diff NNLSChroma.cpp @ 77:ba930176df5b matthiasm-plugin

fixed. warning: I added some arrays with variable length (though not at runtime).

--- a/NNLSChroma.cpp	Wed Nov 10 22:52:46 2010 +0900
+++ b/NNLSChroma.cpp	Thu Nov 11 10:27:58 2010 +0900
@@ -99,7 +99,7 @@
     d2.description = "A Log-Frequency Spectrum (constant Q) that is obtained by cosine filter mapping, then its tuned using the estimated tuning frequency.";
     d2.unit = "";
     d2.hasFixedBinCount = true;
-    d2.binCount = 256;
+    d2.binCount = nNote;
     d2.hasKnownExtents = false;
     d2.isQuantized = false;
     d2.sampleType = OutputDescriptor::FixedSampleRate;
@@ -243,6 +243,9 @@
     float thresh = pow(10,dbThreshold/20);
     // cerr << "tune local ? " << m_tuneLocal << endl;
     int count = 0;
+
+    cerr << nNote;
+    cerr << endl << "-------------------------------------"<< endl;
 		
     for (FeatureList::iterator i = m_logSpectrum.begin(); i != m_logSpectrum.end(); ++i) {
         Feature f1 = *i;
@@ -264,13 +267,14 @@
         }
 		        
         f2.values.push_back(0.0); f2.values.push_back(0.0); f2.values.push_back(0.0); // upper edge
+
         vector<float> runningmean = SpecialConvolution(f2.values,hw);
         vector<float> runningstd;
-        for (int i = 0; i < 256; i++) { // first step: squared values into vector (variance)
+        for (int i = 0; i < nNote; i++) { // first step: squared values into vector (variance)
             runningstd.push_back((f2.values[i] - runningmean[i]) * (f2.values[i] - runningmean[i]));
         }
         runningstd = SpecialConvolution(runningstd,hw); // second step convolve
-        for (int i = 0; i < 256; i++) { 
+        for (int i = 0; i < nNote; i++) { 
             runningstd[i] = sqrt(runningstd[i]); // square root to finally have running std
             if (runningstd[i] > 0) {
                 // f2.values[i] = (f2.values[i] / runningmean[i]) > thresh ? 
@@ -323,12 +327,12 @@
         f6.hasTimestamp = true;
         f6.timestamp = f2.timestamp;
 	        
-        float b[256];
+        float b[nNote];
 	
         bool some_b_greater_zero = false;
         float sumb = 0;
-        for (int i = 0; i < 256; i++) {
-            // b[i] = m_dict[(256 * count + i) % (256 * 84)];
+        for (int i = 0; i < nNote; i++) {
+            // b[i] = m_dict[(nNote * count + i) % (nNote * 84)];
             b[i] = f2.values[i];
             sumb += b[i];
             if (b[i] > 0) {
@@ -376,12 +380,12 @@
                 float zz[84+1000];
                 int indx[84+1000];
                 int mode;
-                int dictsize = 256*signifIndex.size();
+                int dictsize = nNote*signifIndex.size();
                 // cerr << "dictsize is " << dictsize << "and values size" << f3.values.size()<< endl;
                 float *curr_dict = new float[dictsize];
                 for (unsigned iNote = 0; iNote < signifIndex.size(); ++iNote) {
-                    for (unsigned iBin = 0; iBin < 256; iBin++) {
-                        curr_dict[iNote * 256 + iBin] = 1.0 * m_dict[signifIndex[iNote] * 256 + iBin];
+                    for (unsigned iBin = 0; iBin < nNote; iBin++) {
+                        curr_dict[iNote * nNote + iBin] = 1.0 * m_dict[signifIndex[iNote] * nNote + iBin];
                     }
                 }
                 nnls(curr_dict, nNote, nNote, signifIndex.size(), b, x, &rnorm, w, zz, indx, &mode);