Interactive demos¶

Music audio descriptors in the browser¶

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

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, MAEST model). Overall track-level predictions: https://replicate.com/mtg/maest
Music style classification with the Discogs taxonomy (400 styles, Effnet-Discogs model). Overall track-level predictions: https://replicate.com/mtg/effnet-discogs
Music style classification with the Discogs taxonomy (400 styles, Effnet-Discogs model). 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
Jupyter notebook for real-time music auto-tagging and classification.

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.