Latest version
This model runs predictions on CPU hardware.
This demo runs transfer learning models to estimate music approachability and engagement using effnet-discogs embeddings.
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.
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.