public ScalogramContainer CreateScalogram(List <float> frequenciesToAnalyze, float cyclesCount = 3)
{
ProgressRatio = 0;
_frequenciesToAnalyze = frequenciesToAnalyze;
var container = new ScalogramContainer(_signalFT.Length, frequenciesToAnalyze.Count);
//Parallel.ForEach(frequenciesToAnalyze, frequency =>
//{
//Interlocked.Increment(ref _frequenciesCalculated);
//});
for (int frequencyIndex = 0; frequencyIndex < frequenciesToAnalyze.Count; frequencyIndex++)
{
var frequency = frequenciesToAnalyze[frequencyIndex];
var sigma = 6;
var scalogram = WaveletTransformCPU.CreateScalogram(frequency, _signalFT, AudioContainer.SampleRate, cyclesCount, sigma);
for (int i = 0; i < scalogram.Length / 2; i++)
{
var temp = scalogram[i];
scalogram[i] = scalogram[scalogram.Length / 2 + i];
scalogram[scalogram.Length / 2 + i] = temp;
}
container.SetFrequencyData(frequencyIndex, scalogram);
ProgressRatio = (float)(frequencyIndex + 1) / frequenciesToAnalyze.Count;
}
container.Normalize();
IsFinished = true;
return(container);
}
public WaveletGenerationViewModel(SoundDataModel soundDataModel)
{
SoundDataModel = soundDataModel;
GenerateWavelet = new DelegateCommand(() =>
{
//todo: put to wavelet model;
var complex = WaveletTransformCPU.GenerateMorletWavelet(WaveFrequency, SampleRate, CyclesCount, PointsCount, SigmaValue);
var real = complex.Select(v => (float)v.Real).ToArray();
var duration = 1.0 / SampleRate * PointsCount;
var container = new AudioContainer((float)duration, SampleRate, real);
SoundDataModel.LoadContainer.OnNext(container);
});
WaveFrequency = 1;
CyclesCount = 5;
PointsCount = 1024;
SampleRate = 64;
SigmaValue = 5;
}
