Renamed lab1a to spectral_features_knn

notebooks/lab1a.ipynb → notebooks/spectral_features_knn.ipynb

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  "metadata": {
   "name": "",
-  "signature": "sha256:dac21bcd06bbdee8425399e96535c0efb0f06203600f7296c0aa1edd94bcd286"
+  "signature": "sha256:37b907afa0f5a76b50f919ebf9d6804feda3a709475bfd7c09ce3cbccdb32313"
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@@ -12,8 +12,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Section 2: Spectral Features & k-NN\n",
-      "------------------------------------\n",
+      "Spectral Features & k-NN\n",
+      "------------------------\n",
       "\n",
       "My first audio classifier: introducing K-NN!  \n",
       "\n",
@@ -21,7 +21,7 @@
       "\n",
       "We're also going to be using the new features in our arsenal: cherishing those \"spectral moments\" (centroid, bandwidth, skewness, kurtosis) and also examining other spectral statistics. \n",
       " \n",
-      "### TRAINING DATA\n",
+      "### Training Data\n",
       "\n",
       "First off, we want to analyze and feature extract a small collection of audio samples - storing their feature data as our \"training data\".  The commands below read all of the drum example .wav files from the MIR web site into an array, snareFileList.  \n",
       "\n",
@@ -159,13 +159,13 @@
       " frameSize = 0.100 * sample_rate   # 100ms\n",
       " currentFrame = audio[0 : frameSize]\n",
       " # featuresSnare[i, 0] = zcr(currentFrame)\n",
-      " # [centroid, bandwidth, skew, kurtosis] = spectralMoments(currentFrame, sample_rate, 8192)\n",
+      " # centroid, bandwidth, skew, kurtosis = spectralMoments(currentFrame, sample_rate, 8192)\n",
       " # featuresSnare[i, 1:4] = [centroid, bandwidth, skew, kurtosis]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",