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musicinformationretrieval-com
Commit d97dd891 authored by Steve Tjoa's avatar Steve Tjoa
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pca; hpss; pitch transcription

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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy, scipy, matplotlib.pyplot as plt, sklearn, stanford_mir\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[← Back to Index](index.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cross Validation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://en.wikipedia.org/wiki/Cross-validation_(statistics)\">K-fold cross validation</a> is a method for evaluating the correctness of a classifier.\n",
"\n",
"For example, with 10-fold cross validation:\n",
"\n",
"1. Divide the data set into 10 random partitions.\n",
"2. Choose one of the partitions as the test set. Train on the other nine partitions.\n",
"3. Repeat for the partitions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load some features from ten kick drums and ten snare drums:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "[Errno socket error] [Errno 8] nodename nor servname provided, or not known",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-a7910ded3ac4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstanford_mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/steve/stanford-mir/stanford_mir/core.pyc\u001b[0m in \u001b[0;36mget_features\u001b[0;34m(collection, features, scaler)\u001b[0m\n\u001b[1;32m 20\u001b[0m scaler=None):\n\u001b[1;32m 21\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcollection\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'drum_samples_train'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mkick_filepaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnare_filepaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdownload_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'drum_samples_train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m kick_signals = [\n\u001b[1;32m 24\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkick_filepaths\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/steve/stanford-mir/stanford_mir/core.pyc\u001b[0m in \u001b[0;36mdownload_samples\u001b[0;34m(collection)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%s_%02d.wav'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdrum_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m urllib.urlretrieve('http://audio.musicinformationretrieval.com/drum_samples/%s' % filename,\n\u001b[0;32m---> 88\u001b[0;31m filename=os.path.join(collection, filename))\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0mkick_filepaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'kick_%02d.wav'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0msnare_filepaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'snare_%02d.wav'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_urlopener\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0m_urlopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFancyURLopener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_urlopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreporthook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0murlcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_urlopener\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self, url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 241\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 209\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36mopen_http\u001b[0;34m(self, url, data)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrealhost\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mputheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Host'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrealhost\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddheaders\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mputheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetreply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 968\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 971\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m 827\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmessage_body\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 829\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 830\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 831\u001b[0m \u001b[0;31m#message_body was not a string (i.e. it is a file) and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 792\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotConnected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[0;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 771\u001b[0m self.sock = socket.create_connection((self.host,self.port),\n\u001b[0;32m--> 772\u001b[0;31m self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tunnel_host\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/socket.pyc\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maddress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 554\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[0msock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIOError\u001b[0m: [Errno socket error] [Errno 8] nodename nor servname provided, or not known"
]
}
],
"source": [
"training_features, training_labels, scaler = stanford_mir.get_features()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1.]\n"
]
}
],
"source": [
"print training_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot their features:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'training_features' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-f00bdfd12558>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'training_features' is not defined"
]
}
],
"source": [
"plt.scatter(training_features[:,0], training_features[:,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize the classifier:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=3)\n",
"model = sklearn.linear_model.LogisticRegression()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform 5-fold cross validation:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"acc = sklearn.cross_validation.cross_val_score(model, random_features, training_labels, cv=5)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5\n"
]
}
],
"source": [
"print acc.mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[&larr; Back to Index](index.html)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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hpss.html 0 → 100644
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index.html
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......@@ -271,6 +271,7 @@ div#notebook {
<li><a href="zcr.html">Zero Crossing Rate</a> (<a href="zcr.ipynb">ipynb</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="pitch_transcription_exercise.html">Pitch Transcription Exercise</a> (<a href="pitch_transcription_exercise.ipynb">ipynb</a>)</li>
</ol>
</div>
......@@ -293,9 +294,11 @@ div#notebook {
<ol>
<li><a href="basic_supervised_classification.html">Basic Supervised Classification</a> (<a href="basic_supervised_classification.ipynb">ipynb</a>)</li>
<li><a href="knn.html">K-Nearest Neighbor Classification</a> (<a href="knn.ipynb">ipynb</a>)</li>
<li><a href="cross_validation.html">Cross Validation</a> (<a href="cross_validation.ipynb">ipynb</a>)</li>
<li><a href="knn_instrument_classification.html">Exercise: K-Nearest Neighbor Instrument Classification</a> (<a href="knn_instrument_classification.ipynb">ipynb</a>)</li>
<li><a href="kmeans.html">K-Means Clustering</a> (<a href="kmeans.ipynb">ipynb</a>)</li>
<li><a href="kmeans_instrument_classification.html">Exercise: Unsupervised Instrument Classification using K-Means</a> (<a href="kmeans_instrument_classification.ipynb">ipynb</a>)</li>
<li><a href="notebooks/evaluation.ipynb">Evaluation</a></li>
</ol>
</div>
......@@ -306,7 +309,7 @@ div#notebook {
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Chapter-4:-Matrix-Factorization-and-Evaluation">Chapter 4: Matrix Factorization and Evaluation<a class="anchor-link" href="#Chapter-4:-Matrix-Factorization-and-Evaluation">&#182;</a></h2>
<h2 id="Chapter-4:-Matrix-Factorization">Chapter 4: Matrix Factorization<a class="anchor-link" href="#Chapter-4:-Matrix-Factorization">&#182;</a></h2>
</div>
</div>
</div>
......@@ -316,11 +319,11 @@ div#notebook {
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li><a href="pca.html">Principal Component Analysis</a> (<a href="pca.ipynb">ipynb</a>)</li>
<li><a href="nmf.html">Nonnegative Matrix Factorization</a> (<a href="nmf.ipynb">ipynb</a>)</li>
<li><a href="hpss.html">Harmonic-Percussive Source Separation</a> (<a href="hpss.ipynb">ipynb</a>)</li>
<li><a href="nmf_source_separation.html">Exercise: Source Separation using NMF</a> (<a href="nmf_source_separation.ipynb">ipynb</a>)</li>
<li><a href="notebooks/classify_separated_signals.ipynb">Classification of Separated Signals</a></li>
<li><a href="notebooks/cross_validation.ipynb">Cross Validation</a></li>
<li><a href="notebooks/evaluation.ipynb">Evaluation</a></li>
</ol>
</div>
......
index.ipynb
View file @ d97dd891
......@@ -66,7 +66,8 @@
"1. [Basic Feature Extraction](basic_feature_extraction.html) ([ipynb](basic_feature_extraction.ipynb))\n",
"1. [Zero Crossing Rate](zcr.html) ([ipynb](zcr.ipynb))\n",
"1. [Spectral Features](spectral_features.html) ([ipynb](spectral_features.ipynb))\n",
"1. [Mel-Frequency Cepstral Coefficients](mfcc.html) ([ipynb](mfcc.ipynb))"
"1. [Mel-Frequency Cepstral Coefficients](mfcc.html) ([ipynb](mfcc.ipynb))\n",
"1. [Pitch Transcription Exercise](pitch_transcription_exercise.html) ([ipynb](pitch_transcription_exercise.ipynb))"
]
},
{
......@@ -82,27 +83,29 @@
"source": [
"1. [Basic Supervised Classification](basic_supervised_classification.html) ([ipynb](basic_supervised_classification.ipynb))\n",
"1. [K-Nearest Neighbor Classification](knn.html) ([ipynb](knn.ipynb))\n",
"1. [Cross Validation](cross_validation.html) ([ipynb](cross_validation.ipynb))\n",
"1. [Exercise: K-Nearest Neighbor Instrument Classification](knn_instrument_classification.html) ([ipynb](knn_instrument_classification.ipynb))\n",
"1. [K-Means Clustering](kmeans.html) ([ipynb](kmeans.ipynb))\n",
"1. [Exercise: Unsupervised Instrument Classification using K-Means](kmeans_instrument_classification.html) ([ipynb](kmeans_instrument_classification.ipynb))"
"1. [Exercise: Unsupervised Instrument Classification using K-Means](kmeans_instrument_classification.html) ([ipynb](kmeans_instrument_classification.ipynb))\n",
"1. [Evaluation](notebooks/evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chapter 4: Matrix Factorization and Evaluation"
"## Chapter 4: Matrix Factorization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. [Principal Component Analysis](pca.html) ([ipynb](pca.ipynb))\n",
"1. [Nonnegative Matrix Factorization](nmf.html) ([ipynb](nmf.ipynb))\n",
"1. [Harmonic-Percussive Source Separation](hpss.html) ([ipynb](hpss.ipynb))\n",
"1. [Exercise: Source Separation using NMF](nmf_source_separation.html) ([ipynb](nmf_source_separation.ipynb))\n",
"1. [Classification of Separated Signals](notebooks/classify_separated_signals.ipynb)\n",
"1. [Cross Validation](notebooks/cross_validation.ipynb)\n",
"1. [Evaluation](notebooks/evaluation.ipynb)"
"1. [Classification of Separated Signals](notebooks/classify_separated_signals.ipynb)\n"
]
},
{
......
notebooks/cross_validation.ipynb deleted 100644 → 0
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"cells": [
{
"cell_type": "markdown",
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"source": [
"CROSS VALIDATION\n",
"----------------\n",
"\n",
"You'll need some of this code and information to calculate your accuracy rate on your classifiers.\n",
"\n",
"EXAMPLE\n",
"\n",
"Let's say we have 10-fold cross validation...\n",
"\n",
"1. Divide test set into 10 random subsets.\n",
"2. 1 test set is tested using the classifier trained on the remaining 9.\n",
"3. We then do test/train on all of the other sets and average the percentages. \n",
"\n",
"To achieve the first step (divide our training set into k disjoint subsets), use the function [Kfold](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html) in the scikit.learn cross_validation package:\n",
"\n",
" K-Folds cross validation iterator.\n",
" Provides train/test indices to split data in train test sets. Split dataset into k consecutive folds (without shuffling).\n",
"\n",
" You can visit the scikit.learn documentation to look at all the other options."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from sklearn import cross_validation\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn import preprocessing\n",
"from essentia.standard import MonoLoader\n",
"from essentia.standard import ZeroCrossingRate, CentralMoments, Spectrum, Windowing, Centroid, DistributionShape\n",
"import urllib2\n",
"import urllib\n",
"\n",
"def process_corpus(corpus_URL):\n",
" \"\"\"Read a list of files to process from the text file at corpusURL. Return a list of URLs\"\"\" \n",
" # Open and read each line\n",
" url_list_text_data = urllib2.urlopen(corpus_URL) # it's a file like object and works just like a file\n",
" for file_URL in url_list_text_data: # files are iterable\n",
" yield file_URL.rstrip()\n",
" \n",
"def spectral_features(filelist):\n",
" \"\"\"\n",
" Given a list of files, retrieve them, analyse the first 100mS of each file and return\n",
" a feature table.\n",
" \"\"\"\n",
" number_of_files = len(filelist)\n",
" number_of_features = 5\n",
" features = np.zeros([number_of_files, number_of_features])\n",
" sample_rate = 44100\n",
"\n",
" for file_index, url in enumerate(filelist):\n",
" print url\n",
" urllib.urlretrieve(url, filename='/tmp/localfile.wav')\n",
" audio = MonoLoader(filename = '/tmp/localfile.wav', sampleRate = sample_rate)()\n",
" zcr = ZeroCrossingRate()\n",
" hamming_window = Windowing(type = 'hamming') # we need to window the frame to avoid FFT artifacts.\n",
" spectrum = Spectrum()\n",
" central_moments = CentralMoments()\n",
" distributionshape = DistributionShape()\n",
" spectral_centroid = Centroid()\n",
"\n",
" frame_size = int(round(0.100 * sample_rate)) # 100ms\n",
" # Only do the first frame for now.\n",
" # TODO we should generate values for the entire file, probably by averaging the features.\n",
" current_frame = audio[0 : frame_size]\n",
" features[file_index, 0] = zcr(current_frame)\n",
" spectral_magnitude = spectrum(hamming_window(current_frame))\n",
" centroid = spectral_centroid(spectral_magnitude)\n",
" spectral_moments = distributionshape(central_moments(spectral_magnitude))\n",
" features[file_index, 1] = centroid\n",
" features[file_index, 2:5] = spectral_moments\n",
" return features\n",
"\n",
"def crossValidateKNN(features, labels):\n",
" \"\"\"\n",
" This code is provided as a template for your cross-validation\n",
" computation. Pass into the variables \"features\", \"labels\" your own data. \n",
"\n",
" As well, you can replace the code in the \"BUILD\" and \"EVALUATE\" sections\n",
" to be useful with other types of Classifiers.\n",
" \"\"\"\n",
" #\n",
" # CROSS VALIDATION \n",
" # The features array is arranged as rows of instances, columns of features in our training set.\n",
" numInstances, numFeatures = features.shape\n",
" numFolds = min(10, numInstances) # how many cross-validation folds do you want - (default=10)\n",
" # divide test set into 10 random subsets\n",
" indices = cross_validation.KFold(numInstances, n_folds = numFolds)\n",
"\n",
" errors = np.empty(numFolds)\n",
" for foldIndex, (train_index, test_index) in enumerate(indices):\n",
" # SEGMENT DATA INTO FOLDS\n",
" print('Fold: %d' % foldIndex) \n",
" print(\"TRAIN: %s\" % train_index)\n",
" print(\"TEST: %s\" % test_index)\n",
" \n",
" # SCALE\n",
" scaler = preprocessing.MinMaxScaler(feature_range = (-1, 1))\n",
" training_features = scaler.fit_transform(features.take(train_index, 0))\n",
" # BUILD NEW MODEL - ADD YOUR MODEL BUILDING CODE HERE...\n",
" model = KNeighborsClassifier(n_neighbors = 3)\n",
" model.fit(training_features, labels.take(train_index, 0))\n",
" # RESCALE TEST DATA TO TRAINING SCALE SPACE\n",
" testing_features = scaler.transform(features.take(test_index, 0))\n",
" # EVALUATE WITH TEST DATA - ADD YOUR MODEL EVALUATION CODE HERE\n",
" model_output = model.predict(testing_features)\n",
" print(\"KNN prediction %s\" % model_output) # Debugging.\n",
" # CONVERT labels(test,:) LABELS TO SAME FORMAT TO COMPUTE ERROR \n",
" labels_test = labels.take(test_index, 0)\n",
" # COUNT ERRORS. matches is a boolean array, taking the mean does the right thing.\n",
" matches = model_output != labels_test\n",
" errors[foldIndex] = matches.mean()\n",
" print('cross validation error: %f' % errors.mean())\n",
" print('cross validation accuracy: %f' % (1.0 - errors.mean()))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"snares_URL = \"https://ccrma.stanford.edu/workshops/mir2014/SnareCorpus.txt\"\n",
"snare_file_list = [audio_file_URL for audio_file_URL in process_corpus(snares_URL)]\n",
"kicks_URL = \"https://ccrma.stanford.edu/workshops/mir2014/KickCorpus.txt\"\n",
"kick_file_list = [audio_file_URL for audio_file_URL in process_corpus(kicks_URL)]\n",
"all_files = snare_file_list + kick_file_list\n",
"\n",
"drum_labels = np.empty(len(all_files), np.int32)\n",
"num_of_snares = len(snare_file_list)\n",
"drum_labels[0:num_of_snares] = 1 # set labels to the first sample type, e.g. snare\n",
"drum_labels[num_of_snares: num_of_snares + len(kick_file_list)] = 2 # Set next set to the second sample type, e.g kick\n",
"print drum_labels\n",
"\n",
"drum_features = spectral_features(all_files)\n",
"print drum_features\n",
"\n",
"print crossValidateKNN(drum_features, drum_labels)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2]\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_01_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_02_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_04_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_05_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_06_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_07_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_08_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_09_01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/SNARE_10_01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/snares/snare_mono.wav"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_01_V01.WAV\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_02_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_03_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_04_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_05_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_06_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_07_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_08_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_09_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"https://ccrma.stanford.edu/workshops/mir2014/audio/drum%20samples/kicks/Bass_Drum_10_V01.WAV"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[[ 1.29251704e-01 2.20017299e-01 4.07947935e-02 8.77821267e-01\n",
" -8.77261162e-03]\n",
" [ 1.06349207e-01 2.09325567e-01 3.52719985e-02 7.70348966e-01\n",
" -3.22882414e-01]\n",
" [ 1.14965983e-01 1.98087305e-01 3.39526162e-02 8.53771806e-01\n",
" -1.91451073e-01]\n",
" [ 1.09977327e-01 1.89880371e-01 3.07796430e-02 9.61023569e-01\n",
" 2.17356920e-01]\n",
" [ 1.37641728e-01 2.61134952e-01 4.25882451e-02 6.05063379e-01\n",
" -4.61072683e-01]\n",
" [ 1.12925172e-01 2.12166250e-01 3.77560928e-02 9.84243870e-01\n",
" 2.55144835e-01]\n",
" [ 1.43990934e-01 2.32887402e-01 4.25517298e-02 8.88096571e-01\n",
" -2.55122185e-02]\n",
" [ 1.24036282e-01 2.34317511e-01 4.48079035e-02 8.41823637e-01\n",
" -9.77532864e-02]\n",
" [ 1.33560091e-01 2.40808740e-01 3.97951268e-02 7.26550639e-01\n",
" -2.08487749e-01]\n",
" [ 1.14058957e-01 2.19529420e-01 3.08201890e-02 9.37722743e-01\n",
" 6.40640974e-01]\n",
" [ 9.97732393e-03 4.34924886e-02 1.27002802e-02 3.57807994e+00\n",
" 1.42459507e+01]\n",
" [ 7.02947844e-03 2.78101377e-02 6.62755780e-03 4.85479450e+00\n",
" 2.90792427e+01]\n",
" [ 5.66893443e-03 1.62475649e-02 2.56283046e-03 7.87885380e+00\n",
" 8.96873932e+01]\n",
" [ 5.66893443e-03 3.63209695e-02 9.78391711e-03 3.97197366e+00\n",
" 1.83651676e+01]\n",
" [ 8.61677993e-03 2.82980110e-02 8.20268691e-03 5.25167370e+00\n",
" 3.25443840e+01]\n",
" [ 7.93650839e-03 2.40181480e-02 5.99115109e-03 5.35486269e+00\n",
" 3.59657173e+01]\n",
" [ 7.70975044e-03 2.50173863e-02 6.07906608e-03 5.63076735e+00\n",
" 3.99530830e+01]\n",
" [ 5.66893443e-03 3.24563682e-02 9.35256947e-03 4.67505932e+00\n",
" 2.59624958e+01]\n",
" [ 8.84353742e-03 3.49452570e-02 8.03499669e-03 4.01454067e+00\n",
" 2.00484810e+01]\n",
" [ 8.61677993e-03 2.26410441e-02 6.27955049e-03 5.77101851e+00\n",
" 3.90028992e+01]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Fold: 0\n",
"TRAIN: [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [0 1]\n",
"KNN prediction [1 1]\n",
"Fold: 1\n",
"TRAIN: [ 0 1 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [2 3]\n",
"KNN prediction [1 1]\n",
"Fold: 2\n",
"TRAIN: [ 0 1 2 3 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [4 5]\n",
"KNN prediction [1 1]\n",
"Fold: 3\n",
"TRAIN: [ 0 1 2 3 4 5 8 9 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [6 7]\n",
"KNN prediction [1 1]\n",
"Fold: 4\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 10 11 12 13 14 15 16 17 18 19]\n",
"TEST: [8 9]\n",
"KNN prediction [1 1]\n",
"Fold: 5\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 12 13 14 15 16 17 18 19]\n",
"TEST: [10 11]\n",
"KNN prediction [2 2]\n",
"Fold: 6\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 14 15 16 17 18 19]\n",
"TEST: [12 13]\n",
"KNN prediction [2 2]\n",
"Fold: 7\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19]\n",
"TEST: [14 15]\n",
"KNN prediction [2 2]\n",
"Fold: 8\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 18 19]\n",
"TEST: [16 17]\n",
"KNN prediction [2 2]\n",
"Fold: 9\n",
"TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17]\n",
"TEST: [18 19]\n",
"KNN prediction [2 2]\n",
"cross validation error: 0.000000\n",
"cross validation accuracy: 1.000000\n",
"None\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This code is also posted as a template in `sources/crossValidationTemplate.py` "
]
}
],
"metadata": {}
}
]
}
\ No newline at end of file
notebooks/pitch_transcription_exercise.ipynb deleted 100644 → 0
View file @ 082d13d0
{
"metadata": {
"name": "",
"signature": "sha256:3a3278f7c6fb206a34581be477af529e901600f877d2eeeb0456448031912235"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Pitch Transcription Exercise"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Load an audio file."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import essentia.standard as ess\n",
"loader = ess.MonoLoader(filename='../test/test.wav', sampleRate=44100)\n",
"signal_in = loader()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Audio\n",
"#Audio(data=signal_in, rate=44100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Find each note and its pitch."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_onsets(signal_in):\n",
" onset_times, onset_rate = ess.OnsetRate()(signal_in)\n",
" return onset_times"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yin = ess.PitchYinFFT()\n",
"spectrum = ess.Spectrum()\n",
"\n",
"def get_pitch(segment):\n",
" if len(segment) < 4096:\n",
" N = len(segment) if len(segment) % 2 == 0 else len(segment) - 1\n",
" else:\n",
" N = 4096\n",
" pitch, pitch_conf = yin(spectrum(segment[:N]))\n",
" return pitch"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def generate_sine(pitch, n_duration):\n",
" #print midi, f0\n",
" n = arange(n_duration)\n",
" return 0.2*sin(2*pi*pitch*n/44100.0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def transcribe_pitch(signal_in):\n",
" \n",
" signal_out = zeros(len(signal_in))\n",
" \n",
" # get onsets\n",
" onsets = get_onsets(signal_in)\n",
" \n",
" # get pitches\n",
" for i in range(len(onsets)-1):\n",
" n0 = int(onsets[i]*44100)\n",
" n1 = int(onsets[i+1]*44100)\n",
" pitch = get_pitch(signal_in[n0:n1])\n",
" \n",
" signal_out[n0:n1] = generate_sine(pitch, n1-n0)\n",
" \n",
" return signal_out\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"signal_out = transcribe_pitch(signal_in)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Play the synthesized transcription."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import essentia\n",
"#Audio(data=signal_out, rate=44100)\n",
"signal_out = sum([signal_out, signal_in], axis=0)\n",
"ess.MonoWriter(filename='../test/out.wav')(essentia.array(signal_out))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
}
],
"metadata": {}
}
]
}
\ No newline at end of file
pca.html 0 → 100644
View file @ d97dd891
This source diff could not be displayed because it is too large. You can view the blob instead.
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View file @ d97dd891
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy, scipy, matplotlib.pyplot as plt, sklearn, stanford_mir\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[&larr; Back to Index](index.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Principal Component Analysis "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load a file:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, fs = librosa.load('prelude_cmaj_10s.wav')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute its chromagram:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = librosa.feature.chromagram(x, sr=fs)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(12, 431)\n"
]
}
],
"source": [
"print X.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display the chromagram:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x111078790>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+Bvj6lMf+O4qiVy3oyIQQQjxqcxVDfQpYCzwpDMOj93lOz0IPTAghxKM31538\n/wOGST+qeSd7NydLSPexEUII8Tg312fy1SiKrgLOATYB9zb/ux8oLvzQhBBCPFpz3cl/mrSf6y+Y\nOQVy9sTMR8HxfazRJLVJrNZpXqzrtnNjTdzgGa98xwGPs6xrCW961mn4vkuxlCFXylCrxHieQ74n\nS2lZD7vu2kl5rM5t0QgXX305a3qHmWiU2VkemfPYrzr+LF536lEk5TLl+7dS2z3J9jtHuOg/PwvA\nN//tjaxduQbdqKZ501q3c3xdJ83Xf+ar3jnnOT75+lez6qlrKK5ahpvN4WZzAFitqWwf57y3XTKP\n1Uy5ymV591IGciUKQRZrLcesWMItW7Zz1X3XHfgAM/j7089h6OQjYM8u6iN7KD+4i3Pe/P7296+9\n/jIogbUWm8QQ5NN8edLc3mdf9G9zHv/Kb7yX3PIGptFo53Fbo9N1cNJc6Yl77ye3eEX6vWYOvbV7\ns3vjiVGU6+FksignzStWjos1aV2BadRQjjtjnngw0M+33/u39B09jOP7aX2Btbj5PL9+0Ykoz8Pq\nNB/eOM0MY2tQbnr9xuNjaX57kEPNcC+llMPn3vhaBtcP0HPEKqw2ae6zo6jvGsUNMtRGRnGzuTS3\n3Bp0o461tv1eqO1Ir1M3W8B1gnYdwtR6BKtjkppO59kqMmyOE8AkjfZ4rNGg1LT1+NEn30Z2sAcv\nn0N5LvH4JH5XkV++4mT87u60RoQqjp9J17Y5PsfzGN+0jeLatbiZ3Iw5+j3hSv7nPW9m4Ilr26+L\nclysTlCuR2XrDrJDy9o58K1akxaTJJS3bCUYWISbye93DuU4aV1BfuYwZ60hqUzMmidvjeEr//sv\n6VnVT35xH16pkF4DhQKZ7n5e8u9DZAcWo1x/2popx8Hv7iMpl6mP7JwzT14ph+9f8hYKS/vxSnkc\n3wdrsSatGUnKFYqrV+Pmi9PqMdqvmTXoWh0AXZnEC/L7nWOubQ3Obv65atYRCiGEeFyb6xevy4CP\nAOuBXwH/FEXR6KEamBBCiEdvrs/kPwvcAfwjkAU+dEhGJIQQ4qCZ6zP5pVEU/TNAGIY/A246NEMS\nQghxsMwV5Nv71URRFIdhWH8kJwjD8HTga8CtUx7eGUXRSx/J8YQQQszfXEF+vy0rHyEL/CyKopcd\npOMJIYSYp7mC/FFhGG6a8vXSKV/bKIrWzPMcioP3D4YQQoiHYa4gv/4gnueMfTpDXR5F0QcO4vGF\nEELM4IC8KTeBAAASTElEQVSdoR6t5mfyr4+i6IL5/LxJYut4/oKOSQghOk19zw6C3kWPqDPUIdUY\nG8HJBDh+ZtaK1+OOPOeAx1nwitf3nYdyvdkrXs8/G2D/ilc/oDG+i+M3vHDOc8xV8brlx7/mBW+d\nf0brQlW8vujtZ4NSs1a8+qWedsWr26p4bVZaPmH16XMeP614XTJnxevorRGLnnJKs9Jy/4rXxujO\nA1a8utnCjBWv5S33su1XN81Y8aqr1XbFa3ZgEbS6Ek2teB3djfI8soNLZzy+SWJuuPjLB6x47X/S\nsWklZ7NDluNn2u+F3TdcT3bRAPnlq3D9/SteTRKTVCfS99AcFa+Ol5mx4tUkMQ/9/MoZK15NHON3\nd+Nl8+2uTVMrXq3RbPnhlSw96xQypb4ZX+OxaCM7r79rzorXRaecMmvFa1Irs+fmm+g79tgZK17T\nqRqs0cx042h0Qn3PjlkrUq0x3HrpV2eteG2MjsxY8QppK77q1gfQ9Ro9hx8z4/Fba/zA5T9+VBWv\nE/fcQWnt4bOe41AEecv+H9cAPCeKotohOL8QQvzJWvAgH0XRL4ChhT6PEEKI/c2nx6sQQog/UhLk\nhRCig0mQF0KIDiZBXgghOpgEeSGE6GALXgz1cCW1Sasct53H28qNbeXAGp3w+/d8nq7BPPn+PH4h\naD/XxAlu4GONJdNTIOjtRnkuTiYzLYe6lddrajV0o04yWaaydRdu4JFUG3j5gMKKxaAUNtFUtu7A\ny2cxcYLVhqC/m+KqNSjPx8QNbBKTVCaZvPd+TJzQfeR6sgNLpp1zah6tjutM3H07TiYD1mLimHTO\nzX9zlcIrlvCLXc38Y29afuzkfXdy0xd/QybrsvjwQdzAb+aBO+h6jOO7OL6HaaTjVZ6DX0jz7HHS\n43j5LPFEhXiiinIVWFCOaua1G4K+En53Ecf3iccmqI2M4WYzmEaMk/HJDfWTX74yXce4gYkb7L5x\nIzZJc62HTj0FxwtmnD/A7o3X4eXTMZlGY9r8TRyTX74SN2iOeUp+dOt6sNYwdsctNHaPYxJNXK7j\neA66nuB4aR59/wkbcIMsynFQXqadh50ewFAb2U4yMUl99zhOxtt7nVmL1YbCymX43T3pa+D6mEaN\nkWuvI+jrIhjoJ9Pdh5crTbs2rdHoWpmkPEnlwa34xQImSUjKVRx/7xq42QzJZJX8yuVk+xen828e\nRzeq6HqVpFIm6Fu0d8xMzxUfv/s2tv7yVrpW9uJmfGq7JnB8F2ssylEk1ZjS8CCFVStxPA8nyEGz\nRqB1PSWVMvWdO0kqtfS1c1Q71x4gv2wxSjlkevtx/AyT993D+N0P4OUDBk8+EcfPTnttrTFYa7A6\nZtM3f0j/hlUo1yGeqLTz+5WX1r3oeoPC8AqC/qFpHbpMEmNNwp6NN1FYsbw9FuU1z2MMRieYRoM7\nvvYbBtf1E/QWqO4Yx/GbOfWug67FzfXUDD5pXfp+A7xCEaxFOQ7lB7bguC5xuYqJExzPRXkuVhuS\nSprhne3vxgkyFIZX09gzwq7r70A5Cr+Uo/+4Y3BzxXb9h7VpXv62q35BUo3JDXThdxfRlRrWGJyM\n3567k0nrL/yuEpmuHrxCae97xlpQislNEV6pq7m4dr81AChv3oKu1qnsGGft+S/crxhK7uSFEKKD\nSZAXQogOJkFeCCE6mAR5IYToYBLkhRCig0mQF0KIDiZBXgghOpgEeSGE6GAS5IUQooNJkBdCiA4m\nQV4IITqYBHkhhOhgEuSFEKKDSZAXQogOJkFeCCE6mAR5IYToYBLkhRCig0mQF0KIDva4a/83fvet\n1i0UyA0uA6WmtTtTjoPRCUl1Ei9X3K+lXEurfZjjBe3nTaUbVQDcTLP9nE6wOsbEDZJqmaBnEMfz\nZzy2NYb66M60NZ+3t/Vgq2Wa0TET99xBfulyMl39sx5j3zFNVd2xBV2rEvQvwg3y7fZv1hqUckhq\nZXR1Er/U057DfmvQbA02df2miivjuEGu3basNYfWc5VyZhxjqxVbXB7Fy5VwPL/9WKvVn0ka6Mok\nme6B9jEONOepGhO7iSfG8Itd01qrtcbYWuffvPsL/NVnPj/rcX7w8f9Nfkn6GvhdxXZLNcf30bU6\n333Pj3nfFd+Z9fnfft/fUhoeJL9sMW6QpbptG0875+84f8PpPO+Z61l77okUV6xrj0nHNZLJMUZv\nuYPx+3dx3tsumXOe5x11Kn/51rPoe+ITcHNFXD9oz1/Xa4xuvJ2+YzfgFUoo15/ewlI5/O7fP8fr\nLv3cnOd402lnc9aLjiTbVyTTWwJjsdaiPBesZfsf7uGl//djsz7/ex/6B3Zt2sOaZz+BTF8vV3/w\nct7y9cvYsGg9n/re2wn6h9rjNkmMtQbTqBFPjnPSiRfOOTaAKy57N93hetxcvt1KsDG+C5MklDdv\npmt9iONnQDnTWj8C1HfvmNc5AC593avJd2XIlgIKQyWstbgZj898+JdctvGqWZ8XuAEvPOrJrF/W\nx5l/9VQ2/fRWXn3JpwH4syeczhvefS7FVatxg1zaOlNrdK3CCU98ybzGBfClf3w93St7WHzqCbj5\nIqZRw2qN42do7NmFVyzh+BmcTHb6E5vrcOul3+LC938CgI2br5b2f0II8adEgrwQQnQwCfJCCNHB\nJMgLIUQHm/k3lwdJGIanA18DbgUU4AMfjqLo6wt5XiGEEKmFvpO3wBVRFD09iqLTgWcCbw3D8JgF\nPq8QQggWPshPS+eJoqgMfAJ48QKfVwghBI/NZ/LbgYHH4LxCCPEnZ0GLoZqfyb8+iqILpjz2RqAY\nRdG7Z3qOrtesG2Rn+pYQQohZbBg+bcZiqAX9xeu+wjDsAi4CzpvtZ8oP3INUvErFq1S8SsWrVLw+\n/IrXmSx0kLfAGWEYXgno5vneHkXRXQt8XiGEECxwkI+i6BfA0EKeQwghxOykGEoIITqYBHkhhOhg\nEuSFEKKDSZAXQogOJkFeCCE6mAR5IYToYIe0GGo+tly1kfEdFdY960gA4okK8UQVL5fB7yrg+B49\nR23AGo1VDtZoUAqsxZoElAPWpIUJzeKhVrEOpIUUI9dey+hd2xk8fg1+VwmrNfHYBPVdY8TlOsuf\ncybphpmpVvGUtRasobLlAboPP6pdBGONxiR14slxkvIE9/1kI7Xy9Qw/eZhMVyE9iEoL0WyiqY2M\nsfSZZ81azLX159dy383bOfIZh+EGGeLJKtZYdCPBz2fIdBcprB5O50paaDS1SMZag9XxtGKt9lo0\n/7zl0u8yePggpdVL8Lu78fJFrDE4vo9NErxCqV1o1ZqjNQm6UUcpha7XcIMc4KfnM5r67h3YOCap\nVLj5K79j7SnD7dcsHi/j5gIc36O+a4yhp53WLqLZ1+jGW7nuWzcTnriM0qpF6FoDXW9gtSXoK+H4\nHljLr6/fwlf/+Q3kBwoEPXlMrFGOwhpLUm3Qf/zReLl8ui5eprkQBpSDrk5y8hmr+P65b6G0Zgk2\n0SilsNZi4gTTiCkdtgYvX0jnqRzcfJHL/uWvyPcXKK0awmrdHrNyHJTj4gQ5gv5uBvq6eMuZ5/KU\nZ62he81ivGIOqw3KdbDGYuoN/K4i2cXL9ivs8/JduEGOSz9wJX92/m561y/BcV1MnKTXgjZkugtc\nd/M2/uOCC+gbzNM33ENuUTdJuYaT8YjLdVzfJbe4j/yypTh+BuVPWYMp4/7Jf7+TTHcR5bnYZO+c\nTJxQGF7B4IkxwcAilONy0itP4qvr+3FchU2SacdK34dgjSYpT7K6dyVvPvtUjnjGYeSG+trvAZpr\nbLWha33YLvhrFct5+S6sNYze+Ru61oftQrD2aUiLA5XrcuGxZ3DWqavpWVaia/UiknIN5TroWoyX\nD9IiPKUoHbYKADfIotz0+jE64RmnbuEN/3o2bi5AOW4aT1rzTzSm1iDT142byxH0D3Hk0qVcdcYR\n6GodNxeQX7a8XRTYeq+4QY4rv/Fe6qMTfOXjv+OVbzkDr5DD8VziiQpePotpxFS27cbLB/QcfThu\nJsDLd6XXUPNaVcph9403UVq3GjdXSNdon+JGazRuxuWyf/krHrx7z4zvJ7mTF0KIDiZBXgghOpgE\neSGE6GAS5IUQooNJkBdCiA4mQV4IITqYBHkhhOhgEuSFEKKDSZAXQogOJkFeCCE6mAR5IYToYBLk\nhRCig0mQF0KIDiZBXgghOpgEeSGE6GAS5IUQooNJkBdCiA72uOsMdf67/hOAU3/0RO7bs51qUmek\nsousl+OEZYdz5JIhLvrPFTiZAK0cbBKnHZq0xugEAKVU2uUlSDsbKUe1O0aZuMEtl9/Gm7/0ZY4c\nXMepa9cxMlHm6vtuZ2d5BIBrbzgBty99rtEJjdFd4DjoagWA+q6xtPOUMehGBV2rkkxOsON3G5nc\nWeYVH7w0ncx/zT7PG+9+OszQGcoaw303b+fNX/oyfAn6cn3sru5ufz/r5XjpE57C69//Ytxsfm8X\nrCROO1elB8HEMV4+7Uo1tYOR1Qk4Lq+6+FMAHL/kSM5/6gbWHjPEljt2sfak5RSXD1BqduRxXC/t\nejUxiq5WKW/ZCsaiXIfuI7MA6HqVpFLm5k//lLHRGtfeto3LNl4Fn5l9/jdEP4AZOkNZY9hz5zbe\n8vXL4Osw3LOCzaMPkPfzVOIKxy4+nMMXLaYWJ3z79l/xpk+/Dqs1No4xOsFqjeP7mHq93QVIuW67\nixY27RwFsPjUDWS6etI5NOrpzwGmViOpVKavnTWYRp1fX7WZrkKG9UeOsuTY5RSHDwOTdt7StTK6\nWkFX6zjZDC9+3wUEPYOgFNZoHNdvdz+yxmCSenuNp1LKwSqHb9xyNd+45WoG8v3UkwYTjYn2z6zs\nXsbT1x3BCa9+Cn6xhJsvpB3K4gbo9PU3jQZONsDv6gHlpJ2vjE7n3+zmlV28mMLK4XZ3NRM32teR\nrpTT8Xhee/0mN2/nhz+6E4DXnb4B5XhT5tNAV8tMbtrE5ivv4EP/dB4rzn5as4MYKMfD8fx2JzOr\nY1BOu6tSe/6Oi8Llp9+5g5cctrTZGa3ZgcpadD3B8RzqY1XOOftwVj33RLxiKb22lYNp1NtdqHSt\nCsbiZIL2eVqvgQMcfeHTyC5a0pzDlLVpdoGbvG8TbpDFDbLt8dV3jRFXGtRGK+RXLMeaBGtcjI6b\n10yM0RovF/C6D7yE4vBhmLiGNRbH83C8AGsNXWEZ5fnN62JvZyw15d77t1+9kZPOS/DyDxBPVFCu\nk3bB0ybt5KUNWMvaF5/F6iTe7/3UmqcQQogOJUFeCCE6mAR5IYToYBLkhRCig0mQF0KIDrbg2TVh\nGB4FvBfIA0XgB1EUvXOhzyuEEGKB7+TDMOwBvgK8KYqiM4CTgQ1hGL5+Ic8rhBAitdAf15wLXBFF\n0T0AURQZ4JXMmUEthBDiYFnoj2uWAJumPhBFUXmBzymEEKJJtaskF0AYhhcAx0VR9I9THlsNLI+i\n6JczPeeu6F57WLhmwcYkhBCdqDqyldzAUrXv4wsd5EvAb4Bzoyi6NwxDH/ga8OMoimYs+t8wfJoF\nOHV4rm0NXoOTCeAA2xo4s2xr8Mt//9oBtjX4Otm+ISDd1qC+a9u0bQ0m7t7M4FOejOtnZ9zW4GXv\nmWM/g6Yb7/4p7ixl/T9/26XptgbMva1BdvEy3EzwiLY1OP6o5wNzb2vg5Yo4roduVGfZ1uAI3CA3\n+7YGc7gh+gFetjjj/O/8/Dd58Ts+Ahx4W4NrrvvqrNsaBAOLcPzMPtsaGKyx6Ook8eTEnNsa+N3d\nuEEWL5+OM54c5wtv/FxzW4MBlhy7nEVPPTXdhsAakso4ulqhvPkBnGyG3JIlj2hbA2sMRsccu+4Z\nAHNua3DRu8894LYGuaGls25roOs13Eww67YGTpAFa8n0DqCU4sEf/5yvffY6AF73/hdTHD6svVWB\njmvTtjXoXlqa17YG+74PWlsYfOLl7+Ul/3DGnNsaVHZV5rWtgVcq7betgdWapDx5wG0Ngt5e3FwO\nr9hNPDHKnhtvbW9rsPK5p5Dp6sXxgmnbGuy+8UZsosktXTTrtga6NvO2BlN9+28u4aTzjsbLZ2fd\n1mDP3TtZd/5ZmCSeMcgv6GfyURRNAK8CPhmG4ZXAb4EbZgvwQgghDq4FT6GMouh64MyFPo8QQoj9\nSTGUEEJ0MAnyQgjRwSTICyFEB5MgL4QQHUyCvBBCdLAFzZMXQgjx2JI7eSGE6GAS5IUQooNJkBdC\niA4mQV4IITqYBHkhhOhgEuSFEKKD/X8SHYZnhPOd5gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10dd3ab10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"librosa.display.specshow(X, sr=fs, y_axis='chroma')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display a single chroma vector:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 3 artists>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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APTQHUtBct+rW1t9gDfipYdW2Uqfp2x8BN2bm1cBDwG90e41BBXunNWdK8CDN8/mh+TM9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\nVJj/B3AF/eu/gWOrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111078c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.stem(X[:,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a PCA model object."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = sklearn.decomposition.PCA(n_components=11, whiten=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply PCA to the chromagram:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Y = model.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(12, 11)\n"
]
}
],
"source": [
"print Y.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display all of the principal components:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x111dda2d0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ahRB6AR8CPzOzv5wB8zxE/tcl64FnzezFGs/TDlgEXA4cB35hZlbDeQYDc83shhDCZcCS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\nTqisIk6orCJO/B/DiC0ysWZ0AAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bdbf150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(Y, interpolation='nearest', origin='lower')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot one of the principal components:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 3 artists>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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UB4v27CPieuDmBZOvzcy/iIjLT/GyVwObgTva698ZEd/KzGd6LVaSVM2iYZ+ZW4Gty1zn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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bde5b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.stem(Y[:,5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[&larr; Back to Index](index.html)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
pitch_transcription_exercise.html 0 → 100644
View file @ d97dd891
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pitch_transcription_exercise.ipynb 0 → 100644
View file @ d97dd891
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