Show simple item record Correya, Albin Andrew Marcos Fernández, Jorge Joglar-Ongay, Luis Alonso Jiménez, Pablo Serra, Xavier Bogdanov, Dmitry 2021-11-25T11:23:41Z 2021-11-25T11:23:41Z 2021
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.issn 2514-3298
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.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.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
dc.title Audio and music analysis on the web using Essentia.js
dc.type info:eu-repo/semantics/article
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.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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