mtg / music-approachability-engagement

Classification of music approachability and engagement

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Input

Output

Run time and cost

This model runs on CPU hardware. Predictions typically complete within 15 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Classification of music approachability and engagement

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: two classes, three classes, and regression with continuous values:

  • two classes: low, and high approachability and engagement.
  • three classes: low, mid, and high approachability and engagement.
  • regression: continuous values of approachability and engagement from 0 (low) to 1 (high).

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.

License

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