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diff --git a/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py
new file mode 100644
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+++ b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py
@@ -0,0 +1,154 @@
+#!/usr/bin/env python
+# Copyright (c) 2017 The WebRTC project authors. All Rights Reserved.
+#
+# Use of this source code is governed by a BSD-style license
+# that can be found in the LICENSE file in the root of the source
+# tree. An additional intellectual property rights grant can be found
+# in the file PATENTS.  All contributing project authors may
+# be found in the AUTHORS file in the root of the source tree.
+"""Shows boxplots of given score for different values of selected
+parameters. Can be used to compare scores by audioproc_f flag.
+
+Usage: apm_quality_assessment_boxplot.py -o /path/to/output
+                                         -v polqa
+                                         -n /path/to/dir/with/apm_configs
+                                         -z audioproc_f_arg1 [arg2 ...]
+
+Arguments --config_names, --render_names, --echo_simulator_names,
+--test_data_generators, --eval_scores can be used to filter the data
+used for plotting.
+"""
+
+import collections
+import logging
+import matplotlib.pyplot as plt
+import os
+
+import quality_assessment.data_access as data_access
+import quality_assessment.collect_data as collect_data
+
+
+def InstanceArgumentsParser():
+    """Arguments parser factory.
+  """
+    parser = collect_data.InstanceArgumentsParser()
+    parser.description = (
+        'Shows boxplot of given score for different values of selected'
+        'parameters. Can be used to compare scores by audioproc_f flag')
+
+    parser.add_argument('-v',
+                        '--eval_score',
+                        required=True,
+                        help=('Score name for constructing boxplots'))
+
+    parser.add_argument(
+        '-n',
+        '--config_dir',
+        required=False,
+        help=('path to the folder with the configuration files'),
+        default='apm_configs')
+
+    parser.add_argument('-z',
+                        '--params_to_plot',
+                        required=True,
+                        nargs='+',
+                        help=('audioproc_f parameter values'
+                              'by which to group scores (no leading dash)'))
+
+    return parser
+
+
+def FilterScoresByParams(data_frame, filter_params, score_name, config_dir):
+    """Filters data on the values of one or more parameters.
+
+  Args:
+    data_frame: pandas.DataFrame of all used input data.
+
+    filter_params: each config of the input data is assumed to have
+      exactly one parameter from `filter_params` defined. Every value
+      of the parameters in `filter_params` is a key in the returned
+      dict; the associated value is all cells of the data with that
+      value of the parameter.
+
+    score_name: Name of score which value is boxplotted. Currently cannot do
+      more than one value.
+
+    config_dir: path to dir with APM configs.
+
+  Returns: dictionary, key is a param value, result is all scores for
+    that param value (see `filter_params` for explanation).
+  """
+    results = collections.defaultdict(dict)
+    config_names = data_frame['apm_config'].drop_duplicates().values.tolist()
+
+    for config_name in config_names:
+        config_json = data_access.AudioProcConfigFile.Load(
+            os.path.join(config_dir, config_name + '.json'))
+        data_with_config = data_frame[data_frame.apm_config == config_name]
+        data_cell_scores = data_with_config[data_with_config.eval_score_name ==
+                                            score_name]
+
+        # Exactly one of `params_to_plot` must match:
+        (matching_param, ) = [
+            x for x in filter_params if '-' + x in config_json
+        ]
+
+        # Add scores for every track to the result.
+        for capture_name in data_cell_scores.capture:
+            result_score = float(data_cell_scores[data_cell_scores.capture ==
+                                                  capture_name].score)
+            config_dict = results[config_json['-' + matching_param]]
+            if capture_name not in config_dict:
+                config_dict[capture_name] = {}
+
+            config_dict[capture_name][matching_param] = result_score
+
+    return results
+
+
+def _FlattenToScoresList(config_param_score_dict):
+    """Extracts a list of scores from input data structure.
+
+  Args:
+    config_param_score_dict: of the form {'capture_name':
+    {'param_name' : score_value,.. } ..}
+
+  Returns: Plain list of all score value present in input data
+    structure
+  """
+    result = []
+    for capture_name in config_param_score_dict:
+        result += list(config_param_score_dict[capture_name].values())
+    return result
+
+
+def main():
+    # Init.
+    # TODO(alessiob): INFO once debugged.
+    logging.basicConfig(level=logging.DEBUG)
+    parser = InstanceArgumentsParser()
+    args = parser.parse_args()
+
+    # Get the scores.
+    src_path = collect_data.ConstructSrcPath(args)
+    logging.debug(src_path)
+    scores_data_frame = collect_data.FindScores(src_path, args)
+
+    # Filter the data by `args.params_to_plot`
+    scores_filtered = FilterScoresByParams(scores_data_frame,
+                                           args.params_to_plot,
+                                           args.eval_score, args.config_dir)
+
+    data_list = sorted(scores_filtered.items())
+    data_values = [_FlattenToScoresList(x) for (_, x) in data_list]
+    data_labels = [x for (x, _) in data_list]
+
+    _, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
+    axes.boxplot(data_values, labels=data_labels)
+    axes.set_ylabel(args.eval_score)
+    axes.set_xlabel('/'.join(args.params_to_plot))
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()