Show simple item record Correya, Albin Andrew Bogdanov, Dmitry Alonso Jiménez, Pablo Serra, Xavier 2023-01-10T15:42:20Z 2023-01-10T15:42:20Z 2023-01-10
dc.description.abstract We present Essentia API, a web API to access a collection of state-of-the-art music audio analysis and description algorithms based on Essentia, an open-source library and machine learning (ML) models for audio and music analysis. We are developing it as part of a broader project in which we explore strategies for the commercial viability of technologies developed at Music Technology Group (MTG) following open science and open source practices, which involves finding licensing schemes and building custom solutions. Currently, the API supports music auto-tagging and classification algorithms (for genre, instrumentation, mood/emotion, danceability, approachability, and engagement), and algorithms for musical key, tempo, loudness, and many more. In the future, we envision expanding it with new machine learning models developed by the MTG and our collaborators to facilitate their access for a broader community of users.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.rights © A. Correya, D. Bogdanov, P. Alonso-Jiménez, and X. Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: A. Correya, D. Bogdanov, P. Alonso-Jiménez, and X. Serra, “Essentia API: a web API for music audio analysis”, in Extended Abstracts for the Late-Breaking Demo Session of the 23rd Int. Society for Music Information Retrieval Conf., Bengaluru, India, 2022.
dc.title Essentia API: a web API for music audio analysis
dc.type info:eu-repo/semantics/preprint
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/acceptedVersion


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