/// <summary>
/// Simulates a realtime audio stream by generating a sine wave in realtime and ingesting it into the FIFO buffer.
/// </summary>
private static void StartSineWaveRealtimeGenerator(AudioProperties audioProperties, IAudioWriterStream targetStream)
{
// Create a stream that generates 1 second of a sine wave
var sineWaveStream = new SineGeneratorStream(audioProperties.SampleRate, 440, new TimeSpan(0, 0, 1));
// Store the sine wave in a buffer
// We can concatenate this buffer over and over again to create an infinitely long sine wave
var sineWaveBuffer = new byte[sineWaveStream.Length];
var bytesRead = sineWaveStream.Read(sineWaveBuffer, 0, sineWaveBuffer.Length);
if (bytesRead < sineWaveBuffer.Length)
{
throw new Exception("incomplete buffer read");
}
Task.Factory.StartNew(() =>
{
// Each realtime second, write the 1-second sine wave to the target stream to
// simulate an infinitely long realtime sine wave stream.
//
// For low-latency processing use-cases, writes would ideally be shorter and happen
// more frequently to keep the delay between input and output of the FIFO stream
// as low as possible.
while (true)
{
Thread.Sleep(1000);
Console.WriteLine("Writing 1 second into buffer");
targetStream.Write(sineWaveBuffer, 0, sineWaveBuffer.Length);
_dataGenerated += sineWaveBuffer.Length;
}
});
}
public void TransformForwardAndBackward()
{
int size = 4096;
var pffft = new PFFFT(size, Transform.Real);
var dataIn = new float[size];
var dataOut = new float[size];
var dataBack = new float[size];
var generator = new SineGeneratorStream(4096, 100, TimeSpan.FromSeconds(1));
generator.Read(dataIn, 0, size);
pffft.Forward(dataIn, dataOut);
pffft.Backward(dataOut, dataBack);
CollectionAssert.AreEqual(dataIn, dataBack, new FFTComparer());
}
/// <summary>
/// Calculates a normalization offset for the current window function. Every window function leads to
/// a different FFT result peak value, and since the peak value of each windowed FFT means 0dB, this
/// offset can be used to adjust the calculated dB values.
/// </summary>
private void CalculateWindowFunctionNormalizationOffset()
{
if (windowFunction == null)
{
windowFunctionNormalizationDecibelOffset = 0;
}
else
{
SineGeneratorStream sine = new SineGeneratorStream(1024, 16, new TimeSpan(0, 0, 1));
float[] input = new float[WindowSize];
float[] output = new float[input.Length / 2];
WindowFunction wf = WindowUtil.GetFunction(windowFunction.Type, input.Length);
sine.Read(input, 0, input.Length);
wf.Apply(input);
fft.Forward(input);
int maxIndex = FFTUtil.Results(input, output);
float maxValue = output[maxIndex];
windowFunctionNormalizationDecibelOffset = 1f - maxValue;
}
}
private void Generate(FFTLibrary fftLib)
{
// FFT resources:
// http://www.mathworks.de/support/tech-notes/1700/1702.html
// http://stackoverflow.com/questions/6627288/audio-spectrum-analysis-using-fft-algorithm-in-java
// http://stackoverflow.com/questions/1270018/explain-the-fft-to-me
// http://stackoverflow.com/questions/6666807/how-to-scale-fft-output-of-wave-file
/*
* FFT max value test:
* 16Hz, 1024 samplerate, 512 samples, rectangle window
* -> input min/max: -1/1
* -> FFT max: 256
* -> FFT result max: ~1
* -> FFT dB result max: ~1
*
* source: "Windowing Functions Improve FFT Results, Part I"
* => kann zum Normalisieren für Fensterfunktionen verwendet werden
*/
float frequency = float.Parse(frequencyTextBox.Text);
int ws = (int)windowSize.Value;
float frequencyFactor = ws / frequency;
int sampleRate = int.Parse(sampleRateTextBox.Text);
SineGeneratorStream sine = new SineGeneratorStream(sampleRate, frequency, new TimeSpan(0, 0, 1));
float[] input = new float[ws];
sine.Read(input, 0, ws);
//// add second frequency
//SineGeneratorStream sine2 = new SineGeneratorStream(44100, 700, new TimeSpan(0, 0, 1));
//float[] input2 = new float[ws];
//sine2.Read(input2, 0, ws);
//for (int x = 0; x < input.Length; x++) {
// input[x] += input2[x];
// input[x] /= 2;
//}
//// add dc offset
//for (int x = 0; x < input.Length; x++) {
// input[x] += 0.5f;
//}
inputGraph.Values = input;
WindowFunction wf = WindowUtil.GetFunction((WindowType)windowTypes.SelectedValue, ws);
float[] window = (float[])input.Clone();
wf.Apply(window);
input2Graph.Values = window;
float[] fftIO = (float[])window.Clone();
if (fftLib == FFTLibrary.ExocortexDSP)
{
// This function call indirection is needed to allow this method execute even when the
// Exocortex FFT assembly is missing.
ApplyExocortexDSP(fftIO);
}
else if (fftLib == FFTLibrary.FFTW)
{
FFTW.FFTW fftw = new FFTW.FFTW(fftIO.Length);
fftw.Execute(fftIO);
}
else if (fftLib == FFTLibrary.PFFFT)
{
PFFFT.PFFFT pffft = new PFFFT.PFFFT(fftIO.Length, PFFFT.Transform.Real);
pffft.Forward(fftIO, fftIO);
}
//// convert real input to complex input with im part set to zero
//float[] fftIO = new float[ws * 2];
//int i = 0;
//for (int j = 0; j < window.Length; j++) {
// fftIO[i] = window[j];
// i += 2;
//}
//Fourier.FFT(fftIO, fftIO.Length / 2, FourierDirection.Forward);
fftOutputGraph.Values = fftIO;
float[] fftResult = new float[ws / 2];
//FFTUtil.Results(fftIO, fftResult);
// transform complex output to magnitudes
float sum = 0;
int y = 0;
for (int x = 0; x < fftIO.Length; x += 2) // / 2
{
fftResult[y] = FFTUtil.CalculateMagnitude(fftIO[x], fftIO[x + 1]) / ws * 2;
sum += fftResult[y++];
}
//FFTUtil.Results(fftIO, fftResult);
//// adjust values for the sum to become 1
//float sum2 = 0;
//for (int x = 0; x < fftResult.Length; x++) {
// fftResult[x] /= sum;
// sum2 += fftResult[x];
//}
//Debug.WriteLine("sum / sum2: {0} / {1}", sum, sum2);
fftNormalizedOutputGraph.Values = fftResult;
// convert magnitudes to decibel
float[] fftResultdB = new float[fftResult.Length];
//for (int x = 0; x < fftResult.Length; x++) {
// fftResultdB[x] = (float)VolumeUtil.LinearToDecibel(fftResult[x]);
//}
FFTUtil.Results(fftIO, fftResultdB);
fftdBOutputGraph.Values = fftResultdB;
summary.Text = String.Format(
"input min/max: {0}/{1} -> window min/max: {2}/{3} -> fft min/max: {4}/{5} -> result min/max/bins/sum: {6}/{7}/{8}/{9} -> dB min/max: {10:0.000000}/{11:0.000000}",
input.Min(), input.Max(),
window.Min(), window.Max(),
fftIO.Min(), fftIO.Max(),
fftResult.Min(), fftResult.Max(), fftResult.Length, sum,
fftResultdB.Min(), fftResultdB.Max());
}