/// <summary>
/// Process 16 bit sample
/// </summary>
/// <param name="wave"></param>
public void Process(ref byte[] wave)
{
IsEventActive = false;
_waveLeft = new double[wave.Length / 4];
_waveRight = new double[wave.Length / 4];
if (_isTest == false)
{
// Split out channels from sample
int h = 0;
for (int i = 0; i < wave.Length; i += 4)
{
_waveLeft[h] = (double)BitConverter.ToInt16(wave, i);
if (IsDetectingEvents == true)
if (_waveLeft[h] > AmplitudeThreshold || _waveLeft[h] < -AmplitudeThreshold)
IsEventActive = true;
_waveRight[h] = (double)BitConverter.ToInt16(wave, i + 2);
if (IsDetectingEvents == true)
if (_waveLeft[h] > AmplitudeThreshold || _waveLeft[h] < -AmplitudeThreshold)
IsEventActive = true;
h++;
}
}
else
{
// Generate artificial sample for testing
_signalGenerator = new SignalGenerator();
_signalGenerator.SetWaveform("Sine");
_signalGenerator.SetSamplingRate(44100);
_signalGenerator.SetSamples(8192);
_signalGenerator.SetFrequency(4096);
_signalGenerator.SetAmplitude(32768);
_waveLeft = _signalGenerator.GenerateSignal();
_waveRight = _signalGenerator.GenerateSignal();
}
// Generate frequency domain data in decibels
_fftLeft = FourierTransform.FFT(ref _waveLeft);
_fftLeftSpect.Add(_fftLeft);
if (_fftLeftSpect.Count > _maxHeightLeftSpect)
_fftLeftSpect.RemoveAt(0);
_fftRight = FourierTransform.FFT(ref _waveRight);
_fftRightSpect.Add(_fftRight);
if (_fftRightSpect.Count > _maxHeightRightSpect)
_fftRightSpect.RemoveAt(0);
}
/// <summary>
/// Process 16 bit sample
/// </summary>
/// <param name="wave"></param>
public void Process(ref byte[] wave)
{
_waveLeft = new double[wave.Length / 4];
_waveRight = new double[wave.Length / 4];
if (_isTest == false)
{
// Split out channels from sample
int h = 0;
for (int i = 0; i < wave.Length; i += 4)
{
_waveLeft[h] = (double)BitConverter.ToInt16(wave, i);
if (IsDetectingEvents == true)
if (_waveLeft[h] > AmplitudeThreshold || _waveLeft[h] < -AmplitudeThreshold)
IsEventActive = true;
_waveRight[h] = (double)BitConverter.ToInt16(wave, i + 2);
if (IsDetectingEvents == true)
if (_waveLeft[h] > AmplitudeThreshold || _waveLeft[h] < -AmplitudeThreshold)
IsEventActive = true;
h++;
}
}
else
{
// Generate artificial sample for testing
_signalGenerator = new SignalGenerator();
_signalGenerator.SetWaveform("Sine");
_signalGenerator.SetSamplingRate(44100);
_signalGenerator.SetSamples(8192);
_signalGenerator.SetFrequency(4096);
_signalGenerator.SetAmplitude(32768);
_waveLeft = _signalGenerator.GenerateSignal();
_waveRight = _signalGenerator.GenerateSignal();
}
if (takeSample)
{
DateTime currentTime = DateTime.Now;
int difference = (currentTime - initialTime).Seconds;
if (difference < sampleRecordDuration)
{
try
{
double[] soundData = new double[_waveLeft.Length];
Array.Copy(_waveLeft, soundData, _waveLeft.Length);
Array.Sort(soundData);
double min = soundData[0];
double max = soundData[soundData.Length - 1];
if (min < minPerSecond)
{
minPerSecond = min;
}
if (max > maxPerSecond)
{
maxPerSecond = max;
}
minSumPerSecond += min;
maxSumPerSecond += max;
string data = Environment.NewLine + counter + "). " + "min = " +min + " max = " + max;
Byte[] text = new UTF8Encoding(true).GetBytes(data);
output.Write(text, 0, text.Length);
counter++;
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
else
{
minAveragePerSecond = minSumPerSecond / counter;
maxAveragePerSecond = maxSumPerSecond / counter;
string data = Environment.NewLine + "min Per second = " + minPerSecond + Environment.NewLine + "max per second = " + maxPerSecond + Environment.NewLine + "min average = " + minAveragePerSecond + Environment.NewLine + "max average = " + maxAveragePerSecond;
Byte[] text = new UTF8Encoding(true).GetBytes(data);
output.Write(text, 0, text.Length);
counter = 0;
output.Flush();
output.Close();
takeSample = false;
sampleMessageTextLabel.Text = "Finished recording";
// reset variables
minPerSecond = 0;
maxPerSecond = 0;
minSumPerSecond = 0;
maxSumPerSecond = 0;
minAveragePerSecond= 0;
maxAveragePerSecond= 0;
}
}
//--------Record Interaction-------------
if (recordInteraction)
{
DateTime interactCurrentTime = DateTime.Now;
int difference = (interactCurrentTime - interactInitialTime).Seconds;
if (difference < interactRecordDuration)
{
try
{
double[] soundData = new double[_waveLeft.Length];
Array.Copy(_waveLeft, soundData, _waveLeft.Length);
Array.Sort(soundData);
double min = soundData[0];
double max = soundData[soundData.Length - 1];
if (min < interactMinPerSecond)
{
interactMinPerSecond = min;
}
if (max > interactMaxPerSecond)
{
interactMaxPerSecond = max;
}
interactMinSumPerSecond += min;
interactMaxSumPerSecond += max;
interactCounter++;
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
else
{
interactMinAveragePerSecond = interactMinSumPerSecond / interactCounter;
interactMaxAveragePerSecond = interactMaxSumPerSecond / interactCounter;
interactCounter = 0;
recordInteraction = false;
interactLabel.Text = "Interaction finished ";
double [] input = {interactMinPerSecond, interactMaxPerSecond, interactMinAveragePerSecond, interactMaxAveragePerSecond };
String voiceTone = neuralNetwork.computeOutput(input);
neuralOutputLabel.Text = "Classification is: " + voiceTone;
Console.WriteLine("Min per second = " + interactMinPerSecond + " Max per second = " + interactMaxPerSecond + " Min average = " + interactMinAveragePerSecond + " Max average = " + interactMaxAveragePerSecond );
if (voiceTone != "Unrecognised")
{
robot.listen(voiceTone);
}
else
{
unrecognisedVoiceTone = true;
}
// reset variables
interactMinPerSecond = 0;
interactMaxPerSecond = 0;
interactMinSumPerSecond = 0;
interactMaxSumPerSecond = 0;
interactMinAveragePerSecond = 0;
interactMaxAveragePerSecond = 0;
}
}
}
