Essentia 2.1-beta6-dev Documentation
What is Essentia?
Essentia is not a framework, but rather a collection of algorithms (plus some infrastructure) wrapped in a library, designed with a focus on the robustness, performance, and optimality of the provided algorithms, including computational speed and memory usage, as well as ease of use. The flow of the analysis is decided and implemented by the user, while Essentia takes care of the implementation details of the algorithms used. There is a special streaming mode with an advantage of less boilerplate code and less memory consumption in which it is possible to connect algorithms and run them automatically (similarly to PureData or Max/MSP) instead of specifying explicitly the order of execution. We provide some examples with the library, but they should not be considered the only correct way of doing things. A large part of Essentia's algorithms is well-suited for real-time applications.
The provided functionality is easily expandable and allows for both research experiments and the development of large-scale industrial applications. Essentia has served in a large number of research activities conducted at Music Technology Group since 2006. It has been used for music classification, semantic auto-tagging, music similarity and recommendation, visualization and interaction with music, sound indexing, musical instruments detection, cover detection, beat detection, and acoustic analysis of stimuli for neuroimaging studies. You can find a list of highlighted academic publications using Essentia here. Essentia and Gaia have been used extensively in many research projects and industrial applications.
Currently, the following algorithms are included (among others):
- Audio file input/output: ability to read and write nearly all audio file formats (wav, mp3, ogg, flac, etc.)
- Standard signal processing blocks: FFT, DCT, frame cutter, windowing, envelope, smoothing
- Filters (FIR & IIR): low/high/band pass, band reject, DC removal, equal loudness
- Statistical descriptors: median, mean, variance, power means, raw and central moments, spread, kurtosis, skewness, flatness
- Time-domain descriptors: duration, loudness, LARM, Leq, Vickers' loudness, zero-crossing-rate, log attack time and other signal envelope descriptors
- Spectral descriptors: Bark/Mel/ERB bands, MFCC, GFCC, LPC, spectral peaks, complexity, roll-off, contrast, HFC, inharmonicity and dissonance
- Tonal descriptors: Pitch salience function, predominant melody and pitch, HPCP (chroma) related features, chords, key and scale, tuning frequency
- Rhythm descriptors: beat detection, BPM, onset detection, rhythm transform, beat loudness
- Other high-level descriptors: danceability, dynamic complexity, audio segmentation, SVM classifier, TensorFlow wrapper for inference
- Machine learning models: inference with SVM classifiers and TensorFlow models
The library includes Python bindings (Linux and OSX) and various predefined command-line extractors for music descriptors (Linux, OSX, and Windows), which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin (Linux and OSX) to be used with Sonic Visualiser for visualization purposes. There are several third-party extensions to Essentia that allow its use within the frameworks of PureData and Max/MSP, openFrameworks, and Matlab.
Please, credit your use of Essentia properly! If you use the Essentia library in your software, please acknowledge it and specify its origin as http://essentia.upf.edu. If you do some research and publish an article, cite both the Essentia paper  and the specific references mentioned in the documentation of the algorithms used. We would also be very grateful if you let us know how you use Essentia by sending an email to email@example.com.
 Bogdanov, D., Wack N., Gómez E., Gulati S., Herrera P., Mayor O., et al. (2013). ESSENTIA: an Audio Analysis Library for Music Information Retrieval. International Society for Music Information Retrieval Conference (ISMIR'13). 493-498.
- An introduction to Essentia's main concepts
- Building and installing Essentia
- Instructions to get Essentia running on your computer
- Algorithms overview
- A quick description of the main algorithms
- Python tutorial for beginners
- A hands-on introduction to Essentia
- Python examples
- Examples of using Essentia in Python
- Using extractors out-of-box
- Quick results with no programming required
- Music extractor
- Command-line feature extractor
- Using machine learning models
- Inference with pre-trained TensorFlow models and Gaia SVM classifiers
- Web applications with Essentia.js
- Using Essentia in the browser
- Frequently asked questions
- Various tips on how to build and use Essentia
- Standard algorithms
- How to write new "standard" algorithms for Essentia
- Streaming algorithms
- How to write new "streaming" algorithms for Essentia
- AlgorithmComposite algorithms
- The inner workings of the composite algorithms
- Streaming network execution
- How Essentia's streaming scheduler works
- Coding guidelines
- Good practices to follow when developing new algorithms
- How to help us in development of Essentia