/// <summary>
/// Generate a spectrogram array spaced linearily
/// </summary>
/// <param name="samples">audio data</param>
/// <param name="fftWindowsSize">fft window size</param>
/// <param name="fftOverlap">overlap in number of samples (normaly half of the fft window size) [low number = high overlap]</param>
/// <returns>spectrogram jagged array</returns>
public static double[][] CreateSpectrogramFFTWLIB_INPLACE(float[] samples, int fftWindowsSize, int fftOverlap)
{
int numberOfSamples = samples.Length;
// overlap must be an integer smaller than the window size
// half the windows size is quite normal
double[] windowArray = FFTWindow.GetWindowFunction(FFTWindowType.HANNING, fftWindowsSize);
// width of the segment - e.g. split the file into 78 time slots (numberOfSegments) and do analysis on each slot
int numberOfSegments = (numberOfSamples - fftWindowsSize) / fftOverlap;
var frames = new double[numberOfSegments][];
var signal = new double[fftWindowsSize];
for (int i = 0; i < numberOfSegments; i++)
{
// apply Hanning Window
for (int j = 0; j < fftWindowsSize; j++)
{
// Weight by Hann Window
signal[j] = (double)(windowArray[j] * samples[i * fftOverlap + j]);
}
// perform the FFT
FFTW_FFT_R2R(ref signal, ref signal, fftWindowsSize, FFTMethod.DFT);
// get the result
double[] complexDout = FFTUtils.HC2C(signal);
frames[i] = Abs(complexDout);
}
return(frames);
}
static double[] FFTWLIB_INPLACE(double[] signal)
{
int N = signal.Length;
double[] din = signal;
var dout = new double[N];
// perform the FFT
FFTUtils.FFTW_FFT_R2R(ref din, ref dout, N, FFTUtils.FFTMethod.DFT);
// get the result
double[] complexDout = FFTUtils.HC2C(dout);
var spectrum_fft_abs = FFTUtils.Abs(complexDout);
//Export.ExportCSV("audio_buffer_padded.csv", din);
//Export.ExportCSV("spectrum_fft_abs.csv", spectrum_fft_abs, fftSize);
return(spectrum_fft_abs);
}
