public List <bool[]> CreateFingerprintsFromAudioSamples(float[] samples, WorkUnitParameterObject param, out double[][] logSpectrogram)
{
IFingerprintingConfiguration configuration = param.FingerprintingConfiguration;
AudioServiceConfiguration audioServiceConfiguration = new AudioServiceConfiguration
{
LogBins = configuration.LogBins,
LogBase = configuration.LogBase,
MaxFrequency = configuration.MaxFrequency,
MinFrequency = configuration.MinFrequency,
Overlap = configuration.Overlap,
SampleRate = configuration.SampleRate,
WindowSize = configuration.WindowSize,
NormalizeSignal = configuration.NormalizeSignal,
UseDynamicLogBase = configuration.UseDynamicLogBase
};
// store the log spectrogram in the out variable
logSpectrogram = AudioService.CreateLogSpectrogram(
samples, configuration.WindowFunction, audioServiceConfiguration);
return(this.CreateFingerprintsFromLogSpectrum(
logSpectrogram,
configuration.Stride,
configuration.FingerprintLength,
configuration.Overlap,
configuration.TopWavelets));
}
public List<bool[]> CreateFingerprints(float[] samples, IFingerprintingConfiguration fingerprintingConfiguration)
{
float[][] spectrum = spectrumService.CreateLogSpectrogram(samples, fingerprintingConfiguration);
return CreateFingerprintsFromLogSpectrum(
spectrum,
fingerprintingConfiguration.Stride,
fingerprintingConfiguration.FingerprintLength,
fingerprintingConfiguration.Overlap,
fingerprintingConfiguration.TopWavelets);
}
public WinDrawningTool(IFingerprintService fingerprintService, IAudioService audioService, ITagService tagService, IWorkUnitBuilder workUnitBuilder, IFingerprintingConfiguration fingerprintingConfiguration)
{
this.fingerprintService = fingerprintService;
this.audioService = audioService;
this.tagService = tagService;
this.workUnitBuilder = workUnitBuilder;
this.fingerprintingConfiguration = fingerprintingConfiguration;
InitializeComponent();
Icon = Resources.Sound;
_lbImageTypes.Items.Add("Single file");
_lbImageTypes.Items.Add("Separated images");
}
public float[][] CreateLogSpectrogram(float[] samples, IFingerprintingConfiguration configuration)
{
if (configuration.NormalizeSignal)
{
NormalizeInPlace(samples);
}
int width = (samples.Length - configuration.WdftSize) / configuration.Overlap; /*width of the image*/
float[][] frames = new float[width][];
int[] logFrequenciesIndexes = GenerateLogFrequencies(configuration);
for (int i = 0; i < width; i++)
{
float[] complexSignal = fftService.FFTForward(samples, i * configuration.Overlap, configuration.WdftSize);
frames[i] = ExtractLogBins(complexSignal, logFrequenciesIndexes, configuration.LogBins);
}
return frames;
}
public WinDrawningTool(
IAudioService audioService,
ITagService tagService,
IFingerprintUnitBuilder fingerprintUnitBuilder,
IFingerprintingConfiguration fingerprintingConfiguration,
IImageService imageService,
ISpectrumService spectrumService)
{
this.audioService = audioService;
this.tagService = tagService;
this.fingerprintUnitBuilder = fingerprintUnitBuilder;
this.fingerprintingConfiguration = fingerprintingConfiguration;
this.imageService = imageService;
this.spectrumService = spectrumService;
InitializeComponent();
Icon = Resources.Sound;
_lbImageTypes.Items.Add("Single file");
_lbImageTypes.Items.Add("Separated images");
}
/// <summary>
/// Gets the spectrum of the wavelet decomposition before extracting top wavelets and binary transformation
/// </summary>
/// <param name="spectrum">
/// The spectrum.
/// </param>
/// <param name="configuration">
/// The configuration.
/// </param>
/// <returns>
/// Image to be saved
/// </returns>
public static Image GetWaveletSpectralImage(float[][] spectrum, IFingerprintingConfiguration configuration)
{
List<float[][]> wavelets = new List<float[][]>();
int specLen = spectrum.GetLength(0);
int start = configuration.Stride.FirstStrideSize / configuration.Overlap;
int logbins = configuration.LogBins;
int fingerprintLength = configuration.FingerprintLength;
int overlap = configuration.Overlap;
while (start + fingerprintLength < specLen)
{
float[][] frames = new float[fingerprintLength][];
for (int i = 0; i < fingerprintLength; i++)
{
frames[i] = new float[logbins];
Array.Copy(spectrum[start + i], frames[i], logbins);
}
start += fingerprintLength + (configuration.Stride.StrideSize / overlap);
wavelets.Add(frames);
}
const int ImagesPerRow = 5; /*5 bitmap images per line*/
const int SpaceBetweenImages = 10; /*10 pixel space between images*/
int width = wavelets[0].GetLength(0);
int height = wavelets[0][0].Length;
int fingersCount = wavelets.Count;
int rowCount = (int)Math.Ceiling((float)fingersCount / ImagesPerRow);
int imageWidth = (ImagesPerRow * (width + SpaceBetweenImages)) + SpaceBetweenImages;
int imageHeight = (rowCount * (height + SpaceBetweenImages)) + SpaceBetweenImages;
Bitmap image = new Bitmap(imageWidth, imageHeight, PixelFormat.Format16bppRgb565);
/*Change the background of the bitmap*/
for (int i = 0; i < imageWidth; i++)
{
for (int j = 0; j < imageHeight; j++)
{
image.SetPixel(i, j, Color.White);
}
}
double maxValue = wavelets.Max((wavelet) => wavelet.Max((column) => column.Max()));
int verticalOffset = SpaceBetweenImages;
int horizontalOffset = SpaceBetweenImages;
int count = 0;
double max = wavelets.Max(wav => wav.Max(w => w.Max(v => Math.Abs(v))));
foreach (float[][] wavelet in wavelets)
{
for (int i = 0; i < width /*128*/; i++)
{
for (int j = 0; j < height /*32*/; j++)
{
Color color = ValueToBlackWhiteColor(wavelet[i][j], max / 4);
image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
}
}
count++;
if (count % ImagesPerRow == 0)
{
verticalOffset += height + SpaceBetweenImages;
horizontalOffset = SpaceBetweenImages;
}
else
{
horizontalOffset += width + SpaceBetweenImages;
}
}
return image;
}
private int[] GenerateStaticLogFrequencies(IFingerprintingConfiguration configuration)
{
double logMin = Math.Log(configuration.MinFrequency, configuration.LogBase);
double logMax = Math.Log(configuration.MaxFrequency, configuration.LogBase);
double delta = (logMax - logMin) / configuration.LogBins;
int[] indexes = new int[configuration.LogBins + 1];
double accDelta = 0;
for (int i = 0; i <= configuration.LogBins /*32 octaves*/; ++i)
{
float freq = (float)Math.Pow(configuration.LogBase, logMin + accDelta);
accDelta += delta; // accDelta = delta * i
/*Find the start index in array from which to start the summation*/
indexes[i] = FreqToIndex(freq, configuration.SampleRate, configuration.WdftSize);
}
return indexes;
}
private int[] GenerateLogFrequenciesDynamicBase(IFingerprintingConfiguration configuration)
{
double logBase =
Math.Exp(
Math.Log((float)configuration.MaxFrequency / configuration.MinFrequency) / configuration.LogBins);
double mincoef = (float)configuration.WdftSize / configuration.SampleRate * configuration.MinFrequency;
int[] indexes = new int[configuration.LogBins + 1];
for (int j = 0; j < configuration.LogBins + 1; j++)
{
int start = (int)((Math.Pow(logBase, j) - 1.0) * mincoef);
indexes[j] = start + (int)mincoef;
}
return indexes;
}
/// <summary>
/// Get logarithmically spaced indices
/// </summary>
/// <param name="configuration">
/// The configuration for log frequencies
/// </param>
/// <returns>
/// Log indexes
/// </returns>
private int[] GenerateLogFrequencies(IFingerprintingConfiguration configuration)
{
if (configuration.UseDynamicLogBase)
{
return GenerateLogFrequenciesDynamicBase(configuration);
}
return GenerateStaticLogFrequencies(configuration);
}
public static void SaveFingerprintingDebugImages(string fileName, double[][] logSpectrogram, List<bool[]> fingerprints, FingerprintService fingerprintService, IFingerprintingConfiguration fingerprintConfig) {
ImageService imageService = new ImageService(fingerprintService.SpectrumService, fingerprintService.WaveletService);
int fingerprintsPerRow = 2;
imageService.GetSpectrogramImage(logSpectrogram, logSpectrogram.Length, logSpectrogram[0].Length).Save(fileName + "_spectrogram.png");
imageService.GetWaveletsImages(logSpectrogram, fingerprintConfig.Stride, fingerprintConfig.FingerprintLength, fingerprintConfig.Overlap, fingerprintsPerRow).Save(fileName + "_wavelets.png");
imageService.GetLogSpectralImages(logSpectrogram, fingerprintConfig.Stride, fingerprintConfig.FingerprintLength, fingerprintConfig.Overlap, fingerprintsPerRow).Save(fileName + "_spectrograms.png");
imageService.GetImageForFingerprints(fingerprints, fingerprintConfig.FingerprintLength, fingerprintConfig.LogBins, fingerprintsPerRow).Save(fileName + "_fingerprints.png");
}
public void SetUp()
{
modelService = new ModelService(new MsSqlDatabaseProviderFactory(new DefaultConnectionStringFactory()), new ModelBinderFactory());
fingerprintService = new FingerprintService(new FingerprintDescriptor(), new SpectrumService(new CachedFFTWService(new FFTWService86())), new WaveletService(new StandardHaarWaveletDecomposition()));
defaultConfiguration = new DefaultFingerprintingConfiguration();
var mockedPermutations = new Mock<IPermutations>();
mockedPermutations.Setup(perms => perms.GetPermutations()).Returns(new int[1][]);
permutations = mockedPermutations.Object;
fingerprintUnitBuilderWithBass = new FingerprintUnitBuilder(fingerprintService, new BassAudioService(), new MinHashService(permutations));
#pragma warning disable 612,618
fingerprintUnitBuilderWithDirectSound = new FingerprintUnitBuilder(fingerprintService, new DirectSoundAudioService(), new MinHashService(permutations));
#pragma warning restore 612,618
}
public void SetUp()
{
connectionstring = ConnectionString;
dalManager = new DaoGateway(connectionstring);
fingerprintingServiceWithBass = new FingerprintService(
new BassAudioService(), new FingerprintDescriptor(), new HaarWavelet());
fingerprintingServiceWithDirectSound = new FingerprintService(
new DirectSoundAudioService(), new FingerprintDescriptor(), new HaarWavelet());
fingerprintingConfiguration = new DefaultFingerprintingConfiguration();
workUnitBuilder = new WorkUnitBuilder();
}
