Total Harmonic Distorsion plus noise (THD+n) score in APM-QA.

modules/audio_processing/test/py_quality_assessment/quality_assessment/signal_processing.py

 # 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.
 
 """Signal processing utility module.
 """
@@ skipping 131 matching lines @@
       AudioSegment instance.
     """
     generator = pydub.generators.WhiteNoise(
         sample_rate=template.frame_rate,
         bit_depth=template.sample_width * 8)
     return generator.to_audio_segment(
         duration=len(template),
         volume=0.0)
 
   @classmethod
+  def AudioSegmentToRawData(cls, signal):
+    samples = signal.get_array_of_samples()
+    if samples.typecode != 'h':
+      raise exceptions.SignalProcessingException('Unsupported samples type')
+    return np.array(signal.get_array_of_samples(), np.int16)
+
+  @classmethod
   def DetectHardClipping(cls, signal, threshold=2):
     """Detects hard clipping.
 
     Hard clipping is simply detected by counting samples that touch either the
     lower or upper bound too many times in a row (according to |threshold|).
     The presence of a single sequence of samples meeting such property is enough
     to label the signal as hard clipped.
 
     Args:
       signal: AudioSegment instance.
       threshold: minimum number of samples at full-scale in a row.
 
     Returns:
       True if hard clipping is detect, False otherwise.
     """
     if signal.channels != 1:
       raise NotImplementedError('mutliple-channel clipping not implemented')
     if signal.sample_width != 2:  # Note that signal.sample_width is in bytes.
       raise exceptions.SignalProcessingException(
           'hard-clipping detection only supported for 16 bit samples')
-
-    # Get raw samples, check type, cast.
-    samples = signal.get_array_of_samples()
-    if samples.typecode != 'h':
-      raise exceptions.SignalProcessingException(
-          'hard-clipping detection only supported for 16 bit samples')
-    samples = np.array(signal.get_array_of_samples(), np.int16)
+    samples = cls.AudioSegmentToRawData(signal)
 
     # Detect adjacent clipped samples.
     samples_type_info = np.iinfo(samples.dtype)
     mask_min = samples == samples_type_info.min
     mask_max = samples == samples_type_info.max
 
     def HasLongSequence(vector, min_legth=threshold):
       """Returns True if there are one or more long sequences of True flags."""
       seq_length = 0
       for b in vector:
@@ skipping 138 matching lines @@
         logging.debug('  signal (pre): %d ms', len(signal))
         signal = signal + padding
         logging.debug('  signal (post): %d ms', len(signal))
 
         # Update power.
         signal_power = float(signal.dBFS)
 
     # Mix signals using the target SNR.
     gain_db = signal_power - noise_power - target_snr
     return cls.Normalize(signal.overlay(noise.apply_gain(gain_db)))