/// <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);
}
/// <summary>
/// Computes a Scms model from the MFCC representation of a song.
/// </summary>
/// <param name="mfcc">Mirage.Matrix mfcc</param>
/// <returns></returns>
public static Scms GetScms(Matrix mfcc, string name)
{
DbgTimer t = new DbgTimer();
t.Start();
// Mean
Vector m = mfcc.Mean();
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
m.WriteText(name + "_mean_orig.txt");
}
m.DrawMatrixGraph(name + "_mean_orig.png");
}
#endif
// Covariance
Matrix c = mfcc.Covariance(m);
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
c.WriteText(name + "_covariance_orig.txt");
}
c.DrawMatrixGraph(name + "_covariance_orig.png");
}
#endif
// Inverse Covariance
Matrix ic;
try {
ic = c.Inverse();
} catch (MatrixSingularException) {
//throw new ScmsImpossibleException();
Dbg.WriteLine("MatrixSingularException - Scms failed!");
return(null);
}
#if DEBUG
if (Analyzer.DEBUG_INFO_VERBOSE)
{
if (Analyzer.DEBUG_OUTPUT_TEXT)
{
ic.WriteAscii(name + "_inverse_covariance_orig.txt");
}
ic.DrawMatrixGraph(name + "_inverse_covariance_orig.png");
}
#endif
// Store the Mean, Covariance, Inverse Covariance in an optimal format.
int dim = m.rows;
Scms s = new Scms(dim);
int l = 0;
for (int i = 0; i < dim; i++)
{
s.mean[i] = m.d[i, 0];
for (int j = i; j < dim; j++)
{
s.cov[l] = c.d[i, j];
s.icov[l] = ic.d[i, j];
l++;
}
}
Dbg.WriteLine("(Mirage) - scms created in: {0} ms", t.Stop().TotalMilliseconds);
return(s);
}
