RP_extract_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Rhythm and Timbre Analysis from Music</h1></center>\n",
    "<center><h2>Rhythm Pattern Music Features</h2></center>\n",
    "<center><h2>Extraction and Application Tutorial</h2></center>\n",
    "<br>\n",
    "<center><h3>Thomas Lidy and Alexander Schindler</h3>\n",
    "<h3>lidy@ifs.tuwien.ac.at</h3><br>\n",
    "<b>Institute of Software Technology and Interactive Systems</b><br>TU Wien\n",
    "<br>\n",
    "<center><h3>http://www.ifs.tuwien.ac.at/mir</h3></center>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <a href=\"#requirements\">Requirements</a>\n",
    "2. <a href=\"#processing\">Audio Processing</a>\n",
    "3. <a href=\"#extraction\">Audio Feature Extraction</a>\n",
    "4. <a href=\"#application\">Application Scenarios</a><br>\n",
    "    4.1 <a href=\"#getsoundcloud\">Getting Songs from Soundcloud</a><br>\n",
    "    4.2. <a href=\"#similar\">Finding Similar Sounding Songs</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"requirements\"><font color=\"#0404B4\">1. Requirements</font></a>\n",
    "\n",
    "This Tutorial uses iPython Notebook for interactive coding. If you use iPython Notebook, you can interactively execute your code (and the code here in the tutorial) directly in the Web browser. Otherwise you can copy & paste code from here to your prefered Python editor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to install iPython notebook on your computer, use this in Terminal\n",
    "sudo pip install \"ipython[notebook]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RP Extract Library\n",
    "\n",
    "This is our mean library for rhythmic and timbral audio feature analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "<li><a href=\"https://github.com/tuwien-musicir/rp_extract\">RP_extract</a> Rhythm Patterns Audio Feature Extraction Library (includes <a href=\"https://github.com/WarrenWeckesser/wavio\">Wavio</a> for reading wav files (incl. 24 bit)) </li>\n",
    "</ul>\n",
    "\n",
    "download <a href=\"https://github.com/tuwien-musicir/rp_extract/archive/master.zip\">ZIP</a> or check out from GitHub:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in Terminal\n",
    "git clone https://github.com/tuwien-musicir/rp_extract.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Libraries\n",
    "\n",
    "RP_extract depends on the following libraries. If not already included in your Python installation,\n",
    "please install these Python libraries using pip or easy_install:\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"http://www.numpy.org/\">Numpy</a>: the fundamental package for scientific computing with Python. It implements a wide range of fast and powerful algebraic functions.</li>\n",
    "<li><a href=\"http://www.scipy.org/install.html\">Scipy</a>: Scientific Python library</li>\n",
    "<li><a href=\"http://matplotlib.org\">matplotlib</a>: only needed for plotting (if you skipt the plots below, you are fine without) </li>\n",
    "</ul> \n",
    "\n",
    "They can usually be installed via Python PIP installer on command line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in Terminal\n",
    "sudo pip install numpy scipy matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Libraries \n",
    "\n",
    "These libraries are used in the later tutorial steps, but not necessarily needed if you want to use the RP_extract library alone:\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"https://github.com/tuwien-musicir/mir_utils\">mir_utils</a>: these are additional functions used for the Soundcloud Demo data set in the tutorial below</li>\n",
    "<li><a href=\"https://developers.soundcloud.com\">Soundcloud API</a>: used to retrieve and analyze music from Soundcloud.com</li>\n",
    "<li>urllib: for downloading content from the web (may be pre-installed already, then you can skip it)</li>\n",
    "<li><a href=\"https://pypi.python.org/pypi/unicsv/1.0.0\">unicsv</a>: used in rp_extract_files.py for batch iteration over many wav or mp3 files, and storing features in CSV (only needed when you want to do batch feature extraction to CSV)</li>\n",
    "<li><a href=\"http://scikit-learn.org/stable/\">sklearn</a>: Scikit-Learn machine learning package - used in later tutorial steps for finding similar songs and/or using machine learning / classification\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in Terminal\n",
    "sudo pip install soundcloud urllib unicsv scikit-learn\n",
    "\n",
    "git clone https://github.com/tuwien-musicir/mir_utils.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MP3 Decoder\n",
    "If you want to use MP3 files as input, you need to have one of the following MP3 decoders installed in your system:\n",
    "\n",
    "<ul>\n",
    "<li>Windows: FFMpeg (ffmpeg.exe is included in RP_extract library on Github above, nothing to install)</li>\n",
    "<li>Mac: <a href=\"http://www.thalictrum.com/en/products/lame.html\">Lame for Mac</a> or <a href=\"http://ffmpegmac.net\">FFMPeg for Mac</a></li>\n",
    "<li>Linux: please install mpg123, lame or ffmpeg from your Software Install Center or Package Repository</li>\n",
    "</ul>\n",
    "\n",
    "Note: If you don't install it to a path which can be found by the operating system, use this to add path where you installed the MP3 decoder binary to your system PATH so Python can call it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path = '/path/to/ffmpeg/'\n",
    "os.environ['PATH'] += os.pathsep + path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import + Test your Environment\n",
    "If you have installed all required libraries, the follwing imports should run without errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# numerical processing and scientific libraries\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# reading wav and mp3 files\n",
    "from audiofile_read import *  # included in the rp_extract git package\n",
    "\n",
    "# Rhythm Pattern Audio Extraction Library\n",
    "from rp_extract import rp_extract\n",
    "from rp_plot import *   # can be skipped if you don't want to do any plots\n",
    "\n",
    "\n",
    "# misc\n",
    "from urllib import urlopen\n",
    "import urllib2\n",
    "import gzip\n",
    "import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"processing\"><font color=\"#0404B4\">2. Audio Processing</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Extraction is the core of content-based description of audio files. With feature extraction from audio, a computer is able to recognize the content of a piece of music without the need of annotated labels such as artist, song title or genre. This is the essential basis for information retrieval tasks, such as similarity based searches (query-by-example, query-by-humming, etc.), automatic classification into categories, or automatic organization and clustering of music archives.\n",
    "\n",
    "Content-based description requires the development of feature extraction techniques that analyze the acoustic characteristics of the signal. Features extracted from the audio signal are intended to describe the stylistic content of the music, e.g. beat, presence of voice, timbre, etc.\n",
    "\n",
    "We use methods from digital signal processing and consider psycho-acoustic models in order to extract suitable semantic information from music. We developed various feature sets, which are appropriate for different tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Audio Files\n",
    "\n",
    "### Load audio data from wav or mp3 file\n",
    "\n",
    "We provide a library (audiofile_read.py) that is capable of reading WAV and MP3 files (MP3 through an external decoder, see Installation Requirements above).\n",
    "\n",
    "Take any MP3 or WAV file on your disk - or download one from e.g. <a href=\"http://freemusicarchive.org\">freemusicarchive.org</a>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# provide/adjust the path to your wav or mp3 file\n",
    "\n",
    "audiofile = \"music/test.wav\"\n",
    "\n",
    "samplerate, samplewidth, wavedata = audiofile_read(audiofile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note about Normalization:</b> Normalization is automatically done by audiofile_read() above.\n",
    "\n",
    "Usually, an audio file stores integer values for the samples. However, for audio processing we need float values that's why the audiofile_read library already converts the input data to float values in the range of (-1,1). \n",
    "\n",
    "This is taken care of by audiofile_read. In the rare case you don't want to normalize, use this line instead of the one above:\n",
    "\n",
    " samplerate, samplewidth, wavedata = audiofile_read(audiofile, normalize=False)\n",
    "\n",
    "In case you use another library to read in WAV files (such as scipy.io.wavfile.read) please have a look into audiofile_read code to do the normalization in the same way. Note that scipy.io.wavfile.read does not correctly read 24bit WAV files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Audio Information\n",
    "\n",
    "Let's print some information about the audio file just read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully read audio file: music/test.wav\n",
      "44100 Hz, 16 bit, 2 channel(s), 7778304 samples\n"
     ]
    }
   ],
   "source": [
    "nsamples = wavedata.shape[0]\n",
    "nchannels = wavedata.shape[1]\n",
    "\n",
    "print \"Successfully read audio file:\", audiofile\n",
    "print samplerate, \"Hz,\", samplewidth*8, \"bit,\", nchannels, \"channel(s),\", nsamples, \"samples\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Wave form\n",
    "we use this to check if the WAV or MP3 file has been correctly loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Stereo\n"
     ]
    },
    {
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9rLX//OsvkXvu0fHPPuu5rrOzdR8rIjJ1qk6zcKHI2rWa/+9/RXr2NJ998EHNnzkj8tBD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\nDztZ9T/wSjVeAAAAAElFTkSuQmCC\n"
     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "metadata": {}
    }
   ],
   "source": [
    "max_samples_plot = 4 * samplerate # limit number of samples to plot (to 4 sec), to avoid graphical overflow\n",
    "\n",
    "if nsamples < max_samples_plot:\n",
    "    max_samples_plot = nsamples\n",
    "\n",
    "plot_waveform(wavedata[0:max_samples_plot], 16, 5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Audio Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For audio processing and feature extraction, we use a single channel only.\n",
    "\n",
    "Therefore in case we have a stereo signal, we combine the separate channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use combine the channels by calculating their geometric mean\n",
    "wavedata_mono = np.mean(wavedata, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below an example waveform of a mono channel after combining the stereo channels by arithmetic mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Mono\n"
     ]
    },
    {
     "data": {
      "image/png": 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T8QT1iIgIrM7xFHNP8yQnJ+cKjAHtztx0ce58nqG3af5+/+GHC/e8QCIiIiIi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\nJiIiIiIiopDGwJiIiIiIiIhC2v8DlQXWrPs2AQQAAAAASUVORK5CYII=\n"
     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "metadata": {}
    }
   ],
   "source": [
    "plot_waveform(wavedata_mono[0:max_samples_plot], 16, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eMtWNpKrZeFv1VEx9A7FaOeWsOTV5LcNByVTWTM94U9G0o2rmLBgxYdpFkxoq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eV4MxyCh8svQEGvW4VEtgKjsLKyLEJ+FzJpc8juIVPIpM8hk/ikPCu1us9tUUaAflEK5/vbYKt8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\nKmydl8ZZaOQGSiuinkP4VYM4R6wv50ze5+YKpRWYg0uzOUbZun1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cyGktvrpCRcbSbo10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\n"
     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "source": [
    "plotstft(wavedata_mono, samplerate, binsize=512, ignore=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"extraction\"><font color=\"#0404B4\">3. Audio Feature Extraction</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rhythm Patterns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"350\" src=\"http://www.ifs.tuwien.ac.at/mir/audiofeatureextraction/feature_extraction_RP_SSD_RH_web.png\" style=\"float:right;margin-left:20px;margin-bottom:20px\">\n",
    "\n",
    "Rhythm Patterns (also called Fluctuation Patterns) describe modulation amplitudes for a range of modulation frequencies on \"critical bands\" of the human auditory range, i.e. fluctuations (or rhythm) on a number of frequency bands. The feature extraction process for the Rhythm Patterns is composed of two stages:\n",
    "\n",
    "First, the specific loudness sensation in different frequency bands is computed, by using a Short Time FFT, grouping the resulting frequency bands to psycho-acoustically motivated critical-bands, applying spreading functions to account for masking effects and successive transformation into the decibel, Phon and Sone scales. This results in a power spectrum that reflects human loudness sensation (Sonogram).\n",
    "\n",
    "In the second step, the spectrum is transformed into a time-invariant representation based on the modulation frequency, which is achieved by applying another discrete Fourier transform, resulting in amplitude modulations of the loudness in individual critical bands. These amplitude modulations have different effects on human hearing sensation depending on their frequency, the most significant of which, referred to as fluctuation strength, is most intense at 4 Hz and decreasing towards 15 Hz. From that data, reoccurring patterns in the individual critical bands, resembling rhythm, are extracted, which – after applying Gaussian smoothing to diminish small variations – result in a time-invariant, comparable representation of the rhythmic patterns in the individual critical bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing 27 segments\n"
     ]
    }
   ],
   "source": [
    "features = rp_extract(wavedata,                            # the two-channel wave-data of the audio-file\n",
    "                                samplerate,                          # the samplerate of the audio-file\n",
    "                                extract_rp          = True,          # <== extract this feature!\n",
    "                                transform_db        = True,          # apply psycho-accoustic transformation\n",
    "                                transform_phon      = True,          # apply psycho-accoustic transformation\n",
    "                                transform_sone      = True,          # apply psycho-accoustic transformation\n",
    "                                fluctuation_strength_weighting=True, # apply psycho-accoustic transformation\n",
    "                                skip_leadin_fadeout = 1,             # skip lead-in/fade-out. value = number of segments skipped\n",
    "                                step_width          = 1)             #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3prsXuDNr66ft61mTSffQqZFurzhI798w7ShSCqJ5sv974FPZ40+jb/fZC2YB\nh2WPpwMPk56vftk+gPptN6cAtwPz6a/tA/gfwHdpZF/6aftW03pL1n7avp51BHBj09fnZB+9rsb2\nk/0qGvdinZV93Q8WAcfSn9s3DbgLeD39tX2zgSXAMTRe2ffT9q0G9hjV1k/b19ZELl5SlRumvZJ0\naofsc3QT7l5SI/0Hcwf9tX0vI/2HuY7GKat+2r6LgE8C25ra+mn7Rkh/zJYDH8ra+mn72upWqCqP\nnDdM6ysj9P52TweuBT5Oa4WOXt++baRTVbuREuDHjPp+L2/fiaTKPPcSX6fdy9sHcCTwJLAn6Tz9\n6Ffxvb59bU3kV/Y5b5jW89bRqKm0F3EprF4wlTTRX0k6jQP9tX11zwE/IZUg6pft+8/ASaRTHVcD\nbyU9j/2yfZAmeoCngR+Ramv00/a1NZEn+4I3TOtZ1wGnZ49PpzFJ9ppJpPscPUgqOVnXL9s3k8aV\nGjuTbsl6L/2zff+T9IJqDvA+4OfAH9M/2zcN2DV7vAvwNtJ7Z/2yfT3vHaSrOh4DPtPlsXTC1cBv\ngN+S3o/4U9LVAUvo/Uu/5pNOcwzSuMn28fTP9h0C3EPavvtJ57ahf7av2dE0Xlj1y/bNIT13g6RL\ng+vzSb9sn5mZmZmZmZmZmZmZmZmZmZmZmZmZmfWGbaQUZt0UUnrxet09NETr3QpH+zbwKxrX+J9V\ncB0T2RBjb3+zGtvfbM9sQt8bx3rf86Q7Qw4Aw6TU6RqK338kT/8R4BPAD4PvTwa2FlzvRNG392ux\n8TORb5dg/eGnwDuzx6eRUsT1Qve7k+Lp9wG3kVKqkG5DezMp6fhPTf3HMrrfUtKdHO8iFVM5PGtb\nTrp9dv2eKIdnYxgE/oHGq+IzgK81Le8GUroUUtz+l8DdwPdIEXxIr8LPy9rvB16btU8HvpW13Qf8\nASlBfVHT8j8EfLnN9tWAh4BLSPvmJtIf0tHb8BdNPzM526Y7s+9/OGv/S9LtLSDt9xVNyzIzK2Qj\naSL5PvA7pNMrR9M4jfM14K+zx8dk3wf4KvC57PEJpNNBRU7j3AMcTLoN8cLs+1NIk3P9fuan0pjs\n7ifd7gFSMYv7s8dnsP1kfz3wX0j3ybmFdI8cSEUv6tuxGvho9vjPSX+sAC5g+4l8BukPxGOkCRng\n/5D+ExqtXnSjBmwG3pC1XwO8P9iG+h+sDwOfzR7/DukP36tJfxhvAd6dtR0h1mt9xKdxrGwrSJPU\naaQ7RTZFqas4AAACN0lEQVQ7kvQKF9LEvAfpZlVHkSYhSP8Z/HuO9USnca7JPh9ImkiXZF9PJt2n\naLfsY1nWfiXpnkyRScA84CDSHw9IN+r7ZVOf+hjuobF9v0/6A1O3Ifv8c+C/km63O5V0j/x2VtP4\nY3Q3ad+224a3kf7gnpJ9/XLSDQYfJ/0xWwF8nfSflfUxT/Y2Hq4DvkR6Vb/nqO9Fp2jynroZ62ee\nb/reA6Rb+TYbfeOr5mVsYftTnc2nORYDfxSM48Xs81a2/x1T47uU9Mr7IVKN4rG82PR4K43/LpqN\nXs9ZpPGOdgDpv69+LApko/icvY2Hy0nnsUe/ar2VxmmIBaQrdTYC/0ZjIn0H8Ls7sO76xPcw6Q/N\nvOzrqaRX5xuyjyOz9vc3/ewQqVjJJNLtf99M+g/i9qz/a7J+u5BeLbezmMbpHWj8kbmTVA7wj0jv\nZ7wUzxFvw02kc/j1PzoHkG73uxvwFdJ/UXsA73mJ67Ye4cneylS/iuQJGufOm6sBnUfjjcW/o3Ff\n8fNJ58ZXkk7nPN60zJ/QeGM1Wp9q+y3pVMYFNG7DXD9P/afAP9J4z6BuGem0yYOkifHurH096RTI\n1dnYf0njjdjR666v//OkP1orsvUvaOr3vWxdz+XYrtHbWP969DbU2y/Nxn8PjVM2U0jvHywkvWdw\nJqnQ9sxg/WZmfefVjP816tfTWuLQrKP8yt5se5MYv+vaZ5BOL71AeoPazMzMzMzMzMzMzMzMzMzM\nzMzMzMzMuu//A1HOCqHtA3uHAAAAAElFTkSuQmCC\n"
     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "source": [
    "plotrp(features['rp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Spectrum Descriptor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sonogram is calculated as in the first part of the Rhythm Patterns calculation. According to the occurrence of beats or other rhythmic variation of energy on a specific critical band, statistical measures are able to describe the audio content. Our goal is to describe the rhythmic content of a piece of audio by computing the following statistical moments on the Sonogram values of each of the critical bands:\n",
    "\n",
    "  * mean, median, variance, skewness, kurtosis, min- and max-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = rp_extract(wavedata,                            # the two-channel wave-data of the audio-file\n",
    "                                samplerate,                          # the samplerate of the audio-file\n",
    "                                extract_ssd         = True,          # <== extract this feature!\n",
    "                                transform_db        = True,          # apply psycho-accoustic transformation\n",
    "                                transform_phon      = True,          # apply psycho-accoustic transformation\n",
    "                                transform_sone      = True,          # apply psycho-accoustic transformation\n",
    "                                fluctuation_strength_weighting=True, # apply psycho-accoustic transformation\n",
    "                                skip_leadin_fadeout = 1,             # skip lead-in/fade-out. value = number of segments skipped\n",
    "                                step_width          = 1)             #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kCrDYS1IFWOwlqQIs9pJUARZ7SaoAi70kVYDFXpIqwGIvSRXQvSdVPZ07QITP5Q4Q6cgD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\nSZIkSZIkSZIkSZIkSZIkSaP1/8KohVh/Lzj0AAAAAElFTkSuQmCC\n"
     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "source": [
    "plotssd(features['ssd'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rhythm Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Rhythm Histogram features we use are a descriptor for general rhythmics in an audio document. Contrary to the Rhythm Patterns and the Statistical Spectrum Descriptor, information is not stored per critical band. Rather, the magnitudes of each modulation frequency bin of all critical bands are summed up, to form a histogram of \"rhythmic energy\" per modulation frequency. The histogram contains 60 bins which reflect modulation frequency between 0 and 10 Hz. For a given piece of audio, the Rhythm Histogram feature set is calculated by taking the median of the histograms of every 6 second segment processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = rp_extract(wavedata,                            # the two-channel wave-data of the audio-file\n",
    "                                samplerate,                          # the samplerate of the audio-file\n",
    "                                extract_rh          = True,          # <== extract this feature!\n",
    "                                transform_db        = True,          # apply psycho-accoustic transformation\n",
    "                                transform_phon      = True,          # apply psycho-accoustic transformation\n",
    "                                transform_sone      = True,          # apply psycho-accoustic transformation\n",
    "                                fluctuation_strength_weighting=True, # apply psycho-accoustic transformation\n",
    "                                skip_leadin_fadeout = 1,             # skip lead-in/fade-out. value = number of segments skipped\n",
    "                                step_width          = 1)             #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "source": [
    "plotrh(features['rh'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get rough BPM from Rhythm Histogram\n",
    "\n",
    "By looking at the maximum peak of a Rhythm Histogram, we can determine the beats per minute (BPM) very roughly by multiplying the Index of the Rhythm Histogram bin by the modulation frequency resolution (0.168 Hz) * 60. The resolution of this is however only at +/- 10 bpm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.5621337891\n"
     ]
    }
   ],
   "source": [
    "maxbin = features['rh'].argmax(axis=0) + 1  # +1 because it starts from 0\n",
    "\n",
    "mod_freq_res = 1.0 / (2**18/44100.0)  # resolution of modulation frequency axis (0.168 Hz) (= 1/(segment_size/samplerate))\n",
    "#print mod_freq_res * 60 # resolution\n",
    "\n",
    "bpm = maxbin * mod_freq_res * 60\n",
    "\n",
    "print bpm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modulation Frequency Variance Descriptor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This descriptor measures variations over the critical frequency bands for a specific modulation frequency (derived from a rhythm pattern).\n",
    "\n",
    "Considering a rhythm pattern, i.e. a matrix representing the amplitudes of 60 modulation frequencies on 24 critical bands, an MVD vector is derived by computing statistical measures (mean, median, variance, skewness, kurtosis, min and max) for each modulation frequency over the 24 bands. A vector is computed for each of the 60 modulation frequencies. Then, an MVD descriptor for an audio file is computed by the mean of multiple MVDs from the audio file's segments, leading to a 420-dimensional vector. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Statistical Spectrum Descriptor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature sets are frequently computed on a per segment basis and do not incorporate time series aspects. As a consequence, TSSD features describe variations over time by including a temporal dimension. Statistical measures (mean, median, variance, skewness, kurtosis, min and max) are computed over the individual statistical spec- trum descriptors extracted from segments at different time positions within a piece of audio. This captures timbral variations and changes over time in the audio spectrum, for all the critical Bark-bands. Thus, a change of rhythmic, instruments, voices, etc. over time is reflected by this feature set. The dimension is 7 times the dimension of an SSD (i.e. 1176)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Rhythm Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical measures (mean, median, variance, skewness, kurtosis, min and max) are computed over the individual Rhythm Histograms extracted from various segments in a piece of audio. Thus, change and variation of rhythmic aspects in time are captured by this descriptor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract All Features\n",
    "\n",
    "To extract ALL or selected ones of the before described features, you can use this command: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adapt the fext array to your needs:\n",
    "fext = ['rp','ssd','rh','mvd'] # sh, tssd, trh\n",
    "\n",
    "features = rp_extract(wavedata,\n",
    "                  samplerate,\n",
    "                  extract_rp   = ('rp' in fext),          # extract Rhythm Patterns features\n",
    "                  extract_ssd  = ('ssd' in fext),           # extract Statistical Spectrum Descriptor\n",
    "                  extract_sh   = ('sh' in fext),          # extract Statistical Histograms\n",
    "                  extract_tssd = ('tssd' in fext),          # extract temporal Statistical Spectrum Descriptor\n",
    "                  extract_rh   = ('rh' in fext),           # extract Rhythm Histogram features\n",
    "                  extract_trh  = ('trh' in fext),          # extract temporal Rhythm Histogram features\n",
    "                  extract_mvd  = ('mvd' in fext),        # extract Modulation Frequency Variance Descriptor\n",
    "                  spectral_masking=True,\n",
    "                  transform_db=True,\n",
    "                  transform_phon=True,\n",
    "                  transform_sone=True,\n",
    "                  fluctuation_strength_weighting=True,\n",
    "                  skip_leadin_fadeout=1,\n",
    "                  step_width=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ssd', 'rh', 'rp', 'mvd']\n"
     ]
    }
   ],
   "source": [
    "# let's see what we got in our dict\n",
    "print features.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ssd (168,)\n",
      "rh (60,)\n",
      "rp (1440,)\n",
      "mvd (420,)\n"
     ]
    }
   ],
   "source": [
    "# list the feature type dimensions\n",
    "\n",
    "for k in features.keys():\n",
    "    print k, features[k].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"application\"><font color=\"#0404B4\">4. Application Scenarios</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Songs from Soundcloud\n",
    "\n",
    "## <a name=\"getsoundcloud\"><font color=\"#0404B4\">4.1. Getting Songs from Soundcloud</font></a>\n",
    "\n",
    "In this step we are going to analyze songs from Soundcloud, using the Soundcloud API.\n",
    "\n",
    "Please get your own API key first by clicking \"Register New App\" on <a href=\"https://developers.soundcloud.com\">https://developers.soundcloud.com</a>.\n",
    "\n",
    "Then we can start using the Soundcloud API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# START SOUNDCLOUD API\n",
    "import soundcloud\n",
    "import urllib # for mp3 download\n",
    "\n",
    "# To use soundcloud-python, you must first create a Client instance, passing at a minimum the client id you \n",
    "# obtained when you registered your app:\n",
    "\n",
    "# If you only need read-only access to public resources, simply provide a client id when creating a Client instance:\n",
    "my_client_id= 'insert your soundcloud client id here'\n",
    "\n",
    "client = soundcloud.Client(client_id=my_client_id)\n",
    "# if there is no error after this, it should have worked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Track Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRACK ID: 208199477\n",
      "Title: Major Lazer - Be Together (feat. Wild Belle)\n",
      "Artist:  Major Lazer [OFFICIAL]\n",
      "Genre:  Major Lazer\n",
      "None bpm\n",
      "3736959 times played\n",
      "0 times downloaded\n",
      "Downloadable? False\n"
     ]
    }
   ],
   "source": [
    "# GET TRACK INFO\n",
    "\n",
    "#soundcloud_url = 'http://soundcloud.com/forss/flickermood'\n",
    "soundcloud_url = 'https://soundcloud.com/majorlazer/be-together-feat-wild-belle'\n",
    "\n",
    "track = client.get('/resolve', url=soundcloud_url)\n",
    "\n",
    "print \"TRACK ID:\", track.id\n",
    "print \"Title:\", track.title\n",
    "print \"Artist: \", track.user['username']\n",
    "print \"Genre: \", track.genre\n",
    "print track.bpm, \"bpm\"\n",
    "print track.playback_count, \"times played\"\n",
    "print track.download_count, \"times downloaded\"\n",
    "print \"Downloadable?\", track.downloadable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you want to see all information contained in 'track':\n",
    "print vars(track)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Track URLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api.soundcloud.com/tracks/208199477/stream\n",
      "https://ec-media.sndcdn.com/rUaqgMEvIAq2.128.mp3?f10880d39085a94a0418a7ef69b03d522cd6dfee9399eeb9a52202986afbb739d5ae703a547444c2ea4f10b53ee73e1d9907dbfbf42076407dc7bc785d37ea1a66e03f6405\n"
     ]
    }
   ],
   "source": [
    "if hasattr(track, 'download_url'):\n",
    "    print track.download_url\n",
    "print track.stream_url\n",
    "stream = client.get('/tracks/%d/streams' % track.id)\n",
    "#print vars(stream)\n",
    "print stream.http_mp3_128_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Preview MP3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded ./music/Major Lazer - Be Together (feat. Wild Belle).mp3\n"
     ]
    }
   ],
   "source": [
    "# set the MP3 download directory\n",
    "mp3_dir = './music'\n",
    "\n",
    "mp3_file = mp3_dir + os.sep + \"%s.mp3\" % track.title\n",
    "\n",
    "# Download the 128 kbit stream MP3\n",
    "urllib.urlretrieve (stream.http_mp3_128_url, mp3_file)\n",
    "\n",
    "print \"Downloaded \" + mp3_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterate over a List of Soundcloud Tracks\n",
    "This will take a number of Souncloud URLs and get the track info for them and download the mp3 stream if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://soundcloud.com/absencemusik/lana-del-rey-born-to-die-absence-remix\n",
      "Downloaded ./music/Lana Del Rey - Born To Die (Absence Remix).mp3\n",
      "https://soundcloud.com/princefoxmusic/raindrops-feat-kerli-prince-fox-remix\n",
      "Downloaded ./music/SNBRN - Raindrops feat. Kerli (Prince Fox Remix).mp3\n",
      "https://soundcloud.com/octobersveryown/remyboyz-my-way-rmx-ft-drake\n",
      "Downloaded ./music/Remy Boyz - My Way RMX Ft. Drake.mp3\n"
     ]
    }
   ],
   "source": [
    "# use your own soundcloud urls here\n",
    "soundcloud_urls = [\n",
    "    'https://soundcloud.com/absencemusik/lana-del-rey-born-to-die-absence-remix',\n",
    "    'https://soundcloud.com/princefoxmusic/raindrops-feat-kerli-prince-fox-remix',\n",
    "    'https://soundcloud.com/octobersveryown/remyboyz-my-way-rmx-ft-drake'\n",
    "    ]\n",
    "\n",
    "mp3_dir = './music'\n",
    "mp3_files = []\n",
    "own_track_ids = []\n",
    "\n",
    "for url in soundcloud_urls:\n",
    "    print url\n",
    "    track = client.get('/resolve', url=url)\n",
    "    mp3_file = mp3_dir + os.sep + \"%s.mp3\" % track.title\n",
    "    mp3_files.append(mp3_file)\n",
    "    own_track_ids.append(track.id)\n",
    "    \n",
    "    stream = client.get('/tracks/%d/streams' % track.id)\n",
    "\n",
    "    if hasattr(stream, 'http_mp3_128_url'):\n",
    "        mp3_url = stream.http_mp3_128_url\n",
    "    elif hasattr(stream, 'preview_mp3_128_url'): # if we cant get the full mp3 we take the 1:30 preview\n",
    "        mp3_url = stream.preview_mp3_128_url\n",
    "    else:\n",
    "        print \"No MP3 can be downloaded for this song.\"\n",
    "        mp3_url = None # in this case we can't get an mp3\n",
    "    \n",
    "    if not mp3_url == None:\n",
    "        urllib.urlretrieve (mp3_url, mp3_file) # Download the 128 kbit stream MP3\n",
    "        print \"Downloaded \" + mp3_file\n",
    "    \n",
    "# show list of mp3 files we got:\n",
    "# print mp3_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"soundcloudanalysis\"><font color=\"#0404B4\">4.2. Analyzing Songs from Soundcloud</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the previously loaded Songs\n",
    "\n",
    "Now this combines reading all the MP3s we've got and analyzing the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoding mp3 with:  lame --quiet --decode \"./music/Lana Del Rey - Born To Die (Absence Remix).mp3\" \"/var/folders/1_/bnncmvw96qvfqdy3yg98mzjm0000gn/T/tmpAppw6p.wav\"\n",
      "Successfully read audio file: ./music/Lana Del Rey - Born To Die (Absence Remix).mp3\n",
      "44100 Hz, 16 bit, 2 channel(s), 12197376 samples\n",
      "Decoding mp3 with:  lame --quiet --decode \"./music/SNBRN - Raindrops feat. Kerli (Prince Fox Remix).mp3\" \"/var/folders/1_/bnncmvw96qvfqdy3yg98mzjm0000gn/T/tmpH1nCQa.wav\"\n",
      "Successfully read audio file: ./music/SNBRN - Raindrops feat. Kerli (Prince Fox Remix).mp3\n",
      "44100 Hz, 16 bit, 2 channel(s), 8695296 samples\n",
      "Decoding mp3 with:  lame --quiet --decode \"./music/Remy Boyz - My Way RMX Ft. Drake.mp3\" \"/var/folders/1_/bnncmvw96qvfqdy3yg98mzjm0000gn/T/tmpsaGK_v.wav\"\n",
      "Successfully read audio file: ./music/Remy Boyz - My Way RMX Ft. Drake.mp3\n",
      "44100 Hz, 16 bit, 2 channel(s), 3957743 samples\n",
      "Finished analyzing 3 files.\n"
     ]
    }
   ],
   "source": [
    "# mp3_files is the list of downloaded Soundcloud files as stored above (mp3_files.append())\n",
    "\n",
    "# all_features will be a list of dict entries for all files\n",
    "all_features = []\n",
    "\n",
    "for mp3 in mp3_files:\n",
    "\n",
    "    # Read the Audio file\n",
    "    samplerate, samplewidth, wavedata = audiofile_read(mp3)\n",
    "    print \"Successfully read audio file:\", mp3\n",
    "    nsamples = wavedata.shape[0]\n",
    "    nchannels = wavedata.shape[1]\n",
    "    print samplerate, \"Hz,\", samplewidth*8, \"bit,\", nchannels, \"channel(s),\", nsamples, \"samples\"\n",
    "    \n",
    "    # Extract the Audio Features\n",
    "    # (adapt the fext array to your needs)\n",
    "    fext = ['rp','ssd','rh','mvd'] # sh, tssd, trh\n",
    "\n",
    "    features = rp_extract(wavedata,\n",
    "                      samplerate,\n",
    "                      extract_rp   = ('rp' in fext),          # extract Rhythm Patterns features\n",
    "                      extract_ssd  = ('ssd' in fext),           # extract Statistical Spectrum Descriptor\n",
    "                      extract_sh   = ('sh' in fext),          # extract Statistical Histograms\n",
    "                      extract_tssd = ('tssd' in fext),          # extract temporal Statistical Spectrum Descriptor\n",
    "                      extract_rh   = ('rh' in fext),           # extract Rhythm Histogram features\n",
    "                      extract_trh  = ('trh' in fext),          # extract temporal Rhythm Histogram features\n",
    "                      extract_mvd  = ('mvd' in fext),        # extract Modulation Frequency Variance Descriptor\n",
    "                      )\n",
    "    \n",
    "    all_features.append(features)\n",
    "    \n",
    "print \"Finished analyzing\", len(mp3_files), \"files.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note:</b> also see source file <b>rp_extract_files.py</b> on how to iterate over ALL mp3 or wav files in a directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7t3NHY0wdNLfTA2gzdaVMryj2rqo7Rfd37yW9vn1DdeKd/TrShXV3kq7M+SZV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\nmZmZmXXe/wd64DapuZC0sAAAAABJRU5ErkJggg==\n"
     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "data": {
      "image/png": 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     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roughly 242.0 bpm\n"
     ]
    },
    {
     "data": {
      "image/png": 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     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "data": {
      "image/png": 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     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roughly 10.0 bpm\n"
     ]
    },
    {
     "data": {
      "image/png": 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     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "data": {
      "image/png": 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     },
     "output_type": "display_data",
     "metadata": {}
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roughly 30.0 bpm\n"
     ]
    }
   ],
   "source": [
    "# iterates over all featuers (files) we extracted\n",
    "\n",
    "for feat in all_features:\n",
    "    plotrp(feat['rp'])\n",
    "    plotrh(feat['rh'])\n",
    "\n",
    "    maxbin = feat['rh'].argmax(axis=0) + 1  # +1 because it starts from 0\n",
    "    bpm = maxbin * mod_freq_res * 60\n",
    "    print \"roughly\", round(bpm), \"bpm\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Example: Get a list of tracks by Genre\n",
    "\n",
    "This is an example on how to retrieve Songs from Soundcloud by genre and/or bpm.\n",
    "\n",
    "currently this does not work ... (issue on Soundcloud side?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# currently this does not work \n",
    "genre = 'Dancehall'\n",
    "\n",
    "curr_offset = 0 # Note: the API has a limit of 50 items per response, so to get more you have to query multiple times with an offset.\n",
    "tracks = client.get('/tracks', genres=genre, offset=curr_offset)\n",
    "print \"Retrieved\", len(tracks), \"track objects data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# original Soundcloud example, searching for genre and bpm\n",
    "# currently this does not work \n",
    "tracks = client.get('/tracks', genres='punk', bpm={'from': 120})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"similar\"><font color=\"#0404B4\">4.3. Finding Similar Sounding Songs</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these application scenarios we try to find similar songs or classify music into different categories.\n",
    "\n",
    "For these Use Cases we need to import a few additional functions from the sklearn package and from mir_utils (installed from git above in parallel to rp_extract):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING mir_utils (installed from git above in parallel to rp_extract (otherwise ajust path))\n",
    "import sys\n",
    "sys.path.append(\"../mir_utils\")\n",
    "from demo.NotebookUtils import *\n",
    "from demo.PlottingUtils import *\n",
    "from demo.Soundcloud_Demo_Dataset import SoundcloudDemodatasetHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTS for NearestNeighbor Search\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Soundcloud Demo Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Soundcloud Demo Dataset is a collection of commonly known mainstream radio songs hosted on the online streaming platform Soundcloud. The Dataset is available as playlist and is intended to be used to demonstrate the performance of MIR algorithms with the help of well known songs.\n",
    "\n",
    "<!-- not working on Mac\n",
    "<iframe width=\"100%\" height=\"450\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/playlists/106852365&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;visual=true\"></iframe>\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"100%\" height=\"450\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/playlists/106852365&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;visual=false\"></iframe>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "# show the data set as Souncloud playlist\n",
    "\n",
    "iframe = '<iframe width=\"100%\" height=\"450\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/playlists/106852365&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;visual=false\"></iframe>'\n",
    "\n",
    "HTML(iframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *SoundcloudDemodatasetHandler* abstracts the access to the TU-Wien server. On this server the extracted features are stored as csv-files. The *SoundcloudDemodatasetHandler* remotely loads the features and returns them by request. The features have been extracted using the method explained in the previous sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first argument is local file path for downloaded MP3s and local metadata (if present, otherwise None)\n",
    "scds = SoundcloudDemodatasetHandler(None, lazy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding rhythmically similar songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the similarity search object\n",
    "\n",
    "sim_song_search = NearestNeighbors(n_neighbors = 6, metric='euclidean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding rhythmically similar songs using Rhythm Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set feature type\n",
    "feature_set = 'rh'\n",
    "\n",
    "# get features from Soundcloud demo set\n",
    "demoset_features = scds.features[feature_set][\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the extracted features\n",
    "scaled_feature_space = StandardScaler().fit_transform(demoset_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='euclidean',\n",
       "         metric_params=None, n_neighbors=6, p=2, radius=1.0)"
      ]
     },
     "execution_count": 149,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "# Fit the Nearest-Neighbor search object to the extracted features\n",
    "sim_song_search.fit(scaled_feature_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Our query-song:\n",
    "\n",
    "This is a query song from the pre-analyzed data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/68687842&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "query_track_soundcloud_id = 68687842 # Mr. Saxobeat\n",
    "\n",
    "HTML(scds.getPlayerHTMLForID(query_track_soundcloud_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Retrieve the feature vector for the query song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_track_feature_vector = scaled_feature_space[scds.features[feature_set][\"ids\"] == query_track_soundcloud_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Search the nearest neighbors of the query-feature-vector\n",
    "This retrieves the most similar song indices and their distance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.          6.02261617  7.76291163  7.80470254  8.63645209  8.90034496]]\n",
      "[[37  9 24 36 40 10]]\n"
     ]
    }
   ],
   "source": [
    "(distances, similar_songs) = sim_song_search.kneighbors(query_track_feature_vector, return_distance=True)\n",
    " \n",
    "print distances\n",
    "print similar_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For now we use only the song indices without distances\n",
    "similar_songs = sim_song_search.kneighbors(query_track_feature_vector, return_distance=False)[0]\n",
    "\n",
    "# because we are searching in the entire collection, the top-most result is the query song itself. Thus, we can skip it.\n",
    "similar_songs = similar_songs[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lookup the corresponding Soundcloud-IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[108622874  59732818  19505822  70343006  40279580]\n"
     ]
    }
   ],
   "source": [
    "similar_soundcloud_ids = scds.features[feature_set][\"ids\"][similar_songs]\n",
    "\n",
    "print similar_soundcloud_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listen to the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/59732818&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/70343006&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "SoundcloudTracklist(similar_soundcloud_ids, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding rhythmically similar songs using Rhythm Patterns\n",
    "This time we define a function that performs steps analogously to the RH retrieval above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def search_similar_songs_by_id(query_song_id, feature_set, skip_query=True):\n",
    "\n",
    "    scaled_feature_space = StandardScaler().fit_transform(scds.features[feature_set][\"data\"])\n",
    "\n",
    "    sim_song_search.fit(scaled_feature_space);\n",
    "\n",
    "    query_track_feature_vector = scaled_feature_space[scds.features[feature_set][\"ids\"] == query_song_id]\n",
    "\n",
    "    similar_songs = sim_song_search.kneighbors(query_track_feature_vector, return_distance=False)[0]\n",
    "    \n",
    "    if skip_query:\n",
    "        similar_songs = similar_songs[1:]\n",
    "\n",
    "    similar_soundcloud_ids = scds.features[feature_set][\"ids\"][similar_songs]\n",
    "    \n",
    "    return similar_soundcloud_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/59732818&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/5669751&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "similar_soundcloud_ids = search_similar_songs_by_id(query_track_soundcloud_id, \n",
    "                                              feature_set='rp')\n",
    "\n",
    "SoundcloudTracklist(similar_soundcloud_ids, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding songs based on Timbral Similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding songs based on timbral similarity using Statistical Spectral Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/15964713&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/39654171&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "similar_soundcloud_ids = search_similar_songs_by_id(query_track_soundcloud_id, \n",
    "                                              feature_set='ssd')\n",
    "\n",
    "SoundcloudTracklist(similar_soundcloud_ids, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the Results of Timbral and Rhythmic Similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First entry is query-track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='100%' style='border:none'><tr style='border:none'><td style='border:none'><center><b>Statistical Spectrum Descriptors</b></center></td><td style='border:none'><center><b>Rhythm Histograms</b></center></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/68687842&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/68687842&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/59732818&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/15964713&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/70343006&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/39654171&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "track_id = 68687842 # 40439758\n",
    "results_track_1 = search_similar_songs_by_id(track_id, feature_set='ssd', skip_query=False)\n",
    "results_track_2 = search_similar_songs_by_id(track_id, feature_set='rh', skip_query=False)\n",
    "\n",
    "compareSimilarityResults([results_track_1, results_track_2],\n",
    "                          width=100, height=120, visual=False,\n",
    "                          columns=['Statistical Spectrum Descriptors', 'Rhythm Histograms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using your Own Query Song from the self-extracted Souncloud tracks above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'./music/Lana Del Rey - Born To Die (Absence Remix).mp3',\n",
       " u'./music/SNBRN - Raindrops feat. Kerli (Prince Fox Remix).mp3',\n",
       " u'./music/Remy Boyz - My Way RMX Ft. Drake.mp3']"
      ]
     },
     "execution_count": 154,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "# check which files we got\n",
    "mp3_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select from the list above the number of the song you want to use as a query (counting from 1)\n",
    "song_id = 3  # count from 1\n",
    "\n",
    "# select the feature vector type\n",
    "feat_type = 'rp' # 'rh' or 'ssd' or 'rp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the all_features data structure, we get the desired feature vector belonging to that song\n",
    "query_feature_vector = all_features[song_id - 1][feat_type]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all the feature vectors of desired feature type from the Soundcloud demo set\n",
    "demo_features = scds.features[feat_type][\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='euclidean',\n",
       "         metric_params=None, n_neighbors=6, p=2, radius=1.0)"
      ]
     },
     "execution_count": 167,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "# Initialize Neighbour Search space with demo set features\n",
    "\n",
    "sim_song_search.fit(demo_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.60906013  8.62923708  8.75274749  8.77687295  8.77928625  8.82950523]]\n",
      "[[36 40 42  1 37 10]]\n"
     ]
    }
   ],
   "source": [
    "# use our own query_feature_vector for search in the demo set \n",
    "(distances, similar_songs) = sim_song_search.kneighbors(query_feature_vector, return_distance=True)\n",
    " \n",
    "print distances\n",
    "print similar_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 40, 42,  1, 37, 10])"
      ]
     },
     "execution_count": 169,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "# now we got the song indices for similar songs in the demo set\n",
    "similar_songs = similar_songs[0]\n",
    "similar_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19505822, 70343006, 11312719, 12285350, 68687842, 40279580])"
      ]
     },
     "execution_count": 170,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "# and we get the according Soundcloud Track IDs\n",
    "similar_soundcloud_ids = scds.features[feat_type][\"ids\"][similar_songs]\n",
    "\n",
    "similar_soundcloud_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204088308\n"
     ]
    }
   ],
   "source": [
    "# we add our own Track ID at the beginning to show the seed song below:\n",
    "\n",
    "my_track_id = own_track_ids[song_id - 1]\n",
    "print my_track_id\n",
    "result = np.insert(similar_soundcloud_ids,0,my_track_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visual Player with the Songs most similar to our Own Song\n",
    "first song is the query song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Type: rp\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/204088308&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/70343006&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/11312719&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/12285350&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/68687842&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ]
     },
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "print \"Feature Type:\", feat_type\n",
    "SoundcloudTracklist(result, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add On: Combining different Music Descriptors\n",
    "\n",
    "Here we merge SSD and RH features together to account for <b>both</b> timbral and rhythmic similarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def search_similar_songs_with_combined_sets(scds, query_song_id, feature_sets, skip_query=True, n_neighbors=6):\n",
    "    \n",
    "    features = scds.getCombinedFeaturesets(feature_sets)\n",
    "    \n",
    "    sim_song_search = NearestNeighbors(n_neighbors = n_neighbors, metric='l2')\n",
    "\n",
    "    #\n",
    "    scaled_feature_space = StandardScaler().fit_transform(features)\n",
    "\n",
    "    #\n",
    "    sim_song_search.fit(scaled_feature_space);\n",
    "\n",
    "    #\n",
    "    query_track_feature_vector = scaled_feature_space[scds.getFeatureIndexByID(query_song_id, feature_sets[0])]\n",
    "    \n",
    "    #\n",
    "    similar_songs = sim_song_search.kneighbors(query_track_feature_vector, return_distance=False)[0]\n",
    "    \n",
    "    if skip_query:\n",
    "        similar_songs = similar_songs[1:]\n",
    "\n",
    "    #\n",
    "    similar_soundcloud_ids = scds.getIdsByIndex(similar_songs, feature_sets[0])\n",
    "    \n",
    "    return similar_soundcloud_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_sets = ['ssd','rh']\n",
    "\n",
    "compareSimilarityResults([search_similar_songs_with_combined_sets(scds, 68687842, feature_sets=feature_sets, n_neighbors=5),\n",
    "                          search_similar_songs_with_combined_sets(scds, 40439758, feature_sets=feature_sets, n_neighbors=5)],\n",
    "                          width=100, height=120, visual=False,\n",
    "                          columns=[scds.getNameByID(68687842),\n",
    "                                   scds.getNameByID(40439758)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * [Audio Feature Extraction site of the MIR-Team @TU-Wien](http://www.ifs.tuwien.ac.at/mir/audiofeatureextraction.html)\n",
    " * Blog-post: [A gentle Introduction to Music Information Retrieval](http://www.europeanasounds.eu/news/a-gentle-introduction-to-music-information-retrieval-making-computers-understand-music)\n",
    " * [Same Blog-post with Python code](http://wwwnew.schindler.eu.com/blog/mir_intro/blog_with_code.html)"
   ]
  }
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