/// <summary>
///
/// </summary>
/// <param name="ae">an acoustic event</param>
/// <param name="dbArray">The sequence of frame dB over the event</param>
/// <returns></returns>
public static System.Tuple <double, double> KiwiPeakAnalysis(AcousticEvent ae, double[] dbArray)
{
//dbArray = DataTools.filterMovingAverage(dbArray, 3);
bool[] peaks = DataTools.GetPeaks(dbArray); //locate the peaks
double[] peakValues = new double[dbArray.Length];
for (int i = 0; i < dbArray.Length; i++)
{
if (peaks[i])
{
peakValues[i] = dbArray[i];
}
}
//take the top N peaks
int N = 5;
double[] topNValues = new double[N];
for (int p = 0; p < N; p++)
{
int maxID = DataTools.GetMaxIndex(peakValues);
topNValues[p] = peakValues[maxID];
peakValues[maxID] = 0.0;
}
//PROCESS PEAK DECIBELS
double avPeakDB, sdPeakDB;
NormalDist.AverageAndSD(topNValues, out avPeakDB, out sdPeakDB);
return(System.Tuple.Create(avPeakDB, sdPeakDB));
}
public void TestFreqScaleOnArtificialSignal1()
{
int sampleRate = 22050;
double duration = 20; // signal duration in seconds
int[] harmonics = { 500, 1000, 2000, 4000, 8000 };
int windowSize = 512;
var freqScale = new FrequencyScale(sampleRate / 2, windowSize, 1000);
var outputImagePath = Path.Combine(this.outputDirectory.FullName, "Signal1_LinearFreqScale.png");
var recording = DspFilters.GenerateTestRecording(sampleRate, duration, harmonics, WaveType.Cosine);
var sonoConfig = new SonogramConfig
{
WindowSize = freqScale.WindowSize,
WindowOverlap = 0.0,
SourceFName = "Signal1",
NoiseReductionType = NoiseReductionType.Standard,
NoiseReductionParameter = 0.12,
};
var sonogram = new AmplitudeSonogram(sonoConfig, recording.WavReader);
// pick a row, any row
var oneSpectrum = MatrixTools.GetRow(sonogram.Data, 40);
oneSpectrum = DataTools.filterMovingAverage(oneSpectrum, 5);
var peaks = DataTools.GetPeaks(oneSpectrum);
for (int i = 5; i < peaks.Length - 5; i++)
{
if (peaks[i])
{
LoggedConsole.WriteLine($"bin ={freqScale.BinBounds[i, 0]}, Herz={freqScale.BinBounds[i, 1]}-{freqScale.BinBounds[i + 1, 1]} ");
}
}
foreach (int h in harmonics)
{
LoggedConsole.WriteLine($"Harmonic {h}Herz should be in bin {freqScale.GetBinIdForHerzValue(h)}");
}
// spectrogram without framing, annotation etc
var image = sonogram.GetImage();
string title = $"Spectrogram of Harmonics: {DataTools.Array2String(harmonics)} SR={sampleRate} Window={windowSize}";
image = sonogram.GetImageFullyAnnotated(image, title, freqScale.GridLineLocations);
image.Save(outputImagePath);
// Check that image dimensions are correct
Assert.AreEqual(861, image.Width);
Assert.AreEqual(310, image.Height);
Assert.IsTrue(peaks[11]);
Assert.IsTrue(peaks[22]);
Assert.IsTrue(peaks[45]);
Assert.IsTrue(peaks[92]);
Assert.IsTrue(peaks[185]);
}
public static void TestMethod_GenerateSignal1()
{
int sampleRate = 22050;
double duration = 20; // signal duration in seconds
int[] harmonics = { 500, 1000, 2000, 4000, 8000 };
int windowSize = 512;
var freqScale = new FrequencyScale(sampleRate / 2, windowSize, 1000);
string path = @"C:\SensorNetworks\Output\Sonograms\UnitTestSonograms\SineSignal1.png";
var recording = GenerateTestRecording(sampleRate, duration, harmonics, WaveType.Cosine);
var sonoConfig = new SonogramConfig
{
WindowSize = freqScale.WindowSize,
WindowOverlap = 0.0,
SourceFName = "Signal1",
NoiseReductionType = NoiseReductionType.Standard,
NoiseReductionParameter = 0.12,
};
var sonogram = new AmplitudeSonogram(sonoConfig, recording.WavReader);
// pick a row, any row
var oneSpectrum = MatrixTools.GetRow(sonogram.Data, 40);
oneSpectrum = DataTools.normalise(oneSpectrum);
var peaks = DataTools.GetPeaks(oneSpectrum, 0.5);
for (int i = 2; i < peaks.Length - 2; i++)
{
if (peaks[i])
{
LoggedConsole.WriteLine($"bin ={freqScale.BinBounds[i, 0]}, Herz={freqScale.BinBounds[i, 1]}-{freqScale.BinBounds[i + 1, 1]} ");
}
}
if (peaks[11] && peaks[22] && peaks[45] && peaks[92] && peaks[185])
{
LoggedConsole.WriteSuccessLine("Spectral Peaks found at correct places");
}
else
{
LoggedConsole.WriteErrorLine("Spectral Peaks found at INCORRECT places");
}
foreach (int h in harmonics)
{
LoggedConsole.WriteLine($"Harmonic {h}Herz should be in bin {freqScale.GetBinIdForHerzValue(h)}");
}
// spectrogram without framing, annotation etc
var image = sonogram.GetImage();
string title = $"Spectrogram of Harmonics: {DataTools.Array2String(harmonics)} SR={sampleRate} Window={windowSize}";
image = sonogram.GetImageFullyAnnotated(image, title, freqScale.GridLineLocations);
image.Save(path);
}
public static double CalculateSpikeIndex(double[] envelope, double spikeThreshold)
{
int length = envelope.Length;
// int isolatedSpikeCount = 0;
double peakIntenisty = 0.0;
double spikeIntensity = 0.0;
var peaks = DataTools.GetPeaks(envelope);
int peakCount = 0;
for (int i = 1; i < length - 1; i++)
{
if (!peaks[i])
{
continue; //count spikes
}
peakCount++;
double diffMinus1 = Math.Abs(envelope[i] - envelope[i - 1]);
double diffPlus1 = Math.Abs(envelope[i] - envelope[i + 1]);
double avDifference = (diffMinus1 + diffPlus1) / 2;
peakIntenisty += avDifference;
if (avDifference > spikeThreshold)
{
//isolatedSpikeCount++; // count isolated spikes
spikeIntensity += avDifference;
}
}
if (peakCount == 0)
{
return(0.0);
}
return(spikeIntensity / peakIntenisty);
} //CalculateSpikeIndex()
/// <summary>
/// THE KEY ANALYSIS METHOD
/// </summary>
/// <param name="recording">
/// The segment Of Source File.
/// </param>
/// <param name="configDict">
/// The config Dict.
/// </param>
/// <param name="value"></param>
/// <returns>
/// The <see cref="LimnodynastesConvexResults"/>.
/// </returns>
internal static LimnodynastesConvexResults Analysis(
Dictionary <string, double[, ]> dictionaryOfHiResSpectralIndices,
AudioRecording recording,
Dictionary <string, string> configDict,
AnalysisSettings analysisSettings,
SegmentSettingsBase segmentSettings)
{
// for Limnodynastes convex, in the D.Stewart CD, there are peaks close to:
//1. 1950 Hz
//2. 1460 hz
//3. 970 hz These are 490 Hz apart.
// for Limnodynastes convex, in the JCU recording, there are peaks close to:
//1. 1780 Hz
//2. 1330 hz
//3. 880 hz These are 450 Hz apart.
// So strategy is to look for three peaks separated by same amount and in the vicinity of the above,
// starting with highest power (the top peak) and working down to lowest power (bottom peak).
var outputDir = segmentSettings.SegmentOutputDirectory;
TimeSpan segmentStartOffset = segmentSettings.SegmentStartOffset;
//KeyValuePair<string, double[,]> kvp = dictionaryOfHiResSpectralIndices.First();
var spg = dictionaryOfHiResSpectralIndices["RHZ"];
int rhzRowCount = spg.GetLength(0);
int rhzColCount = spg.GetLength(1);
int sampleRate = recording.SampleRate;
double herzPerBin = sampleRate / 2 / (double)rhzRowCount;
double scoreThreshold = (double?)double.Parse(configDict["EventThreshold"]) ?? 3.0;
int minimumFrequency = (int?)int.Parse(configDict["MinHz"]) ?? 850;
int dominantFrequency = (int?)int.Parse(configDict["DominantFrequency"]) ?? 1850;
// # The Limnodynastes call has three major peaks. The dominant peak is at 1850 or as set above.
// # The second and third peak are at equal gaps below. DominantFreq-gap and DominantFreq-(2*gap);
// # Set the gap in the Config file. Should typically be in range 880 to 970
int peakGapInHerz = (int?)int.Parse(configDict["PeakGap"]) ?? 470;
int F1AndF2Gap = (int)Math.Round(peakGapInHerz / herzPerBin);
//int F1AndF2Gap = 10; // 10 = number of freq bins
int F1AndF3Gap = 2 * F1AndF2Gap;
//int F1AndF3Gap = 20;
int hzBuffer = 250;
int bottomBin = 5;
int dominantBin = (int)Math.Round(dominantFrequency / herzPerBin);
int binBuffer = (int)Math.Round(hzBuffer / herzPerBin);;
int dominantBinMin = dominantBin - binBuffer;
int dominantBinMax = dominantBin + binBuffer;
// freqBin + rowID = binCount - 1;
// therefore: rowID = binCount - freqBin - 1;
int minRowID = rhzRowCount - dominantBinMax - 1;
int maxRowID = rhzRowCount - dominantBinMin - 1;
int bottomRow = rhzRowCount - bottomBin - 1;
var list = new List <Point>();
// loop through all spectra/columns of the hi-res spectrogram.
for (int c = 1; c < rhzColCount - 1; c++)
{
double maxAmplitude = -double.MaxValue;
int idOfRowWithMaxAmplitude = 0;
for (int r = minRowID; r <= bottomRow; r++)
{
if (spg[r, c] > maxAmplitude)
{
maxAmplitude = spg[r, c];
idOfRowWithMaxAmplitude = r;
}
}
if (idOfRowWithMaxAmplitude < minRowID)
{
continue;
}
if (idOfRowWithMaxAmplitude > maxRowID)
{
continue;
}
// want a spectral peak.
if (spg[idOfRowWithMaxAmplitude, c] < spg[idOfRowWithMaxAmplitude, c - 1])
{
continue;
}
if (spg[idOfRowWithMaxAmplitude, c] < spg[idOfRowWithMaxAmplitude, c + 1])
{
continue;
}
// peak should exceed thresold amplitude
if (spg[idOfRowWithMaxAmplitude, c] < 3.0)
{
continue;
}
// convert row ID to freq bin ID
int freqBinID = rhzRowCount - idOfRowWithMaxAmplitude - 1;
list.Add(new Point(c, freqBinID));
// we now have a list of potential hits for LimCon. This needs to be filtered.
// Console.WriteLine("Col {0}, Bin {1} ", c, freqBinID);
}
// DEBUG ONLY // ################################ TEMPORARY ################################
// superimpose point on RHZ HiRes spectrogram for debug purposes
bool drawOnHiResSpectrogram = true;
//string filePath = @"G:\SensorNetworks\Output\Frogs\TestOfHiResIndices-2016July\Test\Towsey.HiResIndices\SpectrogramImages\3mile_creek_dam_-_Herveys_Range_1076_248366_20130305_001700_30_0min.CombinedGreyScale.png";
var fileName = Path.GetFileNameWithoutExtension(segmentSettings.SegmentAudioFile.Name);
string filePath = outputDir.FullName + @"\SpectrogramImages\" + fileName + ".CombinedGreyScale.png";
var debugImage = new FileInfo(filePath);
if (!debugImage.Exists)
{
drawOnHiResSpectrogram = false;
}
if (drawOnHiResSpectrogram)
{
// put red dot where max is
Bitmap bmp = new Bitmap(filePath);
foreach (Point point in list)
{
bmp.SetPixel(point.X + 70, 1911 - point.Y, Color.Red);
}
// mark off every tenth frequency bin
for (int r = 0; r < 26; r++)
{
bmp.SetPixel(68, 1911 - (r * 10), Color.Blue);
bmp.SetPixel(69, 1911 - (r * 10), Color.Blue);
}
// mark off upper bound and lower frequency bound
bmp.SetPixel(69, 1911 - dominantBinMin, Color.Lime);
bmp.SetPixel(69, 1911 - dominantBinMax, Color.Lime);
//bmp.SetPixel(69, 1911 - maxRowID, Color.Lime);
string opFilePath = outputDir.FullName + @"\SpectrogramImages\" + fileName + ".CombinedGreyScaleAnnotated.png";
bmp.Save(opFilePath);
}
// END DEBUG ################################ TEMPORARY ################################
// now construct the standard decibel spectrogram WITHOUT noise removal, and look for LimConvex
// get frame parameters for the analysis
double epsilon = Math.Pow(0.5, recording.BitsPerSample - 1);
int frameSize = rhzRowCount * 2;
int frameStep = frameSize; // this default = zero overlap
double frameDurationInSeconds = frameSize / (double)sampleRate;
double frameStepInSeconds = frameStep / (double)sampleRate;
double framesPerSec = 1 / frameStepInSeconds;
//var dspOutput = DSP_Frames.ExtractEnvelopeAndFFTs(recording, frameSize, frameStep);
//// Generate deciBel spectrogram
//double[,] deciBelSpectrogram = MFCCStuff.DecibelSpectra(dspOutput.amplitudeSpectrogram, dspOutput.WindowPower, sampleRate, epsilon);
// i: Init SONOGRAM config
var sonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,
WindowSize = frameSize,
WindowOverlap = 0.0,
NoiseReductionType = NoiseReductionType.None,
};
// init sonogram
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
// remove the DC row of the spectrogram
sonogram.Data = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.Data.GetLength(0) - 1, sonogram.Data.GetLength(1) - 1);
//scores.Add(new Plot("Decibels", DataTools.NormaliseMatrixValues(dBArray), ActivityAndCover.DefaultActivityThresholdDb));
//scores.Add(new Plot("Active Frames", DataTools.Bool2Binary(activity.activeFrames), 0.0));
// convert spectral peaks to frequency
//var tuple_DecibelPeaks = SpectrogramTools.HistogramOfSpectralPeaks(deciBelSpectrogram);
//int[] peaksBins = tuple_DecibelPeaks.Item2;
//double[] freqPeaks = new double[peaksBins.Length];
//int binCount = sonogram.Data.GetLength(1);
//for (int i = 1; i < peaksBins.Length; i++) freqPeaks[i] = (lowerBinBound + peaksBins[i]) / (double)nyquistBin;
//scores.Add(new Plot("Max Frequency", freqPeaks, 0.0)); // location of peaks for spectral images
// create new list of LimCon hits in the standard spectrogram.
double timeSpanOfFrameInSeconds = frameSize / (double)sampleRate;
var newList = new List <int[]>();
int lastFrameID = sonogram.Data.GetLength(0) - 1;
int lastBinID = sonogram.Data.GetLength(1) - 1;
foreach (Point point in list)
{
double secondsFromStartOfSegment = (point.X * 0.1) + 0.05; // convert point.Y to center of time-block.
int framesFromStartOfSegment = (int)Math.Round(secondsFromStartOfSegment / timeSpanOfFrameInSeconds);
// location of max point is uncertain, so search in neighbourhood.
// NOTE: sonogram.data matrix is time*freqBin
double maxValue = -double.MaxValue;
int idOfTMax = framesFromStartOfSegment;
int idOfFMax = point.Y;
for (int deltaT = -4; deltaT <= 4; deltaT++)
{
for (int deltaF = -1; deltaF <= 1; deltaF++)
{
int newT = framesFromStartOfSegment + deltaT;
if (newT < 0)
{
newT = 0;
}
else if (newT > lastFrameID)
{
newT = lastFrameID;
}
double value = sonogram.Data[newT, point.Y + deltaF];
if (value > maxValue)
{
maxValue = value;
idOfTMax = framesFromStartOfSegment + deltaT;
idOfFMax = point.Y + deltaF;
}
}
}
// newList.Add(new Point(frameSpan, point.Y));
int[] array = new int[2];
array[0] = idOfTMax;
array[1] = idOfFMax;
newList.Add(array);
}
// Now obtain more of spectrogram to see if have peaks at two other places characteristic of Limnodynastes convex.
// In the D.Stewart CD, there are peaks close to:
//1. 1950 Hz
//2. 1460 hz
//3. 970 hz These are 490 Hz apart.
// For Limnodynastes convex, in the JCU recording, there are peaks close to:
//1. 1780 Hz
//2. 1330 hz
//3. 880 hz These are 450 Hz apart.
// So strategy is to look for three peaks separated by same amount and in the vicinity of the above,
// starting with highest power (the top peak) and working down to lowest power (bottom peak).
//We have found top/highest peak - now find the other two.
int secondDominantFrequency = 1380;
int secondDominantBin = (int)Math.Round(secondDominantFrequency / herzPerBin);
int thirdDominantFrequency = 900;
int thirdDominantBin = (int)Math.Round(thirdDominantFrequency / herzPerBin);
var acousticEvents = new List <AcousticEvent>();
int Tbuffer = 2;
// First extract a sub-matrix.
foreach (int[] array in newList)
{
// NOTE: sonogram.data matrix is time*freqBin
int Tframe = array[0];
int F1bin = array[1];
double[,] subMatrix = MatrixTools.Submatrix(sonogram.Data, Tframe - Tbuffer, 0, Tframe + Tbuffer, F1bin);
double F1power = subMatrix[Tbuffer, F1bin];
// convert to vector
var spectrum = MatrixTools.GetColumnAverages(subMatrix);
// use the following code to get estimate of background noise
double[,] powerMatrix = MatrixTools.Submatrix(sonogram.Data, Tframe - 3, 10, Tframe + 3, F1bin);
double averagePower = (MatrixTools.GetRowAverages(powerMatrix)).Average();
double score = F1power - averagePower;
// debug - checking what the spectrum looks like.
//for (int i = 0; i < 18; i++)
// spectrum[i] = -100.0;
//DataTools.writeBarGraph(spectrum);
// locate the peaks in lower frequency bands, F2 and F3
bool[] peaks = DataTools.GetPeaks(spectrum);
int F2bin = 0;
double F2power = -200.0; // dB
for (int i = -3; i <= 2; i++)
{
int bin = F1bin - F1AndF2Gap + i;
if ((peaks[bin]) && (F2power < subMatrix[1, bin]))
{
F2bin = bin;
F2power = subMatrix[1, bin];
}
}
if (F2bin == 0)
{
continue;
}
if (F2power == -200.0)
{
continue;
}
score += (F2power - averagePower);
int F3bin = 0;
double F3power = -200.0;
for (int i = -5; i <= 2; i++)
{
int bin = F1bin - F1AndF3Gap + i;
if ((peaks[bin]) && (F3power < subMatrix[1, bin]))
{
F3bin = bin;
F3power = subMatrix[1, bin];
}
}
if (F3bin == 0)
{
continue;
}
if (F3power == -200.0)
{
continue;
}
score += (F3power - averagePower);
score /= 3;
// ignore events where SNR < decibel threshold
if (score < scoreThreshold)
{
continue;
}
// ignore events with wrong power distribution. A good LimnoConvex call has strongest F1 power
if ((F3power > F1power) || (F2power > F1power))
{
continue;
}
//freq Bin ID must be converted back to Matrix row ID
// freqBin + rowID = binCount - 1;
// therefore: rowID = binCount - freqBin - 1;
minRowID = rhzRowCount - F1bin - 2;
maxRowID = rhzRowCount - F3bin - 1;
int F1RowID = rhzRowCount - F1bin - 1;
int F2RowID = rhzRowCount - F2bin - 1;
int F3RowID = rhzRowCount - F3bin - 1;
int maxfreq = dominantFrequency + hzBuffer;
int topBin = (int)Math.Round(maxfreq / herzPerBin);
int frameCount = 4;
double duration = frameCount * frameStepInSeconds;
double startTimeWrtSegment = (Tframe - 2) * frameStepInSeconds;
// Got to here so start initialising an acoustic event
var ae = new AcousticEvent(segmentStartOffset, startTimeWrtSegment, duration, minimumFrequency, maxfreq);
ae.SetTimeAndFreqScales(framesPerSec, herzPerBin);
//var ae = new AcousticEvent(oblong, recording.Nyquist, binCount, frameDurationInSeconds, frameStepInSeconds, frameCount);
//ae.StartOffset = TimeSpan.FromSeconds(Tframe * frameStepInSeconds);
var pointF1 = new Point(2, topBin - F1bin);
var pointF2 = new Point(2, topBin - F2bin);
var pointF3 = new Point(2, topBin - F3bin);
ae.Points = new List <Point>();
ae.Points.Add(pointF1);
ae.Points.Add(pointF2);
ae.Points.Add(pointF3);
//tried using HitElements but did not do what I wanted later on.
//ae.HitElements = new HashSet<Point>();
//ae.HitElements = new SortedSet<Point>();
//ae.HitElements.Add(pointF1);
//ae.HitElements.Add(pointF2);
//ae.HitElements.Add(pointF3);
ae.Score = score;
//ae.MinFreq = Math.Round((topBin - F3bin - 5) * herzPerBin);
//ae.MaxFreq = Math.Round(topBin * herzPerBin);
acousticEvents.Add(ae);
}
// now add in extra common info to the acoustic events
acousticEvents.ForEach(ae =>
{
ae.SpeciesName = configDict[AnalysisKeys.SpeciesName];
ae.SegmentStartSeconds = segmentStartOffset.TotalSeconds;
ae.SegmentDurationSeconds = recording.Duration.TotalSeconds;
ae.Name = abbreviatedName;
ae.BorderColour = Color.Red;
ae.FileName = recording.BaseName;
});
double[] scores = new double[rhzColCount]; // predefinition of score array
double nomalisationConstant = scoreThreshold * 4; // four times the score threshold
double compressionFactor = rhzColCount / (double)sonogram.Data.GetLength(0);
foreach (AcousticEvent ae in acousticEvents)
{
ae.ScoreNormalised = ae.Score / nomalisationConstant;
if (ae.ScoreNormalised > 1.0)
{
ae.ScoreNormalised = 1.0;
}
int frameID = (int)Math.Round(ae.EventStartSeconds / frameDurationInSeconds);
int hiresFrameID = (int)Math.Floor(frameID * compressionFactor);
scores[hiresFrameID] = ae.ScoreNormalised;
}
var plot = new Plot(AnalysisName, scores, scoreThreshold);
// DEBUG ONLY ################################ TEMPORARY ################################
// Draw a standard spectrogram and mark of hites etc.
bool createStandardDebugSpectrogram = true;
var imageDir = new DirectoryInfo(outputDir.FullName + @"\SpectrogramImages");
if (!imageDir.Exists)
{
imageDir.Create();
}
if (createStandardDebugSpectrogram)
{
var fileName2 = Path.GetFileNameWithoutExtension(segmentSettings.SegmentAudioFile.Name);
string filePath2 = Path.Combine(imageDir.FullName, fileName + ".Spectrogram.png");
Bitmap sonoBmp = (Bitmap)sonogram.GetImage();
int height = sonoBmp.Height;
foreach (AcousticEvent ae in acousticEvents)
{
ae.DrawEvent(sonoBmp);
//g.DrawRectangle(pen, ob.ColumnLeft, ob.RowTop, ob.ColWidth-1, ob.RowWidth);
//ae.DrawPoint(sonoBmp, ae.HitElements.[0], Color.OrangeRed);
//ae.DrawPoint(sonoBmp, ae.HitElements[1], Color.Yellow);
//ae.DrawPoint(sonoBmp, ae.HitElements[2], Color.Green);
ae.DrawPoint(sonoBmp, ae.Points[0], Color.OrangeRed);
ae.DrawPoint(sonoBmp, ae.Points[1], Color.Yellow);
ae.DrawPoint(sonoBmp, ae.Points[2], Color.LimeGreen);
}
// draw the original hits on the standard sonogram
foreach (int[] array in newList)
{
sonoBmp.SetPixel(array[0], height - array[1], Color.Cyan);
}
// mark off every tenth frequency bin on the standard sonogram
for (int r = 0; r < 20; r++)
{
sonoBmp.SetPixel(0, height - (r * 10) - 1, Color.Blue);
sonoBmp.SetPixel(1, height - (r * 10) - 1, Color.Blue);
}
// mark off upper bound and lower frequency bound
sonoBmp.SetPixel(0, height - dominantBinMin, Color.Lime);
sonoBmp.SetPixel(0, height - dominantBinMax, Color.Lime);
sonoBmp.Save(filePath2);
}
// END DEBUG ################################ TEMPORARY ################################
return(new LimnodynastesConvexResults
{
Sonogram = sonogram,
Hits = null,
Plot = plot,
Events = acousticEvents,
RecordingDuration = recording.Duration,
});
} // Analysis()
public void TestFreqScaleOnArtificialSignal2()
{
int sampleRate = 64000;
double duration = 30; // signal duration in seconds
int[] harmonics = { 500, 1000, 2000, 4000, 8000 };
var freqScale = new FrequencyScale(FreqScaleType.Linear125Octaves7Tones28Nyquist32000);
var outputImagePath = Path.Combine(this.outputDirectory.FullName, "Signal2_OctaveFreqScale.png");
var recording = DspFilters.GenerateTestRecording(sampleRate, duration, harmonics, WaveType.Cosine);
// init the default sonogram config
var sonoConfig = new SonogramConfig
{
WindowSize = freqScale.WindowSize,
WindowOverlap = 0.2,
SourceFName = "Signal2",
NoiseReductionType = NoiseReductionType.None,
NoiseReductionParameter = 0.0,
};
var sonogram = new AmplitudeSonogram(sonoConfig, recording.WavReader);
sonogram.Data = OctaveFreqScale.ConvertAmplitudeSpectrogramToDecibelOctaveScale(sonogram.Data, freqScale);
// pick a row, any row
var oneSpectrum = MatrixTools.GetRow(sonogram.Data, 40);
oneSpectrum = DataTools.filterMovingAverage(oneSpectrum, 5);
var peaks = DataTools.GetPeaks(oneSpectrum);
var peakIds = new List <int>();
for (int i = 5; i < peaks.Length - 5; i++)
{
if (peaks[i])
{
int peakId = freqScale.BinBounds[i, 0];
peakIds.Add(peakId);
LoggedConsole.WriteLine($"Spectral peak located in bin {peakId}, Herz={freqScale.BinBounds[i, 1]}");
}
}
foreach (int h in harmonics)
{
LoggedConsole.WriteLine($"Harmonic {h}Herz should be in bin {freqScale.GetBinIdForHerzValue(h)}");
}
Assert.AreEqual(5, peakIds.Count);
Assert.AreEqual(129, peakIds[0]);
Assert.AreEqual(257, peakIds[1]);
Assert.AreEqual(513, peakIds[2]);
Assert.AreEqual(1025, peakIds[3]);
Assert.AreEqual(2049, peakIds[4]);
var image = sonogram.GetImage();
string title = $"Spectrogram of Harmonics: {DataTools.Array2String(harmonics)} SR={sampleRate} Window={freqScale.WindowSize}";
image = sonogram.GetImageFullyAnnotated(image, title, freqScale.GridLineLocations);
image.Save(outputImagePath);
// Check that image dimensions are correct
Assert.AreEqual(146, image.Width);
Assert.AreEqual(310, image.Height);
}
/// <summary>
/// New and alternative version of Lconvex recogniser because discovered that the call is more variable than I first realised.
/// </summary>
internal RecognizerResults Gruntwork2(AudioRecording audioRecording, Config configuration, DirectoryInfo outputDirectory, TimeSpan segmentStartOffset)
{
// make a spectrogram
double noiseReductionParameter = configuration.GetDoubleOrNull(AnalysisKeys.NoiseBgThreshold) ?? 0.1;
int frameStep = 512;
int sampleRate = audioRecording.SampleRate;
double frameStepInSeconds = frameStep / (double)sampleRate;
double framesPerSec = 1 / frameStepInSeconds;
var config = new SonogramConfig
{
WindowSize = frameStep, // this default = zero overlap
WindowOverlap = 0.0,
NoiseReductionType = NoiseReductionType.Standard,
NoiseReductionParameter = noiseReductionParameter,
};
// now construct the standard decibel spectrogram WITH noise removal, and look for LimConvex
// get frame parameters for the analysis
var sonogram = (BaseSonogram) new SpectrogramStandard(config, audioRecording.WavReader);
// remove the DC column
// var spg = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.Data.GetLength(0) - 1, sonogram.Data.GetLength(1) - 1);
// sonogram.Data = spg;
var spg = sonogram.Data;
int rowCount = spg.GetLength(0);
int colCount = spg.GetLength(1);
double herzPerBin = sampleRate / 2.0 / colCount;
string abbreviatedSpeciesName = configuration[AnalysisKeys.AbbreviatedSpeciesName] ?? "<no.sp>";
// ## TWO THRESHOLDS
// The threshold dB amplitude in the dominant freq bin required to yield an event
double eventThresholdDb = configuration.GetDoubleOrNull("PeakThresholdDecibels") ?? 3.0;
// minimum score for an acceptable event - that is when processing the score array.
double similarityThreshold = configuration.GetDoubleOrNull(AnalysisKeys.EventThreshold) ?? 0.5;
// IMPORTANT: The following frame durations assume a sampling rate = 22050 and window size of 512.
int callFrameWidth = 5;
int callHalfWidth = callFrameWidth / 2;
// minimum number of bins covering frequency bandwidth of L.convex call
// call has binWidth=25 but we want zero buffer of four bins either side.
int callBinWidth = 25;
int binSilenceBuffer = 4;
int topFrequency = configuration.GetInt("TopFrequency");
// # The Limnodynastes call has a duration of 3-5 frames given the above settings.
// # But we will assume 5-7 because sometimes the three harmonics are not exactly alligned!!
// # The call has three major peaks. The top peak, typically the dominant peak, is at approx 1850, a value which is set in the convig.
// # The second and third peak are at equal gaps below. TopFreq-gap and TopFreq-(2*gap);
// # The gap could be set in the Config file, but this is not implemented yet.
// Instead the algorithm uses three pre-fixed templates that determine the different kinds ogap. Gap is typically close to 500Hz
// In the D.Stewart CD, there are peaks close to:
//1. 1950 Hz
//2. 1460 hz
//3. 970 hz These are 490 Hz apart.
// In the Kiyomi's JCU recording, there are peaks close to:
//1. 1780 Hz
//2. 1330 hz
//3. 880 hz These are 450 Hz apart.
// So the strategy is to look for three peaks separated by same amount and in the vicinity of the above,
// To this end we produce three templates each of length 36, but having 2nd and 3rd peaks at different intervals.
var templates = GetLconvexTemplates(callBinWidth, binSilenceBuffer);
int templateHeight = templates[0].Length;
// NOTE: could give user control over other call features
// Such as frequency gap between peaks. But not in this first iteration of the recognizer.
//int peakGapInHerz = (int)configuration["PeakGap"];
int searchBand = 8;
int topBin = (int)Math.Round(topFrequency / herzPerBin);
int bottomBin = topBin - templateHeight - searchBand + 1;
if (bottomBin < 0)
{
Log.Fatal("Template bandwidth exceeds availble bandwidth given your value for top frequency.");
}
spg = MatrixTools.Submatrix(spg, 0, bottomBin, sonogram.Data.GetLength(0) - 1, topBin);
double[,] frames = MatrixTools.Submatrix(spg, 0, 0, callFrameWidth - 1, spg.GetLength(1) - 1);
double[] spectrum = MatrixTools.GetColumnSums(frames);
// set up arrays for monitoring important event parameters
double[] decibels = new double[rowCount];
int[] bottomBins = new int[rowCount];
double[] scores = new double[rowCount]; // predefinition of score array
int[] templateIds = new int[rowCount];
double[,] hits = new double[rowCount, colCount];
// loop through all spectra/rows of the spectrogram - NB: spg is rotated to vertical.
for (int s = callFrameWidth; s < rowCount; s++)
{
double[] rowToRemove = MatrixTools.GetRow(spg, s - callFrameWidth);
double[] rowToAdd = MatrixTools.GetRow(spg, s);
// shift frame block to the right.
for (int b = 0; b < spectrum.Length; b++)
{
spectrum[b] = spectrum[b] - rowToRemove[b] + rowToAdd[b];
}
// now check if frame block matches a template.
ScanEventScores(spectrum, templates, out double eventScore, out int eventBottomBin, out int templateId);
//hits[rowCount, colCount];
decibels[s - callHalfWidth - 1] = spectrum.Max() / callFrameWidth;
bottomBins[s - callHalfWidth - 1] = eventBottomBin + bottomBin;
scores[s - callHalfWidth - 1] = eventScore;
templateIds[s - callHalfWidth - 1] = templateId;
} // loop through all spectra
// we now have a score array and decibel array and bottom bin array for the entire spectrogram.
// smooth them to find events
scores = DataTools.filterMovingAverageOdd(scores, 5);
decibels = DataTools.filterMovingAverageOdd(decibels, 3);
var peaks = DataTools.GetPeaks(scores);
// loop through the score array and find potential events
var potentialEvents = new List <AcousticEvent>();
for (int s = callHalfWidth; s < scores.Length - callHalfWidth - 1; s++)
{
if (!peaks[s])
{
continue;
}
if (scores[s] < similarityThreshold)
{
continue;
}
if (decibels[s] < eventThresholdDb)
{
continue;
}
// put hits into hits matrix
// put cosine score into the score array
//for (int s = point.X; s <= point.Y; s++)
//{
// hits[s, topBins[s]] = 10;
//}
int bottomBinForEvent = bottomBins[s];
int topBinForEvent = bottomBinForEvent + templateHeight;
int topFreqForEvent = (int)Math.Round(topBinForEvent * herzPerBin);
int bottomFreqForEvent = (int)Math.Round(bottomBinForEvent * herzPerBin);
double startTime = (s - callHalfWidth) * frameStepInSeconds;
double durationTime = callFrameWidth * frameStepInSeconds;
var newEvent = new AcousticEvent(segmentStartOffset, startTime, durationTime, bottomFreqForEvent, topFreqForEvent)
{
//Name = string.Empty, // remove name because it hides spectral content of the event.
Name = "Lc" + templateIds[s],
Score = scores[s],
};
newEvent.SetTimeAndFreqScales(framesPerSec, herzPerBin);
potentialEvents.Add(newEvent);
}
// display the original score array
scores = DataTools.normalise(scores);
var scorePlot = new Plot(this.DisplayName + " scores", scores, similarityThreshold);
DataTools.Normalise(decibels, eventThresholdDb, out double[] normalisedDb, out double normalisedThreshold);
var decibelPlot = new Plot("Decibels", normalisedDb, normalisedThreshold);
var debugPlots = new List <Plot> {
scorePlot, decibelPlot
};
if (this.displayDebugImage)
{
var debugImage = DisplayDebugImage(sonogram, potentialEvents, debugPlots, hits);
var debugPath = outputDirectory.Combine(FilenameHelpers.AnalysisResultName(Path.GetFileNameWithoutExtension(audioRecording.BaseName), this.Identifier, "png", "DebugSpectrogram"));
debugImage.Save(debugPath.FullName);
}
// display the cosine similarity scores
var plot = new Plot(this.DisplayName, scores, similarityThreshold);
var plots = new List <Plot> {
plot
};
// add names into the returned events
string speciesName = configuration[AnalysisKeys.SpeciesName] ?? this.SpeciesName;
foreach (var ae in potentialEvents)
{
ae.Name = abbreviatedSpeciesName;
ae.SpeciesName = speciesName;
}
return(new RecognizerResults()
{
Events = potentialEvents,
Hits = hits,
Plots = plots,
Sonogram = sonogram,
});
}
/// <summary>
/// Counts the number of spectral tracks or harmonics in the passed ferquency band.
/// Also calculates the average amplitude of the peaks to each succeeding trough.
/// </summary>
/// <param name="values">Spectral values in the frequency band.</param>
/// <param name="row">This argument is NOT used. Is included only for debugging purposes.</param>
public static Tuple <double, int, bool[]> CountHarmonicTracks(double[] values, int expectedHarmonicCount)
{
int L = values.Length;
int expectedPeriod = L / expectedHarmonicCount;
int midPeriod = expectedPeriod / 2;
//double[] smooth = DataTools.filterMovingAverage(values, 3);
double[] smooth = values;
bool[] peaks = DataTools.GetPeaks(smooth);
int peakCount = DataTools.CountTrues(peaks);
//return if too far outside limits
int lowerLimit = expectedHarmonicCount / 2;
int upperLimit = expectedHarmonicCount * 2;
if (peakCount <= lowerLimit)
{
return(Tuple.Create(0.0, 0, peaks));
}
else
if (peakCount >= upperLimit)
{
return(Tuple.Create(0.0, peakCount, peaks));
}
// Store peak locations.
var peakLocations = new List <int>();
for (int i = 0; i < values.Length; i++)
{
if (peaks[i])
{
peakLocations.Add(i);
}
}
//// If have too many peaks (local maxima), remove the lowest of them
//if (peakCount > (expectedHarmonicCount + 1))
//{
// var peakValues = new double[peakCount];
// for (int i = 0; i < peakCount; i++) peakValues[i] = values[peakLocations[i]];
// IEnumerable<double> ordered = peakValues.OrderByDescending(d => d);
// double avValue = ordered.Take(expectedHarmonicCount).Average();
// double min = ordered.Last();
// double threshold = min + ((avValue - min) / 2);
// // apply threshold to remove low peaks
// for (int i = 0; i < L; i++)
// {
// if ((peaks[i]) && (values[i] < threshold)) peaks[i] = false;
// }
// // recalculate the number of peaks
// peakCount = -1;
// for (int i = 0; i < L; i++)
// {
// if (peaks[i])
// {
// peakCount++;
// peakLocations[peakCount] = i;
// }
// }
//}
//if (peakCount <= 1) return Tuple.Create(0.0, 0, peaks);
double amplitude = 0.0;
for (int i = 0; i < peakLocations.Count; i++)
{
int troughIndex = peakLocations[i] + midPeriod;
if (troughIndex >= L)
{
troughIndex = peakLocations[i] - midPeriod;
}
double delta = smooth[peakLocations[i]] - smooth[troughIndex];
if (delta > 1.0)
{
amplitude += delta; // dB threshold - required a minimum perceptible difference
}
}
double avAmplitude = amplitude / peakCount;
return(Tuple.Create(avAmplitude, peakCount, peaks));
}
} //DetectBarsInTheRowsOfaMatrix()
/// A METHOD TO DETECT HARMONICS IN THE ROWS of the passed portion of a sonogram.
/// This method assume the matrix is derived from a spectrogram rotated so that the matrix rows are spectral columns of sonogram.
/// Was first developed for crow calls.
/// First looks for a decibel profile that matches the passed call duration and decibel loudness
/// Then samples the centre portion for the correct harmonic period.
/// </summary>
/// <param name="m"></param>
/// <param name="amplitudeThreshold"></param>
/// <returns></returns>
public static Tuple <double[], double[], double[]> DetectHarmonicsInSonogramMatrix(double[,] m, double dBThreshold, int callSpan)
{
int zeroBinCount = 3; //to remove low freq content which dominates the spectrum
int halfspan = callSpan / 2;
double[] dBArray = MatrixTools.GetRowAverages(m);
dBArray = DataTools.filterMovingAverage(dBArray, 3);
bool doNoiseRemoval = true;
if (doNoiseRemoval)
{
double StandardDeviationCount = 0.1; // number of noise SDs to calculate noise threshold - determines severity of noise reduction
SNR.BackgroundNoise bgn = SNR.SubtractBackgroundNoiseFromSignal(dBArray, StandardDeviationCount);
dBArray = bgn.NoiseReducedSignal;
}
bool[] peaks = DataTools.GetPeaks(dBArray);
int rowCount = m.GetLength(0);
int colCount = m.GetLength(1);
var intensity = new double[rowCount]; //an array of period intensity
var periodicity = new double[rowCount]; //an array of the periodicity values
for (int r = halfspan; r < rowCount - halfspan; r++)
{
//APPLY A FILTER: must satisfy the following conditions for a call.
if (!peaks[r])
{
continue;
}
if (dBArray[r] < dBThreshold)
{
continue;
}
double lowerDiff = dBArray[r] - dBArray[r - halfspan];
double upperDiff = dBArray[r] - dBArray[r + halfspan];
if (lowerDiff < dBThreshold || upperDiff < dBThreshold)
{
continue;
}
double[] prevRow = DataTools.DiffFromMean(MatrixTools.GetRow(m, r - 1));
double[] thisRow = DataTools.DiffFromMean(MatrixTools.GetRow(m, r));
var spectrum = AutoAndCrossCorrelation.CrossCorr(prevRow, thisRow);
for (int s = 0; s < zeroBinCount; s++)
{
spectrum[s] = 0.0; //in real data these bins are dominant and hide other frequency content
}
spectrum = DataTools.NormaliseArea(spectrum);
int maxId = DataTools.GetMaxIndex(spectrum);
double intensityValue = spectrum[maxId];
intensity[r] = intensityValue;
double period = 0.0;
if (maxId != 0)
{
period = 2 * colCount / (double)maxId;
}
periodicity[r] = period;
prevRow = thisRow;
} // rows
return(Tuple.Create(dBArray, intensity, periodicity));
} //DetectHarmonicsInSonogramMatrix()
public static void TestMethod_GenerateSignal2()
{
int sampleRate = 64000;
double duration = 30; // signal duration in seconds
int[] harmonics = { 500, 1000, 2000, 4000, 8000 };
var freqScale = new FrequencyScale(FreqScaleType.Linear125Octaves7Tones28Nyquist32000);
string path = @"C:\SensorNetworks\Output\Sonograms\UnitTestSonograms\SineSignal2.png";
var recording = GenerateTestRecording(sampleRate, duration, harmonics, WaveType.Cosine);
// init the default sonogram config
var sonoConfig = new SonogramConfig
{
WindowSize = freqScale.WindowSize,
WindowOverlap = 0.2,
SourceFName = "Signal2",
NoiseReductionType = NoiseReductionType.None,
NoiseReductionParameter = 0.0,
};
var sonogram = new AmplitudeSonogram(sonoConfig, recording.WavReader);
sonogram.Data = OctaveFreqScale.ConvertAmplitudeSpectrogramToDecibelOctaveScale(sonogram.Data, freqScale);
// pick a row, any row
var oneSpectrum = MatrixTools.GetRow(sonogram.Data, 40);
oneSpectrum = DataTools.normalise(oneSpectrum);
var peaks = DataTools.GetPeaks(oneSpectrum, 0.5);
var peakIds = new List <int>();
for (int i = 5; i < peaks.Length - 5; i++)
{
if (peaks[i])
{
int peakId = freqScale.BinBounds[i, 0];
peakIds.Add(peakId);
LoggedConsole.WriteLine($"Spectral peak located in bin {peakId}, Herz={freqScale.BinBounds[i, 1]}");
}
}
//if (peaks[129] && peaks[257] && peaks[513] && peaks[1025] && peaks[2049])
if (peakIds[0] == 129 && peakIds[1] == 257 && peakIds[2] == 513 && peakIds[3] == 1025 && peakIds[4] == 2049)
{
LoggedConsole.WriteSuccessLine("Spectral Peaks found at correct places");
}
else
{
LoggedConsole.WriteErrorLine("Spectral Peaks found at INCORRECT places");
}
foreach (int h in harmonics)
{
LoggedConsole.WriteLine($"Harmonic {h}Hertz should be in bin {freqScale.GetBinIdForHerzValue(h)}");
}
// spectrogram without framing, annotation etc
var image = sonogram.GetImage();
string title = $"Spectrogram of Harmonics: {DataTools.Array2String(harmonics)} SR={sampleRate} Window={freqScale.WindowSize}";
image = sonogram.GetImageFullyAnnotated(image, title, freqScale.GridLineLocations);
image.Save(path);
}
} // end method ConvertODScores2Events()
/*
* public static double PeakEntropy(double[] array)
* {
* bool[] peaks = DataTools.GetPeaks(array);
* int peakCount = DataTools.CountTrues(peaks);
* //set up histogram of peak energies
* double[] histogram = new double[peakCount];
* int count = 0;
* for (int k = 0; k < array.Length; k++)
* {
* if (peaks[k])
* {
* histogram[count] = array[k];
* count++;
* }
* }
* histogram = DataTools.NormaliseMatrixValues(histogram);
* histogram = DataTools.Normalise2Probabilites(histogram);
* double normFactor = Math.Log(histogram.Length) / DataTools.ln2; //normalize for length of the array
* double entropy = DataTools.Entropy(histogram) / normFactor;
* return entropy;
* }
*
*/
/// <summary>
/// returns the periodicity in an array of values.
/// </summary>
public static double[] PeriodicityAnalysis(double[] array)
{
//DataTools.writeBarGraph(array);
var A = AutoAndCrossCorrelation.MyCrossCorrelation(array, array); // do 2/3rds of maximum possible lag
int dctLength = A.Length;
A = DataTools.SubtractMean(A);
//DataTools.writeBarGraph(A);
double[,] cosines = MFCCStuff.Cosines(dctLength, dctLength); //set up the cosine coefficients
double[] dct = MFCCStuff.DCT(A, cosines);
for (int i = 0; i < dctLength; i++)
{
dct[i] = Math.Abs(dct[i]); //convert to absolute values
}
//DataTools.writeBarGraph(dct);
for (int i = 0; i < 3; i++)
{
dct[i] = 0.0; //remove low freq oscillations from consideration
}
dct = DataTools.normalise2UnitLength(dct);
var peaks = DataTools.GetPeaks(dct);
// remove non-peak values and low values
for (int i = 0; i < dctLength; i++)
{
if (!peaks[i] || dct[i] < 0.2)
{
dct[i] = 0.0;
}
}
DataTools.writeBarGraph(dct);
//get periodicity of highest three values
int peakCount = 3;
var period = new double[peakCount];
var maxIndex = new double[peakCount];
for (int i = 0; i < peakCount; i++)
{
int indexOfMaxValue = DataTools.GetMaxIndex(dct);
maxIndex[i] = indexOfMaxValue;
//double oscilFreq = indexOfMaxValue / dctDuration * 0.5; //Times 0.5 because index = Pi and not 2Pi
if ((double)indexOfMaxValue == 0)
{
period[i] = 0.0;
}
else
{
period[i] = dctLength / (double)indexOfMaxValue * 2;
}
dct[indexOfMaxValue] = 0.0; // remove value for next iteration
}
LoggedConsole.WriteLine("Max indices = {0:f0}, {1:f0}, {2:f0}.", maxIndex[0], maxIndex[1], maxIndex[2]);
return(period);
}
