Essentia Lightning Talk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import essentia.standard as ess\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Algo in module essentia.standard:\n",
      "\n",
      "class Algo(Algorithm)\n",
      " |  MonoLoader\n",
      " |  \n",
      " |  \n",
      " |  Outputs:\n",
      " |  \n",
      " |    [vector_real] audio - the audio signal\n",
      " |  \n",
      " |  \n",
      " |  Parameters:\n",
      " |  \n",
      " |    downmix:\n",
      " |      string ∈ {left,right,mix} (default = \"mix\")\n",
      " |      the mixing type for stereo files\n",
      " |  \n",
      " |    filename:\n",
      " |      string\n",
      " |      the name of the file from which to read\n",
      " |  \n",
      " |    sampleRate:\n",
      " |      real ∈ (0,inf) (default = 44100)\n",
      " |      the desired output sampling rate [Hz]\n",
      " |  \n",
      " |  \n",
      " |  Description:\n",
      " |  \n",
      " |    Given an audio file this algorithm outputs the raw audio data downmixed to\n",
      " |    mono. Audio is resampled in case the given sampling rate does not match the\n",
      " |    sampling rate of the input signal.\n",
      " |    \n",
      " |    This algorithm uses AudioLoader and thus inherits all of its input\n",
      " |    requirements and exceptions.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      Algo\n",
      " |      Algorithm\n",
      " |      __builtin__.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, *args)\n",
      " |  \n",
      " |  __init__(self, **kwargs)\n",
      " |  \n",
      " |  __str__(self)\n",
      " |  \n",
      " |  compute(self, *args)\n",
      " |  \n",
      " |  configure(self, **kwargs)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __struct__ = {'description': 'Given an audio file this algorithm outpu...\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from Algorithm:\n",
      " |  \n",
      " |  __compute__(...)\n",
      " |      compute the algorithm\n",
      " |  \n",
      " |  __configure__(...)\n",
      " |      Configure the algorithm\n",
      " |  \n",
      " |  getDoc(...)\n",
      " |      Returns the doc string for the algorithm\n",
      " |  \n",
      " |  getStruct(...)\n",
      " |      Returns the doc struct for the algorithm\n",
      " |  \n",
      " |  inputNames(...)\n",
      " |      Returns the names of the inputs of the algorithm.\n",
      " |  \n",
      " |  inputType(...)\n",
      " |      Returns the type of the input given by its name\n",
      " |  \n",
      " |  name(...)\n",
      " |      Returns the name of the algorithm.\n",
      " |  \n",
      " |  outputNames(...)\n",
      " |      Returns the names of the outputs of the algorithm.\n",
      " |  \n",
      " |  paramType(...)\n",
      " |      Returns the type of the parameter given by its name\n",
      " |  \n",
      " |  paramValue(...)\n",
      " |      Returns the value of the parameter or None if not yet configured\n",
      " |  \n",
      " |  parameterNames(...)\n",
      " |      Returns the names of the parameters for this algorithm.\n",
      " |  \n",
      " |  reset(...)\n",
      " |      Reset the algorithm to its initial state (if any).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from Algorithm:\n",
      " |  \n",
      " |  __new__ = <built-in method __new__ of type object>\n",
      " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ess.MonoLoader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "audio = ess.MonoLoader(filename='example.mp3')()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x106cfe550>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEACAYAAABPiSrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d09850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(audio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class FrameGenerator in module essentia.algorithms:\n",
      "\n",
      "class FrameGenerator(__builtin__.object)\n",
      " |  # FrameGenerator\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, audio, frameSize=1024, hopSize=512, startFromZero=False, validFrameThresholdRatio=0, lastFrameToEndOfFile=False)\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |  \n",
      " |  next(self)\n",
      " |  \n",
      " |  num_frames(self)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __struct__ = {'description': 'The FrameGenerator is a special algorith...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ess.FrameGenerator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Algo in module essentia.standard:\n",
      "\n",
      "class Algo(Algorithm)\n",
      " |  LoudnessVickers\n",
      " |  \n",
      " |  \n",
      " |  Inputs:\n",
      " |  \n",
      " |    [vector_real] signal - the input signal\n",
      " |  \n",
      " |  \n",
      " |  Outputs:\n",
      " |  \n",
      " |    [real] loudness - the Vickers loudness [dB]\n",
      " |  \n",
      " |  \n",
      " |  Parameters:\n",
      " |  \n",
      " |    sampleRate:\n",
      " |      real ∈ [44100,44100] (default = 44100)\n",
      " |      the audio sampling rate of the input signal which is used to create the\n",
      " |      weight vector [Hz] (currently, this algorithm only works on signals with a\n",
      " |      sampling rate of 44100Hz)\n",
      " |  \n",
      " |  \n",
      " |  Description:\n",
      " |  \n",
      " |    This algorithm computes Vickers's loudness for a given audio signal.\n",
      " |    Currently, this algorithm only works for signals with a 44100Hz sampling\n",
      " |    rate. This algorithm is meant to be given frames of audio as input (not\n",
      " |    entire audio signals). The algorithm described in the paper performs a\n",
      " |    weighted average of the loudness value computed for each of the given frames,\n",
      " |    this step is left as a post processing step and is not performed by this\n",
      " |    algorithm.\n",
      " |    \n",
      " |    References:\n",
      " |      [1] E. Vickers, \"Automatic Long-term Loudness and Dynamics Matching,\" in\n",
      " |      The 111th AES Convention, 2001.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      Algo\n",
      " |      Algorithm\n",
      " |      __builtin__.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, *args)\n",
      " |  \n",
      " |  __init__(self, **kwargs)\n",
      " |  \n",
      " |  __str__(self)\n",
      " |  \n",
      " |  compute(self, *args)\n",
      " |  \n",
      " |  configure(self, **kwargs)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __struct__ = {'description': 'This algorithm computes Vickers\\'s loudn...\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from Algorithm:\n",
      " |  \n",
      " |  __compute__(...)\n",
      " |      compute the algorithm\n",
      " |  \n",
      " |  __configure__(...)\n",
      " |      Configure the algorithm\n",
      " |  \n",
      " |  getDoc(...)\n",
      " |      Returns the doc string for the algorithm\n",
      " |  \n",
      " |  getStruct(...)\n",
      " |      Returns the doc struct for the algorithm\n",
      " |  \n",
      " |  inputNames(...)\n",
      " |      Returns the names of the inputs of the algorithm.\n",
      " |  \n",
      " |  inputType(...)\n",
      " |      Returns the type of the input given by its name\n",
      " |  \n",
      " |  name(...)\n",
      " |      Returns the name of the algorithm.\n",
      " |  \n",
      " |  outputNames(...)\n",
      " |      Returns the names of the outputs of the algorithm.\n",
      " |  \n",
      " |  paramType(...)\n",
      " |      Returns the type of the parameter given by its name\n",
      " |  \n",
      " |  paramValue(...)\n",
      " |      Returns the value of the parameter or None if not yet configured\n",
      " |  \n",
      " |  parameterNames(...)\n",
      " |      Returns the names of the parameters for this algorithm.\n",
      " |  \n",
      " |  reset(...)\n",
      " |      Reset the algorithm to its initial state (if any).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from Algorithm:\n",
      " |  \n",
      " |  __new__ = <built-in method __new__ of type object>\n",
      " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ess.LoudnessVickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x107970410>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAFwCAYAAACLn6xhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1071f9690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vickers = ess.LoudnessVickers()\n",
    "dbValues = []\n",
    "hopSize = 1024\n",
    "# hopSize and frameSize need to be 1024, simply because those are the\n",
    "# values we chose in Sonic Visualiser\n",
    "# frameSize by default is already 1024, that leaves hopSize:\n",
    "for frame in ess.FrameGenerator(audio, hopSize=hopSize):\n",
    "    dbValues.append(vickers(frame))\n",
    "\n",
    "dbValues = np.array(dbValues)\n",
    "\n",
    "# Let's plot the values.\n",
    "# Audio sample values are from -1 to +1.\n",
    "# Vicker's Loudness values are from -100 to 0. We know that simply from\n",
    "# looking at Sonic Visualiser (of course there's a more mathematical explanation,\n",
    "# but a look at the scale in Sonic Visualiser is sufficient for now).\n",
    "#\n",
    "# Scale the Vicker's Loudness values to a scale from -1 to +1:\n",
    "scaledValues = ((dbValues + 100) / 100 * 2) - 1\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(audio)\n",
    "plt.plot(np.arange(len(dbValues))*hopSize, scaledValues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks very much like in Sonic Visualiser, doesn't it?"
   ]
  }
 ],
 "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": "2.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}