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Remove the Oscillations2010 class
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Issue #500 Remove the long unmaintained Oscillations2010 class. It s functionality is now replaced by Oscillations2019. The dependency class OscillationRecognizer was also deleted. The remaining dependency, EPR (Event Pattern recognition) class has ben refactored to use the class Oscillations2019. There is still much more work to be done on the EPR class to make it useable in todays AP environment.  The EPR class was originally developed to recognize the Eastern Ground Parrot using recordings from the Sunshine Coast.
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@towsey
towsey committed Jul 2, 2021
1 parent ccc8f31 commit ae8cc1a
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Showing 3 changed files with 64 additions and 790 deletions.
131 changes: 64 additions & 67 deletions src/AnalysisPrograms/EPR.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -19,9 +19,15 @@ namespace AnalysisPrograms
using TowseyLibrary;

/// <summary>
/// ######### 2021 NOTE:
/// This class was refactored in July 2021 to remove all dependencies on the Oscillations2010 class. The 2010 class was long out of date.
/// Instead, the Execute() method now calls the Oscillations2019 class which implements a similar but improved algorithm.
/// However some work remains to be done to remove the previous system of config dictionaries.
///
/// ######## THE FOLLOWING NOTES DATE BACK TO 2010-2011.
/// This program runs an alternative version of Event Pattern Recognition (EPR)
/// It can be used to detect Ground Parrots.
/// It was developed by Michael Towsey in order to address difficulties in the original EPR algorithm - see more below.
/// It was developed by Michael Towsey in 20010 to address difficulties in the original EPR algorithm - see more below.
/// COMMAND LINE ARGUMENTS:
/// string recordingPath = args[0]; //the recording to be scanned
/// string iniPath = args[1]; //the initialisation file containing parameters for AED and EPR
Expand Down Expand Up @@ -113,10 +119,7 @@ namespace AnalysisPrograms
/// iv) Apply template to each valid start point.
/// a) extract AEs at dB threshold apporpriate to the valid AE
/// a) align centroid of valid AE with centroid of first template AE
/// c) calculate the overlap score
///
///
///
/// c) calculate the overlap score.
/// </summary>
public class EPR
{
Expand Down Expand Up @@ -179,98 +182,92 @@ public static void Execute(Arguments arguments)
//double frameOverlap = FeltTemplates_Use.FeltFrameOverlap; // default = 0.5
double frameOverlap = double.Parse(dict["FRAME_OVERLAP"]);

//frequency band
//the search band band
int minHz = int.Parse(dict["MIN_HZ"]);
int maxHz = int.Parse(dict["MAX_HZ"]);

// oscillation OD parameters
double dctDuration = double.Parse(dict[OscillationRecogniser.Key_DCT_DURATION]); // 2.0; // seconds
double dctThreshold = double.Parse(dict[OscillationRecogniser.Key_DCT_THRESHOLD]); // 0.5;
int minOscilFreq = int.Parse(dict[OscillationRecogniser.Key_MIN_OSCIL_FREQ]); // 4;
int maxOscilFreq = int.Parse(dict[OscillationRecogniser.Key_MAX_OSCIL_FREQ]); // 5;
bool normaliseDCT = false;

// iii initialize the sonogram config class.
SonogramConfig sonoConfig = new SonogramConfig(); //default values config
sonoConfig.SourceFName = recording.BaseName;
double dctDuration = double.Parse(dict["dctDuration"]); // 2.0; // seconds
double dctThreshold = double.Parse(dict["dctThreshold"]); // 0.5;
int minOscFreq = int.Parse(dict["minOscillationFrequency"]); // 4;
int maxOscFreq = int.Parse(dict["maxOscillationFrequency"]); // 5;
double decibelThreshold = double.Parse(dict["decibelThreshold"]);

// iii initialize the spectrogram config with default values..
SonogramConfig sonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,

//sonoConfig.WindowSize = windowSize;
sonoConfig.WindowOverlap = frameOverlap;
//sonoConfig.WindowSize = windowSize;
WindowOverlap = frameOverlap,
};

// iv: generate the sonogram
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
// iv: generate the spectrogram
var spectrogram = new SpectrogramStandard(sonoConfig, recording.WavReader);

Log.WriteLine(
"Frames: Size={0}, Count={1}, Duration={2:f1}ms, Overlap={5:f2}%, Offset={3:f1}ms, Frames/s={4:f1}",
sonogram.Configuration.WindowSize, sonogram.FrameCount, sonogram.FrameDuration * 1000,
sonogram.FrameStep * 1000, sonogram.FramesPerSecond, frameOverlap);
int binCount = (int)(maxHz / sonogram.FBinWidth) - (int)(minHz / sonogram.FBinWidth) + 1;
spectrogram.Configuration.WindowSize,
spectrogram.FrameCount,
spectrogram.FrameDuration * 1000,
spectrogram.FrameStep * 1000,
spectrogram.FramesPerSecond,
frameOverlap);

int binCount = (int)(maxHz / spectrogram.FBinWidth) - (int)(minHz / spectrogram.FBinWidth) + 1;
Log.WriteIfVerbose("Freq band: {0} Hz - {1} Hz. (Freq bin count = {2})", minHz, maxHz, binCount);

// v: extract the subband energy array
Log.WriteLine("# Start extracting target event.");
double[] dBArray = SNR.DecibelsInSubband(sonogram.Data, minHz, maxHz, sonogram.FBinWidth);
for (int i = 0; i < sonogram.FrameCount; i++)
{
dBArray[i] /= binCount; // get average dB energy
}
// smooth the spectra in all time-frames.
spectrogram.Data = MatrixTools.SmoothRows(spectrogram.Data, 3);

double Q = 0.0;
double SD = 0.0;
throw new NotImplementedException("Mike changed the API here, I don't know how to fix it.");
dBArray = new[] { 0.0 }; // SNR.NoiseSubtractMode(dBArray, out Q, out SD);
double maxDB = 6.0;
double dBThreshold = 2 * SD / maxDB; //set dB threshold to 2xSD above background noise
dBArray = SNR.NormaliseDecibelArray_ZeroOne(dBArray, maxDB);
dBArray = DataTools.filterMovingAverage(dBArray, 7);
// extract array of decibel values, frame averaged over required frequency band
var decibelArray = SNR.CalculateFreqBandAvIntensity(spectrogram.Data, minHz, maxHz, spectrogram.NyquistFrequency);

//Log.WriteLine("Q ={0}", Q);
//Log.WriteLine("SD={0}", SD);
//Log.WriteLine("Th={0}", dBThreshold); //normalised threshhold
// can noise reduce the decibel array here if it helps.
// but only if noise reduction not already done earlier.

// #############################################################################################################################################
// vi: look for oscillation at required OR for ground parrots.
// vi: look for oscillation at required oscillation rate for ground parrots.
// The following call to the Oscillations2010 class is no longer available. Class deprecated on 2 July 2021.
// Instead use the below call to the Oscillations2019 class.
/*
double[] odScores = Oscillations2010.DetectOscillationsInScoreArray(
dBArray,
dctDuration,
sonogram.FramesPerSecond,
dctThreshold,
normaliseDCT,
minOscilFreq,
maxOscilFreq);

//odScores = SNR.NoiseSubtractMode(odScores, out Q, out SD);
double maxOD = 1.0;
odScores = SNR.NormaliseDecibelArray_ZeroOne(odScores, maxOD);
odScores = DataTools.filterMovingAverage(odScores, 5);

//odScores = DataTools.NormaliseMatrixValues(odScores); //NormaliseMatrixValues 0 - 1
//double odThreshold = (10 * SD) / maxOD; //set od threshold to 2xSD above background noise
//double odThreshold = dctThreshold;
double odThreshold = 0.4;
Log.WriteLine("Max={0}", odScores.Max());
minOscilRate,
maxOscilRate);
*/

// vi: look for oscillation at required oscillation rate for ground parrots.
var framesPerSecond = spectrogram.FramesPerSecond;
Oscillations2019.DetectOscillations(
decibelArray,
framesPerSecond,
decibelThreshold,
dctDuration,
minOscFreq,
maxOscFreq,
dctThreshold,
out var dctScores,
out var oscFreq);

//Log.WriteLine("Q ={0}", Q);
//Log.WriteLine("SD ={0}", SD);
Log.WriteLine("Th ={0}", dctThreshold); //normalised threshhold
double maxOctScore = 1.0;
dctScores = SNR.NormaliseDecibelArray_ZeroOne(dctScores, maxOctScore);
dctScores = DataTools.filterMovingAverage(dctScores, 5);

// #############################################################################################################################################
// vii: LOOK FOR GROUND PARROTS USING TEMPLATE
var template = GroundParrotRecogniser.ReadGroundParrotTemplateAsList(sonogram);
double[] gpScores = DetectEPR(template, sonogram, odScores, odThreshold);
var template = GroundParrotRecogniser.ReadGroundParrotTemplateAsList(spectrogram);
double[] gpScores = DetectEPR(template, spectrogram, dctScores, dctThreshold);
gpScores = DataTools.normalise(gpScores); //NormaliseMatrixValues 0 - 1

// #############################################################################################################################################

// iv: SAVE extracted event as matrix of dB intensity values
//FileTools.WriteMatrix2File(template, targetPath); // write template values to file PRIOR to noise removal.
//FileTools.WriteMatrix2File(templateMinusNoise, targetNoNoisePath); // write template values to file AFTER to noise removal.
//FileTools.WriteArray2File(noiseSubband, noisePath);

// v: SAVE image of extracted event in the original sonogram
// viii: SAVE image of extracted event in the original sonogram
string sonogramImagePath = outputDir + Path.GetFileNameWithoutExtension(recordingFileName) + ".png";

//DrawSonogram(sonogram, sonogramImagePath, dBArray, dBThreshold / maxDB, odScores, dctThreshold, gpScores, template);
}

public static double[] DetectEPR(List<AcousticEvent> template, BaseSonogram sonogram, double[] odScores, double odThreshold)
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