/// <summary>
/// Do your analysis. This method is called once per segment (typically one-minute segments).
/// </summary>
public override RecognizerResults Recognize(
AudioRecording recording,
Config configuration,
TimeSpan segmentStartOffset,
Lazy <IndexCalculateResult[]> getSpectralIndexes,
DirectoryInfo outputDirectory,
int?imageWidth)
{
//string speciesName = (string)configuration[AnalysisKeys.SpeciesName] ?? "<no species>";
//string abbreviatedSpeciesName = (string)configuration[AnalysisKeys.AbbreviatedSpeciesName] ?? "<no.sp>";
// this default framesize seems to work for Lewin's Rail
const int frameSize = 512;
// DO NOT SET windowOverlap. Calculate it below.
if (imageWidth == null)
{
throw new ArgumentNullException(nameof(imageWidth));
}
// check the sample rate. Must be 22050
if (recording.WavReader.SampleRate != 22050)
{
throw new InvalidOperationException("Requires a 22050Hz file");
}
TimeSpan recordingDuration = recording.WavReader.Time;
// check for the profiles in the config file
bool hasProfiles = ConfigFile.HasProfiles(configuration);
if (!hasProfiles)
{
throw new ConfigFileException("The Config file for L.pectoralis must contain a profiles object.");
}
// get the profile names
string[] profileNames = ConfigFile.GetProfileNames(configuration);
var recognizerConfig = new LewinsRailConfig();
var prunedEvents = new List <AcousticEvent>();
var plots = new List <Plot>();
BaseSonogram sonogram = null;
// cycle through the profiles and analyse recording using each of them
foreach (var name in profileNames)
{
Log.Debug($"Reading profile <{name}>.");
recognizerConfig.ReadConfigFile(configuration, name);
// ignore oscillations above this threshold freq
int maxOscilRate = (int)Math.Ceiling(1 / recognizerConfig.MinPeriod);
// calculate frame overlap and ignor any user inut.
double windowOverlap = Oscillations2012.CalculateRequiredFrameOverlap(
recording.SampleRate,
frameSize,
maxOscilRate);
// i: MAKE SONOGRAM
var sonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,
//set default values - ignore those set by user
WindowSize = frameSize,
WindowOverlap = windowOverlap,
// the default window is HAMMING
//WindowFunction = WindowFunctions.HANNING.ToString(),
//WindowFunction = WindowFunctions.NONE.ToString(),
// if do not use noise reduction can get a more sensitive recogniser.
//NoiseReductionType = NoiseReductionType.NONE,
NoiseReductionType = SNR.KeyToNoiseReductionType("STANDARD"),
};
//#############################################################################################################################################
//DO THE ANALYSIS AND RECOVER SCORES OR WHATEVER
var results = Analysis(recording, sonoConfig, recognizerConfig, this.ReturnDebugImage, segmentStartOffset);
// ######################################################################
if (results == null)
{
return(null); //nothing to process
}
sonogram = results.Item1;
//var hits = results.Item2;
var scoreArray = results.Item3;
var predictedEvents = results.Item4;
var debugImage = results.Item5;
//#############################################################################################################################################
if (debugImage == null)
{
Log.Debug("DebugImage is null, not writing file");
}
else if (MainEntry.InDEBUG)
{
var imageName = AnalysisResultName(recording.BaseName, this.SpeciesName, "png", "DebugSpectrogram");
var debugPath = outputDirectory.Combine(imageName);
//debugImage.Save(debugPath.FullName);
}
foreach (var ae in predictedEvents)
{
// add additional info
if (!(ae.Score > recognizerConfig.EventThreshold))
{
continue;
}
ae.Name = recognizerConfig.AbbreviatedSpeciesName;
ae.SpeciesName = recognizerConfig.SpeciesName;
ae.SegmentStartSeconds = segmentStartOffset.TotalSeconds;
ae.SegmentDurationSeconds = recordingDuration.TotalSeconds;
prunedEvents.Add(ae);
}
// increase very low scores
for (int j = 0; j < scoreArray.Length; j++)
{
scoreArray[j] *= 4;
if (scoreArray[j] > 1.0)
{
scoreArray[j] = 1.0;
}
}
var plot = new Plot(this.DisplayName, scoreArray, recognizerConfig.EventThreshold);
plots.Add(plot);
}
return(new RecognizerResults()
{
Sonogram = sonogram,
Hits = null,
Plots = plots,
Events = prunedEvents,
});
}
/// <summary>
/// THE KEY ANALYSIS METHOD.
/// </summary>
private static Tuple <BaseSonogram, double[, ], double[], List <AcousticEvent>, Image> Analysis(
AudioRecording recording,
SonogramConfig sonoConfig,
LewinsRailConfig lrConfig,
bool returnDebugImage,
TimeSpan segmentStartOffset)
{
if (recording == null)
{
LoggedConsole.WriteLine("AudioRecording == null. Analysis not possible.");
return(null);
}
int sr = recording.SampleRate;
int upperBandMinHz = lrConfig.UpperBandMinHz;
int upperBandMaxHz = lrConfig.UpperBandMaxHz;
int lowerBandMinHz = lrConfig.LowerBandMinHz;
int lowerBandMaxHz = lrConfig.LowerBandMaxHz;
//double decibelThreshold = lrConfig.DecibelThreshold; //dB
//int windowSize = lrConfig.WindowSize;
double eventThreshold = lrConfig.EventThreshold; //in 0-1
double minDuration = lrConfig.MinDuration; // seconds
double maxDuration = lrConfig.MaxDuration; // seconds
double minPeriod = lrConfig.MinPeriod; // seconds
double maxPeriod = lrConfig.MaxPeriod; // seconds
//double freqBinWidth = sr / (double)windowSize;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
//i: MAKE SONOGRAM
double framesPerSecond = freqBinWidth;
//the Xcorrelation-FFT technique requires number of bins to scan to be power of 2.
//assuming sr=17640 and window=1024, then 64 bins span 1100 Hz above the min Hz level. i.e. 500 to 1600
//assuming sr=17640 and window=1024, then 128 bins span 2200 Hz above the min Hz level. i.e. 500 to 2700
int upperBandMinBin = (int)Math.Round(upperBandMinHz / freqBinWidth) + 1;
int upperBandMaxBin = (int)Math.Round(upperBandMaxHz / freqBinWidth) + 1;
int lowerBandMinBin = (int)Math.Round(lowerBandMinHz / freqBinWidth) + 1;
int lowerBandMaxBin = (int)Math.Round(lowerBandMaxHz / freqBinWidth) + 1;
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
int rowCount = sonogram.Data.GetLength(0);
int colCount = sonogram.Data.GetLength(1);
//ALTERNATIVE IS TO USE THE AMPLITUDE SPECTRUM
//var results2 = DSP_Frames.ExtractEnvelopeAndFFTs(recording.GetWavReader().Samples, sr, frameSize, windowOverlap);
//double[,] matrix = results2.Item3; //amplitude spectrogram. Note that column zero is the DC or average energy value and can be ignored.
//double[] avAbsolute = results2.Item1; //average absolute value over the minute recording
////double[] envelope = results2.Item2;
//double windowPower = results2.Item4;
double[] lowerArray = MatrixTools.GetRowAveragesOfSubmatrix(sonogram.Data, 0, lowerBandMinBin, rowCount - 1, lowerBandMaxBin);
double[] upperArray = MatrixTools.GetRowAveragesOfSubmatrix(sonogram.Data, 0, upperBandMinBin, rowCount - 1, upperBandMaxBin);
int step = (int)Math.Round(framesPerSecond); //take one second steps
int stepCount = rowCount / step;
int sampleLength = 64; //64 frames = 3.7 seconds. Suitable for Lewins Rail.
double[] intensity = new double[rowCount];
double[] periodicity = new double[rowCount];
//######################################################################
//ii: DO THE ANALYSIS AND RECOVER SCORES
for (int i = 0; i < stepCount; i++)
{
int start = step * i;
double[] lowerSubarray = DataTools.Subarray(lowerArray, start, sampleLength);
double[] upperSubarray = DataTools.Subarray(upperArray, start, sampleLength);
if (lowerSubarray.Length != sampleLength || upperSubarray.Length != sampleLength)
{
break;
}
var spectrum = AutoAndCrossCorrelation.CrossCorr(lowerSubarray, upperSubarray);
int zeroCount = 3;
for (int s = 0; s < zeroCount; 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 period = 2 * sampleLength / (double)maxId / framesPerSecond; //convert maxID to period in seconds
if (period < minPeriod || period > maxPeriod)
{
continue;
}
// lay down score for sample length
for (int j = 0; j < sampleLength; j++)
{
if (intensity[start + j] < spectrum[maxId])
{
intensity[start + j] = spectrum[maxId];
}
periodicity[start + j] = period;
}
}
//######################################################################
//iii: CONVERT SCORES TO ACOUSTIC EVENTS
intensity = DataTools.filterMovingAverage(intensity, 5);
var predictedEvents = AcousticEvent.ConvertScoreArray2Events(
intensity,
lowerBandMinHz,
upperBandMaxHz,
sonogram.FramesPerSecond,
freqBinWidth,
eventThreshold,
minDuration,
maxDuration,
segmentStartOffset);
CropEvents(predictedEvents, upperArray, segmentStartOffset);
var hits = new double[rowCount, colCount];
//######################################################################
var scorePlot = new Plot("L.pect", intensity, lrConfig.IntensityThreshold);
Image debugImage = null;
if (returnDebugImage)
{
// display a variety of debug score arrays
DataTools.Normalise(intensity, lrConfig.DecibelThreshold, out var normalisedScores, out var normalisedThreshold);
var intensityPlot = new Plot("Intensity", normalisedScores, normalisedThreshold);
DataTools.Normalise(periodicity, 10, out normalisedScores, out normalisedThreshold);
var periodicityPlot = new Plot("Periodicity", normalisedScores, normalisedThreshold);
var debugPlots = new List <Plot> {
scorePlot, intensityPlot, periodicityPlot
};
debugImage = DrawDebugImage(sonogram, predictedEvents, debugPlots, hits);
}
return(Tuple.Create(sonogram, hits, intensity, predictedEvents, debugImage));
} //Analysis()
