Factored out the classification of lab4 to classify_separated_signals

notebooks/classify_separated_signals.ipynb

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+     "source": [
+      "Classification of Separated Signals\n",
+      "-----------------------------------\n",
+      "\n",
+      "Follow the K-NN example in Lab 1, but classify the *separated* signals.\n",
+      "\n",
+      "As in Lab 1, extract features from each training sample in the kick and snare drum directories.\n",
+      "\n",
+      "1.  Train a K-NN model using the kick and snare drum samples."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "labels = np.empty(20, np.int32)\n",
+      "labels[0:9] = 1 # First 10 are the first sample type, e.g. kick\n",
+      "labels[10:19] = 2 # Second 10 are the second sample type, e.g snare\n",
+      "\n",
+      "model_snare = KNeighborsClassifier(n_neighbors = 1)\n",
+      "model.fit(scaledTrainingFeatures, labels.take(train_index, 0))\n",
+      "model_output = model_snare.predict(scaledTestingFeatures)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "2.  Extract features from the drum signals that you separated in Lab 4 Section 1. \n",
+      "\n",
+      "3.  Classify them using the K-NN model that you built.\n",
+      "\n",
+      "    Does K-NN accurately classify the separated signals?\n",
+      "\n",
+      "4.  Repeat for different numbers of separated signals (i.e., the parameter `K` in NMF). \n",
+      "\n",
+      "5.  Overseparate the signal using `K = 20` or more. For those separated components that are classified as snare, add them together using `sum}. The listen to the sum signal. Is it coherent, i.e., does it sound like a single separated drum?\n",
+      "\n",
+      "...and more!\n",
+      "\n",
+      "*  If you have another idea that you would like to try out, please ask me!\n",
+      "*  Feel free to collaborate with a partner.  Together, brainstorm your own problems, if you want!\n",
+      "\n",
+      "Good luck!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

notebooks/ipython_audio.ipynb

@@ -358,4 +358,4 @@
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notebooks/lab4.ipynb

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@@ -124,39 +124,7 @@
       "\n",
       "    Compared to the noisy input signal, how much noise is left in the output signals? \n",
       "\n",
-      "    Which output contains more noise? Why?\n",
-      "\n",
-      "\n",
-      "Section 3: Classification\n",
-      "-------------------------\n",
-      "\n",
-      "Follow the K-NN example in Lab 1, but classify the *separated* signals.\n",
-      "\n",
-      "As in Lab 1, extract features from each training sample in the kick and snare drum directories.\n",
-      "\n",
-      "1.  Train a K-NN model using the kick and snare drum samples.\n",
-      "\n",
-      "        labels=[[ones(10,1) zeros(10,1)];\n",
-      "                [zeros(10,1) ones(10,1)]];\n",
-      "        model_snare = knn(5, 2, 1, trainingFeatures, labels);\n",
-      "        [voting, model_output] = knnfwd(model_snare, featuresScaled)\n",
-      "\n",
-      "2.  Extract features from the drum signals that you separated in Lab 4 Section 1. \n",
-      "\n",
-      "3.  Classify them using the K-NN model that you built.\n",
-      "\n",
-      "    Does K-NN accurately classify the separated signals?\n",
-      "\n",
-      "4.  Repeat for different numbers of separated signals (i.e., the parameter `K` in NMF). \n",
-      "\n",
-      "5.  Overseparate the signal using `K = 20` or more. For those separated components that are classified as snare, add them together using `sum}. The listen to the sum signal. Is it coherent, i.e., does it sound like a single separated drum?\n",
-      "\n",
-      "...and more!\n",
-      "\n",
-      "*  If you have another idea that you would like to try out, please ask me!\n",
-      "*  Feel free to collaborate with a partner.  Together, brainstorm your own problems, if you want!\n",
-      "\n",
-      "Good luck!\n"
+      "    Which output contains more noise? Why?\n"
      ]
     },
     {