Essentia API: a web API for music audio analysis

Correya, Albin Andrew; Bogdanov, Dmitry; Alonso Jiménez, Pablo; Serra, Xavier

Essentia API: a web API for music audio analysis

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Citation

Correya, A, Bogdanov, D, Alonso Jiménez, P, Serra, X. Essentia API: a web API for music audio analysis. 2023. handle: http://hdl.handle.net/10230/55249

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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.