/// <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,
});
}
