A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4364-4373, 2018.

Abstract

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 https://goo.gl/magenta/musicvae-code.

Cite this Paper


BibTeX
@InProceedings{pmlr-v80-roberts18a, title = {A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music}, author = {Roberts, Adam and Engel, Jesse and Raffel, Colin and Hawthorne, Curtis and Eck, Douglas}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {4364--4373}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/roberts18a/roberts18a.pdf}, url = {http://proceedings.mlr.press/v80/roberts18a.html}, abstract = {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 https://goo.gl/magenta/musicvae-code.} }
Endnote
%0 Conference Paper %T A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music %A Adam Roberts %A Jesse Engel %A Colin Raffel %A Curtis Hawthorne %A Douglas Eck %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-roberts18a %I PMLR %P 4364--4373 %U http://proceedings.mlr.press/v80/roberts18a.html %V 80 %X 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 https://goo.gl/magenta/musicvae-code.
APA
Roberts, A., Engel, J., Raffel, C., Hawthorne, C. & Eck, D.. (2018). A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:4364-4373 Available from http://proceedings.mlr.press/v80/roberts18a.html.

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