We are currently preparing deb packages for Ubuntu and Debian. Meanwhile, you need to compile Essentia from source (see below).
Cross-compile Essentia from Linux/OSX (see below).
You can install those dependencies on a Debian/Ubuntu system from official repositories using the commands provided below:
sudo apt-get install build-essential libyaml-dev libfftw3-dev libavcodec-dev libavformat-dev libavutil-dev libavresample-dev python-dev libsamplerate0-dev libtag1-dev
In order to use python bindings for the library, you might also need to install python-numpy-dev (or python-numpy on Ubuntu) and python-yaml for YAML support in python:
sudo apt-get install python-numpy-dev python-numpy python-yaml
Note that, depending on the version of Essentia, different versions of libav* and libtag1-dev packages are required. See release notes for official releases. In the case of Essentia’s master branch, the required version of TagLib (libtag1-dev) is greater or equal to 1.9. The required version of LibAv (libavcodec-dev, libavformat-dev, libavutil-dev and libavresample-dev) is greater or equal to 10. The appropriate versions are distributed in Ubuntu Utopic (14.10) repository, and in Debian wheezy-backports.
Install Command Line Tools for Xcode. Even if you install Xcode from the app store you must configure command-line compilation by running:
Install Homebrew package manager.
Insert the Homebrew directory at the top of your PATH environment variable by adding the following line at the bottom of your ~/.profile file:
brew install pkg-config gcc readline sqlite gdbm freetype libpng
Install Essentia’s dependencies:
brew install libyaml fftw ffmpeg libsamplerate libtag
Install python environment using Homebrew (Note that you are advised to do as described here and there are good reasons to do so. You will most probably encounter installation errors when using python/numpy preinstalled with OSX.):
brew install python --framework pip install ipython numpy matplotlib pyyaml
Go into its source code directory and start by configuring it:
./waf configure --mode=release --build-static --with-python --with-cpptests --with-examples --with-vamp --with-gaia
NOTE: you must always configure at least once before building!
The following will give you a full list of options:
To compile everything you’ve configured:
To install the C++ library and the python bindings (if configured successfully; you might need to run this command with sudo):
All built examples (including the out-of-box features extractors) will be located in build/src/examples/ folder, as well as the vamp plugin file libvamp_essentia.so. In order to use the plugin you will need to place this file to the the standard vamp plugin folder of your system (such as /usr/local/lib/vamp/ on Linux).
If you want to assure that Essentia works correctly, do the tests.
To run the C++ base unit tests (only test basic library behavior):
To run the python unit tests (include all unittests on algorithms, need python bindings installed first):
All documentation is provided on the official website of Essentia library. To generate it by your own follow the steps below.
Install doxigen and pip, if you are on Linux:
sudo apt-get install doxygen python-pip
Install additiona dependencies (you might need to run this command with sudo):
sudo pip install sphinx pyparsing sphinxcontrib-doxylink docutils
Make sure to install Essentia with python bindings and run:
Documentation will be located in doc/sphinxdoc/_build/html/ folder.
Essentia C++ library and extractors based on it can be compiled and run correctly on Windows, but python bindings are currently not supported. The easiest way to build Essentia is by cross-compilation on Linux using MinGW. However the resulting library binaries are only compatible within C++ projects using MinGW compilers, and therefore they are not compatible with Visual Studio. If you want to use Visual Studio, there is no project readily available, so you will have to setup one yourself and compile the dependencies too.
A lightweight version of Essentia can be cross-compiled for Android from Linux or Mac OSX.
Essentia includes a number of pre-trained classifier models for genres, moods and instrumentation. In order to use them you need to:
You can train your own classifier models.