Regression of musical arousal and valence values
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Run time and cost

Predictions run on CPU hardware. Predictions typically complete within 25 seconds.

This demo runs a series of transfer learning regression models trained to predict musical arousal and valence values.
These classifiers were trained on a mixture of public and in-house MTG datasets.

Source models

  • MusiCNN. A musically motivated CNN with two variants trained on the Million Song Dataset and the MagnaTagATune.
  • VGGish. A large VGG variant trained on a preliminary version of the AudioSet Dataset.

Transfer learning classifiers

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.