static void audioGraph_FFTCalculated(object sender, SampleProcessor.FftEventArgs e)
{
var gainer = new MFCCGainer(26, e.Result.Count, e.SampleRate);
buffer.Add(gainer.getCoefficents(e.Result));
var notes = classifier.getNotes(e.Result);
//Console.WriteLine(String.Join(", ", notes.Distinct()));
if (buffer.Count == 15)
{
if (!isTest)
{
Console.WriteLine("Current ErrorSum: {0}", network.errorSum);
var results = network.Teach(buffer, MarkedValues[currentInstrument]);
for (int i = 0; i < results.Count; i++)
{
Console.WriteLine("{0} - {1:00.00}%", instruments[i], results[i] * 100);
}
}
else
{
var results = network.GetResults(buffer);
for (int i = 0; i < results.Count; i++)
{
if (results[i] > 0.5)
{
testMap[currentInstrument].Add(instruments[i]);
//Console.WriteLine("{0} - {1}", currentInstrument, instruments[i]);
}
}
}
buffer.RemoveAt(0);
}
}
static void audioGraph_FFTCalculated(object sender, SampleProcessor.FftEventArgs e)
{
var gainer = new MFCCGainer(26, e.Result.Count, e.SampleRate);
buffer.Add(gainer.getCoefficents(e.Result));
if (buffer.Count == 10)
{
var result = network.GetResults(buffer);
for (int i = 0; i < result.Count; i++)
{
if (result[i] > 0.4)
{
Console.WriteLine("{0} - {1}%", instruments[i], result[i] * 100);
}
}
buffer.Clear();
}
}