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.
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.