Title: A Classifying Variational Autoencoder with Application to Polyphonic Music Generation,
Abstract: The variational autoencoder (VAE) is a popular probabilistic generative
model. However, one shortcoming of VAEs is that the latent variables cannot be
discrete, which makes it difficult to generate data from different modes of a
distribution. Here, we propose an extension of the VAE framework that
incorporates a classifier to infer the discrete class of the modeled data. To
model sequential data, we can combine our Classifying VAE with a recurrent
neural network such as an LSTM. We apply this model to algorithmic music
generation, where our model learns to generate musical sequences in different
keys. Most previous work in this area avoids modeling key by transposing data
into only one or two keys, as opposed to the 10+ different keys in the original
music. We show that our Classifying VAE and Classifying VAE+LSTM models
outperform the corresponding non-classifying models in generating musical
samples that stay in key. This benefit is especially apparent when trained on
untransposed music data in the original keys.