(nrows=n_modes, ncols=2, figsize=(12, 2 * n_modes), gridspec_kw={'width_ratios': [3, 1]})
 
     for mod in range(n_modes):
-        res = callback(e_time, e_filtered[mod], s_filtered[mod], freq_rtol=0.3)
-
         freq = df[df['mode'] == mod].get('freq')
         phase = df[df['mode'] == mod].get('phase')
         delay = df[df['mode'] == mod].get('delay')