Audio and music analysis on the web using Essentia.js
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- dc.contributor.author Correya, Albin Andrew
- dc.contributor.author Marcos Fernández, Jorge
- dc.contributor.author Joglar-Ongay, Luis
- dc.contributor.author Alonso Jiménez, Pablo
- dc.contributor.author Serra, Xavier
- dc.contributor.author Bogdanov, Dmitry
- dc.date.accessioned 2021-11-25T11:23:41Z
- dc.date.available 2021-11-25T11:23:41Z
- dc.date.issued 2021
- dc.description.abstract Open-source software libraries have a significant impact on the development of Audio Signal Processing and Music Information Retrieval (MIR) systems. Despite the abundance of such tools, there is a lack of an extensive and easy-to-use reference library for audio feature extraction on Web clients. In this article, we present Essentia.js, an open-source JavaScript (JS) library for audio and music analysis on both web clients and JS engines. Along with the Web Audio API, it can be used for both offline and real-time audio feature extraction on web browsers. Essentia.js is modular, lightweight, and easy-to-use, deploy, maintain, and integrate into the existing plethora of JS libraries and web technologies. It is powered by a WebAssembly back end cross-compiled from the Essentia C++ library, which facilitates a JS interface to a wide range of low-level and high-level audio features, including signal processing MIR algorithms as well as pre-trained TensorFlow.js machine learning models. It also provides a higher-level JS API and add-on MIR utility modules along with extensive documentation, usage examples, and tutorials. We benchmark the proposed library on two popular web browsers and the Node.js engine, and four devices, including mobile Android and iOS, comparing it to the native performance of Essentia and the Meyda JS library.
- dc.description.sponsorship The work on Essentia.js has been partially funded by the Ministry of Science and Innovation of the Spanish Government under the grant agreement PID2019-111403GB-I00 (Musical AI).
- dc.format.mimetype application/pdf
- dc.identifier.citation Correya A, Marcos-Fernández J, Joglar-Ongay L, Alonso-Jiménez P, Serra X, Bogdanov D. Audio and music analysis on the web using Essentia.js. Transactions of the International Society for Music Information Retrieval. 2021;4(1):167-81. DOI: 10.5334/tismir.111
- dc.identifier.doi http://dx.doi.org/10.5334/tismir.111
- dc.identifier.issn 2514-3298
- dc.identifier.uri http://hdl.handle.net/10230/49060
- dc.language.iso eng
- dc.publisher Ubiquity Press
- dc.relation.ispartof Transactions of the International Society for Music Information Retrieval. 2021;4(1):167-81.
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
- dc.rights © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Software
- dc.subject.keyword Web audio
- dc.subject.keyword Audio analysis
- dc.subject.keyword Music signal processing
- dc.subject.keyword Music audio classification
- dc.subject.keyword Deep learning
- dc.title Audio and music analysis on the web using Essentia.js
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion