annotate src/Modules/BMM/ModuleGammatone_test.py @ 16:2a5354042241

-Updated the Slaney IIR gammatone to use a cascase of four second-order filters as per the implementtion in Slaney's auditory toolbox. This is more numerically stable at high sample rates and low centre frequencies.

#!/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
import wave
import scipy

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, frame_count) = expected_output.shape
  buffer_length = 20000;
  
  input_sig = aimc.SignalBank()
  input_sig.Initialize(1, buffer_length, 48000)
  parameters = aimc.Parameters()
  parameters.Load("src/Modules/BMM/testdata/gammatone.cfg")
  mod_gt = aimc.ModuleGammatone(parameters)
  mod_gt.Initialize(input_sig)
  
  correct_count = 0;
  incorrect_count  = 0;
  
  out = scipy.zeros((channel_count, buffer_length))
  
  cfs = scipy.zeros((channel_count))

  for i in range(0, buffer_length):
    input_sig.set_sample(0, i, input_wave[i][0])
  mod_gt.Process(input_sig)
  out_sig = mod_gt.GetOutputBank()
  for ch in range(0, out_sig.channel_count()):
    cfs[ch] = out_sig.centre_frequency(ch);
    for i in range(0, buffer_length):  
      out[ch, i] = out_sig.sample(ch, i)
  
  outmat = dict(filterbank_out=out, centre_frequencies_out=cfs)
  io.savemat("src/Modules/BMM/testdata/out_v2.mat", outmat)

  pass


if __name__ == '__main__':
  main()