view src/Modules/BMM/ModuleGammatone_test.py @ 5:3c782dec2fc0

- Ported over HTK file output
- Added some more meat to the Slaney IIR gammatone implementation
- Ported over the AIM-MAT sf2003 parabola strobe algorithm
- Finished making the SAI implementation compile
- Ported over the strobe list class (now uses STL deques internally)

#!/usr/bin/env python
# encoding: utf-8
#
# AIM-C: A C++ implementation of the Auditory Image Model
# http://www.acousticscale.org/AIMC
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""
ModuleGammatone_test.py

Created by Thomas Walters on 2010-02-15.
Copyright 2010 Thomas Walters <[e-mail]>
Test for the Slaney IIR gammatone.
"""

import aimc
from scipy import io

def main():
  data_file = "src/Modules/BMM/testdata/gammatone.mat"
  data = io.loadmat(data_file)
  
  # The margin of error allowed between the returned values from AIM-C and
  # the stored MATLAB values.
  epsilon = 0.000001;
  
  input_wave = data["input_wave"]
  sample_rate = data["sample_rate"]
  centre_frequencies = data["centre_frequencies"]
  expected_output = data["expected_output"]
  
  (channel_count, buffer_length, frame_count) = expected_output.shape
  
  input_sig = aimc.SignalBank()
  input_sig.Initialize(1, buffer_length, 44100)
  parameters = aimc.Parameters()
  mod_gt = aimc.ModuleGammatone(parameters)
  mod_gt.Initialize(input_sig)
  
  correct_count = 0;
  incorrect_count  = 0;
  for p in range(0, profile_count):
    profile = given_profiles[p]
    features = matlab_features[p]
    for i in range(0, channel_count):
      profile_sig.set_sample(i, 0, profile[i])
    mod_gauss.Process(profile_sig)
    out_sig = mod_gauss.GetOutputBank()
    error = False;
    for j in range(0, out_sig.channel_count()):
      if (abs(out_sig.sample(j, 0) - features[j]) > epsilon):
        error = True;
        incorrect_count += 1;
      else:
        correct_count += 1;
    if error:
      print("Mismatch at profile %d" % (p))
      print("AIM-C values: %f %f %f %f" % (out_sig.sample(0, 0), out_sig.sample(1, 0), out_sig.sample(2, 0), out_sig.sample(3, 0)))
      print("MATLAB values: %f %f %f %f" % (features[0], features[1], features[2], features[3]))
      print("")
  percent_correct = 100 * correct_count / (correct_count + incorrect_count)
  print("Total correct: %f percent" % (percent_correct))
  if percent_correct == 100:
    print("=== TEST PASSED ===")
  else:
    print("=== TEST FAILED! ===")

  pass


if __name__ == '__main__':
  main()