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We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global representation with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and melody. Empirically, we demonstrate the effectiveness of our method on various music generation tasks on the MAESTRO dataset and a YouTube dataset with 10,000+ hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to baselines.
Author Information
Kristy Choi (Stanford University)
Curtis "Fjord" Hawthorne (Google Research)
Ian Simon (Google Brain)
Monica Dinculescu (Google Brain)
JesseEngel Engel (Google Brain)
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2020 Poster: Fair Generative Modeling via Weak Supervision »
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2018 Poster: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music »
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2018 Oral: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music »
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2017 Poster: Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders »
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