/// <summary>
/// This algorithm is used for broad band events such as a bird chorus.
/// It selects acoustic content over a band of several kHz and calculates a content score based on a template match to what is in the band.
/// </summary>
/// <param name="oneMinuteOfIndices">Derived from the source recording.</param>
/// <param name="template">A previously prepared template.</param>
/// <param name="templateIndices">The actual dictionary of template arrays.</param>
/// <returns>A similarity score.</returns>
public static double GetBroadbandContent1(Dictionary <string, double[]> oneMinuteOfIndices, TemplateManifest template, Dictionary <string, double[]> templateIndices)
{
// copy over the recording indices required by the template.
var requiredIndices = DataProcessing.GetRequiredIndices(oneMinuteOfIndices, templateIndices.Keys.ToArray());
//reduce indices and convert to vector.
var reductionFactor = template.SpectralReductionFactor;
int freqBinCount = ContentSignatures.FreqBinCount / reductionFactor;
int bottomFreq = template.BandMinHz; //Hertz
int topFreq = template.BandMaxHz; //Hertz
var freqBinBounds = DataProcessing.GetFreqBinBounds(bottomFreq, topFreq, freqBinCount);
var reducedIndices = DataProcessing.ReduceIndicesByFactor(requiredIndices, reductionFactor);
// remove top freq bins and bottom freq bins;
reducedIndices = DataProcessing.ApplyBandPass(reducedIndices, freqBinBounds[0], freqBinBounds[1]);
var oneMinuteVector = DataProcessing.ConvertDictionaryToVector(reducedIndices);
var templateVector = DataProcessing.ConvertDictionaryToVector(templateIndices);
//Get Euclidean distance and normalize the distance
var distance = DataTools.EuclideanDistance(templateVector, oneMinuteVector);
// Normalize the distance
distance /= Math.Sqrt(templateVector.Length);
return(1 - distance);
}
/// <summary>
/// This algorithm is used for full band width events such as a rain and wind.
/// It calculates a content score based on a template match to what is in the full spectrum.
/// </summary>
/// <param name="oneMinuteOfIndices">Derived from the source recording.</param>
/// <param name="template">A previously prepared template.</param>
/// <param name="templateIndices">The actual dictionary of template arrays.</param>
/// <returns>A similarity score.</returns>
public static double GetFullBandContent1(Dictionary <string, double[]> oneMinuteOfIndices, TemplateManifest template, Dictionary <string, double[]> templateIndices)
{
// copy over the recording indices required by the template.
var requiredIndices = DataProcessing.GetRequiredIndices(oneMinuteOfIndices, templateIndices.Keys.ToArray());
//reduce indices and convert to vector.
var reducedIndices = DataProcessing.ReduceIndicesByFactor(requiredIndices, template.SpectralReductionFactor);
var oneMinuteVector = DataProcessing.ConvertDictionaryToVector(reducedIndices);
var templateVector = DataProcessing.ConvertDictionaryToVector(templateIndices);
var distance = DataTools.EuclideanDistance(templateVector, oneMinuteVector);
// Normalize the distance
distance /= Math.Sqrt(templateVector.Length);
return(1 - distance);
}
/// <summary>
/// This algorithm is used for narrow band events such as an insect bird chorus or content due to narrow band calls of a single bird species.
/// It searches the full spectrum for a match to the template and then
/// calculates how much of the match weight is in the correct narrow freq band.
/// </summary>
/// <param name="oneMinuteOfIndices">Derived from the source recording.</param>
/// <param name="template">A previously prepared template.</param>
/// <param name="templateIndices">The actual dictionary of template arrays.</param>
/// <returns>A similarity score.</returns>
public static double GetNarrowBandContent1(Dictionary <string, double[]> oneMinuteOfIndices, TemplateManifest template, Dictionary <string, double[]> templateIndices)
{
// copy over the recording indices required by the template.
var requiredIndices = DataProcessing.GetRequiredIndices(oneMinuteOfIndices, templateIndices.Keys.ToArray());
//reduce indices and convert to vector.
var reductionFactor = template.SpectralReductionFactor;
var reducedIndices = DataProcessing.ReduceIndicesByFactor(requiredIndices, reductionFactor);
// Now pass the template up the full frequency spectrum to get a spectrum of scores.
var spectralScores = DataProcessing.ScanSpectrumWithTemplate(templateIndices, reducedIndices);
// Now check how much of spectral weight is in the correct freq band ie between 3-4 kHz.
int freqBinCount = ContentSignatures.FreqBinCount / reductionFactor;
int bottomFreq = template.BandMinHz; //Hertz
int topFreq = template.BandMaxHz; //Hertz
var freqBinBounds = DataProcessing.GetFreqBinBounds(bottomFreq, topFreq, freqBinCount);
double callSum = DataTools.Subarray(spectralScores, freqBinBounds[0], freqBinBounds[1]).Sum();
double totalSum = DataTools.Subarray(spectralScores, 1, spectralScores.Length - 3).Sum();
double score = callSum / totalSum;
return(score);
}
