public void RecognizeAsync(ISoundSignalReader signal, HiddenMarkovClassifier hmm, Action<string> handleMessage,
SignalVisitor voiceVisitor = null)
{
Action<List<double[]>> action = features =>
{
var observations = _codeBook.Quantize(features.Select(item => new Point(item)).ToArray());
double[] responsabilities;
var ret = hmm.Compute(observations, out responsabilities);
var models = hmm.Models;
var likelyHoodValue = Double.MinValue;
foreach (var model in models)
{
var val = model.Evaluate(observations);
if (val > likelyHoodValue)
{
likelyHoodValue = val;
}
}
handleMessage(hmm[ret].Tag.ToString());
};
var featureUtility = new FeatureUtility(_engineParameters);
featureUtility.ExtractFeaturesAsync(signal, action, voiceVisitor);
}
public void RecognizeAsync(ISoundSignalReader signal, Action<string> handleMessage,
SignalVisitor visitor = null)
{
Action<List<double[]>> action = features =>
{
var observations = _codeBook.Quantize(features.Select(item => new Point(item)).ToArray());
var likelyHoodValue = Double.MinValue;
HiddenMarkovModel bestFit = null;
var modelsToSearchFor = _models.Values.Where(item =>
{
var idProp = (IdentificationProperties) item.Tag;
var rateLength = Math.Abs(idProp.MeanFeaturesLength - observations.Length)/idProp.MeanFeaturesLength;
return rateLength < 0.1;
}).ToArray();
foreach (var model in modelsToSearchFor)
{
var val = model.Evaluate(observations);
if (val > likelyHoodValue)
{
likelyHoodValue = val;
bestFit = model;
}
}
if (bestFit != null)
{
var idProp = (IdentificationProperties)bestFit.Tag;
handleMessage(idProp.Label);
}
};
var featureUtility = new FeatureUtility(_engineParameters);
featureUtility.ExtractFeaturesAsync(signal, action, visitor);
}