Essentia allows us to extract "simple" music features (loudness, bpm, danceability) as well as more complex features through ML (pre-trained?) models (genre, mood, instrumentation, voiciness, valence, gender, acousticness, ...)
We don't have to stick to Spotify's feature list (acousticness, danceability, energy, instrumentalness, liveness, speechiness, valence) as long as we find some vectors that allow for
playlist creation based on human-understandable descriptions of musical feature space
track matching based on similarity of features, that gives satisfyingly "similar" tracks
varied enough vectors (i.e. 3 axes would be too little, 7 is more than enough)
https://essentia.upf.edu/models.html
Essentia allows us to extract "simple" music features (loudness, bpm, danceability) as well as more complex features through ML (pre-trained?) models (genre, mood, instrumentation, voiciness, valence, gender, acousticness, ...)
5Go / 8M+ sqlite dataset of spotify tracks and their audio features: https://www.kaggle.com/datasets/maltegrosse/8-m-spotify-tracks-genre-audio-features
We don't have to stick to Spotify's feature list (acousticness, danceability, energy, instrumentalness, liveness, speechiness, valence) as long as we find some vectors that allow for