  It has been shown recently that deep convolutional generative adversarial
networks (GANs) can learn to generate music in the form of piano-rolls, which
represent music by binary-valued time-pitch matrices. However, existing models
can only generate real-valued piano-rolls and require further post-processing,
such as hard thresholding (HT) or Bernoulli sampling (BS), to obtain the final
binary-valued results. In this paper, we study whether we can have a
convolutional GAN model that directly creates binary-valued piano-rolls by
using binary neurons. Specifically, we propose to append to the generator an
additional refiner network, which uses binary neurons at the output layer. The
whole network is trained in two stages. Firstly, the generator and the
discriminator are pretrained. Then, the refiner network is trained along with
the discriminator to learn to binarize the real-valued piano-rolls the
pretrained generator creates. Experimental results show that using binary
neurons instead of HT or BS indeed leads to better results in a number of
objective measures. Moreover, deterministic binary neurons perform better than
stochastic ones in both objective measures and a subjective test. The source
code, training data and audio examples of the generated results can be found at
https://salu133445.github.io/bmusegan/ .
