# (Re)train the localisation stage¶

In the localisation stage of the Two!Ears Auditory Model the Location knowledge source: LocationKS is involved which has a learned lookup table for ITDs and ILDs. For the default setting of LocationKS this was trained with the impulse_responses/qu_kemar_anechoic/QU_KEMAR_anechoic_3m.sofa from the database convolved with white noise. The training was done for a resolution of 1°. This examples shows you how you can retrain the model for other HRTFs, other angular resolutions, and other source materials. This example can be found in the examples/train_localisation_model folder which consists of the following files:

BlackboardMIT.xml
BlackboardQU.xml
localise.m
SceneDescriptionMIT.xml
SceneDescriptionQU.xml
train.m


Before starting with the training of LocationKS we have to define the binaural signals that should be used during training. As we would like to train on the MIT KEMAR HRTFs we do the definition in the SceneDescriptionMIT.xml file, which looks like this:

<?xml version="1.0" encoding="utf-8"?>
<scene
BlockSize="4096"
SampleRate="44100"
MaximumDelay="0.0"
LengthOfSimulation = "30"
HRIRs="impulse_responses/mit_kemar_anechoic/MIT_KEMAR_anechoic_1.7m_large.sofa">
Mute="false"
Type="point"
Name="SoundSource">
<buffer ChannelMapping="1"
Type="noise"/>
</source>
Position="0 0 0"
UnitX="1 0 0"
UnitZ="0 0 1"/>
</scene>


The most important parts are the setting of HRIRs to the corresponding file from the Database impulse_responses/mit_kemar_anechoic/MIT_KEMAR_anechoic_1.7m_large.sofa, and the specifying of the used audio material during training, which is set to white noise with the Type="noise" option and a length of 30 s with by LengthOfSimulation = "30". For more details on how to configure the binaural simulator or how to include WAV-files as audio material see Configuration using XML Scene Description. After setting up the audio scene we can train the LocationKS knowledge source with the following command:

>> train('mit', 'SceneDescriptionMIT.xml', 5);


This will create the folder learned_models/LocationKS/mit where the result is stored. In this case we trained for an angular resolution of 5°. For looking into the details how the training is working, we look at the single steps that are performed by the train() function:

% Create a LocationKS in training mode
loc = LocationKS('mit', 5, true);
% Generate binaural signals and extract ITD, ILD for learning.
loc.generateTrainingData('SceneDescriptionMIT.xml');
% Train model and remove tmp data afterwards
loc.train();
loc.removeTrainingData();


Now we would like to compare the localisation results of the default and the mit LocationKS. For both cases we have to configure a blackboard which is done inside BlackboardQU.xml and BlackboardMIT.xml. On details how to configure the blackboard see Setting up the blackboard. The important parts in our case are:

<KS Name="loc" Type="LocationKS">
<Param Type="char">default</Param>
</KS>


inside BlackboardQU.xml and:

<KS Name="loc" Type="LocationKS">
<Param Type="char">mit</Param>
</KS>


inside BlackboardMIT.xml. The first parameter specifies which learned model to use. Now, we are generating an audio scene with the HRTFs used for the default setting and localise with both models:

>> localise

------------------------------------------------------------------
Source direction        Model w QU HRTF       Model w MIT HRTF
------------------------------------------------------------------
30                       30                      30
88                       88                      85
160                      160                     160
-103                     -103                    -110
------------------------------------------------------------------


This is done by the following loop inside localise.m which loops over the specified source directions:

for direction = sourceAngles

% Localisation using QU KEMAR HRTFs
sim = simulator.SimulatorConvexRoom('SceneDescriptionQU.xml');
sim.Verbose = false;
sim.Sources{1}.set('Azimuth', direction);
sim.Init = true;
bbs = BlackboardSystem(0);
bbs.setRobotConnect(sim);
bbs.buildFromXml('BlackboardQU.xml');
bbs.run();
predictedLocations = bbs.blackboard.getData('perceivedLocations');
predictedAzimuthQu = evaluateLocalisationResults(predictedLocations, direction);
sim.ShutDown = true;

% Localisation using MIT KEMAR HRTFs
sim = simulator.SimulatorConvexRoom('SceneDescriptionQU.xml');
sim.Verbose = false;
sim.Sources{1}.set('Azimuth', direction);