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Merge pull request #1512 from xaviliz/fix/essentia-js-backlinks
Reorganize interactive demos section and improve demo links
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doc/sphinxdoc/demos.rst

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Interactive demos
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=================
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Music audio descriptors in the browser
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Essentia.js browser demos — (real-time) analysis in the browser (tagging, genre, mood, key, BPM, onset detection, audio metering, chroma, pitch, mel-spectrogram)
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https://mtg.github.io/essentia.js/examples/#/demos/
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Examples of music audio analysis with Essentia algorithms using Essentia.js
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Replicate demos — cloud-hosted inference demos (tagging, genre, mood, arousal/valence, approachability, engagement, BPM)
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https://replicate.com/mtg/
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https://mtg.github.io/essentia.js/examples/
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AcousticBrainz — large-scale music analysis database with descriptors for millions of tracks
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https://acousticbrainz.org
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Tempo estimation
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----------------
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Tempo BPM estimation with Essentia: https://replicate.com/mtg/essentia-bpm
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Essentia TensorFlow models
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--------------------------
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Examples of inference with the pre-trained TensorFlow models for music auto-tagging and classification tasks:
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- Music classification by genre, mood, danceability, instrumentation: https://replicate.com/mtg/music-classifiers
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- Music style classification with the Discogs taxonomy (400 styles, MAEST model). Overall track-level predictions: https://replicate.com/mtg/maest
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- Music style classification with the Discogs taxonomy (400 styles, Effnet-Discogs model). Overall track-level predictions: https://replicate.com/mtg/effnet-discogs
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- 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
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- Real-time music autotagging (50 tags) in the browser with Essentia.js: https://mtg.github.io/essentia.js/examples/demos/autotagging-rt/
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- Mood classification in the browser with Essentia.js: https://mtg.github.io/essentia.js/examples/demos/mood-classifiers/
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- Music emotion arousal/valence regression: https://replicate.com/mtg/music-arousal-valence
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- Music approachability and engagement: https://replicate.com/mtg/music-approachability-engagement
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- Jupyter notebook for real-time music `auto-tagging <https://github.com/MTG/essentia/blob/master/src/examples/python/tutorial_tensorflow_real-time_auto-tagging.ipynb>`_ and `classification <https://github.com/MTG/essentia/blob/master/src/examples/python/tutorial_tensorflow_real-time_simultaneous_classifiers.ipynb>`_.
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.. raw:: html
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<iframe width="560" height="315" src="https://www.youtube.com/embed/xMUcY7_n4kQ" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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<iframe width="560" height="315" src="https://www.youtube.com/embed/yssBE6oafLs" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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Essentia SVM models
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-------------------
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Examples of inference with older SVM models for music classification tasks:
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- `AcousticBrainz <https://acousticbrainz.org>`_ is using our pre-trained SVM classifiers for large-scale music analysis on millions of tracks.
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- `AcousticBrainz Moods Playlist Generator <http://mtg.upf.edu/demos/acousticbrainz/moods>`_ is using SVM mood classifiers.
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Cosine.club — interactive music similarity exploration
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https://cosine.club/

doc/sphinxdoc/python_examples.rst

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* `Music auto-tagging, classification, and embedding extraction <tutorial_tensorflow_auto-tagging_classification_embeddings.html>`_
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* `Real-time music auto-tagging <tutorial_tensorflow_real-time_auto-tagging.html>`_
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* `Simultaneous real-time inference with multiple classifiers <tutorial_tensorflow_real-time_simultaneous_classifiers.html>`_
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* `Music auto-tagging <https://github.com/MTG/essentia/blob/master/src/examples/python/tutorial_tensorflow_real-time_auto-tagging.ipynb>`_
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* `Music classification <https://github.com/MTG/essentia/blob/master/src/examples/python/tutorial_tensorflow_real-time_simultaneous_classifiers.ipynb>`_
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Fingerprinting & similarity
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* `Signal-to-noise ratio estimation <tutorial_audioproblems_snr.html>`_
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* `Start/stop cuts detection <tutorial_audioproblems_startstopcut.html>`_
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* `True-peak detection <tutorial_audioproblems_truepeakdetector.html>`_
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