cleaned up wav files

notebooks/ipython_audio.ipynb

 {
  "metadata": {
   "name": "",
-  "signature": "sha256:04dd1d827aa0536306a14b823f0b3f49da92cb76becdea5ddf064be18b5f38a2"
+  "signature": "sha256:dc2d519cf8158ddde08869c1e4bdc1bf230dda51aeea74b84739eedd05d4272e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -90,13 +90,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 44,
+       "prompt_number": 1,
        "text": [
-        "('simpleLoop.wav', <httplib.HTTPMessage instance at 0x64434d0>)"
+        "('simpleLoop.wav', <httplib.HTTPMessage instance at 0x4276098>)"
        ]
       }
      ],
-     "prompt_number": 44
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -109,7 +109,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%ls"
+      "%ls *.wav"
      ],
      "language": "python",
      "metadata": {},
@@ -118,13 +118,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "about_this_workshop.ipynb         get_good_at_ipython.ipynb  lab1.ipynb  lab4.ipynb  \u001b[0m\u001b[00;36mnoise.wav\u001b[0m            segmentation.ipynb       \u001b[00;36mtemp.wav\u001b[0m\r\n",
-        "classify_separated_signals.ipynb  ipython_audio.ipynb        lab2.ipynb  \u001b[00;36mnoise1.wav\u001b[0m  numpy_basics.ipynb   \u001b[00;36msimpleLoop.wav\u001b[0m\r\n",
-        "cross_validation.ipynb            knn.ipynb                  lab3.ipynb  \u001b[00;36mnoise2.wav\u001b[0m  python_basics.ipynb  spectral_features.ipynb\r\n"
+        "\u001b[0m\u001b[00;36msimpleLoop.wav\u001b[0m\r\n"
        ]
       }
      ],
-     "prompt_number": 45
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -177,13 +175,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 46,
+       "prompt_number": 3,
        "text": [
         "(132300,)"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -200,9 +198,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 47,
+       "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x642a090>"
+        "<matplotlib.text.Text at 0x43906d0>"
        ]
       },
       {
@@ -210,11 +208,11 @@
        "output_type": "display_data",
        "png": 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P7hIFLuivyezZs7F27VoMGzYM69atw+zZswEAhw8fRkFBAQCgtrYW2dnZSE9P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\ngg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x5e1a650>"
+        "<matplotlib.figure.Figure at 0x4398310>"
        ]
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -252,7 +250,7 @@
        ]
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -298,13 +296,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 49,
+       "prompt_number": 6,
        "text": [
-        "<IPython.lib.display.Audio at 0x5e14ad0>"
+        "<IPython.lib.display.Audio at 0x61d5050>"
        ]
       }
      ],
-     "prompt_number": 49
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -326,13 +324,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 50,
+       "prompt_number": 7,
        "text": [
-        "<IPython.lib.display.Audio at 0x32efc90>"
+        "<IPython.lib.display.Audio at 0x64dd250>"
        ]
       }
      ],
-     "prompt_number": 50
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -365,13 +363,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 51,
+       "prompt_number": 8,
        "text": [
-        "<IPython.lib.display.Audio at 0x686b2d0>"
+        "<IPython.lib.display.Audio at 0x64fa250>"
        ]
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 8
     },
     {
      "cell_type": "heading",
@@ -422,9 +420,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 52,
+       "prompt_number": 9,
        "text": [
-        "<matplotlib.text.Text at 0x6850290>"
+        "<matplotlib.text.Text at 0x64fb810>"
        ]
       },
       {
@@ -432,11 +430,11 @@
        "output_type": "display_data",
        "png": 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UQpiplkUGLBASMYtAKL1rvHEf484wi5uJ4w/tGTIE8Pc3774sesECIZERI2j7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\nYRzy/won3wKGcFuVAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x6867150>"
+        "<matplotlib.figure.Figure at 0x4867910>"
        ]
       }
      ],
-     "prompt_number": 52
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -459,9 +457,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 53,
+       "prompt_number": 10,
        "text": [
-        "<matplotlib.text.Text at 0x7ba1dd0>"
+        "<matplotlib.text.Text at 0x6813fd0>"
        ]
       },
       {
@@ -469,11 +467,11 @@
        "output_type": "display_data",
        "png": 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vdXU1brcbALfbTXV1tZVtXJBV/Z1pzZo1pKamWlB986zqb8uWLXg8HoYOHWpx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\nKHBERMQW/x+fal+1runOsgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x686bed0>"
+        "<matplotlib.figure.Figure at 0x4271750>"
        ]
       }
      ],
-     "prompt_number": 53
+     "prompt_number": 10
     },
     {
      "cell_type": "heading",
@@ -505,7 +503,7 @@
       "from essentia.standard import MonoWriter\n",
       "noise = 0.1*randn(44100)\n",
       "MonoWriter(filename='noise1.wav')(single(noise))\n",
-      "%ls"
+      "%ls *.wav"
      ],
      "language": "python",
      "metadata": {},
@@ -514,13 +512,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "about_this_workshop.ipynb         get_good_at_ipython.ipynb  lab1.ipynb  lab4.ipynb  \u001b[0m\u001b[00;36mnoise.wav\u001b[0m            segmentation.ipynb       \u001b[00;36mtemp.wav\u001b[0m\r\n",
-        "classify_separated_signals.ipynb  ipython_audio.ipynb        lab2.ipynb  \u001b[00;36mnoise1.wav\u001b[0m  numpy_basics.ipynb   \u001b[00;36msimpleLoop.wav\u001b[0m\r\n",
-        "cross_validation.ipynb            knn.ipynb                  lab3.ipynb  \u001b[00;36mnoise2.wav\u001b[0m  python_basics.ipynb  spectral_features.ipynb\r\n"
+        "\u001b[0m\u001b[00;36mnoise1.wav\u001b[0m  \u001b[00;36msimpleLoop.wav\u001b[0m\r\n"
        ]
       }
      ],
-     "prompt_number": 54
+     "prompt_number": 11
     },
     {
      "cell_type": "heading",
@@ -544,7 +540,7 @@
       "import librosa\n",
       "noise = 0.1*randn(44100)\n",
       "librosa.output.write_wav('noise2.wav', noise, 44100)\n",
-      "%ls"
+      "%ls *.wav"
      ],
      "language": "python",
      "metadata": {},
@@ -553,13 +549,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "about_this_workshop.ipynb         get_good_at_ipython.ipynb  lab1.ipynb  lab4.ipynb  \u001b[0m\u001b[00;36mnoise.wav\u001b[0m            segmentation.ipynb       \u001b[00;36mtemp.wav\u001b[0m\r\n",
-        "classify_separated_signals.ipynb  ipython_audio.ipynb        lab2.ipynb  \u001b[00;36mnoise1.wav\u001b[0m  numpy_basics.ipynb   \u001b[00;36msimpleLoop.wav\u001b[0m\r\n",
-        "cross_validation.ipynb            knn.ipynb                  lab3.ipynb  \u001b[00;36mnoise2.wav\u001b[0m  python_basics.ipynb  spectral_features.ipynb\r\n"
+        "\u001b[0m\u001b[00;36mnoise1.wav\u001b[0m  \u001b[00;36mnoise2.wav\u001b[0m  \u001b[00;36msimpleLoop.wav\u001b[0m\r\n"
        ]
       }
      ],
-     "prompt_number": 55
+     "prompt_number": 12
     }
    ],
    "metadata": {}