mtg / music-arousal-valence

Regression of musical arousal and valence values

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  • 5.3K runs
  • GitHub
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Input

Output

Examples

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Run time and cost

This model costs approximately $0.00024 to run on Replicate, or 4166 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on CPU hardware. Predictions typically complete within 3 seconds.

Readme

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.

License

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