public List <bool[]> CreateFingerprintsFromLogSpectrum(
double[][] logarithmizedSpectrum, IStride stride, int fingerprintLength, int overlap, int topWavelets)
{
DbgTimer t = new DbgTimer();
t.Start();
// Cut the logaritmic spectrogram into smaller spectrograms with one stride between each
List <double[][]> spectralImages = SpectrumService.CutLogarithmizedSpectrum(logarithmizedSpectrum, stride, fingerprintLength, overlap);
// Then apply the wavelet transform on them to later reduce the resolution
// do this in place
WaveletService.ApplyWaveletTransformInPlace(spectralImages);
// Then for each of the wavelet reduce the resolution by only keeping the top wavelets
// and ignore the magnitude of the top wavelets.
// Instead, we can simply keep the sign of it (+/-).
// This information is enough to keep the extract perceptual characteristics of a song.
List <bool[]> fingerprints = new List <bool[]>();
foreach (var spectralImage in spectralImages)
{
bool[] image = FingerprintDescriptor.ExtractTopWavelets(spectralImage, topWavelets);
fingerprints.Add(image);
}
Dbg.WriteLine("Created {1} Fingerprints from Log Spectrum - Execution Time: {0} ms", t.Stop().TotalMilliseconds, fingerprints.Count);
return(fingerprints);
}
public Image GetWaveletsImages(
double[][] spectrum,
IStride strideBetweenConsecutiveImages,
int fingerprintLength,
int overlap,
int imagesPerRow)
{
List <double[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(
spectrum, strideBetweenConsecutiveImages, fingerprintLength, overlap);
waveletService.ApplyWaveletTransformInPlace(spectralImages);
int width = spectralImages[0].GetLength(0);
int height = spectralImages[0][0].Length;
int fingersCount = spectralImages.Count;
int rowCount = (int)Math.Ceiling((float)fingersCount / imagesPerRow);
int imageWidth = (rowCount == 1 ? width + 2 * SpaceBetweenImages : ((imagesPerRow * (width + SpaceBetweenImages)) + SpaceBetweenImages));
int imageHeight = (rowCount * (height + SpaceBetweenImages)) + SpaceBetweenImages;
Bitmap image = new Bitmap(imageWidth, imageHeight, PixelFormat.Format16bppRgb565);
SetBackground(image, Color.White);
int verticalOffset = SpaceBetweenImages;
int horizontalOffset = SpaceBetweenImages;
int count = 0;
foreach (double[][] spectralImage in spectralImages)
{
double average = spectralImage.Average(col => col.Average(v => Math.Abs(v)));
for (int i = 0; i < width /*128*/; i++)
{
for (int j = 0; j < height /*32*/; j++)
{
Color color = ValueToBlackWhiteColor(spectralImage[i][j], average);
image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
}
}
count++;
if (count % imagesPerRow == 0)
{
verticalOffset += height + SpaceBetweenImages;
horizontalOffset = SpaceBetweenImages;
}
else
{
horizontalOffset += width + SpaceBetweenImages;
}
}
return(image);
}