static public NeuralNet.Sample GetSampleFromModel(FrequencyModel model)
{
double[] data = new double[model.Data.Count];
int i = 0;
foreach (var x in model.GetHighestData())
{
data[i] = x.Value;
i++;
}
return(new NeuralNet.Sample(data, new double[61]));
}
static public NeuralNet.Sample GetSampleFromModel(FrequencyModel model, Note n)
{
double[] data = new double[model.Data.Count];
int i = 0;
foreach (var x in model.GetHighestData())
{
data[i] = x.Value;
i++;
}
double[] pred = new double[61];
pred[n.ToInt()] = 1;
return(new NeuralNet.Sample(data, pred));
}
private void Capturer_NewPick(object sender, AudioPickEventArgs e)
{
if (DateTime.Now - lastSound > new TimeSpan(0, 0, 0, 0, 150))
{
var t0 = DateTime.Now;
FrequencyModel test = new FrequencyModel(new Dictionary <float, float>());
Task t = Task.Factory.StartNew(() => { test = SoundCalculator.GetFrequencyModel(e.pickData); });
renderer.Render(e.pickData, 0);
t.Wait();
appInfo.SetFrequency(test.FirstTone);
Note n = noteFinder.getNoteFromModel(test);
appInfo.SetNote(n);
testSong.AddNote(n, t0);
}
lastSound = DateTime.Now;
}
public Note getNoteFromModel(FrequencyModel model)
{
return((Note)(m_noteNetwork.Predict(SoundCalculator.GetSampleFromModel(model))));
}