# (Re)train the segmentation stage¶

The SegmentationKS knowledge source of the Two!Ears Auditory Model depends on a localisation model which is based on support vector machine regression. This regression model has to be trained using a set of HRTFs. A demo of how a specific instance of the SegmentationKS can be trained is provided by the script demo_train_segmentation.m in the examples/segmentation folder. This script shows how the default setting of the SegmentationKS knowledge source which is used for all demos is generated.

Before starting with the training of a new model, the configuration of the Binaural simulator for which this model should be used has to be specified. This is done by setting up a training scene. In this case, the training scene is specified in the training_scene.xml file:

<?xml version="1.0" encoding="utf-8"?>
<scene
BlockSize="4096"
SampleRate="44100"
MaximumDelay="0.0"
LengthOfSimulation = "5"
HRIRs="impulse_responses/qu_kemar_anechoic/QU_KEMAR_anechoic_3m.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 only parameter that is relevant for the training process is the set of HRTFs, which is taken from the Database impulse_responses/qu_kemar_anechoic/QU_KEMAR_anechoic_3m.sofa for this demo. All other parameters only have to match the HRTFs specifications. The current implementation of the training framework uses white noise as a stimulus signal during training.

Besides the scene description file, the only requirement to generate a training script is to provide a unique identifier for the SegmentationKS instance that should be trained. In the file demo_train_segmentation.m this is done via

 1 ksName = 'DemoKS'; 

Furthermore, some additional parameters that should be used for training can be specified directly in the training script. The additional parameters are optional and will be initialised by the default Auditory front-end values if not explicitly specified. The possible configuration parameters are provided as an example in demo_train_segmentation.m:

 1 2 3 4 5 nChannels = 32; % Number of filterbank channels winSize = 0.02; % Size of the processing window in [s] hopSize = 0.01; % Frame shift in [s] fLow = 80; % Lowest filterbank center frequency in [Hz] fHigh = 8000; % Highest filterbank center frequency in [Hz] 

If all of the described prerequisites are met, an instance of the SegmentationKS can be created:

 1 2 3 4 5 6 7 segKS = SegmentationKS(ksName, ... 'NumChannels', nChannels, ... 'WindowSize', winSize, ... 'HopSize', hopSize, ... 'FLow', fLow, ... 'FHigh', fHigh, ... 'Verbosity', true); % Enable status messages during training 

This instance can subsequently be used to automatically generate all files required for the training process:

 1 2 xmlSceneDescription = 'training_scene.xml'; segKS.generateTrainingData(xmlSceneDescription); 

If this is completed, the train() function can be used to start training of the regression models.

 1 segKS.train(); 

The train() command will produce an error message if a set of trained models already exist for the identifier the SegmentationKS was instantiated with. Overwriting existing models has to be explicitly enforced by calling the train() method with a additional doOverwrite flag which has to be set to true:

 1 segKS.train(true); 

Note

The training process may take up to several hours depending on the available computational ressources. It is generally recommended to set the Verbosity flag to true at instantiation, in order to receive important status and progress messages during the training process.

If training is completed, the generated training files are not needed anymore and can be deleted if no re-training should be performed. This can be done by calling the removeTrainingData method:

 1 segKS.removeTrainingData();