public double[][] CreateSpectrogram(string pathToFilename, IWindowFunction windowFunction, int sampleRate, int overlap, int wdftSize)
{
// read 5512 Hz, Mono, PCM, with a specific proxy
float[] samples = ReadMonoFromFile(pathToFilename, sampleRate, 0, 0);
NormalizeInPlace(samples);
int width = (samples.Length - wdftSize) / overlap; /*width of the image*/
double[][] frames = new double[width][];
double[] complexSignal = new double[2 * wdftSize]; /*even - Re, odd - Img, thats how Exocortex works*/
double[] window = windowFunction.GetWindow();
for (int i = 0; i < width; i++)
{
// take 371 ms each 11.6 ms (2048 samples each 64 samples)
for (int j = 0; j < wdftSize; j++)
{
// Weight by Hann Window
complexSignal[2 * j] = window[j] * samples[(i * overlap) + j];
// need to clear out as fft modifies buffer (phase)
complexSignal[(2 * j) + 1] = 0;
}
lomonFFT.TableFFT(complexSignal, true);
// When the input is purely real, its transform is Hermitian,
// i.e., the component at frequency f_k is the complex conjugate of the component
// at frequency -f_k, which means that for real inputs there is no information
// in the negative frequency components that is not already available from the
// positive frequency components.
// Thus, n input points produce n/2+1 complex output points.
// The inverses of this family assumes the same symmetry of its input,
// and for an output of n points uses n/2+1 input points.
// Transform output contains, for a transform of size N,
// N/2+1 complex numbers, i.e. 2*(N/2+1) real numbers
// our transform is of size N+1, because the histogram has n+1 bins
double[] band = new double[(wdftSize / 2)]; // Don't add te last band, i.e. + 1 is removed
for (int j = 0; j < (wdftSize / 2); j++) // Don't add te last band, i.e. + 1 is removed
{
double re = complexSignal[2 * j];
double img = complexSignal[(2 * j) + 1];
band[j] = Math.Sqrt(((re * re) + (img * img)) * wdftSize);
}
frames[i] = band;
}
return(frames);
}
public DerivedNoteData FindNote(double[] audioBuffer)
{
if (audioBuffer.Length != _fftSize)
{
throw new InvalidOperationException(
$"{nameof(audioBuffer)} must be of length {_fftSize}");
}
_soFilter.Process(audioBuffer);
_hanWindow.ApplyWindow(audioBuffer);
{
for (var i = 0; i < _fftSize; ++i)
{
_fftBufferTmp[i * 2] = audioBuffer[i];
_fftBufferTmp[i * 2 + 1] = 0;
}
_fft.TableFFT(new Span <double>(_fftBufferTmp), true);
for (var i = 0; i < _fftSize; ++i)
{
audioBuffer[i] = _fftBufferTmp[i * 2];
}
}
return(_frequencyNotes.DeriveNoteData(audioBuffer));
}
public void ComputeMatrixUsingLomontTableFFT(ref 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 = FFTUtils.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++;
}
}
public void Run()
{
if (!ChEnable.Checked)
{
return;
}
if (AudioCapturer == null)
{
return;
}
//for (int j = 0; j < 92; j++)
{
// reset mem
for (int i = 0; i < 256; i++)
{
audioBuffer[i] = 0;
fftData[i] = 0;
fft[i] = 0;
}
AudioCapturer.ReadSamples(audioBuffer, 256);
for (int i = 0; i < 256; i++)
{
fft[i] = (audioBuffer[i] - 128) * amplitude;
}
fftTransoformer.TableFFT(fft, true);
for (int i = 0; i < 256; i += 2)
{
double fftmag = Math.Sqrt((fft[i] * fft[i]) + (fft[i + 1] * fft[i + 1]));
fftavg += fftmag;
fftData[i] = (byte)fftmag;
fftData[i + 1] = fftData[i];
}
//fftavg /= 10;
Main.Keyboard.WriteAudio(fft);
}
}
public void TimetoFreq(ref double[] audioSamples, int fftType = 0)
{
switch (fftType)
{
case 0:
{
_fft.FFT(audioSamples, true);
}
break;
case 1:
{
_fft.RealFFT(ref audioSamples, true);
}
break;
case 2:
{
_fft.TableFFT(audioSamples, true);
}
break;
}
}