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fixed bug in feature extraction
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@mkholghi
mkholghi committed Dec 5, 2018
1 parent 895396f commit b52daae
Showing 1 changed file with 51 additions and 90 deletions.
141 changes: 51 additions & 90 deletions src/AudioAnalysisTools/DSP/FeatureExtraction.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ public class FeatureExtraction
{
/// <summary>
/// Apply feature learning process on a set of target (1-minute) recordings (inputPath)
/// according to the a set of centroids learnt using feature learning process.
/// according to the a set of centroids learned using feature learning process.
/// Output feature vectors (outputPath)
/// </summary>
public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config, List<double[][]> allCentroids,
Expand Down Expand Up @@ -77,11 +77,11 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
//*****
// lists of features for all processing files
// the key is the file name, and the value is the features for different bands
Dictionary<string, List<List<double[,]>>> allFilesMinFeatureVectors = new Dictionary<string, List<List<double[,]>>>();
Dictionary<string, List<List<double[,]>>> allFilesMeanFeatureVectors = new Dictionary<string, List<List<double[,]>>>();
Dictionary<string, List<List<double[,]>>> allFilesMaxFeatureVectors = new Dictionary<string, List<List<double[,]>>>();
Dictionary<string, List<List<double[,]>>> allFilesStdFeatureVectors = new Dictionary<string, List<List<double[,]>>>();
Dictionary<string, List<List<double[,]>>> allFilesSkewnessFeatureVectors = new Dictionary<string, List<List<double[,]>>>();
Dictionary<string, List<double[,]>> allFilesMinFeatureVectors = new Dictionary<string, List<double[,]>>();
Dictionary<string, List<double[,]>> allFilesMeanFeatureVectors = new Dictionary<string, List<double[,]>>();
Dictionary<string, List<double[,]>> allFilesMaxFeatureVectors = new Dictionary<string, List<double[,]>>();
Dictionary<string, List<double[,]>> allFilesStdFeatureVectors = new Dictionary<string, List<double[,]>>();
Dictionary<string, List<double[,]>> allFilesSkewnessFeatureVectors = new Dictionary<string, List<double[,]>>();

double[,] inputMatrix;
List<AudioRecording> recordings = new List<AudioRecording>();
Expand All @@ -90,13 +90,6 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
{
FileInfo fileInfo = filePath.ToFileInfo();

// lists of features for subsegments of a file
List<List<double[,]>> allSegmentsMinFeatureVectors = new List<List<double[,]>>();
List<List<double[,]>> allSegmentsMeanFeatureVectors = new List<List<double[,]>>();
List<List<double[,]>> allSegmentsMaxFeatureVectors = new List<List<double[,]>>();
List<List<double[,]>> allSegmentsStdFeatureVectors = new List<List<double[,]>>();
List<List<double[,]>> allSegmentSkewnessFeatureVectors = new List<List<double[,]>>();

// process the wav file if it is not empty
if (fileInfo.Length != 0)
{
Expand Down Expand Up @@ -370,24 +363,17 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
allSkewnessFeatureVectors.Add(skewnessFeatureVectors.ToArray().ToMatrix());
}

// add the features of all subsegments to a list
allSegmentsMinFeatureVectors.Add(allMinFeatureVectors);
allSegmentsMeanFeatureVectors.Add(allMeanFeatureVectors);
allSegmentsMaxFeatureVectors.Add(allMaxFeatureVectors);
allSegmentsStdFeatureVectors.Add(allStdFeatureVectors);
allSegmentSkewnessFeatureVectors.Add(allSkewnessFeatureVectors);

//*****
// the keys of the following dictionaries contain file name
// and their values are a list<list<double[,]>> which the list.count is
// the number of subsegments for which features are extracted
// and for each subsegment, the number of freq bands defined as an user-defined parameter.
// and their values are a list<double[,]> which the list.count is
// the number of all subsegments for which features are extracted
// the number of freq bands defined as an user-defined parameter.
// the 2D-array is the feature vectors.
allFilesMinFeatureVectors.Add(fileInfo.Name, allSegmentsMinFeatureVectors);
allFilesMeanFeatureVectors.Add(fileInfo.Name, allSegmentsMeanFeatureVectors);
allFilesMaxFeatureVectors.Add(fileInfo.Name, allSegmentsMaxFeatureVectors);
allFilesStdFeatureVectors.Add(fileInfo.Name, allSegmentsStdFeatureVectors);
allFilesSkewnessFeatureVectors.Add(fileInfo.Name, allSegmentSkewnessFeatureVectors);
allFilesMinFeatureVectors.Add(fileInfo.Name + "-" + s.ToString(), allMinFeatureVectors);
allFilesMeanFeatureVectors.Add(fileInfo.Name + "-" + s.ToString(), allMeanFeatureVectors);
allFilesMaxFeatureVectors.Add(fileInfo.Name + "-" + s.ToString(), allMaxFeatureVectors);
allFilesStdFeatureVectors.Add(fileInfo.Name + "-" + s.ToString(), allStdFeatureVectors);
allFilesSkewnessFeatureVectors.Add(fileInfo.Name + "-" + s.ToString(), allSkewnessFeatureVectors);

// +++++++++++++++++++++++++++++++++++Temporal Summarization

Expand Down Expand Up @@ -480,7 +466,7 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
// First, concatenate mean, max, std for each second.
// Then, write the features of each pre-defined frequency band into a separate CSV file.
var filesName = allFilesMeanFeatureVectors.Keys.ToArray();
var minFeatures = allFilesMinFeatureVectors.Values.ToArray(); // list<list<double[,]>>[]
var minFeatures = allFilesMinFeatureVectors.Values.ToArray();
var meanFeatures = allFilesMeanFeatureVectors.Values.ToArray();
var maxFeatures = allFilesMaxFeatureVectors.Values.ToArray();
var stdFeatures = allFilesStdFeatureVectors.Values.ToArray();
Expand All @@ -489,92 +475,71 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
// The number of elements in the list shows the number of freq bands
// the size of each element in the list shows the number of files processed to generate feature for.
// the dimensions of the matrix shows the number of feature vectors generated for each file and the length of feature vector
var allMins = new List<List<List<double[,]>>>();
var allMeans = new List<List<List<double[,]>>>();
var allMaxs = new List<List<List<double[,]>>>();
var allStds = new List<List<List<double[,]>>>();
var allSkewness = new List<List<List<double[,]>>>();
var allMins = new List<double[][,]>();
var allMeans = new List<double[][,]>();
var allMaxs = new List<double[][,]>();
var allStds = new List<double[][,]>();
var allSkewness = new List<double[][,]>();


// looping over freq bands
for (int i = 0; i < meanFeatures[0].ToArray()[0].Count; i++)
for (int i = 0; i < meanFeatures[0].Count; i++)
{
var mins = new List<List<double[,]>>();
var means = new List<List<double[,]>>();
var maxs = new List<List<double[,]>>();
var stds = new List<List<double[,]>>();
var skewnesses = new List<List<double[,]>>();
var mins = new List<double[,]>();
var means = new List<double[,]>();
var maxs = new List<double[,]>();
var stds = new List<double[,]>();
var skewnesses = new List<double[,]>();

// looping over all files
for (int k = 0; k < meanFeatures.Length; k++)
{
var minsSeg = new List<double[,]>();
var meansSeg = new List<double[,]>();
var maxsSeg = new List<double[,]>();
var stdsSeg = new List<double[,]>();
var skewnessesSeg = new List<double[,]>();

// looping over segments
for (int j = 0; j < meanFeatures[k].ToArray().Length; j++)
{
minsSeg.Add(minFeatures[k].ToArray()[j].ToArray()[i]);
meansSeg.Add(meanFeatures[k].ToArray()[j].ToArray()[i]);
maxsSeg.Add(maxFeatures[k].ToArray()[j].ToArray()[i]);
stdsSeg.Add(stdFeatures[k].ToArray()[j].ToArray()[i]);
skewnessesSeg.Add(skewnessFeatures[k].ToArray()[j].ToArray()[i]);
}

mins.Add(minsSeg);
means.Add(meansSeg);
maxs.Add(maxsSeg);
stds.Add(stdsSeg);
skewnesses.Add(skewnessesSeg);
mins.Add(minFeatures[k].ToArray()[i]);
means.Add(meanFeatures[k].ToArray()[i]);
maxs.Add(maxFeatures[k].ToArray()[i]);
stds.Add(stdFeatures[k].ToArray()[i]);
skewnesses.Add(skewnessFeatures[k].ToArray()[i]);
}

allMins.Add(mins);
allMeans.Add(means);
allMaxs.Add(maxs);
allStds.Add(stds);
allSkewness.Add(skewnesses);
allMins.Add(mins.ToArray());
allMeans.Add(means.ToArray());
allMaxs.Add(maxs.ToArray());
allStds.Add(stds.ToArray());
allSkewness.Add(skewnesses.ToArray());
}

// each element of meanFeatures array is a list of features for different frequency bands.
// looping over the number of freq bands
for (int i = 0; i < allMeans.ToArray().Length; i++)
for (int i = 0; i < allMeans.ToArray().GetLength(0); i++)
{
// creating output feature file based on the number of freq bands
var outputFeatureFile = Path.Combine(outputPath, "FeatureVectors-" + i.ToString() + ".csv");

// creating the header for CSV file
List<string> header = new List<string>();
header.Add("file name");
for (int j = 0; j < allMins.ToArray()[i].ToArray().ToArray().GetLength(1); j++)
{
header.Add("min" + j.ToString());
}

/*
for (int j = 0; j < allMins.ToArray()[i][0].GetLength(1); j++)
{
header.Add("min" + j.ToString());
}
*/

for (int j = 0; j < allMeans.ToArray()[i].ToArray().ToArray().GetLength(1); j++)
for (int j = 0; j < allMeans.ToArray()[i][0].GetLength(1); j++)
{
header.Add("mean" + j.ToString());
}

for (int j = 0; j < allMaxs.ToArray()[i].ToArray().ToArray().GetLength(1); j++)
for (int j = 0; j < allMaxs.ToArray()[i][0].GetLength(1); j++)
{
header.Add("max" + j.ToString());
}

for (int j = 0; j < allStds.ToArray()[i].ToArray().ToArray().GetLength(1); j++)
for (int j = 0; j < allStds.ToArray()[i][0].GetLength(1); j++)
{
header.Add("std" + j.ToString());
}

for (int j = 0; j < allSkewness.ToArray()[i].ToArray().ToArray().GetLength(1); j++)
for (int j = 0; j < allSkewness.ToArray()[i][0].GetLength(1); j++)
{
header.Add("skewness" + j.ToString());
}
Expand All @@ -591,31 +556,26 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
var allFilesFeatureVectors = new Dictionary<string, double[,]>();

// looping over files
for (int j = 0; j < allMeans.ToArray()[i].ToArray().Length; j++)
for (int j = 0; j < allMeans.ToArray()[i].GetLength(0); j++)
{

// looping over segments
for (int k = 0; k < allMeans.ToArray()[i].ToArray().GetLength(0); k++)
{

// concatenating mean, std, and max vector together for the pre-defined resolution
List<double[]> featureVectors = new List<double[]>();
for (int l = 0; l < allMeans.ToArray()[i].ToArray()[j].ToArray()[k].ToJagged().GetLength(0); l++)
for (int k = 0; k < allMeans.ToArray()[i][j].ToJagged().GetLength(0); k++)
{
List<double[]> featureList = new List<double[]>
{
allMins.ToArray()[i].ToArray()[j].ToArray()[k].ToJagged()[l],
allMeans.ToArray()[i].ToArray()[j].ToArray()[k].ToJagged()[l],
allMaxs.ToArray()[i].ToArray()[j].ToArray()[k].ToJagged()[l],
allStds.ToArray()[i].ToArray()[j].ToArray()[k].ToJagged()[l],
allSkewness.ToArray()[i].ToArray()[j].ToArray()[k].ToJagged()[l],
allMins.ToArray()[i][j].ToJagged()[k],
allMeans.ToArray()[i][j].ToJagged()[k],
allMaxs.ToArray()[i][j].ToJagged()[k],
allStds.ToArray()[i][j].ToJagged()[k],
allSkewness.ToArray()[i][j].ToJagged()[k],
};
double[] featureVector = DataTools.ConcatenateVectors(featureList);
featureVectors.Add(featureVector);
}

allFilesFeatureVectors.Add(filesName[j] + "Second_" + k, featureVectors.ToArray().ToMatrix());
}
allFilesFeatureVectors.Add(filesName[j], featureVectors.ToArray().ToMatrix());
}

// writing feature vectors to CSV file
Expand All @@ -633,6 +593,7 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,

File.WriteAllText(outputFeatureFile, csv.ToString());
}

//*****
}
}
Expand Down

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