static void Main(string[] args)
{
String path = @"C:\Users\PauloRenato\Documents\base_balanceada_14-02-2019";
double[][] data = AudioFeatures.getDataFromBase(path, "");
//double[][] data;
//AudioFeatures.LoadData(@"C:\Projeto\autoleitura\paulorenato\AudioCSharp\Caracteristicas\4125_mfcc.txt", out data, 4125, 101); ;
//AudioFeatures.scaleData(data);
//double[][] trainData, testData;
//AudioFeatures.MakeTrainTestByRef(data, 42,out trainData,out testData);
//Train(trainData, @"C:\Projeto\autoleitura\paulorenato\AudioCSharp\Modelo\audioSVMTrain.xml");
//Test(testData, @"C:\Projeto\autoleitura\paulorenato\AudioCSharp\Modelo\audioSVMTrain.xml");
//Console.WriteLine(RecognizeAudio(@"C:\Users\PauloRenato\Documents\base_balanceada_14-02-2019\5\5_F_14-01-2019_21_22_23.wav", @"C:\Projeto\autoleitura\paulorenato\AudioCSharp\Modelo\audioSVMTrain.xml"));
//Console.ReadKey();
}
static double[] FeaturesFromMFCC(double[,] mfcc_mass)
{
int count_frames = mfcc_mass.Length / 20;
int features_size = 100;
double[] features = new double[features_size];
int feature_index = 0;
for (int i = 0; i < 20; i++)
{
double media = 0.0;
double max = Double.MinValue;
double min = Double.MaxValue;
double[] values = new double[count_frames];
for (int k = 0; k < count_frames; k++)
{
if (mfcc_mass[k, i] < min)
{
min = mfcc_mass[k, i];
}
if (mfcc_mass[k, i] > max)
{
max = mfcc_mass[k, i];
}
media += mfcc_mass[k, i];
values[k] = mfcc_mass[k, i];
}
media = media / count_frames;
double variance = AudioFeatures.Variance(values, media);
double mediana = AudioFeatures.GetMedian(values);
features[feature_index++] = (float)mediana;
features[feature_index++] = (float)media;
features[feature_index++] = (float)variance;
features[feature_index++] = (float)min;
features[feature_index++] = (float)max;
}
return(features);
}
static float RecognizeAudio(string path, string svmModel)
{
SVMAudio mySVM = new SVMAudio();
mySVM.LoadSVMFromFile(svmModel);
double[] y = AudioFeatures.load2(path);
double[,] mfcc_mass = MFCC_calculating.MFCC_20_calculation(y);
double[] features_vector = FeaturesFromMFCC(mfcc_mass);
int features_size = 100;
Image <Gray, float> features = new Image <Gray, float>(features_size, 1);
Image <Gray, float> results = new Image <Gray, float>(1, 1);
AudioFeatures.NormalizeFromFile(@"C:\Projeto\autoleitura\paulorenato\AudioCSharp\Modelo\scaleSVMTrain.txt", features_vector);
for (int i = 0; i < features_size; i++)
{
features.Data[0, i, 0] = (float)features_vector[i];
}
mySVM.Predict(features, results);
return(results.Data[0, 0, 0]);
}
