spectral features; mfcc; knn instrument classification

index.html

@@ -264,9 +264,9 @@ div#notebook {
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <ol>
-<li><a href="notebooks/spectral_features.ipynb">Spectral Features</a></li>
-<li><a href="notebooks/mfcc.ipynb">Mel-Frequency Cepstral Coefficients</a></li>
-<li><a href="notebooks/knn_instrument_classification.ipynb">K-Nearest Neighbor Instrument Classification</a></li>
+<li><a href="spectral_features.html">Spectral Features</a> (<a href="spectral_features.ipynb">ipynb</a>)</li>
+<li><a href="mfcc.html">Mel-Frequency Cepstral Coefficients</a> (<a href="mfcc.ipynb">ipynb</a>)</li>
+<li><a href="knn_instrument_classification.html">K-Nearest Neighbor Instrument Classification</a> (<a href="knn_instrument_classification.ipynb">ipynb</a>)</li>
 </ol>
 
 </div>

index.ipynb

@@ -60,9 +60,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "1.  [Spectral Features](notebooks/spectral_features.ipynb)\n",
-    "1.  [Mel-Frequency Cepstral Coefficients](notebooks/mfcc.ipynb)\n",
-    "1.  [K-Nearest Neighbor Instrument Classification](notebooks/knn_instrument_classification.ipynb)"
+    "1.  [Spectral Features](spectral_features.html) ([ipynb](spectral_features.ipynb))\n",
+    "1.  [Mel-Frequency Cepstral Coefficients](mfcc.html) ([ipynb](mfcc.ipynb))\n",
+    "1.  [K-Nearest Neighbor Instrument Classification](knn_instrument_classification.html) ([ipynb](knn_instrument_classification.ipynb))"
    ]
   },
   {

notebooks/knn_instrument_classification.ipynb → knn_instrument_classification.ipynb

 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7b9044e3893b9f87e8347d7d3d1679d5cfd0f54fb88c291634c51876f7fff24b"
+  "signature": "sha256:e470213ca2db1aa50204e5d556bfe9ba3cda6b08f319067efe328611f64362c2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -336,11 +336,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#model = KNeighborsClassifier(n_neighbors=1)\n",
+      "model = KNeighborsClassifier(n_neighbors=1)\n",
       "#model = SVC()\n",
       "#model = LogisticRegression()\n",
       "#model = GaussianNB()\n",
-      "model = AdaBoostClassifier()\n",
+      "#model = AdaBoostClassifier()\n",
       "model.fit(training_features, training_labels)"
      ],
      "language": "python",