Mercurial > hg > aimc
diff src/Scripts/Multi-slice_test.py @ 32:9122efd2b227
-New AIMCopy main for the SSI features (temporary hack till I get a working module load system)
-LocalMax strobe criterion. This is faster and better than the parabola version, which still seems buggy.
-Noise generator module. Adds noise to a signal. Uses boost for the random number generator.
-New options for the SSI
-Slice now respects all its flags (oops!).
-MATLAB functions for visualisation
-Scripts for generating data to view in MATLAB
-Script to download and build HTK - useful for running experiments
author | tomwalters |
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date | Thu, 25 Feb 2010 22:02:00 +0000 |
parents | |
children | c5f5e9569863 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/Scripts/Multi-slice_test.py Thu Feb 25 22:02:00 2010 +0000 @@ -0,0 +1,213 @@ +#!/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/>. +""" +Profiles_test.py + +Created by Thomas Walters on 2010-02-22. +Copyright 2010 Thomas Walters <tom@acousticscale.org> +Test the AIM-C model from filterbank to SSI profiles +""" + +import aimc +from scipy.io import wavfile +from scipy import io +import scipy +import pylab +from itertools import izip, chain, repeat + +def grouper(n, iterable, padvalue=None): + "grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')" + return izip(*[chain(iterable, repeat(padvalue, n-1))]*n) + +def BankToArray(out_bank): + channel_count = out_bank.channel_count() + out_buffer_length = out_bank.buffer_length() + out = scipy.zeros((channel_count, out_buffer_length)) + for ch in range(0, channel_count): + for i in range(0, out_buffer_length): + out[ch, i] = out_bank.sample(ch, i) + return out + +def StrobesToList(bank): + channel_count = bank.channel_count() + strobes = [] + for ch in range(0, channel_count): + s = [] + for i in range(0, bank.strobe_count(ch)): + s.append(bank.strobe(ch, i)) + strobes.append(s) + +def main(): + wave_path = "/Users/Tom/Documents/Work/PhD/HTK-AIM/Sounds/" + #features_path = "/Users/Tom/Documents/Work/PhD/HTK-AIM/work08-jess-original-rec_rubber/features/" + + file_name = "ii/ii172.5p112.5s100.0t+000itd" + + wave_suffix = ".wav" + features_suffix = ".mat" + + frame_period_ms = 10; + + wave_filename = wave_path + file_name + wave_suffix + #features_filename = features_path + file_name + features_suffix + + (sample_rate, input_wave) = wavfile.read(wave_filename) + wave_length = input_wave.size + buffer_length = int(frame_period_ms * sample_rate / 1000) + + #pylab.plot(input_wave) + #pylab.show() + + input_sig = aimc.SignalBank() + input_sig.Initialize(1, buffer_length, sample_rate) + parameters = aimc.Parameters() + parameters.SetFloat("sai.frame_period_ms", 10.0) + parameters.SetInt("input.buffersize", 480) + + mod_gt = aimc.ModuleGammatone(parameters) + mod_hl = aimc.ModuleHCL(parameters) + mod_strobes = aimc.ModuleLocalMax(parameters) + mod_sai = aimc.ModuleSAI(parameters) + parameters.SetBool("ssi.pitch_cutoff", True) + parameters.SetBool("ssi.weight_by_cutoff", False) + parameters.SetBool("ssi.weight_by_scaling", True) + parameters.SetBool("ssi.log_cycles_axis", True) + mod_ssi = aimc.ModuleSSI(parameters) + + parameters.SetFloat("nap.lowpass_cutoff", 100.0) + mod_nap_smooth = aimc.ModuleHCL(parameters) + mod_scaler = aimc.ModuleScaler(parameters) + + parameters.SetBool("slice.all", False) + parameters.SetInt("slice.lower_index", 77) + parameters.SetInt("slice.upper_index", 150) + slice_1 = aimc.ModuleSlice(parameters) + + parameters.SetInt("slice.lower_index", 210) + parameters.SetInt("slice.upper_index", 240) + slice_2 = aimc.ModuleSlice(parameters) + + parameters.SetInt("slice.lower_index", 280) + parameters.SetInt("slice.upper_index", 304) + slice_3 = aimc.ModuleSlice(parameters) + + parameters.SetInt("slice.lower_index", 328) + parameters.SetInt("slice.upper_index", 352) + slice_4 = aimc.ModuleSlice(parameters) + + parameters.SetBool("slice.all", True) + slice_5 = aimc.ModuleSlice(parameters) + + nap_profile = aimc.ModuleSlice(parameters) + + features_1 = aimc.ModuleGaussians(parameters) + features_2 = aimc.ModuleGaussians(parameters) + features_3 = aimc.ModuleGaussians(parameters) + features_4 = aimc.ModuleGaussians(parameters) + features_5 = aimc.ModuleGaussians(parameters) + + mod_gt.AddTarget(mod_hl) + mod_gt.AddTarget(mod_nap_smooth) + mod_nap_smooth.AddTarget(nap_profile) + nap_profile.AddTarget(mod_scaler) + mod_hl.AddTarget(mod_strobes) + mod_strobes.AddTarget(mod_sai) + mod_sai.AddTarget(mod_ssi) + mod_ssi.AddTarget(slice_1) + mod_ssi.AddTarget(slice_2) + mod_ssi.AddTarget(slice_3) + mod_ssi.AddTarget(slice_4) + mod_ssi.AddTarget(slice_5) + + slice_1.AddTarget(features_1) + slice_2.AddTarget(features_2) + slice_3.AddTarget(features_3) + slice_4.AddTarget(features_4) + slice_5.AddTarget(features_5) + + mod_gt.Initialize(input_sig) + + correct_count = 0; + incorrect_count = 0; + + scaled_wave = [] + for sample in input_wave: + scaled_wave.append(float(sample / float(pow(2,15) - 1))) + i = 0 + + wave_chunks = grouper(buffer_length, scaled_wave, 0) + + out_bmm = [] + out_nap = [] + out_smooth_nap_profile = [] + out_strobes = [] + out_sais = [] + out_ssis = [] + out_slice_1 = [] + out_slice_2 = [] + out_slice_3 = [] + out_slice_4 = [] + out_slice_5 = [] + out_feat_1 = [] + out_feat_2 = [] + out_feat_3 = [] + out_feat_4 = [] + out_feat_5 = [] + for chunk in wave_chunks: + i = 0 + for sample in chunk: + input_sig.set_sample(0, i, float(sample)) + i += 1 + mod_gt.Process(input_sig) + + #out_bmm.append(BankToArray(mod_gt.GetOutputBank())) + #out_nap.append(BankToArray(mod_hl.GetOutputBank())) + out_smooth_nap_profile.append(BankToArray(mod_scaler.GetOutputBank())) + #out_strobes.append(BankToArray(mod_strobes.GetOutputBank())) + #out_sais.append(BankToArray(mod_sai.GetOutputBank())) + out_ssis.append(BankToArray(mod_ssi.GetOutputBank())) + out_slice_1.append(BankToArray(slice_1.GetOutputBank())) + out_slice_2.append(BankToArray(slice_2.GetOutputBank())) + out_slice_3.append(BankToArray(slice_3.GetOutputBank())) + out_slice_4.append(BankToArray(slice_4.GetOutputBank())) + out_slice_5.append(BankToArray(slice_5.GetOutputBank())) + out_feat_1.append(BankToArray(features_1.GetOutputBank())) + out_feat_2.append(BankToArray(features_2.GetOutputBank())) + out_feat_3.append(BankToArray(features_3.GetOutputBank())) + out_feat_4.append(BankToArray(features_4.GetOutputBank())) + out_feat_5.append(BankToArray(features_5.GetOutputBank())) + + out_bank = mod_gt.GetOutputBank() + channel_count = out_bank.channel_count() + cfs = scipy.zeros((channel_count)) + for ch in range(0, channel_count): + cfs[ch] = out_bank.centre_frequency(ch) + outmat = dict(bmm=out_bmm, nap=out_nap, sais=out_sais, + ssis=out_ssis, slice1=out_slice_1, slice2=out_slice_2, + slice3=out_slice_3, slice4=out_slice_4, slice5=out_slice_5, + feat1=out_feat_1, feat2=out_feat_2, feat3=out_feat_3, + feat4=out_feat_4, feat5=out_feat_5, + nap_smooth=out_smooth_nap_profile, centre_freqs=cfs) + io.savemat("src/Scripts/profile_out.mat", outmat, oned_as='column') + + pass + + +if __name__ == '__main__': + main()