"When we feature extract a sample collection, we need to sequentially access audio files, segment them (or not), and feature extract them. Loading a lot of audio files into memory is not always a feasible or desirable operation, so you will create a loop which loads an audio file, feature extracts it, and closes the audio file. Note that the only information that we retain in memory are the features that are extracted.\n",
"When we feature extract a sample collection, we need to sequentially access audio files, segment them (or not), and feature extract them. Loading a lot of audio files into memory is not always a feasible or desirable operation, so you will create a loop which loads an audio file, feature extracts it, and closes the audio file. Note that the only information that we retain in memory are the features that are extracted.\n",
"\n",
"\n",
"Create a loop which reads in an audio file, extracts the zero crossing rate, and some spectral statistics. You can use the \"in\" operator to retrieve each audio file URL from process_corpus(), as used above. The feature information for each audio file (the \"feature vector\") should be stored as a feature array, with columns being the features and rows for each file. For example:\n",
"Create a loop which reads in an audio file, extracts the zero crossing rate, and some spectral statistics. You can use the \"in\" operator to retrieve each audio file URL from process_corpus(), as used above. The feature information for each audio file (the \"feature vector\") should be stored as a feature array, with columns being the features and rows for each file. For example:"
"\u001b[0;32m/sw/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 842\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 843\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--> 844\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 845\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 846\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",
"Here's an example of how to feature extract the first frame from the current audio file..."
"Here's an example of how to feature extract the first frame from the current audio file, using Essentia's [ZeroCrossingRate](http://essentia.upf.edu/documentation/reference/streaming_ZeroCrossingRate.html) and [CentralMoments](http://essentia.upf.edu/documentation/reference/std_CentralMoments.html) classes..."
"4. First, extract all of the feature data for the kick drums and store it in a feature array. (For my example, above, I'd put it in \"featuresKick\")\n",
"4. First, extract all of the feature data for the kick drums and store it in a feature array. (For my example, above, I'd put it in \"features_kick\")\n",
"\n",
"\n",
"5. Next, extract all of the feature data for the snares, storing them in a different array. \n",
"5. Next, extract all of the feature data for the snares, storing them in a different array. \n",
"Again, the kick and snare features should be separated in two different arrays!\n",
"Again, the kick and snare features should be separated in two different arrays!\n",