A Hierarchical Latent Vector Model for Learning Long-Term Structure in MusicA Hierarchical Latent Vector Model for Learning Long-Term Structure in MusicAdam Roberts and Jesse Engel and Colin Raffel and Curtis Hawthorne and Douglas Eck2018
Paper summarydecodyngI’ve spent the last few days pretty deep in the weeds of GAN theory - with all its attendant sample-squinting and arcane training diagnosis - and so today I’m shifting gears to an applied paper, that mostly showcases some clever modifications of an underlying technique. The goal of the MusicVAE is as you might expect: to make music. But the goal isn’t just the ability to produce patterns of notes that sound musical, it’s the ability to learn a vector space where we can modify the values along each dimension, and cause the music we produce to vary along conceptually meaningful directions. In an ideal world, we might learn a dimension that corresponds to tempo, another that corresponds to the key we’re in, etc.
To achieve this goal, the modelers use the structure of a Variational AutoEncoder, a model where we pass in the input, compress it down to some latent code (read: a low-dimensional vector of continuous values), and then, starting from that latent code, use a decoder to try to recreate (or “reconstruct”) the output. Think of this as describing a scene to a friend behind their back, and trying to describe it in a maximally informative way, so that they can draw it themselves, and get as close as possible to the original. Ideally, this set of constraints incentives you to learn an informative code, which will contain the kind of conceptually meaningful information that we want it to.
One problem this can run into is that, given certain mathematical facts about the structure of autoencoders, if you use a decoder with a lot of capacity, like a RNN, the model can “decide” to use the RNN to model the data directly, storing all that conceptual information we’d like to have pulled out in the latent code in the parameters of the RNN instead. And, so, to solve this, the authors of the paper came up with a clever solution: instead of generating the full piece of music at once, they would instead build a hierarchical model, with a “conductor” layer that prescribes what a medium-sized chunk of the reconstructed piece will sound like, and a lower level “decoder” layer that takes the conductor’s direction for that chunk, and unspools it into a series of notes. On a more mechanical level, when the encoder spits out a latent code for a given piece of music, we pass that to the conductor. The conductor then produces - say - 10 embeddings, with each embedding corresponding to a set of 4 measures. Each decoder only sees the embedding for its chunk, and is only responsible for mapping that embedding into a series of concrete notes. This inability of each decoder to see what the decoders before and after it are doing means that, in order for the piece to sound coherent, the network needs to learn to develop a condensed set of instructions to give to the conductor.
In practice, they come up with some really neat results: the example they show on the linked page demonstrates a learned concept-dimension that maps to “how much is this piece composed of long, held notes, vs short staccato ones”. They show that they can “interpolate” across this dimension (that is: slowly change its value) and see that the output slowly morphs from very long held notes, to a high density of different ones.
First published: 2018/03/13 (2 years ago) 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 http://g.co/magenta/musicvae-code.