public InverseSTFT(IAudioWriterStream stream, int windowSize, int hopSize, int fftSize, WindowType windowType, float windowNormalizationFactor)
: base(stream, windowSize, hopSize)
{
if (fftSize < windowSize)
{
throw new ArgumentOutOfRangeException("fftSize must be >= windowSize");
}
frameBuffer = new float[fftSize];
fft = FFTFactory.CreateInstance(fftSize);
synthesisWindow = WindowUtil.GetFunction(windowType, windowSize, windowNormalizationFactor);
}
/// <summary>
/// Initializes a new STFT for the specified stream with the specified window, hop, and FFT size.
/// An FFT size larger than the window size can be used to increase frequency resolution. Window will be right-padded with zeros for FFT.
/// </summary>
/// <param name="stream">the stream to read the audio data to process from</param>
/// <param name="windowSize">the window size in the dimension of samples</param>
/// <param name="hopSize">the hop size in the dimension of samples</param>
/// <param name="fftSize">the FFT size, must be >= windowSize</param>
/// <param name="windowType">the type of the window function to apply</param>
/// <param name="outputFormat">format of the output data, e.g. raw FFT complex numbers or dB spectrum</param>
public STFT(IAudioStream stream, int windowSize, int hopSize, int fftSize, WindowType windowType, OutputFormat outputFormat, int bufferSize = DEFAULT_STREAM_INPUT_BUFFER_SIZE)
: base(stream, windowSize, hopSize, windowType, bufferSize)
{
if (fftSize < windowSize)
{
throw new ArgumentOutOfRangeException("fftSize must be >= windowSize");
}
frameBuffer = new float[fftSize];
fftBuffer = new float[fftSize];
Array.Clear(frameBuffer, 0, frameBuffer.Length); // init with zeros (assure zero padding)
fft = FFTFactory.CreateInstance(fftSize);
this.outputFormat = outputFormat;
}
public void ComputeTest_Impulse()
{
Complex[] data = new Complex[4];
data[0] = new Complex(0.25, 0);
data[1] = new Complex(0.25, 0);
data[2] = new Complex(0.25, 0);
data[3] = new Complex(0.25, 0);
var output = IFFT.Compute(data);
Assert.AreEqual(1, output[0].Real);
Assert.AreEqual(0, output[0].Imaginary);
for (int i = 1; i < data.Length; i++)
{
Assert.AreEqual(0, output[i].Real);
Assert.AreEqual(0, output[i].Imaginary);
}
}
public void ComputeTest_Impulse_Nyq_Bounded()
{
Complex[] data = new Complex[3];
data[0] = new Complex(0.25, 0);
data[1] = new Complex(0.25, 0);
data[2] = new Complex(0.25, 0);
var output = IFFT.Compute(data, true, true);
Assert.AreEqual(1, output[0].Real);
Assert.AreEqual(0, output[0].Imaginary);
for (int i = 1; i < output.Length; i++)
{
Assert.AreEqual(0, output[i].Real);
Assert.AreEqual(0, output[i].Imaginary);
}
Assert.AreEqual(4, output.Length);
//not scaled
data[0] = new Complex(1, 0);
data[1] = new Complex(1, 0);
data[2] = new Complex(1, 0);
output = IFFT.Compute(data, false, true);
Assert.AreEqual(1, output[0].Real);
Assert.AreEqual(0, output[0].Imaginary);
for (int i = 1; i < output.Length; i++)
{
Assert.AreEqual(0, output[i].Real);
Assert.AreEqual(0, output[i].Imaginary);
}
Assert.AreEqual(4, output.Length);
}
public FFTAnalyzer(int windowSize)
{
WindowSize = windowSize;
fft = FFTFactory.CreateInstance(windowSize);
windowFunctionNormalizationDecibelOffset = 0;
}
private static void FrequencyDistributionBlockProcess(float[] buffer, double[] target, IFFT fft)
{
windowFunction.Apply(buffer);
fft.Forward(buffer);
for (int i = 0; i < buffer.Length; i += 2)
{
buffer[i / 2] = FFTUtil.CalculateMagnitude(buffer[i], buffer[i + 1]) / buffer.Length * 2;
}
for (int i = 0; i < target.Length; i++)
{
target[i] += buffer[i];
}
}
