public AudioIn(int sampleRate, int device, int bufferSize)
{
if (device > WaveIn.DeviceCount)
{
throw new Exception();
}
this.sampleRate = sampleRate;
this.bufferSize = bufferSize;
audioDataTrue = new short[bufferSize + 20286];
tempOdd = new double[audioDataTrue.Length];
tempTrue = new double[audioDataTrue.Length];
realIn = new PinnedArray <double>(audioDataTrue.Length);
comOut = new FftwArrayComplex(DFT.GetComplexBufferSize(realIn.GetSize()));
fft = FftwPlanRC.Create(realIn, comOut, DftDirection.Forwards);
waveInDevices = WaveIn.DeviceCount;
waveEvent = new WaveInEvent();
waveEvent.DeviceNumber = device;
waveEvent.DataAvailable += OnWaveIn;
channels = WaveIn.GetCapabilities(device).Channels;
waveEvent.WaveFormat = new WaveFormat(sampleRate, 1);
string path = Path.GetDirectoryName(Assembly.GetEntryAssembly().Location) + "/NetworkSave01.json";
// Load Neural network
if (File.Exists(path))
{
Console.WriteLine("exists");
network = NeuralNetwork.LoadNetwork(path);
}
else
{
throw new FileNotFoundException();
}
//network.PrintWeights();
waveEvent.StartRecording();
}
static void NetworkTraining(int inputSize, int width, int height, int itterations)
{
int outputSize = DataSample.labelSize;
string databasePath1 = @"C:/Users/Mikkel/Documents/MTA18434_SemesterProject/ML_Sound_Samples/Assets/Resources/SampleDatabase/Database.json";
string networkPath1 = Path.GetDirectoryName(Assembly.GetEntryAssembly().Location) + "/NetworkSave01.json";
if (File.Exists(networkPath1))
{
network = NeuralNetwork.LoadNetwork(networkPath1);
if (network.inputSize != inputSize)
{
throw new Exception();
}
if (network.hiddenDimension != width)
{
throw new Exception();
}
if (network.hiddenSize != height)
{
throw new Exception();
}
}
else
{
Console.WriteLine("Initializing NN");
network = new NeuralNetwork(inputSize, width, height, outputSize);
Console.WriteLine("NN Initialized");
}
if (File.Exists(databasePath1))
{
Console.WriteLine(File.Exists(databasePath1) ? "Database exists." : "File does not exist.");
SampleDatabase temp = null;
using (StreamReader r = new StreamReader("C:/Users/Mikkel/Documents/MTA18434_SemesterProject/ML_Sound_Samples/Assets/Resources/SampleDatabase/Database.json"))
{
using (JsonReader reader = new JsonTextReader(r))
{
JsonSerializer serializer = new JsonSerializer();
Console.WriteLine("Deserializing");
temp = serializer.Deserialize <SampleDatabase>(reader);
}
}
Console.WriteLine(temp.database[0].data.Length);
DataSample[] trainingSamples = new DataSample[10];
Random rand = new Random();
for (int i = 0; i < itterations; i++)
{
Console.WriteLine("Progress: " + i + " / " + itterations);
// pick 10 samples
for (int j = 0; j < 10; j++)
{
int num = rand.Next(0, temp.database.Length);
trainingSamples[j] = new DataSample(temp.database[num].data, temp.database[num].label);
//Console.WriteLine("Database sample " + temp.database[num].data[0] + " " + temp.database[num].label);
}
network.TrainNetwork(trainingSamples);
}
Console.WriteLine("Saving network");
network.SaveNetwork(networkPath1);
Console.WriteLine("Network saved");
}
else
{
Console.WriteLine(File.Exists(databasePath1) ? "File exists." : "File does not exist.");
throw new Exception();
}
}
