public void ComputeInverseComirvaMatrixUsingLomontTableFFT(Comirva.Audio.Util.Maths.Matrix m, int column, ref double[] signal, int winsize, int hopsize)
{
double[] spectrogramWindow = m.GetColumn(column);
// extend window with the inverse duplicate array
int len = spectrogramWindow.Length;
double[] extendedWindow = new double[len * 2];
Array.Copy(spectrogramWindow, extendedWindow, len);
for (int i = 1; i < len; i++)
{
extendedWindow[len + i] = spectrogramWindow[len - i];
}
double[] complexSignal = FFTUtilsLomont.DoubleToComplexDouble(extendedWindow);
lomonFFT.TableFFT(complexSignal, false);
double[] window = win.GetWindow();
// multiply by window w/ overlap-add
int N = complexSignal.Length / 2;
double[] returnArray = new double[N];
for (int j = 0; j < N; j++)
{
double re = complexSignal[2 * j] / Math.Sqrt(winsize);
//double img = complexSignal[2*j + 1];
returnArray[j] = re * window[j]; // smooth yet another time (also did this when doing FFT)
// overlap-add method
// scale with 2 just because the volume got so much lower when using a second smoothing filter when reconstrcting
signal[j + hopsize * column] = signal[j + hopsize * column] + returnArray[j] * 2;
}
}
public void ComputeComirvaMatrixUsingLomontTableFFT(ref Comirva.Audio.Util.Maths.Matrix m, int column, float[] audiodata, int pos)
{
// apply the window method (e.g HammingWindow, HannWindow etc)
win.Apply(ref data, audiodata, pos);
double[] complexSignal = FFTUtilsLomont.FloatToComplexDouble(data);
lomonFFT.TableFFT(complexSignal, true);
int row = 0;
for (int i = 0; i < complexSignal.Length / 4; i += 2)
{
double re = complexSignal[2 * i];
double img = complexSignal[2 * i + 1];
m.MatrixData[row][column] = Math.Sqrt((re * re + img * img) * complexSignal.Length / 2);
row++;
}
}