/// <summary>
/// This method was set up as a TESTMETHOD in May 2017 but has not yet been debugged.
/// It was transferred from Sandpit.cls. It is several years old.
/// Updated May 2017.
/// </summary>
public static void TESTMETHOD_SpectralClustering(string wavFilePath, string outputDir, int frameSize)
{
string imageViewer = @"C:\Windows\system32\mspaint.exe";
var recording = new AudioRecording(wavFilePath); // get recording segment
// test clustering using amplitude spectrogram
// double binaryThreshold = 0.07; // An amplitude threshold of 0.03 = -30 dB. A threshold of 0.05 = -26dB.
// double[,] spectrogramData = GetAmplitudeSpectrogramNoiseReduced(recording, frameSize);
// test clustering using decibel spectrogram
double binaryThreshold = DefaultBinaryThresholdInDecibels; // A decibel threshold for converting to binary
double[,] spectrogramData = GetDecibelSpectrogramNoiseReduced(recording, frameSize);
//#######################################################################################################################################
int nyquistFreq = recording.Nyquist;
int frameCount = spectrogramData.GetLength(0);
int freqBinCount = spectrogramData.GetLength(1);
double binWidth = nyquistFreq / (double)freqBinCount;
// We only use the midband of the Spectrogram, i.e. the band between lowerBinBound and upperBinBound.
// int lowFreqBound = 1000;
// int upperBinBound = freqBinCount - 5;
// In June 2016, the mid-band was set to lowerBound=1000Hz, upperBound=8000hz, because this band contains most bird activity, i.e. it is the Bird-Band
// This was done in order to make the cluster summary indices more reflective of bird call activity.
// int lowerFreqBound = 482;
int lowerFreqBound = 1000;
int lowerBinBound = (int)Math.Ceiling(lowerFreqBound / binWidth);
int upperFreqBound = 8000;
int upperBinBound = (int)Math.Ceiling(upperFreqBound / binWidth);
var midBandSpectrogram = MatrixTools.Submatrix(spectrogramData, 0, lowerBinBound, spectrogramData.GetLength(0) - 1, upperBinBound);
var clusterInfo = ClusterTheSpectra(midBandSpectrogram, lowerBinBound, upperBinBound, binaryThreshold);
// transfer cluster info to spectral index results
var clusterSpectrum1 = RestoreFullLengthSpectrum(clusterInfo.ClusterSpectrum, freqBinCount, lowerBinBound);
Console.WriteLine("Lower BinBound=" + lowerBinBound);
Console.WriteLine("Upper BinBound=" + upperBinBound);
Console.WriteLine("Binary Threshold=" + binaryThreshold);
Console.WriteLine("Cluster Count =" + clusterInfo.ClusterCount);
Console.WriteLine("Three Gram Count=" + clusterInfo.TriGramUniqueCount);
// ###################################################################################
// Now repeat the entire process again. This time we want to get intermediate results to superimpose on spectrogram
// Need to specify additional parameters. ACTIVITY THRESHOLD - require activity in at least N bins to include spectrum for training
double rowSumThreshold = DefaultRowSumThreshold;
var parameters = new ClusteringParameters(lowerBinBound, upperBinBound, binaryThreshold, rowSumThreshold);
TrainingDataInfo data = GetTrainingDataForClustering(midBandSpectrogram, parameters);
// make a normal standard decibel spectogram on which to superimpose cluster results
var stdSpectrogram = GetStandardSpectrogram(recording, frameSize);
var image = DrawSonogram(stdSpectrogram, null, null, 0.0, null);
SaveAndViewSpectrogramImage(image, outputDir, "test0Spectrogram.png", imageViewer);
// Debug.Assert(data.TrainingDataAsSpectrogram != null, "data.TrainingDataAsSpectrogram != null");
double[,] overlay = ConvertOverlayToSpectrogramSize(data.TrainingDataAsSpectrogram, lowerBinBound, frameCount, freqBinCount);
image = DrawSonogram(stdSpectrogram, null, null, 0.0, overlay);
SaveAndViewSpectrogramImage(image, outputDir, "test1Spectrogram.png", imageViewer);
// Return if no suitable training data for clustering
if (data.TrainingData.Count <= 8)
{
Console.WriteLine("Abort clustering. Only {0} spectra available for training data. Must be at least 9.", data.TrainingData.Count);
}
else
{
// the following are pruning parameters
double wtThreshold = rowSumThreshold; // used to remove wt vectors whose sum of wts <= threshold
int hitThreshold = DefaultHitThreshold; // used to remove wt vectors which have < N hits, i.e. cluster size < N
clusterInfo = ClusterAnalysis(data.TrainingData, wtThreshold, hitThreshold, data.SelectedFrames);
Console.WriteLine("Binary Threshold=" + binaryThreshold);
Console.WriteLine("Weight Threshold=" + wtThreshold);
Console.WriteLine("Hit Threshold=" + hitThreshold);
Console.WriteLine("Cluster Count=" + clusterInfo.ClusterCount);
Console.WriteLine("Three Gram Count=" + clusterInfo.TriGramUniqueCount);
image = DrawClusterSpectrogram(stdSpectrogram, clusterInfo, data, lowerBinBound);
SaveAndViewSpectrogramImage(image, outputDir, "test2Spectrogram.png", imageViewer);
// transfer cluster info to spectral index results
var clusterSpectrum2 = RestoreFullLengthSpectrum(clusterInfo.ClusterSpectrum, freqBinCount, lowerBinBound);
TestTools.CompareTwoArrays(clusterSpectrum1, clusterSpectrum2);
}
}