// OTHER CONSTANTS
//private const string ImageViewer = @"C:\Windows\system32\mspaint.exe";
/// <summary>
/// Do your analysis. This method is called once per segment (typically one-minute segments).
/// </summary>
/// <param name="recording"></param>
/// <param name="configuration"></param>
/// <param name="segmentStartOffset"></param>
/// <param name="getSpectralIndexes"></param>
/// <param name="outputDirectory"></param>
/// <param name="imageWidth"></param>
/// <returns></returns>
public override RecognizerResults Recognize(AudioRecording recording, Config configuration, TimeSpan segmentStartOffset, Lazy <IndexCalculateResult[]> getSpectralIndexes, DirectoryInfo outputDirectory, int?imageWidth)
{
var recognizerConfig = new LitoriaWatjulumConfig();
recognizerConfig.ReadConfigFile(configuration);
//int maxOscilRate = (int)Math.Ceiling(1 / lwConfig.MinPeriod);
if (recording.WavReader.SampleRate != 22050)
{
throw new InvalidOperationException("Requires a 22050Hz file");
}
TimeSpan recordingDuration = recording.WavReader.Time;
// this default framesize seems to work
const int frameSize = 128;
double windowOverlap = 0.0;
// calculate the overlap instead
//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
var results = Analysis(recording, sonoConfig, recognizerConfig, MainEntry.InDEBUG, segmentStartOffset);
//######################################################################
if (results == null)
{
return(null); //nothing to process
}
var sonogram = results.Item1;
var hits = results.Item2;
var scoreArray = results.Item3;
var predictedEvents = results.Item4;
var debugImage = results.Item5;
// old way of creating a path:
//var debugPath = outputDirectory.Combine(FilenameHelpers.AnalysisResultName(Path.GetFileNameWithoutExtension(recording.FileName), SpeciesName, "png", "DebugSpectrogram"));
var debugPath = FilenameHelpers.AnalysisResultPath(outputDirectory, recording.BaseName, this.SpeciesName, "png", "DebugSpectrogram");
debugImage.Save(debugPath);
//#############################################################################################################################################
// Prune events here if required i.e. remove those below threshold score if this not already done. See other recognizers.
foreach (var ae in predictedEvents)
{
// add additional info
ae.Name = recognizerConfig.AbbreviatedSpeciesName;
ae.SpeciesName = recognizerConfig.SpeciesName;
ae.SegmentDurationSeconds = recordingDuration.TotalSeconds;
ae.SegmentStartSeconds = segmentStartOffset.TotalSeconds;
}
// do a recognizer TEST.
if (false)
{
var testDir = new DirectoryInfo(outputDirectory.Parent.Parent.FullName);
TestTools.RecognizerScoresTest(recording.BaseName, testDir, recognizerConfig.AnalysisName, scoreArray);
AcousticEvent.TestToCompareEvents(recording.BaseName, testDir, recognizerConfig.AnalysisName, predictedEvents);
}
var plot = new Plot(this.DisplayName, scoreArray, recognizerConfig.EventThreshold);
return(new RecognizerResults()
{
Sonogram = sonogram,
Hits = hits,
Plots = plot.AsList(),
Events = predictedEvents,
});
}
/// <summary>
/// The CORE ANALYSIS METHOD.
/// </summary>
public static Tuple <BaseSonogram, double[, ], Plot, List <AcousticEvent>, TimeSpan> Analysis(FileInfo fiSegmentOfSourceFile, Dictionary <string, string> configDict, TimeSpan segmentStartOffset)
{
//set default values -
int frameLength = 1024;
if (configDict.ContainsKey(AnalysisKeys.FrameLength))
{
frameLength = int.Parse(configDict[AnalysisKeys.FrameLength]);
}
double windowOverlap = 0.0;
int minHz = int.Parse(configDict["MIN_HZ"]);
int minFormantgap = int.Parse(configDict["MIN_FORMANT_GAP"]);
int maxFormantgap = int.Parse(configDict["MAX_FORMANT_GAP"]);
double decibelThreshold = double.Parse(configDict["DECIBEL_THRESHOLD"]); //dB
double harmonicIntensityThreshold = double.Parse(configDict["INTENSITY_THRESHOLD"]); //in 0-1
double callDuration = double.Parse(configDict["CALL_DURATION"]); // seconds
AudioRecording recording = new AudioRecording(fiSegmentOfSourceFile.FullName);
//i: MAKE SONOGRAM
var sonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,
WindowSize = frameLength,
WindowOverlap = windowOverlap,
NoiseReductionType = SNR.KeyToNoiseReductionType("STANDARD"),
}; //default values config
TimeSpan tsRecordingtDuration = recording.Duration;
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
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 numberOfBins = 64;
int minBin = (int)Math.Round(minHz / freqBinWidth) + 1;
int maxbin = minBin + numberOfBins - 1;
int maxHz = (int)Math.Round(minHz + (numberOfBins * freqBinWidth));
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
int rowCount = sonogram.Data.GetLength(0);
int colCount = sonogram.Data.GetLength(1);
double[,] subMatrix = MatrixTools.Submatrix(sonogram.Data, 0, minBin, rowCount - 1, maxbin);
int callSpan = (int)Math.Round(callDuration * framesPerSecond);
//#############################################################################################################################################
//ii: DETECT HARMONICS
var results = CrossCorrelation.DetectHarmonicsInSonogramMatrix(subMatrix, decibelThreshold, callSpan);
double[] dBArray = results.Item1;
double[] intensity = results.Item2; //an array of periodicity scores
double[] periodicity = results.Item3;
//intensity = DataTools.filterMovingAverage(intensity, 3);
int noiseBound = (int)(100 / freqBinWidth); //ignore 0-100 hz - too much noise
double[] scoreArray = new double[intensity.Length];
for (int r = 0; r < rowCount; r++)
{
if (intensity[r] < harmonicIntensityThreshold)
{
continue;
}
//ignore locations with incorrect formant gap
double herzPeriod = periodicity[r] * freqBinWidth;
if (herzPeriod < minFormantgap || herzPeriod > maxFormantgap)
{
continue;
}
//find freq having max power and use info to adjust score.
//expect humans to have max < 1000 Hz
double[] spectrum = MatrixTools.GetRow(sonogram.Data, r);
for (int j = 0; j < noiseBound; j++)
{
spectrum[j] = 0.0;
}
int maxIndex = DataTools.GetMaxIndex(spectrum);
int freqWithMaxPower = (int)Math.Round(maxIndex * freqBinWidth);
double discount = 1.0;
if (freqWithMaxPower < 1200)
{
discount = 0.0;
}
if (intensity[r] > harmonicIntensityThreshold)
{
scoreArray[r] = intensity[r] * discount;
}
}
//transfer info to a hits matrix.
var hits = new double[rowCount, colCount];
double threshold = harmonicIntensityThreshold * 0.75; //reduced threshold for display of hits
for (int r = 0; r < rowCount; r++)
{
if (scoreArray[r] < threshold)
{
continue;
}
double herzPeriod = periodicity[r] * freqBinWidth;
for (int c = minBin; c < maxbin; c++)
{
//hits[r, c] = herzPeriod / (double)380; //divide by 380 to get a relativePeriod;
hits[r, c] = (herzPeriod - minFormantgap) / maxFormantgap; //to get a relativePeriod;
}
}
//iii: CONVERT TO ACOUSTIC EVENTS
double maxPossibleScore = 0.5;
int halfCallSpan = callSpan / 2;
var predictedEvents = new List <AcousticEvent>();
for (int i = 0; i < rowCount; i++)
{
//assume one score position per crow call
if (scoreArray[i] < 0.001)
{
continue;
}
double startTime = (i - halfCallSpan) / framesPerSecond;
AcousticEvent ev = new AcousticEvent(segmentStartOffset, startTime, callDuration, minHz, maxHz);
ev.SetTimeAndFreqScales(framesPerSecond, freqBinWidth);
ev.Score = scoreArray[i];
ev.ScoreNormalised = ev.Score / maxPossibleScore; // normalised to the user supplied threshold
//ev.Score_MaxPossible = maxPossibleScore;
predictedEvents.Add(ev);
} //for loop
Plot plot = new Plot("CROW", intensity, harmonicIntensityThreshold);
return(Tuple.Create(sonogram, hits, plot, predictedEvents, tsRecordingtDuration));
} //Analysis()
/// <summary>
/// ################ THE KEY ANALYSIS METHOD.
/// </summary>
public static Tuple <BaseSonogram, double[, ], List <Plot>, List <AcousticEvent>, TimeSpan> Analysis(FileInfo fiSegmentOfSourceFile, Dictionary <string, string> configDict, TimeSpan segmentStartOffset)
{
//set default values - ignore those set by user
int frameSize = 128;
double windowOverlap = 0.5;
double intensityThreshold = double.Parse(configDict["INTENSITY_THRESHOLD"]); //in 0-1
double minDuration = double.Parse(configDict["MIN_DURATION"]); // seconds
double maxDuration = double.Parse(configDict["MAX_DURATION"]); // seconds
double minPeriod = double.Parse(configDict["MIN_PERIOD"]); // seconds
double maxPeriod = double.Parse(configDict["MAX_PERIOD"]); // seconds
AudioRecording recording = new AudioRecording(fiSegmentOfSourceFile.FullName);
//i: MAKE SONOGRAM
SonogramConfig sonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,
WindowSize = frameSize,
WindowOverlap = windowOverlap,
NoiseReductionType = SNR.KeyToNoiseReductionType("STANDARD"),
}; //default values config
//sonoConfig.NoiseReductionType = SNR.Key2NoiseReductionType("NONE");
TimeSpan tsRecordingtDuration = recording.Duration;
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
double frameOffset = sonoConfig.GetFrameOffset(sr);
double framesPerSecond = 1 / frameOffset;
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
int rowCount = sonogram.Data.GetLength(0);
int colCount = sonogram.Data.GetLength(1);
//#############################################################################################################################################
//window sr frameDuration frames/sec hz/bin 64frameDuration hz/64bins hz/128bins
// 1024 22050 46.4ms 21.5 21.5 2944ms 1376hz 2752hz
// 256 17640 14.5ms 68.9 68.9 ms hz hz
// 512 17640 29.0ms 34.4 34.4 ms hz hz
// 1024 17640 58.0ms 17.2 17.2 3715ms 1100hz 2200hz
// 2048 17640 116.1ms 8.6 8.6 7430ms 551hz 1100hz
//The Xcorrelation-FFT technique requires number of bins to scan to be power of 2.
// Assuming sr=17640 and window=256, then binWidth = 68.9Hz and 1500Hz = bin 21.7..
// Therefore do a Xcorrelation between bins 21 and 22.
// Number of frames to span must power of 2. Try 16 frames which covers 232ms - almost 1/4 second.
int midHz = 1500;
int lowerBin = (int)(midHz / freqBinWidth) + 1; //because bin[0] = DC
int upperBin = lowerBin + 4;
int lowerHz = (int)Math.Floor((lowerBin - 1) * freqBinWidth);
int upperHz = (int)Math.Ceiling((upperBin - 1) * freqBinWidth);
//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.GetColumn(sonogram.Data, lowerBin);
double[] upperArray = MatrixTools.GetColumn(sonogram.Data, upperBin);
lowerArray = DataTools.NormaliseInZeroOne(lowerArray, 0, 60); //## ABSOLUTE NORMALISATION 0-60 dB #######################################################################
upperArray = DataTools.NormaliseInZeroOne(upperArray, 0, 60); //## ABSOLUTE NORMALISATION 0-60 dB #######################################################################
int step = (int)(framesPerSecond / 40); //take one/tenth second steps
int stepCount = rowCount / step;
int sampleLength = 32; //16 frames = 232ms - almost 1/4 second.
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 == null || upperSubarray == null)
{
break;
}
if (lowerSubarray.Length != sampleLength || upperSubarray.Length != sampleLength)
{
break;
}
var spectrum = AutoAndCrossCorrelation.CrossCorr(lowerSubarray, upperSubarray);
int zeroCount = 2;
for (int s = 0; s < zeroCount; s++)
{
spectrum[s] = 0.0; //in real data these bins are dominant and hide other frequency content
}
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, 3);
intensity = DataTools.NormaliseInZeroOne(intensity, 0, 0.5); //## ABSOLUTE NORMALISATION 0-0.5 #######################################################################
List <AcousticEvent> predictedEvents = AcousticEvent.ConvertScoreArray2Events(
intensity,
lowerHz,
upperHz,
sonogram.FramesPerSecond,
freqBinWidth,
intensityThreshold,
minDuration,
maxDuration,
segmentStartOffset);
CropEvents(predictedEvents, upperArray, segmentStartOffset);
var hits = new double[rowCount, colCount];
var plots = new List <Plot>();
//plots.Add(new Plot("lowerArray", DataTools.Normalise(lowerArray, 0, 100), 10.0));
//plots.Add(new Plot("lowerArray", DataTools.Normalise(lowerArray, 0, 100), 10.0));
//plots.Add(new Plot("lowerArray", DataTools.NormaliseMatrixValues(lowerArray), 0.25));
//plots.Add(new Plot("upperArray", DataTools.NormaliseMatrixValues(upperArray), 0.25));
//plots.Add(new Plot("intensity", DataTools.NormaliseMatrixValues(intensity), intensityThreshold));
plots.Add(new Plot("intensity", intensity, intensityThreshold));
return(Tuple.Create(sonogram, hits, plots, predictedEvents, tsRecordingtDuration));
} //Analysis()
/// <summary>
/// ################ THE KEY ANALYSIS METHOD for TRILLS
///
/// See Anthony's ExempliGratia.Recognize() method in order to see how to use methods for config profiles.
/// </summary>
/// <param name="recording"></param>
/// <param name="sonoConfig"></param>
/// <param name="lwConfig"></param>
/// <param name="returnDebugImage"></param>
/// <param name="segmentStartOffset"></param>
/// <returns></returns>
private static Tuple <BaseSonogram, double[, ], double[], List <AcousticEvent>, Image> Analysis(
AudioRecording recording,
SonogramConfig sonoConfig,
LitoriaWatjulumConfig lwConfig,
bool returnDebugImage,
TimeSpan segmentStartOffset)
{
double intensityThreshold = lwConfig.IntensityThreshold;
double minDuration = lwConfig.MinDurationOfTrill; // seconds
double maxDuration = lwConfig.MaxDurationOfTrill; // seconds
double minPeriod = lwConfig.MinPeriod; // seconds
double maxPeriod = lwConfig.MaxPeriod; // seconds
if (recording == null)
{
LoggedConsole.WriteLine("AudioRecording == null. Analysis not possible.");
return(null);
}
//i: MAKE SONOGRAM
//TimeSpan tsRecordingtDuration = recording.Duration();
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
double framesPerSecond = freqBinWidth;
// duration of DCT in seconds - want it to be about 3X or 4X the expected maximum period
double dctDuration = 4 * maxPeriod;
// duration of DCT in frames
int dctLength = (int)Math.Round(framesPerSecond * dctDuration);
// set up the cosine coefficients
double[,] cosines = MFCCStuff.Cosines(dctLength, dctLength);
int upperBandMinBin = (int)Math.Round(lwConfig.UpperBandMinHz / freqBinWidth) + 1;
int upperBandMaxBin = (int)Math.Round(lwConfig.UpperBandMaxHz / freqBinWidth) + 1;
int lowerBandMinBin = (int)Math.Round(lwConfig.LowerBandMinHz / freqBinWidth) + 1;
int lowerBandMaxBin = (int)Math.Round(lwConfig.LowerBandMaxHz / freqBinWidth) + 1;
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
int rowCount = sonogram.Data.GetLength(0);
//int colCount = sonogram.Data.GetLength(1);
double[] lowerArray = MatrixTools.GetRowAveragesOfSubmatrix(sonogram.Data, 0, lowerBandMinBin, rowCount - 1, lowerBandMaxBin);
double[] upperArray = MatrixTools.GetRowAveragesOfSubmatrix(sonogram.Data, 0, upperBandMinBin, rowCount - 1, upperBandMaxBin);
//lowerArray = DataTools.filterMovingAverage(lowerArray, 3);
//upperArray = DataTools.filterMovingAverage(upperArray, 3);
double[] amplitudeScores = DataTools.SumMinusDifference(lowerArray, upperArray);
double[] differenceScores = DspFilters.SubtractBaseline(amplitudeScores, 7);
// Could smooth here rather than above. Above seemed slightly better?
//amplitudeScores = DataTools.filterMovingAverage(amplitudeScores, 7);
//differenceScores = DataTools.filterMovingAverage(differenceScores, 7);
//iii: CONVERT decibel sum-diff SCORES TO ACOUSTIC TRILL EVENTS
var predictedTrillEvents = AcousticEvent.ConvertScoreArray2Events(
amplitudeScores,
lwConfig.LowerBandMinHz,
lwConfig.UpperBandMaxHz,
sonogram.FramesPerSecond,
freqBinWidth,
lwConfig.DecibelThreshold,
minDuration,
maxDuration,
segmentStartOffset);
for (int i = 0; i < differenceScores.Length; i++)
{
if (differenceScores[i] < 1.0)
{
differenceScores[i] = 0.0;
}
}
// LOOK FOR TRILL EVENTS
// init the score array
double[] scores = new double[rowCount];
// var hits = new double[rowCount, colCount];
double[,] hits = null;
// init confirmed events
var confirmedEvents = new List <AcousticEvent>();
// add names into the returned events
foreach (var ae in predictedTrillEvents)
{
int eventStart = ae.Oblong.RowTop;
int eventWidth = ae.Oblong.RowWidth;
int step = 2;
double maximumIntensity = 0.0;
// scan the event to get oscillation period and intensity
for (int i = eventStart - (dctLength / 2); i < eventStart + eventWidth - (dctLength / 2); i += step)
{
// Look for oscillations in the difference array
double[] differenceArray = DataTools.Subarray(differenceScores, i, dctLength);
double oscilFreq;
double period;
double intensity;
Oscillations2014.GetOscillation(differenceArray, framesPerSecond, cosines, out oscilFreq, out period, out intensity);
bool periodWithinBounds = period > minPeriod && period < maxPeriod;
//Console.WriteLine($"step={i} period={period:f4}");
if (!periodWithinBounds)
{
continue;
}
for (int j = 0; j < dctLength; j++) //lay down score for sample length
{
if (scores[i + j] < intensity)
{
scores[i + j] = intensity;
}
}
if (maximumIntensity < intensity)
{
maximumIntensity = intensity;
}
}
// add abbreviatedSpeciesName into event
if (maximumIntensity >= intensityThreshold)
{
ae.Name = $"{lwConfig.AbbreviatedSpeciesName}.{lwConfig.ProfileNames[0]}";
ae.Score_MaxInEvent = maximumIntensity;
ae.Profile = lwConfig.ProfileNames[0];
confirmedEvents.Add(ae);
}
}
//######################################################################
// LOOK FOR TINK EVENTS
// CONVERT decibel sum-diff SCORES TO ACOUSTIC EVENTS
double minDurationOfTink = lwConfig.MinDurationOfTink; // seconds
double maxDurationOfTink = lwConfig.MaxDurationOfTink; // seconds
// want stronger threshold for tink because brief.
double tinkDecibelThreshold = lwConfig.DecibelThreshold + 3.0;
var predictedTinkEvents = AcousticEvent.ConvertScoreArray2Events(
amplitudeScores,
lwConfig.LowerBandMinHz,
lwConfig.UpperBandMaxHz,
sonogram.FramesPerSecond,
freqBinWidth,
tinkDecibelThreshold,
minDurationOfTink,
maxDurationOfTink,
segmentStartOffset);
foreach (var ae2 in predictedTinkEvents)
{
// Prune the list of potential acoustic events, for example using Cosine Similarity.
//rowtop, rowWidth
//int eventStart = ae2.Oblong.RowTop;
//int eventWidth = ae2.Oblong.RowWidth;
//int step = 2;
//double maximumIntensity = 0.0;
// add abbreviatedSpeciesName into event
//if (maximumIntensity >= intensityThreshold)
//{
ae2.Name = $"{lwConfig.AbbreviatedSpeciesName}.{lwConfig.ProfileNames[1]}";
//ae2.Score_MaxInEvent = maximumIntensity;
ae2.Profile = lwConfig.ProfileNames[1];
confirmedEvents.Add(ae2);
//}
}
//######################################################################
var scorePlot = new Plot(lwConfig.SpeciesName, scores, intensityThreshold);
Image debugImage = null;
if (returnDebugImage)
{
// display a variety of debug score arrays
double[] normalisedScores;
double normalisedThreshold;
DataTools.Normalise(amplitudeScores, lwConfig.DecibelThreshold, out normalisedScores, out normalisedThreshold);
var sumDiffPlot = new Plot("Sum Minus Difference", normalisedScores, normalisedThreshold);
DataTools.Normalise(differenceScores, lwConfig.DecibelThreshold, out normalisedScores, out normalisedThreshold);
var differencePlot = new Plot("Baseline Removed", normalisedScores, normalisedThreshold);
var debugPlots = new List <Plot> {
scorePlot, sumDiffPlot, differencePlot
};
debugImage = DrawDebugImage(sonogram, confirmedEvents, debugPlots, hits);
}
// return new sonogram because it makes for more easy interpretation of the image
var returnSonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,
WindowSize = 512,
WindowOverlap = 0,
// 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"),
};
BaseSonogram returnSonogram = new SpectrogramStandard(returnSonoConfig, recording.WavReader);
return(Tuple.Create(returnSonogram, hits, scores, confirmedEvents, debugImage));
} //Analysis()
public static void Main(Arguments arguments)
{
//1. set up the necessary files
//DirectoryInfo diSource = arguments.Source.Directory;
FileInfo fiSourceRecording = arguments.Source;
FileInfo fiConfig = arguments.Config.ToFileInfo();
FileInfo fiImage = arguments.Output.ToFileInfo();
fiImage.CreateParentDirectories();
string title = "# CREATE FOUR (4) SONOGRAMS FROM AUDIO RECORDING";
string date = "# DATE AND TIME: " + DateTime.Now;
LoggedConsole.WriteLine(title);
LoggedConsole.WriteLine(date);
LoggedConsole.WriteLine("# Input audio file: " + fiSourceRecording.Name);
LoggedConsole.WriteLine("# Output image file: " + fiImage);
//2. get the config dictionary
Config configuration = ConfigFile.Deserialize(fiConfig);
//below three lines are examples of retrieving info from Config config
//string analysisIdentifier = configuration[AnalysisKeys.AnalysisName];
//bool saveIntermediateWavFiles = (bool?)configuration[AnalysisKeys.SaveIntermediateWavFiles] ?? false;
//scoreThreshold = (double?)configuration[AnalysisKeys.EventThreshold] ?? scoreThreshold;
//3 transfer conogram parameters to a dictionary to be passed around
var configDict = new Dictionary <string, string>();
// #Resample rate must be 2 X the desired Nyquist. Default is that of recording.
configDict["ResampleRate"] = (configuration.GetIntOrNull(AnalysisKeys.ResampleRate) ?? 17640).ToString();
configDict["FrameLength"] = configuration[AnalysisKeys.FrameLength] ?? "512";
int frameSize = configuration.GetIntOrNull(AnalysisKeys.FrameLength) ?? 512;
// #Frame Overlap as fraction: default=0.0
configDict["FrameOverlap"] = configuration[AnalysisKeys.FrameOverlap] ?? "0.0";
double windowOverlap = configuration.GetDoubleOrNull(AnalysisKeys.FrameOverlap) ?? 0.0;
// #MinHz: 500
// #MaxHz: 3500
// #NOISE REDUCTION PARAMETERS
configDict["DoNoiseReduction"] = configuration["DoNoiseReduction"] ?? "true";
configDict["BgNoiseThreshold"] = configuration["BgNoiseThreshold"] ?? "3.0";
configDict["ADD_AXES"] = configuration["ADD_AXES"] ?? "true";
configDict["AddSegmentationTrack"] = configuration["AddSegmentationTrack"] ?? "true";
// 3: GET RECORDING
var startOffsetMins = TimeSpan.Zero;
var endOffsetMins = TimeSpan.Zero;
FileInfo fiOutputSegment = fiSourceRecording;
if (!(startOffsetMins == TimeSpan.Zero && endOffsetMins == TimeSpan.Zero))
{
var buffer = new TimeSpan(0, 0, 0);
fiOutputSegment = new FileInfo(Path.Combine(fiImage.DirectoryName, "tempWavFile.wav"));
//This method extracts segment and saves to disk at the location fiOutputSegment.
var resampleRate = configuration.GetIntOrNull(AnalysisKeys.ResampleRate) ?? AppConfigHelper.DefaultTargetSampleRate;
AudioRecording.ExtractSegment(fiSourceRecording, startOffsetMins, endOffsetMins, buffer, resampleRate, fiOutputSegment);
}
var recording = new AudioRecording(fiOutputSegment.FullName);
// EXTRACT ENVELOPE and SPECTROGRAM
var dspOutput = DSP_Frames.ExtractEnvelopeAndFfts(recording, frameSize, windowOverlap);
// average absolute value over the minute recording
////double[] avAbsolute = dspOutput.Average;
// (A) ################################## EXTRACT INDICES FROM THE SIGNAL WAVEFORM ##################################
// var wavDuration = TimeSpan.FromSeconds(recording.WavReader.Time.TotalSeconds);
// double totalSeconds = wavDuration.TotalSeconds;
// double[] signalEnvelope = dspOutput.Envelope;
// double avSignalEnvelope = signalEnvelope.Average();
// double[] frameEnergy = dspOutput.FrameEnergy;
// double highAmplIndex = dspOutput.HighAmplitudeCount / totalSeconds;
// double binWidth = dspOutput.BinWidth;
// int nyquistBin = dspOutput.NyquistBin;
// dspOutput.WindowPower,
// dspOutput.FreqBinWidth
int nyquistFreq = dspOutput.NyquistFreq;
double epsilon = recording.Epsilon;
// i: prepare amplitude spectrogram
double[,] amplitudeSpectrogramData = dspOutput.AmplitudeSpectrogram; // get amplitude spectrogram.
var image1 = ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(amplitudeSpectrogramData));
// ii: prepare decibel spectrogram prior to noise removal
double[,] decibelSpectrogramdata = MFCCStuff.DecibelSpectra(dspOutput.AmplitudeSpectrogram, dspOutput.WindowPower, recording.SampleRate, epsilon);
decibelSpectrogramdata = MatrixTools.NormaliseMatrixValues(decibelSpectrogramdata);
var image2 = ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(decibelSpectrogramdata));
// iii: Calculate background noise spectrum in decibels
// Calculate noise value for each freq bin.
double sdCount = 0.0; // number of SDs above the mean for noise removal
var decibelProfile = NoiseProfile.CalculateModalNoiseProfile(decibelSpectrogramdata, sdCount);
// DataTools.writeBarGraph(dBProfile.NoiseMode);
// iv: Prepare noise reduced spectrogram
decibelSpectrogramdata = SNR.TruncateBgNoiseFromSpectrogram(decibelSpectrogramdata, decibelProfile.NoiseThresholds);
//double dBThreshold = 1.0; // SPECTRAL dB THRESHOLD for smoothing background
//decibelSpectrogramdata = SNR.RemoveNeighbourhoodBackgroundNoise(decibelSpectrogramdata, dBThreshold);
var image3 = ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(decibelSpectrogramdata));
// prepare new sonogram config and draw second image going down different code pathway
var config = new SonogramConfig
{
MinFreqBand = 0,
MaxFreqBand = 10000,
NoiseReductionType = SNR.KeyToNoiseReductionType("Standard"),
NoiseReductionParameter = 1.0,
WindowSize = frameSize,
WindowOverlap = windowOverlap,
};
//var mfccConfig = new MfccConfiguration(config);
int bandCount = config.mfccConfig.FilterbankCount;
bool doMelScale = config.mfccConfig.DoMelScale;
int ccCount = config.mfccConfig.CcCount;
int fftBins = config.FreqBinCount; //number of Hz bands = 2^N +1 because includes the DC band
int minHz = config.MinFreqBand ?? 0;
int maxHz = config.MaxFreqBand ?? nyquistFreq;
var standardSonogram = new SpectrogramStandard(config, recording.WavReader);
var image4 = standardSonogram.GetImage();
// TODO next line crashes - does not produce cepstral sonogram.
//SpectrogramCepstral cepSng = new SpectrogramCepstral(config, recording.WavReader);
//Image image5 = cepSng.GetImage();
//var mti = SpectrogramTools.Sonogram2MultiTrackImage(sonogram, configDict);
//var image = mti.GetImage();
//Image image = SpectrogramTools.Matrix2SonogramImage(deciBelSpectrogram, config);
//Image image = SpectrogramTools.Audio2SonogramImage(FileInfo fiAudio, Dictionary<string, string> configDict);
//prepare sonogram images
var protoImage6 = new Image_MultiTrack(standardSonogram.GetImage(doHighlightSubband: false, add1KHzLines: true, doMelScale: false));
protoImage6.AddTrack(ImageTrack.GetTimeTrack(standardSonogram.Duration, standardSonogram.FramesPerSecond));
protoImage6.AddTrack(ImageTrack.GetWavEnvelopeTrack(recording, protoImage6.SonogramImage.Width));
protoImage6.AddTrack(ImageTrack.GetSegmentationTrack(standardSonogram));
var image6 = protoImage6.GetImage();
var list = new List <Image <Rgb24> >();
list.Add(image1); // amplitude spectrogram
list.Add(image2); // decibel spectrogram before noise removal
list.Add(image3); // decibel spectrogram after noise removal
list.Add(image4); // second version of noise reduced spectrogram
//list.Add(image5); // ceptral sonogram
list.Add(image6.CloneAs <Rgb24>()); // multitrack image
Image finalImage = ImageTools.CombineImagesVertically(list);
finalImage.Save(fiImage.FullName);
////2: NOISE REMOVAL
//double[,] originalSg = sonogram.Data;
//double[,] mnr = sonogram.Data;
//mnr = ImageTools.WienerFilter(mnr, 3);
//double backgroundThreshold = 4.0; //SETS MIN DECIBEL BOUND
//var output = SNR.NoiseReduce(mnr, NoiseReductionType.STANDARD, backgroundThreshold);
//double ConfigRange = 70; //sets the the max dB
//mnr = SNR.SetConfigRange(output.Item1, 0.0, ConfigRange);
////3: Spectral tracks sonogram
//byte[,] binary = MatrixTools.IdentifySpectralRidges(mnr);
//binary = MatrixTools.ThresholdBinarySpectrum(binary, mnr, 10);
//binary = MatrixTools.RemoveOrphanOnesInBinaryMatrix(binary);
////binary = MatrixTools.PickOutLines(binary); //syntactic approach
//sonogram.SetBinarySpectrum(binary);
////sonogram.Data = SNR.SpectralRidges2Intensity(binary, originalSg);
//image = new Image_MultiTrack(sonogram.GetImage(doHighlightSubband, false));
//image.AddTrack(ImageTrack.GetTimeTrack(sonogram.Duration, sonogram.FramesPerSecond));
//image.AddTrack(ImageTrack.GetWavEnvelopeTrack(recording, image.sonogramImage.Width));
//image.AddTrack(ImageTrack.GetSegmentationTrack(sonogram));
//fn = outputFolder + wavFileName + "_tracks.png";
//image.Save(fn);
//LoggedConsole.WriteLine("Spectral tracks sonogram to file: " + fn);
//3: prepare image of spectral peaks sonogram
//sonogram.Data = SNR.NoiseReduce_Peaks(originalSg, dynamicRange);
//image = new Image_MultiTrack(sonogram.GetImage(doHighlightSubband, add1kHzLines));
//image.AddTrack(ImageTrack.GetTimeTrack(sonogram.Duration));
//image.AddTrack(ImageTrack.GetWavEnvelopeTrack(recording, image.Image.Width));
//image.AddTrack(ImageTrack.GetSegmentationTrack(sonogram));
//fn = outputFolder + wavFileName + "_peaks.png";
//image.Save(fn);
//LoggedConsole.WriteLine("Spectral peaks sonogram to file: " + fn);
//4: Sobel approach
//sonogram.Data = SNR.NoiseReduce_Sobel(originalSg, dynamicRange);
//image = new Image_MultiTrack(sonogram.GetImage(doHighlightSubband, add1kHzLines));
//image.AddTrack(ImageTrack.GetTimeTrack(sonogram.Duration));
//image.AddTrack(ImageTrack.GetWavEnvelopeTrack(recording, image.Image.Width));
//image.AddTrack(ImageTrack.GetSegmentationTrack(sonogram));
//fn = outputFolder + wavFileName + "_sobel.png";
//image.Save(fn);
//LoggedConsole.WriteLine("Sobel sonogram to file: " + fn);
// I1.txt contains the sonogram matrix produced by matlab
//string matlabFile = @"C:\SensorNetworks\Software\AudioAnalysis\AED\Test\matlab\GParrots_JB2_20090607-173000.wav_minute_3\I1.txt";
//double[,] matlabMatrix = Util.fileToMatrix(matlabFile, 256, 5166);
//LoggedConsole.WriteLine(matrix[0, 2] + " vs " + matlabMatrix[254, 0]);
//LoggedConsole.WriteLine(matrix[0, 3] + " vs " + matlabMatrix[253, 0]);
// TODO put this back once sonogram issues resolved
/*
* LoggedConsole.WriteLine("START: AED");
* IEnumerable<Oblong> oblongs = AcousticEventDetection.detectEvents(3.0, 100, matrix);
* LoggedConsole.WriteLine("END: AED");
*
*
* //set up static variables for init Acoustic events
* //AcousticEvent. doMelScale = config.DoMelScale;
* AcousticEvent.FreqBinCount = config.FreqBinCount;
* AcousticEvent.FreqBinWidth = config.FftConfig.NyquistFreq / (double)config.FreqBinCount;
* // int minF = (int)config.MinFreqBand;
* // int maxF = (int)config.MaxFreqBand;
* AcousticEvent.FrameDuration = config.GetFrameOffset();
*
*
* var events = new List<EventPatternRecog.Rectangle>();
* foreach (Oblong o in oblongs)
* {
* var e = new AcousticEvent(o);
* events.Add(new EventPatternRecog.Rectangle(e.StartTime, (double) e.MaxFreq, e.StartTime + e.Duration, (double)e.MinFreq));
* //LoggedConsole.WriteLine(e.StartTime + "," + e.Duration + "," + e.MinFreq + "," + e.MaxFreq);
* }
*
* LoggedConsole.WriteLine("# AED events: " + events.Count);
*
* LoggedConsole.WriteLine("START: EPR");
* IEnumerable<EventPatternRecog.Rectangle> eprRects = EventPatternRecog.detectGroundParrots(events);
* LoggedConsole.WriteLine("END: EPR");
*
* var eprEvents = new List<AcousticEvent>();
* foreach (EventPatternRecog.Rectangle r in eprRects)
* {
* var ae = new AcousticEvent(r.Left, r.Right - r.Left, r.Bottom, r.Top, false);
* LoggedConsole.WriteLine(ae.WriteProperties());
* eprEvents.Add(ae);
* }
*
* string imagePath = Path.Combine(outputFolder, "RESULTS_" + Path.GetFileNameWithoutExtension(recording.BaseName) + ".png");
*
* bool doHighlightSubband = false; bool add1kHzLines = true;
* var image = new Image_MultiTrack(sonogram.GetImage(doHighlightSubband, add1kHzLines));
* //image.AddTrack(ImageTrack.GetTimeTrack(sonogram.Duration));
* //image.AddTrack(ImageTrack.GetWavEnvelopeTrack(recording, image.Image.Width));
* //image.AddTrack(ImageTrack.GetSegmentationTrack(sonogram));
* image.AddEvents(eprEvents);
* image.Save(outputFolder + wavFileName + ".png");
*/
LoggedConsole.WriteLine("\nFINISHED!");
}
} //Analysis()
public static Tuple <BaseSonogram, double[, ], double[], List <AcousticEvent> > DetectHarmonics(
AudioRecording recording,
double intensityThreshold,
int minHz,
int minFormantgap,
int maxFormantgap,
double minDuration,
int windowSize,
double windowOverlap,
TimeSpan segmentStartOffset)
{
//i: MAKE SONOGRAM
int numberOfBins = 32;
double binWidth = recording.SampleRate / (double)windowSize;
int sr = recording.SampleRate;
double frameDuration = windowSize / (double)sr; // Duration of full frame or window in seconds
double frameOffset = frameDuration * (1 - windowOverlap); //seconds between starts of consecutive frames
double framesPerSecond = 1 / frameOffset;
//double framesPerSecond = sr / (double)windowSize;
//int frameOffset = (int)(windowSize * (1 - overlap));
//int frameCount = (length - windowSize + frameOffset) / frameOffset;
double epsilon = Math.Pow(0.5, recording.BitsPerSample - 1);
var results2 = DSP_Frames.ExtractEnvelopeAndAmplSpectrogram(
recording.WavReader.Samples,
sr,
epsilon,
windowSize,
windowOverlap);
double[] avAbsolute = results2.Average; //average absolute value over the minute recording
//double[] envelope = results2.Item2;
double[,]
matrix = results2
.AmplitudeSpectrogram; //amplitude spectrogram. Note that column zero is the DC or average energy value and can be ignored.
double windowPower = results2.WindowPower;
//window sr frameDuration frames/sec hz/bin 64frameDuration hz/64bins hz/128bins
// 1024 22050 46.4ms 21.5 21.5 2944ms 1376hz 2752hz
// 1024 17640 58.0ms 17.2 17.2 3715ms 1100hz 2200hz
// 2048 17640 116.1ms 8.6 8.6 7430ms 551hz 1100hz
//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 minBin = (int)Math.Round(minHz / binWidth);
int maxHz = (int)Math.Round(minHz + (numberOfBins * binWidth));
int rowCount = matrix.GetLength(0);
int colCount = matrix.GetLength(1);
int maxbin = minBin + numberOfBins;
double[,] subMatrix = MatrixTools.Submatrix(matrix, 0, minBin + 1, rowCount - 1, maxbin);
//ii: DETECT HARMONICS
int zeroBinCount = 5; //to remove low freq content which dominates the spectrum
var results = CrossCorrelation.DetectBarsInTheRowsOfaMatrix(subMatrix, intensityThreshold, zeroBinCount);
double[] intensity = results.Item1; //an array of periodicity scores
double[] periodicity = results.Item2;
//transfer periodicity info to a hits matrix.
//intensity = DataTools.filterMovingAverage(intensity, 3);
double[] scoreArray = new double[intensity.Length];
var hits = new double[rowCount, colCount];
for (int r = 0; r < rowCount; r++)
{
double relativePeriod = periodicity[r] / numberOfBins / 2;
if (intensity[r] > intensityThreshold)
{
for (int c = minBin; c < maxbin; c++)
{
hits[r, c] = relativePeriod;
}
}
double herzPeriod = periodicity[r] * binWidth;
if (herzPeriod > minFormantgap && herzPeriod < maxFormantgap)
{
scoreArray[r] = 2 * intensity[r] * intensity[r]; //enhance high score wrt low score.
}
}
scoreArray = DataTools.filterMovingAverage(scoreArray, 11);
//iii: CONVERT TO ACOUSTIC EVENTS
double maxDuration = 100000.0; //abitrary long number - do not want to restrict duration of machine noise
List <AcousticEvent> predictedEvents = AcousticEvent.ConvertScoreArray2Events(
scoreArray,
minHz,
maxHz,
framesPerSecond,
binWidth,
intensityThreshold,
minDuration,
maxDuration,
segmentStartOffset);
hits = null;
//set up the songogram to return. Use the existing amplitude sonogram
int bitsPerSample = recording.WavReader.BitsPerSample;
TimeSpan duration = recording.Duration;
NoiseReductionType nrt = SNR.KeyToNoiseReductionType("STANDARD");
var sonogram = (BaseSonogram)SpectrogramStandard.GetSpectralSonogram(
recording.BaseName,
windowSize,
windowOverlap,
bitsPerSample,
windowPower,
sr,
duration,
nrt,
matrix);
sonogram.DecibelsNormalised = new double[rowCount];
//foreach frame or time step
for (int i = 0; i < rowCount; i++)
{
sonogram.DecibelsNormalised[i] = 2 * Math.Log10(avAbsolute[i]);
}
sonogram.DecibelsNormalised = DataTools.normalise(sonogram.DecibelsNormalised);
return(Tuple.Create(sonogram, hits, scoreArray, predictedEvents));
} //end Execute_HDDetect
public static void Execute(Arguments arguments)
{
const string Title = "# DETERMINING SIGNAL TO NOISE RATIO IN RECORDING";
string date = "# DATE AND TIME: " + DateTime.Now;
Log.WriteLine(Title);
Log.WriteLine(date);
Log.Verbosity = 1;
var input = arguments.Source;
var sourceFileName = input.Name;
var outputDir = arguments.Output;
var fileNameWithoutExtension = Path.GetFileNameWithoutExtension(input.FullName);
var outputTxtPath = Path.Combine(outputDir.FullName, fileNameWithoutExtension + ".txt").ToFileInfo();
Log.WriteIfVerbose("# Recording file: " + input.FullName);
Log.WriteIfVerbose("# Config file: " + arguments.Config);
Log.WriteIfVerbose("# Output folder =" + outputDir.FullName);
FileTools.WriteTextFile(outputTxtPath.FullName, date + "\n# Recording file: " + input.FullName);
//READ PARAMETER VALUES FROM INI FILE
// load YAML configuration
Config configuration = ConfigFile.Deserialize(arguments.Config);
//ii: SET SONOGRAM CONFIGURATION
SonogramConfig sonoConfig = new SonogramConfig(); //default values config
sonoConfig.SourceFName = input.FullName;
sonoConfig.WindowSize = configuration.GetIntOrNull(AnalysisKeys.KeyFrameSize) ?? 512;
sonoConfig.WindowOverlap = configuration.GetDoubleOrNull(AnalysisKeys.FrameOverlap) ?? 0.5;
sonoConfig.WindowFunction = configuration[AnalysisKeys.KeyWindowFunction];
sonoConfig.NPointSmoothFFT = configuration.GetIntOrNull(AnalysisKeys.KeyNPointSmoothFft) ?? 256;
sonoConfig.NoiseReductionType = SNR.KeyToNoiseReductionType(configuration[AnalysisKeys.NoiseReductionType]);
int minHz = configuration.GetIntOrNull("MIN_HZ") ?? 0;
int maxHz = configuration.GetIntOrNull("MAX_HZ") ?? 11050;
double segK1 = configuration.GetDoubleOrNull("SEGMENTATION_THRESHOLD_K1") ?? 0;
double segK2 = configuration.GetDoubleOrNull("SEGMENTATION_THRESHOLD_K2") ?? 0;
double latency = configuration.GetDoubleOrNull("K1_K2_LATENCY") ?? 0;
double vocalGap = configuration.GetDoubleOrNull("VOCAL_GAP") ?? 0;
double minVocalLength = configuration.GetDoubleOrNull("MIN_VOCAL_DURATION") ?? 0;
//bool DRAW_SONOGRAMS = (bool?)configuration.DrawSonograms ?? true; //options to draw sonogram
//double intensityThreshold = Acoustics.AED.Default.intensityThreshold;
//if (dict.ContainsKey(key_AED_INTENSITY_THRESHOLD)) intensityThreshold = Double.Parse(dict[key_AED_INTENSITY_THRESHOLD]);
//int smallAreaThreshold = Acoustics.AED.Default.smallAreaThreshold;
//if( dict.ContainsKey(key_AED_SMALL_AREA_THRESHOLD)) smallAreaThreshold = Int32.Parse(dict[key_AED_SMALL_AREA_THRESHOLD]);
// COnvert input recording into wav
var convertParameters = new AudioUtilityRequest {
TargetSampleRate = 17640
};
var fileToAnalyse = new FileInfo(Path.Combine(outputDir.FullName, "temp.wav"));
if (File.Exists(fileToAnalyse.FullName))
{
File.Delete(fileToAnalyse.FullName);
}
var convertedFileInfo = AudioFilePreparer.PrepareFile(
input,
fileToAnalyse,
convertParameters,
outputDir);
// (A) ##########################################################################################################################
AudioRecording recording = new AudioRecording(fileToAnalyse.FullName);
int signalLength = recording.WavReader.Samples.Length;
TimeSpan wavDuration = TimeSpan.FromSeconds(recording.WavReader.Time.TotalSeconds);
double frameDurationInSeconds = sonoConfig.WindowSize / (double)recording.SampleRate;
TimeSpan frameDuration = TimeSpan.FromTicks((long)(frameDurationInSeconds * TimeSpan.TicksPerSecond));
int stepSize = (int)Math.Floor(sonoConfig.WindowSize * (1 - sonoConfig.WindowOverlap));
double stepDurationInSeconds = sonoConfig.WindowSize * (1 - sonoConfig.WindowOverlap)
/ recording.SampleRate;
TimeSpan stepDuration = TimeSpan.FromTicks((long)(stepDurationInSeconds * TimeSpan.TicksPerSecond));
double framesPerSecond = 1 / stepDuration.TotalSeconds;
int frameCount = signalLength / stepSize;
// (B) ################################## EXTRACT ENVELOPE and SPECTROGRAM ##################################
var dspOutput = DSP_Frames.ExtractEnvelopeAndFfts(
recording,
sonoConfig.WindowSize,
sonoConfig.WindowOverlap);
//double[] avAbsolute = dspOutput.Average; //average absolute value over the minute recording
// (C) ################################## GET SIGNAL WAVEFORM ##################################
double[] signalEnvelope = dspOutput.Envelope;
double avSignalEnvelope = signalEnvelope.Average();
// (D) ################################## GET Amplitude Spectrogram ##################################
double[,] amplitudeSpectrogram = dspOutput.AmplitudeSpectrogram; // get amplitude spectrogram.
// (E) ################################## Generate deciBel spectrogram from amplitude spectrogram
double epsilon = Math.Pow(0.5, recording.BitsPerSample - 1);
double[,] deciBelSpectrogram = MFCCStuff.DecibelSpectra(
dspOutput.AmplitudeSpectrogram,
dspOutput.WindowPower,
recording.SampleRate,
epsilon);
LoggedConsole.WriteLine("# Finished calculating decibel spectrogram.");
StringBuilder sb = new StringBuilder();
sb.AppendLine("\nSIGNAL PARAMETERS");
sb.AppendLine("Signal Duration =" + wavDuration);
sb.AppendLine("Sample Rate =" + recording.SampleRate);
sb.AppendLine("Min Signal Value =" + dspOutput.MinSignalValue);
sb.AppendLine("Max Signal Value =" + dspOutput.MaxSignalValue);
sb.AppendLine("Max Absolute Ampl =" + signalEnvelope.Max().ToString("F3") + " (See Note 1)");
sb.AppendLine("Epsilon Ampl (1 bit)=" + epsilon);
sb.AppendLine("\nFRAME PARAMETERS");
sb.AppendLine("Window Size =" + sonoConfig.WindowSize);
sb.AppendLine("Frame Count =" + frameCount);
sb.AppendLine("Envelope length=" + signalEnvelope.Length);
sb.AppendLine("Frame Duration =" + frameDuration.TotalMilliseconds.ToString("F3") + " ms");
sb.AppendLine("Frame overlap =" + sonoConfig.WindowOverlap);
sb.AppendLine("Step Size =" + stepSize);
sb.AppendLine("Step duration =" + stepDuration.TotalMilliseconds.ToString("F3") + " ms");
sb.AppendLine("Frames Per Sec =" + framesPerSecond.ToString("F1"));
sb.AppendLine("\nFREQUENCY PARAMETERS");
sb.AppendLine("Nyquist Freq =" + dspOutput.NyquistFreq + " Hz");
sb.AppendLine("Freq Bin Width =" + dspOutput.FreqBinWidth.ToString("F2") + " Hz");
sb.AppendLine("Nyquist Bin =" + dspOutput.NyquistBin);
sb.AppendLine("\nENERGY PARAMETERS");
double val = dspOutput.FrameEnergy.Min();
sb.AppendLine(
"Minimum dB / frame =" + (10 * Math.Log10(val)).ToString("F2") + " (See Notes 2, 3 & 4)");
val = dspOutput.FrameEnergy.Max();
sb.AppendLine("Maximum dB / frame =" + (10 * Math.Log10(val)).ToString("F2"));
sb.AppendLine("\ndB NOISE SUBTRACTION");
double noiseRange = 2.0;
//sb.AppendLine("Noise (estimate of mode) =" + sonogram.SnrData.NoiseSubtracted.ToString("F3") + " dB (See Note 5)");
//double noiseSpan = sonogram.SnrData.NoiseRange;
//sb.AppendLine("Noise range =" + noiseSpan.ToString("F2") + " to +" + (noiseSpan * -1).ToString("F2") + " dB (See Note 6)");
//sb.AppendLine("SNR (max frame-noise) =" + sonogram.SnrData.Snr.ToString("F2") + " dB (See Note 7)");
//sb.Append("\nSEGMENTATION PARAMETERS");
//sb.Append("Segment Thresholds K1: {0:f2}. K2: {1:f2} (See Note 8)", segK1, segK2);
//sb.Append("# Event Count = " + predictedEvents.Count());
FileTools.Append2TextFile(outputTxtPath.FullName, sb.ToString());
FileTools.Append2TextFile(outputTxtPath.FullName, GetSNRNotes(noiseRange).ToString());
// (F) ################################## DRAW IMAGE 1: original spectorgram
Log.WriteLine("# Start drawing noise reduced sonograms.");
TimeSpan X_AxisInterval = TimeSpan.FromSeconds(1);
//int Y_AxisInterval = (int)Math.Round(1000 / dspOutput.FreqBinWidth);
int nyquist = recording.SampleRate / 2;
int hzInterval = 1000;
var image1 = DrawSonogram(deciBelSpectrogram, wavDuration, X_AxisInterval, stepDuration, nyquist, hzInterval);
// (G) ################################## Calculate modal background noise spectrum in decibels
//double SD_COUNT = -0.5; // number of SDs above the mean for noise removal
//NoiseReductionType nrt = NoiseReductionType.MODAL;
//System.Tuple<double[,], double[]> tuple = SNR.NoiseReduce(deciBelSpectrogram, nrt, SD_COUNT);
//double upperPercentileBound = 0.2; // lowest percentile for noise removal
//NoiseReductionType nrt = NoiseReductionType.LOWEST_PERCENTILE;
//System.Tuple<double[,], double[]> tuple = SNR.NoiseReduce(deciBelSpectrogram, nrt, upperPercentileBound);
// (H) ################################## Calculate BRIGGS noise removal from amplitude spectrum
int percentileBound = 20; // low energy percentile for noise removal
//double binaryThreshold = 0.6; //works for higher SNR recordings
double binaryThreshold = 0.4; //works for lower SNR recordings
//double binaryThreshold = 0.3; //works for lower SNR recordings
double[,] m = NoiseRemoval_Briggs.BriggsNoiseFilterAndGetMask(
amplitudeSpectrogram,
percentileBound,
binaryThreshold);
string title = "TITLE";
var image2 = NoiseRemoval_Briggs.DrawSonogram(
m,
wavDuration,
X_AxisInterval,
stepDuration,
nyquist, hzInterval,
title);
//Image image2 = NoiseRemoval_Briggs.BriggsNoiseFilterAndGetSonograms(amplitudeSpectrogram, upperPercentileBound, binaryThreshold,
// wavDuration, X_AxisInterval, stepDuration, Y_AxisInterval);
// (I) ################################## Calculate MEDIAN noise removal from amplitude spectrum
//double upperPercentileBound = 0.8; // lowest percentile for noise removal
//NoiseReductionType nrt = NoiseReductionType.MEDIAN;
//System.Tuple<double[,], double[]> tuple = SNR.NoiseReduce(deciBelSpectrogram, nrt, upperPercentileBound);
//double[,] noiseReducedSpectrogram1 = tuple.Item1; //
//double[] noiseProfile = tuple.Item2; // smoothed modal profile
//SNR.NoiseProfile dBProfile = SNR.CalculateNoiseProfile(deciBelSpectrogram, SD_COUNT); // calculate noise value for each freq bin.
//double[] noiseProfile = DataTools.filterMovingAverage(dBProfile.noiseThresholds, 7); // smooth modal profile
//double[,] noiseReducedSpectrogram1 = SNR.TruncateBgNoiseFromSpectrogram(deciBelSpectrogram, dBProfile.noiseThresholds);
//Image image2 = DrawSonogram(noiseReducedSpectrogram1, wavDuration, X_AxisInterval, stepDuration, Y_AxisInterval);
var combinedImage = ImageTools.CombineImagesVertically(image1, image2);
string imagePath = Path.Combine(outputDir.FullName, fileNameWithoutExtension + ".png");
combinedImage.Save(imagePath);
// (G) ################################## Calculate modal background noise spectrum in decibels
Log.WriteLine("# Finished recording:- " + input.Name);
}
/// <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>
/// Does the Analysis
/// Returns a DataTable
/// </summary>
/// <param name="fiSegmentOfSourceFile"></param>
/// <param name="configDict"></param>
/// <param name="diOutputDir"></param>
/// <param name="opFileName"></param>
/// <param name="segmentStartOffset"></param>
/// <param name="config"></param>
/// <param name="segmentAudioFile"></param>
public static Tuple <BaseSonogram, double[, ], double[], List <AcousticEvent>, TimeSpan> Analysis(FileInfo fiSegmentOfSourceFile, Dictionary <string, string> configDict, DirectoryInfo diOutputDir, string opFileName, TimeSpan segmentStartOffset)
{
//set default values
int bandWidth = 500; //detect bars in bands of this width.
int frameSize = 1024;
double windowOverlap = 0.0;
double intensityThreshold = double.Parse(configDict[key_INTENSITY_THRESHOLD]);
//intensityThreshold = 0.01;
AudioRecording recording = AudioRecording.GetAudioRecording(fiSegmentOfSourceFile, RESAMPLE_RATE, diOutputDir.FullName, opFileName);
if (recording == null)
{
LoggedConsole.WriteLine("############ WARNING: Recording could not be obtained - likely file does not exist.");
return(null);
}
int sr = recording.SampleRate;
double binWidth = recording.SampleRate / (double)frameSize;
double frameDuration = frameSize / (double)sr;
double frameOffset = frameDuration * (1 - windowOverlap); //seconds between start of each frame
double framesPerSecond = 1 / frameOffset;
TimeSpan tsRecordingtDuration = recording.Duration;
int colStep = (int)Math.Round(bandWidth / binWidth);
//i: GET SONOGRAM AS MATRIX
double epsilon = Math.Pow(0.5, recording.BitsPerSample - 1);
var results2 = DSP_Frames.ExtractEnvelopeAndAmplSpectrogram(recording.WavReader.Samples, sr, epsilon, frameSize, windowOverlap);
double[] avAbsolute = results2.Average; //average absolute value over the minute recording
//double[] envelope = results2.Item2;
double[,] spectrogram = results2.AmplitudeSpectrogram; //amplitude spectrogram. Note that column zero is the DC or average energy value and can be ignored.
double windowPower = results2.WindowPower;
//############################ NEXT LINE FOR DEBUGGING ONLY
//spectrogram = GetTestSpectrogram(spectrogram.GetLength(0), spectrogram.GetLength(1), 0.01, 0.03);
var output = DetectGratingEvents(spectrogram, colStep, intensityThreshold);
var amplitudeArray = output.Item2; //for debug purposes only
//convert List of Dictionary events to List of ACousticevents.
//also set up the hits matrix.
int rowCount = spectrogram.GetLength(0);
int colCount = spectrogram.GetLength(1);
var hitsMatrix = new double[rowCount, colCount];
var acousticEvents = new List <AcousticEvent>();
double minFrameCount = 8; //this assumes that the minimum grid is 2 * 4 = 8 long
foreach (Dictionary <string, double> item in output.Item1)
{
int minRow = (int)item[key_START_FRAME];
int maxRow = (int)item[key_END_FRAME];
int frameCount = maxRow - minRow + 1;
if (frameCount < minFrameCount)
{
continue; //only want events that are over a minimum length
}
int minCol = (int)item[key_MIN_FREQBIN];
int maxCol = (int)item[key_MAX_FREQBIN];
double periodicity = item[key_PERIODICITY];
double[] subarray = DataTools.Subarray(avAbsolute, minRow, maxRow - minRow + 1);
double severity = 0.1;
int[] bounds = DataTools.Peaks_CropToFirstAndLast(subarray, severity);
minRow = minRow + bounds[0];
maxRow = minRow + bounds[1];
if (maxRow >= rowCount)
{
maxRow = rowCount - 1;
}
Oblong o = new Oblong(minRow, minCol, maxRow, maxCol);
var ae = new AcousticEvent(segmentStartOffset, o, results2.NyquistFreq, frameSize, frameDuration, frameOffset, frameCount);
ae.Name = string.Format("p={0:f0}", periodicity);
ae.Score = item[key_SCORE];
ae.ScoreNormalised = item[key_SCORE] / 0.5;
acousticEvents.Add(ae);
//display event on the hits matrix
for (int r = minRow; r < maxRow; r++)
{
for (int c = minCol; c < maxCol; c++)
{
hitsMatrix[r, c] = periodicity;
}
}
} //foreach
//set up the songogram to return. Use the existing amplitude sonogram
int bitsPerSample = recording.WavReader.BitsPerSample;
//NoiseReductionType nrt = SNR.Key2NoiseReductionType("NONE");
NoiseReductionType nrt = SNR.KeyToNoiseReductionType("STANDARD");
var sonogram = (BaseSonogram)SpectrogramStandard.GetSpectralSonogram(recording.BaseName, frameSize, windowOverlap, bitsPerSample, windowPower, sr, tsRecordingtDuration, nrt, spectrogram);
sonogram.DecibelsNormalised = new double[sonogram.FrameCount];
for (int i = 0; i < sonogram.FrameCount; i++) //foreach frame or time step
{
sonogram.DecibelsNormalised[i] = 2 * Math.Log10(avAbsolute[i]);
}
sonogram.DecibelsNormalised = DataTools.normalise(sonogram.DecibelsNormalised);
return(Tuple.Create(sonogram, hitsMatrix, amplitudeArray, acousticEvents, tsRecordingtDuration));
} //Analysis()
/// <summary>
/// THE KEY ANALYSIS METHOD.
/// </summary>
public static Tuple <BaseSonogram, double[, ], double[], List <AcousticEvent>, Image> Analysis(
AudioRecording recording,
SonogramConfig sonoConfig,
LitoriaBicolorConfig lbConfig,
bool drawDebugImage,
TimeSpan segmentStartOffset)
{
double decibelThreshold = lbConfig.DecibelThreshold; //dB
double intensityThreshold = lbConfig.IntensityThreshold;
//double eventThreshold = lbConfig.EventThreshold; //in 0-1
if (recording == null)
{
LoggedConsole.WriteLine("AudioRecording == null. Analysis not possible.");
return(null);
}
//i: MAKE SONOGRAM
//TimeSpan tsRecordingtDuration = recording.Duration();
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
double framesPerSecond = freqBinWidth;
// duration of DCT in seconds - want it to be about 3X or 4X the expected maximum period
double dctDuration = 3 * lbConfig.MaxPeriod;
// duration of DCT in frames
int dctLength = (int)Math.Round(framesPerSecond * dctDuration);
// set up the cosine coefficients
double[,] cosines = MFCCStuff.Cosines(dctLength, dctLength);
int upperBandMinBin = (int)Math.Round(lbConfig.UpperBandMinHz / freqBinWidth) + 1;
int upperBandMaxBin = (int)Math.Round(lbConfig.UpperBandMaxHz / freqBinWidth) + 1;
int lowerBandMinBin = (int)Math.Round(lbConfig.LowerBandMinHz / freqBinWidth) + 1;
int lowerBandMaxBin = (int)Math.Round(lbConfig.LowerBandMaxHz / freqBinWidth) + 1;
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
int rowCount = sonogram.Data.GetLength(0);
int colCount = sonogram.Data.GetLength(1);
double[] lowerArray = MatrixTools.GetRowAveragesOfSubmatrix(sonogram.Data, 0, lowerBandMinBin, rowCount - 1, lowerBandMaxBin);
double[] upperArray = MatrixTools.GetRowAveragesOfSubmatrix(sonogram.Data, 0, upperBandMinBin, rowCount - 1, upperBandMaxBin);
//lowerArray = DataTools.filterMovingAverage(lowerArray, 3);
//upperArray = DataTools.filterMovingAverage(upperArray, 3);
double[] amplitudeScores = DataTools.SumMinusDifference(lowerArray, upperArray);
double[] differenceScores = DspFilters.PreEmphasis(amplitudeScores, 1.0);
// Could smooth here rather than above. Above seemed slightly better?
amplitudeScores = DataTools.filterMovingAverage(amplitudeScores, 7);
differenceScores = DataTools.filterMovingAverage(differenceScores, 7);
//iii: CONVERT decibel sum-diff SCORES TO ACOUSTIC EVENTS
var predictedEvents = AcousticEvent.ConvertScoreArray2Events(
amplitudeScores,
lbConfig.LowerBandMinHz,
lbConfig.UpperBandMaxHz,
sonogram.FramesPerSecond,
freqBinWidth,
decibelThreshold,
lbConfig.MinDuration,
lbConfig.MaxDuration,
segmentStartOffset);
for (int i = 0; i < differenceScores.Length; i++)
{
if (differenceScores[i] < 1.0)
{
differenceScores[i] = 0.0;
}
}
// init the score array
double[] scores = new double[rowCount];
//iii: CONVERT SCORES TO ACOUSTIC EVENTS
// var hits = new double[rowCount, colCount];
double[,] hits = null;
// init confirmed events
var confirmedEvents = new List <AcousticEvent>();
// add names into the returned events
foreach (var ae in predictedEvents)
{
//rowtop, rowWidth
int eventStart = ae.Oblong.RowTop;
int eventWidth = ae.Oblong.RowWidth;
int step = 2;
double maximumIntensity = 0.0;
// scan the event to get oscillation period and intensity
for (int i = eventStart - (dctLength / 2); i < eventStart + eventWidth - (dctLength / 2); i += step)
{
// Look for oscillations in the difference array
double[] differenceArray = DataTools.Subarray(differenceScores, i, dctLength);
Oscillations2014.GetOscillationUsingDct(differenceArray, framesPerSecond, cosines, out var oscilFreq, out var period, out var intensity);
bool periodWithinBounds = period > lbConfig.MinPeriod && period < lbConfig.MaxPeriod;
//Console.WriteLine($"step={i} period={period:f4}");
if (!periodWithinBounds)
{
continue;
}
// lay down score for sample length
for (int j = 0; j < dctLength; j++)
{
if (scores[i + j] < intensity)
{
scores[i + j] = intensity;
}
}
if (maximumIntensity < intensity)
{
maximumIntensity = intensity;
}
}
// add abbreviatedSpeciesName into event
if (maximumIntensity >= intensityThreshold)
{
ae.Name = "L.b";
ae.Score_MaxInEvent = maximumIntensity;
confirmedEvents.Add(ae);
}
}
//######################################################################
// calculate the cosine similarity scores
var scorePlot = new Plot(lbConfig.SpeciesName, scores, intensityThreshold);
//DEBUG IMAGE this recognizer only. MUST set false for deployment.
Image debugImage = null;
if (drawDebugImage)
{
// display a variety of debug score arrays
//DataTools.Normalise(scores, eventDecibelThreshold, out normalisedScores, out normalisedThreshold);
//var debugPlot = new Plot("Score", normalisedScores, normalisedThreshold);
//DataTools.Normalise(upperArray, eventDecibelThreshold, out normalisedScores, out normalisedThreshold);
//var upperPlot = new Plot("Upper", normalisedScores, normalisedThreshold);
//DataTools.Normalise(lowerArray, eventDecibelThreshold, out normalisedScores, out normalisedThreshold);
//var lowerPlot = new Plot("Lower", normalisedScores, normalisedThreshold);
DataTools.Normalise(amplitudeScores, decibelThreshold, out var normalisedScores, out var normalisedThreshold);
var sumDiffPlot = new Plot("SumMinusDifference", normalisedScores, normalisedThreshold);
DataTools.Normalise(differenceScores, 3.0, out normalisedScores, out normalisedThreshold);
var differencePlot = new Plot("Difference", normalisedScores, normalisedThreshold);
var debugPlots = new List <Plot> {
scorePlot, sumDiffPlot, differencePlot
};
// other debug plots
//var debugPlots = new List<Plot> { scorePlot, upperPlot, lowerPlot, sumDiffPlot, differencePlot };
debugImage = DisplayDebugImage(sonogram, confirmedEvents, debugPlots, hits);
}
// return new sonogram because it makes for more easy interpretation of the image
var returnSonoConfig = new SonogramConfig
{
SourceFName = recording.BaseName,
WindowSize = 512,
WindowOverlap = 0,
// 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"),
};
BaseSonogram returnSonogram = new SpectrogramStandard(returnSonoConfig, recording.WavReader);
return(Tuple.Create(returnSonogram, hits, scores, confirmedEvents, debugImage));
} //Analysis()
//Analyze()
/// <summary>
/// ################ THE KEY ANALYSIS METHOD
/// Returns a DataTable
/// </summary>
/// <param name="fiSegmentOfSourceFile"></param>
/// <param name="analysisSettings"></param>
/// <param name="originalSampleRate"></param>
/// <param name="segmentStartOffset"></param>
/// <param name="configDict"></param>
/// <param name="diOutputDir"></param>
public static Tuple <BaseSonogram, double[, ], List <Plot>, List <AcousticEvent>, TimeSpan> Analysis(FileInfo fiSegmentOfSourceFile, AnalysisSettings analysisSettings, int originalSampleRate, TimeSpan segmentStartOffset)
{
Dictionary <string, string> configDict = analysisSettings.ConfigDict;
int originalAudioNyquist = originalSampleRate / 2; // original sample rate can be anything 11.0-44.1 kHz.
//set default values - ignore those set by user
int frameSize = 32;
double windowOverlap = 0.3;
int xCorrelationLength = 256; //for Xcorrelation - 256 frames @801 = 320ms, almost 1/3 second.
//int xCorrelationLength = 128; //for Xcorrelation - 128 frames @801 = 160ms, almost 1/6 second.
//int xCorrelationLength = 64; //for Xcorrelation - 64 frames @128 = 232ms, almost 1/4 second.
//int xCorrelationLength = 16; //for Xcorrelation - 16 frames @128 = 232ms, almost 1/4 second.
double dBThreshold = 12.0;
// read frog data to datatable
var dt = CsvTools.ReadCSVToTable(configDict[key_FROG_DATA], true); // read file contining parameters of frog calls to a table
double intensityThreshold = double.Parse(configDict[AnalysisKeys.IntensityThreshold]); //in 0-1
double minDuration = double.Parse(configDict[AnalysisKeys.MinDuration]); // seconds
double maxDuration = double.Parse(configDict[AnalysisKeys.MaxDuration]); // seconds
double minPeriod = double.Parse(configDict[AnalysisKeys.MinPeriodicity]); // seconds
double maxPeriod = double.Parse(configDict[AnalysisKeys.MaxPeriodicity]); // seconds
AudioRecording recording = new AudioRecording(fiSegmentOfSourceFile.FullName);
if (recording == null)
{
LoggedConsole.WriteLine("AudioRecording == null. Analysis not possible.");
return(null);
}
//i: MAKE SONOGRAM
SonogramConfig sonoConfig = new SonogramConfig(); //default values config
sonoConfig.SourceFName = recording.BaseName;
sonoConfig.WindowSize = frameSize;
sonoConfig.WindowOverlap = windowOverlap;
//sonoConfig.NoiseReductionType = SNR.Key2NoiseReductionType("NONE");
sonoConfig.NoiseReductionType = SNR.KeyToNoiseReductionType("STANDARD"); //must do noise removal
TimeSpan tsRecordingtDuration = recording.Duration;
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
double frameOffset = sonoConfig.GetFrameOffset(sr);
double framesPerSecond = 1 / frameOffset;
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
//iii: GET TRACKS
int nhLimit = 3; //limit of neighbourhood around maximum
var peaks = DataTools.GetPeakValues(sonogram.DecibelsPerFrame);
var tuple = SpectralTrack.GetSpectralMaxima(sonogram.DecibelsPerFrame, sonogram.Data, dBThreshold, nhLimit);
var maxFreqArray = tuple.Item1; //array (one element per frame) indicating which freq bin has max amplitude.
var hitsMatrix = tuple.Item2;
int herzOffset = 0;
int maxFreq = 6000;
var tracks = SpectralTrack.GetSpectralTracks(maxFreqArray, framesPerSecond, freqBinWidth, herzOffset, SpectralTrack.MIN_TRACK_DURATION, SpectralTrack.MAX_INTRASYLLABLE_GAP, maxFreq);
double severity = 0.5;
double dynamicRange = 60; // deciBels above background noise. BG noise has already been removed from each bin.
// convert sonogram to a list of frequency bin arrays
var listOfFrequencyBins = SpectrogramTools.Sonogram2ListOfFreqBinArrays(sonogram, dynamicRange);
int minFrameLength = SpectralTrack.FrameCountEquivalent(SpectralTrack.MIN_TRACK_DURATION, framesPerSecond);
for (int i = tracks.Count - 1; i >= 0; i--)
{
tracks[i].CropTrack(listOfFrequencyBins, severity);
if (tracks[i].Length < minFrameLength)
{
tracks.Remove(tracks[i]);
}
} // foreach track
foreach (SpectralTrack track in tracks) // find any periodicity in the track and calculate its score.
{
SpectralTrack.DetectTrackPeriodicity(track, xCorrelationLength, listOfFrequencyBins, sonogram.FramesPerSecond);
} // foreach track
int rowCount = sonogram.Data.GetLength(0);
int MAX_FREQ_BOUND = 6000;
int topBin = (int)Math.Round(MAX_FREQ_BOUND / freqBinWidth);
var plots = CreateScorePlots(tracks, rowCount, topBin);
//iv: CONVERT TRACKS TO ACOUSTIC EVENTS
List <AcousticEvent> frogEvents = SpectralTrack.ConvertTracks2Events(tracks, segmentStartOffset);
// v: GET FROG IDs
//var frogEvents = new List<AcousticEvent>();
foreach (AcousticEvent ae in frogEvents)
{
double oscRate = 1 / ae.Periodicity;
// ae.DominantFreq
// ae.Score
// ae.Duration
//ClassifyFrogEvent(ae);
string[] names = ClassifyFrogEvent(ae.DominantFreq, oscRate, dt);
ae.Name = names[0];
ae.Name2 = names[1];
}
return(Tuple.Create(sonogram, hitsMatrix, plots, frogEvents, tsRecordingtDuration));
} //Analysis()
} //Analyze()
/// <summary>
/// ################ THE KEY ANALYSIS METHOD
/// Returns a DataTable
/// </summary>
/// <param name="fiSegmentOfSourceFile"></param>
/// <param name="configDict"></param>
/// <param name="segmentStartOffset"></param>
/// <param name="diOutputDir"></param>
public static Tuple <BaseSonogram, double[, ], double[], List <AcousticEvent>, TimeSpan> Analysis(FileInfo fiSegmentOfSourceFile, Dictionary <string, string> configDict, TimeSpan segmentStartOffset)
{
//set default values - ignor those set by user
int frameSize = 1024;
double windowOverlap = 0.0;
int upperBandMinHz = int.Parse(configDict[KeyUpperfreqbandBtm]);
int upperBandMaxHz = int.Parse(configDict[KeyUpperfreqbandTop]);
int lowerBandMinHz = int.Parse(configDict[KeyLowerfreqbandBtm]);
int lowerBandMaxHz = int.Parse(configDict[KeyLowerfreqbandTop]);
double decibelThreshold = double.Parse(configDict[KeyDecibelThreshold]);; //dB
double intensityThreshold = double.Parse(configDict[KeyIntensityThreshold]); //in 0-1
double minDuration = double.Parse(configDict[KeyMinDuration]); // seconds
double maxDuration = double.Parse(configDict[KeyMaxDuration]); // seconds
double minPeriod = double.Parse(configDict[KeyMinPeriod]); // seconds
double maxPeriod = double.Parse(configDict[KeyMaxPeriod]); // seconds
AudioRecording recording = new AudioRecording(fiSegmentOfSourceFile.FullName);
if (recording == null)
{
LoggedConsole.WriteLine("AudioRecording == null. Analysis not possible.");
return(null);
}
//i: MAKE SONOGRAM
SonogramConfig sonoConfig = new SonogramConfig(); //default values config
sonoConfig.SourceFName = recording.BaseName;
sonoConfig.WindowSize = frameSize;
sonoConfig.WindowOverlap = windowOverlap;
//sonoConfig.NoiseReductionType = SNR.Key2NoiseReductionType("NONE");
sonoConfig.NoiseReductionType = SNR.KeyToNoiseReductionType("STANDARD");
TimeSpan tsRecordingtDuration = recording.Duration;
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
double framesPerSecond = freqBinWidth;
//#############################################################################################################################################
//window sr frameDuration frames/sec hz/bin 64frameDuration hz/64bins hz/128bins
// 1024 22050 46.4ms 21.5 21.5 2944ms 1376hz 2752hz
// 1024 17640 58.0ms 17.2 17.2 3715ms 1100hz 2200hz
// 2048 17640 116.1ms 8.6 8.6 7430ms 551hz 1100hz
//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;
}
for (int j = 0; j < sampleLength; j++) //lay down score for sample length
{
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);
List <AcousticEvent> predictedEvents = AcousticEvent.ConvertScoreArray2Events(
intensity,
lowerBandMinHz,
upperBandMaxHz,
sonogram.FramesPerSecond,
freqBinWidth,
intensityThreshold,
minDuration,
maxDuration,
segmentStartOffset);
CropEvents(predictedEvents, upperArray);
var hits = new double[rowCount, colCount];
return(Tuple.Create(sonogram, hits, intensity, predictedEvents, tsRecordingtDuration));
} //Analysis()
/// <summary>
/// THIS IS THE CORE DETECTION METHOD
/// Detects the human voice
/// </summary>
public static Tuple <BaseSonogram, double[, ], Plot, List <AcousticEvent>, TimeSpan> Analysis(FileInfo fiSegmentOfSourceFile, Dictionary <string, string> configDict, TimeSpan segmentStartOffset)
{
//set default values
int frameLength = 1024;
if (configDict.ContainsKey(AnalysisKeys.FrameLength))
{
frameLength = int.Parse(configDict[AnalysisKeys.FrameLength]);
}
double windowOverlap = 0.0;
int minHz = int.Parse(configDict["MIN_HZ"]);
int minFormantgap = int.Parse(configDict["MIN_FORMANT_GAP"]);
int maxFormantgap = int.Parse(configDict["MAX_FORMANT_GAP"]);
double intensityThreshold = double.Parse(configDict["INTENSITY_THRESHOLD"]); //in 0-1
double minDuration = double.Parse(configDict["MIN_DURATION"]); // seconds
double maxDuration = double.Parse(configDict["MAX_DURATION"]); // seconds
AudioRecording recording = new AudioRecording(fiSegmentOfSourceFile.FullName);
//i: MAKE SONOGRAM
SonogramConfig sonoConfig = new SonogramConfig
{
//default values config
SourceFName = recording.BaseName,
WindowSize = frameLength,
WindowOverlap = windowOverlap,
NoiseReductionType = SNR.KeyToNoiseReductionType("STANDARD"),
};
var tsRecordingtDuration = recording.Duration;
int sr = recording.SampleRate;
double freqBinWidth = sr / (double)sonoConfig.WindowSize;
//#############################################################################################################################################
//window sr frameDuration frames/sec hz/bin 64frameDuration hz/64bins hz/128bins
// 1024 22050 46.4ms 21.5 21.5 2944ms 1376hz 2752hz
// 1024 17640 58.0ms 17.2 17.2 3715ms 1100hz 2200hz
// 2048 17640 116.1ms 8.6 8.6 7430ms 551hz 1100hz
//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 numberOfBins = 64;
int minBin = (int)Math.Round(minHz / freqBinWidth) + 1;
int maxbin = minBin + numberOfBins - 1;
int maxHz = (int)Math.Round(minHz + (numberOfBins * freqBinWidth));
BaseSonogram sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
int rowCount = sonogram.Data.GetLength(0);
int colCount = sonogram.Data.GetLength(1);
double[,] subMatrix = MatrixTools.Submatrix(sonogram.Data, 0, minBin, rowCount - 1, maxbin);
//ii: DETECT HARMONICS
int zeroBinCount = 4; //to remove low freq content which dominates the spectrum
var results = CrossCorrelation.DetectBarsInTheRowsOfaMatrix(subMatrix, intensityThreshold, zeroBinCount);
double[] intensity = results.Item1;
double[] periodicity = results.Item2; //an array of periodicity scores
//intensity = DataTools.filterMovingAverage(intensity, 3);
//expect humans to have max power >100 and < 1000 Hz. Set these bounds
int lowerHumanMaxBound = (int)(100 / freqBinWidth); //ignore 0-100 hz - too much noise
int upperHumanMaxBound = (int)(3000 / freqBinWidth); //ignore above 2500 hz
double[] scoreArray = new double[intensity.Length];
for (int r = 0; r < rowCount; r++)
{
if (intensity[r] < intensityThreshold)
{
continue;
}
//ignore locations with incorrect formant gap
double herzPeriod = periodicity[r] * freqBinWidth;
if (herzPeriod < minFormantgap || herzPeriod > maxFormantgap)
{
continue;
}
//find freq having max power and use info to adjust score.
double[] spectrum = MatrixTools.GetRow(sonogram.Data, r);
for (int j = 0; j < lowerHumanMaxBound; j++)
{
spectrum[j] = 0.0;
}
for (int j = upperHumanMaxBound; j < spectrum.Length; j++)
{
spectrum[j] = 0.0;
}
double[] peakvalues = DataTools.GetPeakValues(spectrum);
int maxIndex1 = DataTools.GetMaxIndex(peakvalues);
peakvalues[maxIndex1] = 0.0;
int maxIndex2 = DataTools.GetMaxIndex(peakvalues);
int avMaxBin = (maxIndex1 + maxIndex2) / 2;
//int freqWithMaxPower = (int)Math.Round(maxIndex * freqBinWidth);
int freqWithMaxPower = (int)Math.Round(avMaxBin * freqBinWidth);
double discount = 1.0;
if (freqWithMaxPower > 1000)
{
discount = 0.0;
}
else
if (freqWithMaxPower < 500)
{
discount = 0.0;
}
//set scoreArray[r] - ignore locations with low intensity
if (intensity[r] > intensityThreshold)
{
scoreArray[r] = intensity[r] * discount;
}
}
//transfer info to a hits matrix.
var hits = new double[rowCount, colCount];
double threshold = intensityThreshold * 0.75; //reduced threshold for display of hits
for (int r = 0; r < rowCount; r++)
{
if (scoreArray[r] < threshold)
{
continue;
}
double herzPeriod = periodicity[r] * freqBinWidth;
for (int c = minBin; c < maxbin; c++)
{
//hits[r, c] = herzPeriod / (double)380; //divide by 380 to get a relativePeriod;
hits[r, c] = (herzPeriod - minFormantgap) / maxFormantgap; //to get a relativePeriod;
}
}
//iii: CONVERT TO ACOUSTIC EVENTS
List <AcousticEvent> predictedEvents = AcousticEvent.ConvertScoreArray2Events(
scoreArray,
minHz,
maxHz,
sonogram.FramesPerSecond,
freqBinWidth,
intensityThreshold,
minDuration,
maxDuration,
segmentStartOffset);
//remove isolated speech events - expect humans to talk like politicians
//predictedEvents = Human2.FilterHumanSpeechEvents(predictedEvents);
Plot plot = new Plot(AnalysisName, intensity, intensityThreshold);
return(Tuple.Create(sonogram, hits, plot, predictedEvents, tsRecordingtDuration));
} //Analysis()
