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A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
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The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the "posterior collapse" problem, which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a "flat" baseline model. An implementation of our "MusicVAE" is available online at http://g.co/magenta/musicvae-code.
Forward citations
Cited by 4 Pith papers
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Translating Visual Art into Music
An unsupervised image-to-music model, SynVAE, lets human listeners match generated music to its source image with up to 73% accuracy.
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Workflow-Based Evaluation of Music Generation Systems
A single-producer workflow evaluation of eight music AI tools finds they work as idea and sound generators but not as complete composers, and proposes a reusable framework.
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Calliope: An Online Generative Music System for Symbolic Multi-Track Composition
Calliope is a browser-based system that wraps the MMM transformer model in an interface for multi-track MIDI generation, editing, batch sampling, and DAW streaming.
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Apollo: An Interactive Environment for Generating Symbolic Musical Phrases using Corpus-based Style Imitation
Apollo is a new interactive desktop application that combines user-managed MIDI corpora, trainable style imitation models, and graphical controls for generating symbolic musical phrases.
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