/// <summary>
/// Computes a Scms model from the MFCC representation of a song.
/// </summary>
/// <param name="mfcc">Comirva.Audio.Util.Maths.Matrix mfcc</param>
/// <returns></returns>
public static Scms GetScmsNoInverse(Comirva.Audio.Util.Maths.Matrix mfccs, string name)
{
DbgTimer t = new DbgTimer();
t.Start();
Comirva.Audio.Util.Maths.Matrix mean = mfccs.Mean(2);
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
mean.WriteText(name + "_mean.txt");
}
mean.DrawMatrixGraph(name + "_mean.png");
}
#endif
// Covariance
Comirva.Audio.Util.Maths.Matrix covarMatrix = mfccs.Cov(mean);
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
covarMatrix.WriteText(name + "_covariance.txt");
}
covarMatrix.DrawMatrixGraph(name + "_covariance.png");
}
#endif
Comirva.Audio.Util.Maths.Matrix covarMatrixInv = new Comirva.Audio.Util.Maths.Matrix(covarMatrix.Rows, covarMatrix.Columns);
// Store the Mean, Covariance, Inverse Covariance in an optimal format.
int dim = mean.Rows;
Scms s = new Scms(dim);
int l = 0;
for (int i = 0; i < dim; i++)
{
s.mean[i] = (float)mean.MatrixData[i][0];
for (int j = i; j < dim; j++)
{
s.cov[l] = (float)covarMatrix.MatrixData[i][j];
s.icov[l] = (float)covarMatrixInv.MatrixData[i][j];
l++;
}
}
Dbg.WriteLine("GetScmsNoInverse - Execution Time: {0} ms", t.Stop().TotalMilliseconds);
return(s);
}
public AudioFeature Calculate(double[] input)
{
//pack the mfccs into a pointlist
double[][] mfccCoefficients = mfcc.Process(input);
//check if element 0 exists
if(mfccCoefficients.Length == 0)
throw new ArgumentException("The input stream is to short to process;");
//create mfcc matrix
Matrix mfccs = new Matrix(mfccCoefficients);
#if DEBUG
mfccs.WriteText("mfccdata-mandelellis.txt");
mfccs.DrawMatrixGraph("mfccdata-mandelellis.png");
#endif
// compute mean
//Matrix mean = mfccs.Mean(1).Transpose();
Matrix mean = mfccs.Mean(2);
#if DEBUG
mean.WriteText("mean-mandelellis.txt");
mean.DrawMatrixGraph("mean-mandelellis.png");
#endif
// create covariance matrix
Matrix covarMatrix = mfccs.Cov();
#if DEBUG
covarMatrix.WriteText("covariance-mandelellis.txt");
covarMatrix.DrawMatrixGraph("covariance-mandelellis.png");
#endif
// Inverse Covariance
Matrix covarMatrixInv;
try {
//covarMatrixInv = covarMatrix.Inverse();
covarMatrixInv = covarMatrix.InverseGausJordan();
} catch (Exception) {
Console.Error.WriteLine("Mandel Ellis Extraction Failed!");
return null;
}
#if DEBUG
covarMatrixInv.WriteText("inverse_covariance-mandelellis.txt");
covarMatrixInv.DrawMatrixGraph("inverse_covariance-mandelellis.png");
#endif
MandelEllis.GmmMe gmmMe = new MandelEllis.GmmMe(mean, covarMatrix, covarMatrixInv);
MandelEllis mandelEllis = new MandelEllis(gmmMe);
return mandelEllis;
}
public double[][] idct(double[][] F)
{
// convert two dimensional data to a Comirva Matrix
Matrix mat = new Matrix(F, rows, columns);
#if DEBUG
mat.WriteText("idct-before.txt");
#endif
// Perform the IDCT (Inverse Discrete Cosine Transform)
Matrix iDctResult = dctMatrix.Transpose() * mat;
#if DEBUG
iDctResult.WriteText("idct-after.txt");
#endif
// return as two dimensional array
return iDctResult.MatrixData;
}
public double[][] dct(double[][] f)
{
// convert two dimensional data to a Comirva Matrix
Matrix mat = new Matrix(f, rows, columns);
#if DEBUG
mat.WriteText("dct-before.txt");
#endif
// Perform the DCT (Discrete Cosine Transform)
Matrix dctResult = dctMatrix * mat;
#if DEBUG
dctResult.WriteText("dct-after.txt");
#endif
// return as two dimensional array
return dctResult.MatrixData;
}
/// <summary>
/// Computes a Scms model from the MFCC representation of a song.
/// </summary>
/// <param name="mfcc">Comirva.Audio.Util.Maths.Matrix mfcc</param>
/// <returns></returns>
public static Scms GetScms(Comirva.Audio.Util.Maths.Matrix mfccs, string name)
{
DbgTimer t = new DbgTimer();
t.Start();
Comirva.Audio.Util.Maths.Matrix mean = mfccs.Mean(2);
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
mean.WriteText(name + "_mean.txt");
}
mean.DrawMatrixGraph(name + "_mean.png");
}
#endif
// Covariance
Comirva.Audio.Util.Maths.Matrix covarMatrix = mfccs.Cov(mean);
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
covarMatrix.WriteText(name + "_covariance.txt");
}
covarMatrix.DrawMatrixGraph(name + "_covariance.png");
}
#endif
// Inverse Covariance
Comirva.Audio.Util.Maths.Matrix covarMatrixInv;
try {
covarMatrixInv = covarMatrix.InverseGausJordan();
} catch (Exception) {
Dbg.WriteLine("MatrixSingularException - Scms failed!");
return(null);
}
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
covarMatrixInv.WriteAscii(name + "_inverse_covariance.ascii");
}
covarMatrixInv.DrawMatrixGraph(name + "_inverse_covariance.png");
}
#endif
// Store the Mean, Covariance, Inverse Covariance in an optimal format.
int dim = mean.Rows;
Scms s = new Scms(dim);
int l = 0;
for (int i = 0; i < dim; i++)
{
s.mean[i] = (float)mean.MatrixData[i][0];
for (int j = i; j < dim; j++)
{
s.cov[l] = (float)covarMatrix.MatrixData[i][j];
s.icov[l] = (float)covarMatrixInv.MatrixData[i][j];
l++;
}
}
Dbg.WriteLine("Compute Scms - Execution Time: {0} ms", t.Stop().TotalMilliseconds);
return(s);
}