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 ...
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.
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