Learning a Latent Space of Style-Aware Symbolic Music Representations by Adversarial Autoencoders

Valenti Andrea
Valenti Andrea
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Abstract:

We address the challenging open problem of learning an effective latent space for symbolic music data in generative music modeling. We focus on leveraging adversarial regularization as a flexible and natural mean to imbue variational autoencoders with context information concerning music genre and style. Through the paper, we show how G...More

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