Interactive demos¶

Music audio descriptors in the browser¶

Examples of music audio analysis with Essentia algorithms using Essentia.js

https://mtg.github.io/essentia.js/examples/

Essentia TensorFlow models¶

Examples of inference with the pre-trained TensorFlow models for music auto-tagging and classification tasks:

• Music classification by genre, mood, danceability, instrumentation: https://replicate.com/mtg/music-classifiers

• Music style classification with the Discogs taxonomy (400 styles). Overall track-level predictions: https://replicate.com/mtg/effnet-discogs

• Music style classification with the Discogs taxonomy (400 styles). Segment-level real-time predictions with Essentia.js: https://essentia.upf.edu/essentiajs-discogs

• Real-time music autotagging (50 tags) in the browser with Essentia.js: https://mtg.github.io/essentia.js/examples/demos/autotagging-rt/

• Mood classification in the browser with Essentia.js: https://mtg.github.io/essentia.js/examples/demos/mood-classifiers/

• Music emotion arousal/valence regression: https://replicate.com/mtg/music-arousal-valence

• Music approachability and engagement: https://replicate.com/mtg/music-approachability-engagement

Essentia SVM models¶

Examples of inference with older SVM models for music classification tasks:

• AcousticBrainz is using our pre-trained SVM classifiers for large-scale music analysis on millions of tracks.

• AcousticBrainz Moods Playlist Generator is using SVM mood classifiers.