public List <SpectralImage> CreateLogSpectrogram(AudioSamples audioSamples, SpectrogramConfig configuration)
{
using (new DebugTimer("CreateLogSpectrogram()"))
{
int wdftSize = configuration.WdftSize;
int width = (audioSamples.Samples.Length - wdftSize) / configuration.Overlap;
if (width < 1)
{
return(new List <SpectralImage>());
}
float[] frames = new float[width * configuration.LogBins];
ushort[] logFrequenciesIndexes = logUtility.GenerateLogFrequenciesRanges(audioSamples.SampleRate, configuration);
float[] window = configuration.Window.GetWindow(wdftSize);
float[] samples = audioSamples.Samples;
// PIN: reverted the following FFT to use lomontFFT with managed code (not the unsafe changed made by the original author due to the issues on my computers)
// NOTE! When using Parallell.For the result becomes different from time to time
// when running in Release mode.
// Therefore make sure to use for loop instead
for (int index = 0; index < width; index++)
// Parallel.For(0, width, index =>
{
var fftArray = CopyAndWindow(samples, index * configuration.Overlap, window);
lomontFFT.RealFFT(fftArray, true);
// after the lomont realfft the fft input array will contain the FFT values
// r0, r(n/2), r1, i1, r2, i2 ...
// since the extract log bins method only uses lowBound index above 2 we can ignore the fact
// that the first and second values are "special": r0, r(n/2)
// see https://github.com/perivar/FindSimilar/blob/6b658b1c54d1504136e25e933f39b7c303da5d9e/Mirage/Fft.cs
ExtractLogBins(fftArray, logFrequenciesIndexes, configuration.LogBins, wdftSize, frames, index);
}
// );
if (configuration.Verbosity == Verbosity.Verbose)
{
var imageService = new FindSimilarImageService();
using (Image image = imageService.GetSpectrogramImage(frames, width, configuration.LogBins, width, configuration.LogBins))
{
var fileName = Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(audioSamples.Origin) + "_spectrogram.png"));
if (fileName != null)
{
image.Save(fileName, ImageFormat.Png);
}
}
WriteOutputUtils.WriteCSV(frames, Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(audioSamples.Origin) + "_frames.csv")));
}
var spectralImages = CutLogarithmizedSpectrum(frames, audioSamples.SampleRate, configuration);
if (configuration.Verbosity == Verbosity.Verbose)
{
if (spectralImages.Count > 0)
{
var spectralImageList = new List <float[]>();
foreach (var spectralImage in spectralImages)
{
spectralImageList.Add(spectralImage.Image);
}
var spectralImageArray = spectralImageList.ToArray();
WriteOutputUtils.WriteCSV(spectralImageArray, Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(audioSamples.Origin) + "_spectral_images.csv")), ";");
}
}
ScaleFullSpectrum(spectralImages, configuration);
return(spectralImages);
}
}
public List <HashedFingerprint> CreateFingerprints(AudioSamples samples, FingerprintConfiguration configuration)
{
// Explode samples to the range of 16 bit shorts (–32,768 to 32,767)
// Matlab multiplies with 2^15 (32768)
const int AUDIO_MULTIPLIER = 65536; // 32768 still makes alot of mfcc feature computations fail!
// Explode samples to the range of 16 bit shorts (–32,768 to 32,767)
// Matlab multiplies with 2^15 (32768)
// e.g. if( max(abs(speech))<=1 ), speech = speech * 2^15; end;
float[] audiodata = samples.Samples;
MathUtils.Multiply(ref audiodata, AUDIO_MULTIPLIER);
// zero pad if the audio file is too short to perform a fft
if (audiodata.Length < (configuration.SpectrogramConfig.WdftSize + configuration.SpectrogramConfig.Overlap))
{
int lenNew = configuration.SpectrogramConfig.WdftSize + configuration.SpectrogramConfig.Overlap;
Array.Resize <float>(ref audiodata, lenNew);
}
samples.Samples = audiodata;
if (configuration.SpectrogramConfig.Verbosity == Verbosity.Verbose)
{
WriteOutputUtils.WriteCSV(audiodata, Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(samples.Origin) + "_audiodata.csv")));
}
// create log spectrogram
var spectralImages = spectrumService.CreateLogSpectrogram(samples, configuration.SpectrogramConfig);
if (configuration.SpectrogramConfig.Verbosity == Verbosity.Verbose)
{
if (spectralImages.Count > 0)
{
var imageService = new FindSimilarImageService();
using (Image image = imageService.GetLogSpectralImages(spectralImages, spectralImages.Count > 5 ? 5 : spectralImages.Count))
{
var fileName = Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(samples.Origin) + "_spectral_images.png"));
if (fileName != null)
{
image.Save(fileName, ImageFormat.Png);
}
}
}
}
var fingerprints = CreateFingerprintsFromLogSpectrum(spectralImages, configuration);
if (configuration.SpectrogramConfig.Verbosity == Verbosity.Verbose)
{
if (fingerprints.Count > 0)
{
var imageService = new FindSimilarImageService();
using (Image image = imageService.GetImageForFingerprints(fingerprints, 128, 32, fingerprints.Count > 5 ? 5 : fingerprints.Count))
{
var fileName = Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(samples.Origin) + "_fingerprints.png"));
if (fileName != null)
{
image.Save(fileName, ImageFormat.Png);
}
}
}
}
var hashedFingerprints = HashFingerprints(fingerprints, configuration);
if (configuration.SpectrogramConfig.Verbosity == Verbosity.Verbose)
{
if (hashedFingerprints.Count > 0)
{
var hashedFingerprintList = new List <int[]>();
foreach (var hashedFingerprint in hashedFingerprints)
{
hashedFingerprintList.Add(hashedFingerprint.HashBins);
}
var hashedFingerprinArray = hashedFingerprintList.ToArray();
WriteOutputUtils.WriteCSV(hashedFingerprinArray, Path.Combine(SoundFingerprinter.DEBUG_DIRECTORY_PATH, (Path.GetFileNameWithoutExtension(samples.Origin) + "_hashbins.csv")), ";");
}
}
return(hashedFingerprints);
}
