Sampling Variations of Lead Sheets
  Machine-learning techniques have been recently used with spectacular results
to generate artefacts such as music or text. However, these techniques are
still unable to capture and generate artefacts that are convincingly
structured. In this paper we present an approach to generate structured musical
sequences. We introduce a mechanism for sampling efficiently variations of
musical sequences. Given a input sequence and a statistical model, this
mechanism samples a set of sequences whose distance to the input sequence is
approximately within specified bounds. This mechanism is implemented as an
extension of belief propagation, and uses local fields to bias the generation.
We show experimentally that sampled sequences are indeed closely correlated to
the standard musical similarity measure defined by Mongeau and Sankoff. We then
show how this mechanism can used to implement composition strategies that
enforce arbitrary structure on a musical lead sheet generation problem.
