public static AudioToSonogramResult AnalyseOneRecording(
FileInfo sourceRecording,
Dictionary <string, string> configDict,
TimeSpan localEventStart,
TimeSpan localEventEnd,
int minHz,
int maxHz,
DirectoryInfo outDirectory)
{
// set a threshold for determining energy distribution in call
// NOTE: value of this threshold depends on whether working with decibel, energy or amplitude values
const double threshold = 9.0;
int resampleRate = AppConfigHelper.DefaultTargetSampleRate;
if (configDict.ContainsKey(AnalysisKeys.ResampleRate))
{
resampleRate = int.Parse(configDict[AnalysisKeys.ResampleRate]);
}
configDict[ConfigKeys.Recording.Key_RecordingCallName] = sourceRecording.FullName;
configDict[ConfigKeys.Recording.Key_RecordingFileName] = sourceRecording.Name;
// 1: GET RECORDING and make temporary copy
// put temp audio FileSegment in same directory as the required output image.
var tempAudioSegment = TempFileHelper.NewTempFile(outDirectory, "wav");
// delete the temp audio file if it already exists.
if (File.Exists(tempAudioSegment.FullName))
{
File.Delete(tempAudioSegment.FullName);
}
// This line creates a temporary version of the source file downsampled as per entry in the config file
MasterAudioUtility.SegmentToWav(sourceRecording, tempAudioSegment, new AudioUtilityRequest()
{
TargetSampleRate = resampleRate
});
// 2: Generate sonogram image files
AudioToSonogramResult result = GenerateSpectrogramImages(tempAudioSegment, configDict, outDirectory);
// 3: GET the SNR statistics
TimeSpan eventDuration = localEventEnd - localEventStart;
result.SnrStatistics = SNR.Calculate_SNR_ShortRecording(tempAudioSegment, configDict, localEventStart, eventDuration, minHz, maxHz, threshold);
// 4: Delete the temp file
File.Delete(tempAudioSegment.FullName);
return(result);
}
public static AudioToSonogramResult GenerateFourSpectrogramImages(
FileInfo sourceRecording,
FileInfo path2SoxSpectrogram,
Dictionary <string, string> configDict,
bool dataOnly = false,
bool makeSoxSonogram = false)
{
var result = new AudioToSonogramResult();
if (dataOnly && makeSoxSonogram)
{
throw new ArgumentException("Can't produce data only for a SoX sonogram");
}
if (makeSoxSonogram)
{
SpectrogramTools.MakeSonogramWithSox(sourceRecording, configDict, path2SoxSpectrogram);
result.Path2SoxImage = path2SoxSpectrogram;
}
else if (dataOnly)
{
var recordingSegment = new AudioRecording(sourceRecording.FullName);
var sonoConfig = new SonogramConfig(configDict); // default values config
// disable noise removal
sonoConfig.NoiseReductionType = NoiseReductionType.None;
Log.Warn("Noise removal disabled!");
var sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
result.DecibelSpectrogram = sonogram;
}
else
{
// init the image stack
var list = new List <Image>();
// IMAGE 1) draw amplitude spectrogram
var recordingSegment = new AudioRecording(sourceRecording.FullName);
var sonoConfig = new SonogramConfig(configDict); // default values config
// disable noise removal for first two spectrograms
var disabledNoiseReductionType = sonoConfig.NoiseReductionType;
sonoConfig.NoiseReductionType = NoiseReductionType.None;
BaseSonogram sonogram = new AmplitudeSonogram(sonoConfig, recordingSegment.WavReader);
// remove the DC bin if it has not already been removed.
// Assume test of divisible by 2 is good enough.
int binCount = sonogram.Data.GetLength(1);
if (!binCount.IsEven())
{
sonogram.Data = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.FrameCount - 1, binCount - 1);
}
//save spectrogram data at this point - prior to noise reduction
var spectrogramDataBeforeNoiseReduction = sonogram.Data;
const double neighbourhoodSeconds = 0.25;
int neighbourhoodFrames = (int)(sonogram.FramesPerSecond * neighbourhoodSeconds);
const double lcnContrastLevel = 0.001;
LoggedConsole.WriteLine("LCN: FramesPerSecond (Prior to LCN) = {0}", sonogram.FramesPerSecond);
LoggedConsole.WriteLine("LCN: Neighbourhood of {0} seconds = {1} frames", neighbourhoodSeconds, neighbourhoodFrames);
const int lowPercentile = 20;
sonogram.Data = NoiseRemoval_Briggs.NoiseReduction_byLowestPercentileSubtraction(sonogram.Data, lowPercentile);
sonogram.Data = NoiseRemoval_Briggs.NoiseReduction_byLCNDivision(sonogram.Data, neighbourhoodFrames, lcnContrastLevel);
//sonogram.Data = NoiseRemoval_Briggs.NoiseReduction_byLowestPercentileSubtraction(sonogram.Data, lowPercentile);
var image = sonogram.GetImageFullyAnnotated("AMPLITUDE SPECTROGRAM + Bin LCN (Local Contrast Normalisation)");
list.Add(image);
//string path2 = @"C:\SensorNetworks\Output\Sonograms\dataInput2.png";
//Histogram.DrawDistributionsAndSaveImage(sonogram.Data, path2);
// double[,] matrix = sonogram.Data;
double[,] matrix = ImageTools.WienerFilter(sonogram.Data, 3);
double ridgeThreshold = 0.25;
byte[,] hits = RidgeDetection.Sobel5X5RidgeDetectionExperiment(matrix, ridgeThreshold);
hits = RidgeDetection.JoinDisconnectedRidgesInMatrix(hits, matrix, ridgeThreshold);
image = SpectrogramTools.CreateFalseColourAmplitudeSpectrogram(spectrogramDataBeforeNoiseReduction, null, hits);
image = sonogram.GetImageAnnotatedWithLinearHerzScale(image, "AMPLITUDE SPECTROGRAM + LCN + ridge detection");
list.Add(image);
Image envelopeImage = ImageTrack.DrawWaveEnvelopeTrack(recordingSegment, image.Width);
list.Add(envelopeImage);
// IMAGE 2) now draw the standard decibel spectrogram
sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
result.DecibelSpectrogram = (SpectrogramStandard)sonogram;
image = sonogram.GetImageFullyAnnotated("DECIBEL SPECTROGRAM");
list.Add(image);
Image segmentationImage = ImageTrack.DrawSegmentationTrack(
sonogram,
EndpointDetectionConfiguration.K1Threshold,
EndpointDetectionConfiguration.K2Threshold,
image.Width);
list.Add(segmentationImage);
// keep the sonogram data for later use
double[,] dbSpectrogramData = (double[, ])sonogram.Data.Clone();
// 3) now draw the noise reduced decibel spectrogram
// #NOISE REDUCTION PARAMETERS - restore noise reduction ##################################################################
sonoConfig.NoiseReductionType = disabledNoiseReductionType;
sonoConfig.NoiseReductionParameter = double.Parse(configDict[AnalysisKeys.NoiseBgThreshold] ?? "2.0");
// #NOISE REDUCTION PARAMETERS - MARINE HACK ##################################################################
//sonoConfig.NoiseReductionType = NoiseReductionType.FIXED_DYNAMIC_RANGE;
//sonoConfig.NoiseReductionParameter = 80.0;
sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
image = sonogram.GetImageFullyAnnotated("DECIBEL SPECTROGRAM + Lamel noise subtraction");
list.Add(image);
// keep the sonogram data for later use
double[,] nrSpectrogramData = sonogram.Data;
// 4) A FALSE-COLOUR VERSION OF SPECTROGRAM
// ########################### SOBEL ridge detection
ridgeThreshold = 3.5;
matrix = ImageTools.WienerFilter(dbSpectrogramData, 3);
hits = RidgeDetection.Sobel5X5RidgeDetectionExperiment(matrix, ridgeThreshold);
// ########################### EIGEN ridge detection
//double ridgeThreshold = 6.0;
//double dominanceThreshold = 0.7;
//var rotatedData = MatrixTools.MatrixRotate90Anticlockwise(dbSpectrogramData);
//byte[,] hits = RidgeDetection.StructureTensorRidgeDetection(rotatedData, ridgeThreshold, dominanceThreshold);
//hits = MatrixTools.MatrixRotate90Clockwise(hits);
// ########################### EIGEN ridge detection
image = SpectrogramTools.CreateFalseColourDecibelSpectrogram(dbSpectrogramData, nrSpectrogramData, hits);
image = sonogram.GetImageAnnotatedWithLinearHerzScale(image, "DECIBEL SPECTROGRAM - Colour annotated");
list.Add(image);
// 5) TODO: ONE OF THESE YEARS FIX UP THE CEPTRAL SONOGRAM
////SpectrogramCepstral cepgram = new SpectrogramCepstral((AmplitudeSonogram)amplitudeSpg);
////var mti3 = SpectrogramTools.Sonogram2MultiTrackImage(sonogram, configDict);
////var image3 = mti3.GetImage();
////image3.Save(fiImage.FullName + "3", ImageFormat.Png);
// 6) COMBINE THE SPECTROGRAM IMAGES
result.CompositeImage = ImageTools.CombineImagesVertically(list);
}
return(result);
}
/// <summary>
/// Calculates the following spectrograms as per content of config.yml file:
/// Waveform: true.
/// DifferenceSpectrogram: true.
/// DecibelSpectrogram: true.
/// DecibelSpectrogram_NoiseReduced: true.
/// DecibelSpectrogram_Ridges: true.
/// AmplitudeSpectrogram_LocalContrastNormalization: true.
/// SoxSpectrogram: false.
/// Experimental: true.
/// </summary>
/// <param name="sourceRecording">The name of the original recording.</param>
/// <param name="configInfo">Contains parameter info to make spectrograms.</param>
/// <param name="sourceRecordingName">.Name of source recording. Required only spectrogram labels.</param>
public static AudioToSonogramResult GenerateSpectrogramImages(
FileInfo sourceRecording,
AnalyzerConfig configInfo,
string sourceRecordingName)
{
//int signalLength = recordingSegment.WavReader.GetChannel(0).Length;
var recordingSegment = new AudioRecording(sourceRecording.FullName);
int sampleRate = recordingSegment.WavReader.SampleRate;
var result = new AudioToSonogramResult();
// init the image stack
var list = new List <Image>();
bool doWaveForm = configInfo.GetBoolOrNull("Waveform") ?? false;
bool doDecibelSpectrogram = configInfo.GetBoolOrNull("DecibelSpectrogram") ?? false;
bool doNoiseReducedSpectrogram = configInfo.GetBoolOrNull("DecibelSpectrogram_NoiseReduced") ?? true;
bool doDifferenceSpectrogram = configInfo.GetBoolOrNull("DifferenceSpectrogram") ?? false;
bool doLcnSpectrogram = configInfo.GetBoolOrNull("AmplitudeSpectrogram_LocalContrastNormalization") ?? false;
bool doCepstralSpectrogram = configInfo.GetBoolOrNull("CepstralSpectrogram") ?? false;
bool doExperimentalSpectrogram = configInfo.GetBoolOrNull("Experimental") ?? false;
//Don't do SOX spectrogram.
//bool doSoxSpectrogram = configInfo.GetBool("SoxSpectrogram");
int frameSize = configInfo.GetIntOrNull("FrameLength") ?? 512;
int frameStep = configInfo.GetIntOrNull("FrameStep") ?? 0;
// must calculate this because used later on.
double frameOverlap = (frameSize - frameStep) / (double)frameSize;
// Default noiseReductionType = Standard
var bgNoiseThreshold = configInfo.GetDoubleOrNull("BgNoiseThreshold") ?? 3.0;
// EXTRACT ENVELOPE and SPECTROGRAM FROM RECORDING SEGMENT
var dspOutput1 = DSP_Frames.ExtractEnvelopeAndFfts(recordingSegment, frameSize, frameStep);
var sonoConfig = new SonogramConfig()
{
epsilon = recordingSegment.Epsilon,
SampleRate = sampleRate,
WindowSize = frameSize,
WindowStep = frameStep,
WindowOverlap = frameOverlap,
WindowPower = dspOutput1.WindowPower,
Duration = recordingSegment.Duration,
NoiseReductionType = NoiseReductionType.Standard,
NoiseReductionParameter = bgNoiseThreshold,
};
// IMAGE 1) draw the WAVEFORM
if (doWaveForm)
{
var minValues = dspOutput1.MinFrameValues;
var maxValues = dspOutput1.MaxFrameValues;
int height = configInfo.GetIntOrNull("WaveformHeight") ?? 180;
var waveformImage = GetWaveformImage(minValues, maxValues, height);
// add in the title bar and time scales.
string title = $"WAVEFORM - {sourceRecordingName} (min value={dspOutput1.MinSignalValue:f3}, max value={dspOutput1.MaxSignalValue:f3})";
var titleBar = BaseSonogram.DrawTitleBarOfGrayScaleSpectrogram(title, waveformImage.Width);
var startTime = TimeSpan.Zero;
var xAxisTicInterval = TimeSpan.FromSeconds(1);
TimeSpan xAxisPixelDuration = TimeSpan.FromSeconds(frameStep / (double)sampleRate);
var labelInterval = TimeSpan.FromSeconds(5);
waveformImage = BaseSonogram.FrameSonogram(waveformImage, titleBar, startTime, xAxisTicInterval, xAxisPixelDuration, labelInterval);
list.Add(waveformImage);
}
// Draw various decibel spectrograms
if (doDecibelSpectrogram || doNoiseReducedSpectrogram || doDifferenceSpectrogram || doExperimentalSpectrogram)
{
// disable noise removal for first spectrogram
var disabledNoiseReductionType = sonoConfig.NoiseReductionType;
sonoConfig.NoiseReductionType = NoiseReductionType.None;
//Get the decibel spectrogram
var decibelSpectrogram = new SpectrogramStandard(sonoConfig, dspOutput1.AmplitudeSpectrogram);
result.DecibelSpectrogram = decibelSpectrogram;
double[,] dbSpectrogramData = (double[, ])decibelSpectrogram.Data.Clone();
// IMAGE 2) DecibelSpectrogram
if (doDecibelSpectrogram)
{
var image3 = decibelSpectrogram.GetImageFullyAnnotated($"DECIBEL SPECTROGRAM ({sourceRecordingName})");
list.Add(image3);
}
if (doNoiseReducedSpectrogram || doExperimentalSpectrogram || doDifferenceSpectrogram)
{
sonoConfig.NoiseReductionType = disabledNoiseReductionType;
sonoConfig.NoiseReductionParameter = bgNoiseThreshold;
double[] spectralDecibelBgn = NoiseProfile.CalculateBackgroundNoise(decibelSpectrogram.Data);
decibelSpectrogram.Data = SNR.TruncateBgNoiseFromSpectrogram(decibelSpectrogram.Data, spectralDecibelBgn);
decibelSpectrogram.Data = SNR.RemoveNeighbourhoodBackgroundNoise(decibelSpectrogram.Data, nhThreshold: bgNoiseThreshold);
// IMAGE 3) DecibelSpectrogram - noise reduced
if (doNoiseReducedSpectrogram)
{
var image4 = decibelSpectrogram.GetImageFullyAnnotated($"DECIBEL SPECTROGRAM + Lamel noise subtraction. ({sourceRecordingName})");
list.Add(image4);
}
// IMAGE 4) EXPERIMENTAL Spectrogram
if (doExperimentalSpectrogram)
{
sonoConfig.NoiseReductionType = disabledNoiseReductionType;
var image5 = GetDecibelSpectrogram_Ridges(dbSpectrogramData, decibelSpectrogram, sourceRecordingName);
list.Add(image5);
}
// IMAGE 5) draw difference spectrogram
if (doDifferenceSpectrogram)
{
var differenceThreshold = configInfo.GetDoubleOrNull("DifferenceThreshold") ?? 3.0;
var image6 = GetDifferenceSpectrogram(dbSpectrogramData, differenceThreshold);
image6 = BaseSonogram.GetImageAnnotatedWithLinearHertzScale(image6, sampleRate, frameStep, $"DECIBEL DIFFERENCE SPECTROGRAM ({sourceRecordingName})");
list.Add(image6);
}
}
}
// IMAGE 6) Cepstral Spectrogram
if (doCepstralSpectrogram)
{
var image6 = GetCepstralSpectrogram(sonoConfig, recordingSegment, sourceRecordingName);
list.Add(image6);
}
// 7) AmplitudeSpectrogram_LocalContrastNormalization
if (doLcnSpectrogram)
{
var neighbourhoodSeconds = configInfo.GetDoubleOrNull("NeighbourhoodSeconds") ?? 0.5;
var lcnContrastParameter = configInfo.GetDoubleOrNull("LcnContrastLevel") ?? 0.4;
var image8 = GetLcnSpectrogram(sonoConfig, recordingSegment, sourceRecordingName, neighbourhoodSeconds, lcnContrastParameter);
list.Add(image8);
}
// 8) SOX SPECTROGRAM
//if (doSoxSpectrogram)
//{
//Log.Warn("SoX spectrogram set to true but is ignored when running as an IAnalyzer");
// The following parameters were once used to implement a sox spectrogram.
//bool makeSoxSonogram = configuration.GetBoolOrNull(AnalysisKeys.MakeSoxSonogram) ?? false;
//configDict[AnalysisKeys.SonogramTitle] = configuration[AnalysisKeys.SonogramTitle] ?? "Sonogram";
//configDict[AnalysisKeys.SonogramComment] = configuration[AnalysisKeys.SonogramComment] ?? "Sonogram produced using SOX";
//configDict[AnalysisKeys.SonogramColored] = configuration[AnalysisKeys.SonogramColored] ?? "false";
//configDict[AnalysisKeys.SonogramQuantisation] = configuration[AnalysisKeys.SonogramQuantisation] ?? "128";
//configDict[AnalysisKeys.AddTimeScale] = configuration[AnalysisKeys.AddTimeScale] ?? "true";
//configDict[AnalysisKeys.AddAxes] = configuration[AnalysisKeys.AddAxes] ?? "true";
//configDict[AnalysisKeys.AddSegmentationTrack] = configuration[AnalysisKeys.AddSegmentationTrack] ?? "true";
// var soxFile = new FileInfo(Path.Combine(output.FullName, sourceName + "SOX.png"));
// SpectrogramTools.MakeSonogramWithSox(sourceRecording, configDict, path2SoxSpectrogram);
// list.Add(image7);
//}
// COMBINE THE SPECTROGRAM IMAGES
result.CompositeImage = ImageTools.CombineImagesVertically(list);
return(result);
}
public static AudioToSonogramResult GenerateSpectrogramImages(FileInfo sourceRecording, Dictionary <string, string> configDict, DirectoryInfo outputDirectory)
{
// the source name was set up in the Analyse() method. But it could also be obtained directly from recording.
string sourceName = configDict[ConfigKeys.Recording.Key_RecordingFileName];
sourceName = Path.GetFileNameWithoutExtension(sourceName);
var result = new AudioToSonogramResult();
// init the image stack
var list = new List <Image>();
// 1) draw amplitude spectrogram
var recordingSegment = new AudioRecording(sourceRecording.FullName);
// default values config except disable noise removal for first two spectrograms
SonogramConfig sonoConfig = new SonogramConfig(configDict)
{
NoiseReductionType = NoiseReductionType.None
};
BaseSonogram sonogram = new AmplitudeSonogram(sonoConfig, recordingSegment.WavReader);
// remove the DC bin
sonogram.Data = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.FrameCount - 1, sonogram.Configuration.FreqBinCount);
// save spectrogram data at this point - prior to noise reduction
double[,] spectrogramDataBeforeNoiseReduction = sonogram.Data;
const int lowPercentile = 20;
const double neighbourhoodSeconds = 0.25;
int neighbourhoodFrames = (int)(sonogram.FramesPerSecond * neighbourhoodSeconds);
const double lcnContrastLevel = 0.25;
////LoggedConsole.WriteLine("LCN: FramesPerSecond (Prior to LCN) = {0}", sonogram.FramesPerSecond);
////LoggedConsole.WriteLine("LCN: Neighbourhood of {0} seconds = {1} frames", neighbourhoodSeconds, neighbourhoodFrames);
sonogram.Data = NoiseRemoval_Briggs.NoiseReduction_ShortRecordings_SubtractAndLCN(sonogram.Data, lowPercentile, neighbourhoodFrames, lcnContrastLevel);
// draw amplitude spectrogram unannotated
FileInfo outputImage1 = new FileInfo(Path.Combine(outputDirectory.FullName, sourceName + ".amplitd.bmp"));
ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(sonogram.Data), outputImage1.FullName);
// draw amplitude spectrogram annotated
var image = sonogram.GetImageFullyAnnotated("AMPLITUDE SPECTROGRAM + Bin LCN (Local Contrast Normalisation)");
list.Add(image);
////string path2 = @"C:\SensorNetworks\Output\Sonograms\dataInput2.png";
////Histogram.DrawDistributionsAndSaveImage(sonogram.Data, path2);
// 2) A FALSE-COLOUR VERSION OF AMPLITUDE SPECTROGRAM
double ridgeThreshold = 0.20;
double[,] matrix = ImageTools.WienerFilter(sonogram.Data, 3);
byte[,] hits = RidgeDetection.Sobel5X5RidgeDetectionExperiment(matrix, ridgeThreshold);
hits = RidgeDetection.JoinDisconnectedRidgesInMatrix(hits, matrix, ridgeThreshold);
image = SpectrogramTools.CreateFalseColourAmplitudeSpectrogram(spectrogramDataBeforeNoiseReduction, null, hits);
image = sonogram.GetImageAnnotatedWithLinearHerzScale(image, "AMPLITUDE SPECTROGRAM + LCN + ridge detection");
list.Add(image);
Image envelopeImage = ImageTrack.DrawWaveEnvelopeTrack(recordingSegment, image.Width);
list.Add(envelopeImage);
// 3) now draw the standard decibel spectrogram
sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
// remove the DC bin
sonogram.Data = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.FrameCount - 1, sonogram.Configuration.FreqBinCount);
// draw decibel spectrogram unannotated
FileInfo outputImage2 = new FileInfo(Path.Combine(outputDirectory.FullName, sourceName + ".deciBel.bmp"));
ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(sonogram.Data), outputImage2.FullName);
image = sonogram.GetImageFullyAnnotated("DECIBEL SPECTROGRAM");
list.Add(image);
Image segmentationImage = ImageTrack.DrawSegmentationTrack(
sonogram,
EndpointDetectionConfiguration.K1Threshold,
EndpointDetectionConfiguration.K2Threshold,
image.Width);
list.Add(segmentationImage);
// keep the sonogram data (NOT noise reduced) for later use
double[,] dbSpectrogramData = (double[, ])sonogram.Data.Clone();
// 4) now draw the noise reduced decibel spectrogram
sonoConfig.NoiseReductionType = NoiseReductionType.Standard;
sonoConfig.NoiseReductionParameter = 3;
////sonoConfig.NoiseReductionType = NoiseReductionType.SHORT_RECORDING;
////sonoConfig.NoiseReductionParameter = 50;
sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
// draw decibel spectrogram unannotated
FileInfo outputImage3 = new FileInfo(Path.Combine(outputDirectory.FullName, sourceName + ".noNoise_dB.bmp"));
ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(sonogram.Data), outputImage3.FullName);
image = sonogram.GetImageFullyAnnotated("DECIBEL SPECTROGRAM + Lamel noise subtraction");
list.Add(image);
// keep the sonogram data for later use
double[,] nrSpectrogramData = sonogram.Data;
// 5) A FALSE-COLOUR VERSION OF DECIBEL SPECTROGRAM
ridgeThreshold = 2.5;
matrix = ImageTools.WienerFilter(dbSpectrogramData, 3);
hits = RidgeDetection.Sobel5X5RidgeDetectionExperiment(matrix, ridgeThreshold);
image = SpectrogramTools.CreateFalseColourDecibelSpectrogram(dbSpectrogramData, nrSpectrogramData, hits);
image = sonogram.GetImageAnnotatedWithLinearHerzScale(image, "DECIBEL SPECTROGRAM - Colour annotated");
list.Add(image);
// 6) COMBINE THE SPECTROGRAM IMAGES
Image compositeImage = ImageTools.CombineImagesVertically(list);
FileInfo outputImage = new FileInfo(Path.Combine(outputDirectory.FullName, sourceName + ".5spectro.png"));
compositeImage.Save(outputImage.FullName, ImageFormat.Png);
result.SpectrogramFile = outputImage;
// 7) Generate the FREQUENCY x OSCILLATIONS Graphs and csv data
////bool saveData = true;
////bool saveImage = true;
////double[] oscillationsSpectrum = Oscillations2014.GenerateOscillationDataAndImages(sourceRecording, configDict, saveData, saveImage);
return(result);
}
/// <summary>
/// This method written 18-09-2014 to process Mangalam's CNN recordings.
/// Calculate the SNR statistics for each recording and then write info back to csv file
/// </summary>
public static void Main(Arguments arguments)
{
// set preprocessing parameter
bool doPreprocessing = true;
var output = arguments.Output;
if (!output.Exists)
{
output.Create();
}
Log.InfoFormat("# PRE-PROCESS SHORT AUDIO RECORDINGS FOR Convolutional DNN");
Log.InfoFormat("# DATE AND TIME: " + DateTime.Now);
Log.InfoFormat("# Input .csv file: " + arguments.Source);
Log.InfoFormat("# Configure file: " + arguments.Config);
Log.InfoFormat("# Output directry: " + arguments.Output);
// 1. set up the necessary files
FileInfo csvFileInfo = arguments.Source;
FileInfo configFile = arguments.Config.ToFileInfo();
// 2. get the config dictionary
var configDict = GetConfigurationForConvCnn(configFile);
// print out the parameters
LoggedConsole.WriteLine("\nPARAMETERS");
foreach (var kvp in configDict)
{
LoggedConsole.WriteLine("{0} = {1}", kvp.Key, kvp.Value);
}
// set up header of the output file
string outputPath = Path.Combine(output.FullName, "SNRInfoForConvDnnDataset.csv");
using (StreamWriter writer = new StreamWriter(outputPath))
{
string header = AudioToSonogramResult.GetCsvHeader();
writer.WriteLine(header);
}
// following int's are counters to monitor file availability
int lineNumber = 0;
int fileExistsCount = 0;
int fileLocationNotInCsv = 0;
int fileInCsvDoesNotExist = 0;
// keep track of species names and distribution of classes.
// following dictionaries are to monitor species numbers
var speciesCounts = new SpeciesCounts();
// read through the csv file containing info about recording locations and call bounds
try
{
var file = new FileStream(csvFileInfo.FullName, FileMode.Open);
var sr = new StreamReader(file);
// read the header and discard
string strLine;
lineNumber++;
while ((strLine = sr.ReadLine()) != null)
{
lineNumber++;
if (lineNumber % 5000 == 0)
{
Console.WriteLine(lineNumber);
}
// cannot use next line because reads the entire file
////var data = Csv.ReadFromCsv<string[]>(csvFileInfo).ToList();
// read single record from csv file
var record = CsvDataRecord.ReadLine(strLine);
if (record.Path == null)
{
fileLocationNotInCsv++;
////string warning = String.Format("######### WARNING: line {0} NULL PATH FIELD >>>null<<<", count);
////LoggedConsole.WriteWarnLine(warning);
continue;
}
var sourceRecording = record.Path;
var sourceDirectory = sourceRecording.Directory;
string parentDirectoryName = sourceDirectory.Parent.Name;
//var imageOpDir = new DirectoryInfo(output.FullName + @"\" + parentDirectoryName);
////DirectoryInfo imageOpDir = new DirectoryInfo(outDirectory.FullName + @"\" + parentDirectoryName + @"\" + directoryName);
////DirectoryInfo localSourceDir = new DirectoryInfo(@"C:\SensorNetworks\WavFiles\ConvDNNData");
////sourceRecording = Path.Combine(localSourceDir.FullName + @"\" + parentDirectoryName + @"\" + directoryName, fileName).ToFileInfo();
////record.path = sourceRecording;
/* ####################################### */
// TO TEST SMALL PORTION OF DATA because total data is too large.
doPreprocessing = false || parentDirectoryName.Equals("0");
if (!sourceRecording.Exists)
{
fileInCsvDoesNotExist++;
string warning = string.Format("FILE DOES NOT EXIST >>>," + sourceRecording.Name);
using (var writer = new StreamWriter(outputPath, true))
{
writer.WriteLine(warning);
}
continue;
}
// ####################################################################
if (doPreprocessing)
{
AudioToSonogramResult result = AnalyseOneRecording(record, configDict, output);
string line = result.WriteResultAsLineOfCsv();
// It is helpful to write to the output file as we go, so as to keep a record of where we are up to.
// This requires to open and close the output file at each iteration
using (var writer = new StreamWriter(outputPath, true))
{
writer.WriteLine(line);
}
}
// everything should be OK - have jumped through all the hoops.
fileExistsCount++;
// keep track of species names and distribution of classes.
speciesCounts.AddSpeciesCount(record.CommonTags);
speciesCounts.AddSpeciesId(record.CommonTags, record.SpeciesTags);
speciesCounts.AddSiteName(record.SiteName);
} // end while()
string classDistributionOpPath = Path.Combine(output.FullName, "ClassDistributionsForConvDnnDataset.csv");
speciesCounts.Save(classDistributionOpPath);
}
catch (IOException e)
{
LoggedConsole.WriteLine("Something went seriously bloody wrong!");
LoggedConsole.WriteLine(e.ToString());
return;
}
LoggedConsole.WriteLine("fileLocationNotInCsv =" + fileLocationNotInCsv);
LoggedConsole.WriteLine("fileInCsvDoesNotExist=" + fileInCsvDoesNotExist);
LoggedConsole.WriteLine("fileExistsCount =" + fileExistsCount);
LoggedConsole.WriteLine("\n##### FINISHED FILE ############################\n");
}
