diff --git a/doc/sphinxdoc/demos.rst b/doc/sphinxdoc/demos.rst index fe15dab2b..a90736923 100644 --- a/doc/sphinxdoc/demos.rst +++ b/doc/sphinxdoc/demos.rst @@ -1,45 +1,14 @@ Interactive demos ================= -Music audio descriptors in the browser --------------------------------------- +Essentia.js browser demos — (real-time) analysis in the browser (tagging, genre, mood, key, BPM, onset detection, audio metering, chroma, pitch, mel-spectrogram) +https://mtg.github.io/essentia.js/examples/#/demos/ -Examples of music audio analysis with Essentia algorithms using Essentia.js +Replicate demos — cloud-hosted inference demos (tagging, genre, mood, arousal/valence, approachability, engagement, BPM) +https://replicate.com/mtg/ -https://mtg.github.io/essentia.js/examples/ +AcousticBrainz — large-scale music analysis database with descriptors for millions of tracks +https://acousticbrainz.org - -Tempo estimation ----------------- - -Tempo BPM estimation with Essentia: https://replicate.com/mtg/essentia-bpm - - -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, 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 `_. - - .. raw:: html - - - - - -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. +Cosine.club — interactive music similarity exploration +https://cosine.club/ diff --git a/doc/sphinxdoc/python_examples.rst b/doc/sphinxdoc/python_examples.rst index a0d67cf47..44b85f2fb 100644 --- a/doc/sphinxdoc/python_examples.rst +++ b/doc/sphinxdoc/python_examples.rst @@ -68,3 +68,8 @@ Audio problems * `Start/stop cuts detection `_ * `True-peak detection `_ + +Jupyter notebook real-time +-------------------------- +* `Music auto-tagging `_ +* `Music classification `_