Join us at Uncanny Spaces, a series of talks about ML and creativity. 🚀


Classification of music approachability and engagement
148 runs


This model runs predictions on CPU hardware.


This demo runs transfer learning models to estimate music approachability and engagement using effnet-discogs embeddings.

  • Approachability measures whether the music is likely to be accessible for the general public (e.g., belonging to common mainstream music genres vs. niche and experimental genres).
  • Engagement measures whether the music evokes active attention of the listener (high-engagement "lean forward" active listening vs. low-engagement "lean back" background listening).

We include three model types, providing different outcome formats: three-class and binary classification and regression with continuous values.

These classifiers were trained on in-house MTG datasets.

Source models

effnet-discogs is an EfficientNet architecture trained to predict music styles for 400 of the most popular Discogs music styles.

Transfer learning models

Our models consist of single-hidden-layer MLPs trained on the considered embeddings.


These models are part of Essentia Models made by MTG-UPF and are publicly available under CC by-nc-sa and commercial license.