diff --git a/PyHa/microfaune_package/microfaune/data_augmentation.py b/PyHa/microfaune_package/microfaune/data_augmentation.py
index e8a5c09..bf37db4 100644
--- a/PyHa/microfaune_package/microfaune/data_augmentation.py
+++ b/PyHa/microfaune_package/microfaune/data_augmentation.py
@@ -49,14 +49,14 @@ def generate_augmentation(self, spec, y_val, my_range=5, to_display=False):
         """ data augmentation of one Spectrogram
         Parameters
         ----------
-        spec spectogram
+        spec spectrogram
         y_val classification value
 
         Returns
         -------
         list_s
-            list of Spectograms with Y list (duplicate from y input)
-            the first Spectograms is the given input S
+            list of Spectrograms with Y list (duplicate from y input)
+            the first Spectrograms is the given input S
         list_y
             All y have the value of the given y
         """
diff --git a/PyHa/microfaune_package/microfaune/dataaugmentation.py b/PyHa/microfaune_package/microfaune/dataaugmentation.py
deleted file mode 100644
index e8a5c09..0000000
--- a/PyHa/microfaune_package/microfaune/dataaugmentation.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Sat Oct  5 16:18:32 2019
-
-
-@author: christian
-"""
-import numpy as np
-from keras.preprocessing.image import ImageDataGenerator
-from microfaune import plot
-
-class DataAugmentation:
-    """Class to generate image data for rnn modeling
-    """
-    datagenerator_list = None
-
-    def __init__(self, width_shift_range=None, horizontal_flip=True, \
-                 brightness_range=None):
-        """Initialization data generators
-
-        Parameters
-        ----------
-        width_shift_range: list
-            width of the random horizontal shift
-        horizontal_flip: bool
-            moving all pixels of the image horizontally,
-            while keeping the image dimensions the same.
-        brightness_range: list
-        The brightness can be augmented by randomly darkening images,
-        where has no effect on darkness
-
-        Initiate
-        -------
-        datagenerator_list: list
-            image data generators
-        """
-        if width_shift_range is None:
-            width_shift_range = [-40, 40]
-        if brightness_range is None:
-            brightness_range = [0.4, 0.9]
-
-        datagen_width_shift = ImageDataGenerator(width_shift_range=width_shift_range)
-        datagen_horizontal_flip = ImageDataGenerator(horizontal_flip=horizontal_flip)
-        datagen_brightness = ImageDataGenerator(brightness_range=brightness_range)
-        self.datagenerator_list = [datagen_width_shift, datagen_horizontal_flip, datagen_brightness]
-
-    def generate_augmentation(self, spec, y_val, my_range=5, to_display=False):
-        """ data augmentation of one Spectrogram
-        Parameters
-        ----------
-        spec spectogram
-        y_val classification value
-
-        Returns
-        -------
-        list_s
-            list of Spectograms with Y list (duplicate from y input)
-            the first Spectograms is the given input S
-        list_y
-            All y have the value of the given y
-        """
-        list_s = [spec]
-        list_y = [y_val]
-        for datagen in self.datagenerator_list:
-            data = np.expand_dims(spec, axis=2)
-
-            # expand dimension to one sample
-            samples = np.expand_dims(data, 0)
-            # prepare iterator
-            cursor = datagen.flow(samples, batch_size=1)
-            # generate samples and plot
-            for _ in range(my_range):
-                batch = cursor.next()
-                # convert to unsigned integers for viewing
-                image = batch[0].astype('uint8')
-                image = image[:, :, 0]
-                list_s.append(image)
-                list_y.append(y_val)
-                if to_display:
-                    plot.plot_spec(image)
-        return list_s, list_y
-
-    def generate_augmentation_list(self, list_s, list_y, my_range=5, to_display=False):
-        """ data augmentation of a  list of Spectrograms
-        Parameters
-        ----------
-        list_s  vector of spectograms
-        list_y  vector of y
-
-        Returns
-        -------
-        list_s_augmented
-            list of Spectograms augmented
-        list_y_augmented
-            list of y augmented with duplicate values
-        """
-        list_s_augmented = []
-        list_y_augmented = []
-        for spec, y_val in zip(list_s, list_y):
-            lstx, lsty = self.generate_augmentation(spec, y_val, my_range, to_display)
-            list_s_augmented += lstx
-            list_y_augmented += lsty
-        return list_s_augmented, list_y_augmented
diff --git a/PyHa/microfaune_package/microfaune/labeling.py b/PyHa/microfaune_package/microfaune/labeling.py
index e193125..de28af6 100644
--- a/PyHa/microfaune_package/microfaune/labeling.py
+++ b/PyHa/microfaune_package/microfaune/labeling.py
@@ -246,7 +246,7 @@ def extract_labels(json_path, start_time, duration):
         Returns
         -------
             labels: list
-                List of labelson the audio extract, each label is a dictionary with keys 'id', 'start', 'end' and 'annotation'
+                List of labels on the audio extract, each label is a dictionary with keys 'id', 'start', 'end' and 'annotation'
         """
     data_dict = read_json_file(json_path)
     labels = []
diff --git a/PyHa/statistics.py b/PyHa/statistics.py
index c3bf017..7f2ad59 100644
--- a/PyHa/statistics.py
+++ b/PyHa/statistics.py
@@ -367,7 +367,7 @@ def clip_IoU(automated_df, manual_df):
 
 def matrix_IoU_Scores(IoU_Matrix, manual_df, threshold):
     """
-    Function that takes in the IoU Matrix from the clip_IoU function and ouputs
+    Function that takes in the IoU Matrix from the clip_IoU function and outputs
     the number of true positives and false positives, as well as calculating
     the precision, recall, and f1 metrics.