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Denoising Diffusion Probabilistic models have emerged as simple yet very
powerful generative models. Unlike other generative models, diffusion models do
not suffer from mode collapse or require a discriminator to generate
high-quality samples. In this paper, a diffusion model that uses a binomial
prior distribution to generate piano rolls is proposed. The paper also proposes
an efficient method to train the model and generate samples. The generated
music has coherence at time scales up to the length of the training piano roll
segments. The paper demonstrates how this model is conditioned on the input and
can be used to harmonize a given melody, complete an incomplete piano roll, or
generate a variation of a given piece. The code is publicly shared to encourage
the use and development of the method by the community.
