  The field of automatic music composition has seen great progress in the last
few years, much of which can be attributed to advances in deep neural networks.
There are numerous studies that present different strategies for generating
sheet music from scratch. The inclusion of high-level musical characteristics
(e.g., perceived emotional qualities), however, as conditions for controlling
the generation output remains a challenge. In this paper, we present a novel
approach for calculating the valence (the positivity or negativity of the
perceived emotion) of a chord progression within a lead sheet, using
pre-defined mood tags proposed by music experts. Based on this approach, we
propose a novel strategy for conditional lead sheet generation that allows us
to steer the music generation in terms of valence, phrasing, and time
signature. Our approach is similar to a Neural Machine Translation (NMT)
problem, as we include high-level conditions in the encoder part of the
sequence-to-sequence architectures used (i.e., long-short term memory networks,
and a Transformer network). We conducted experiments to thoroughly analyze
these two architectures. The results show that the proposed strategy is able to
generate lead sheets in a controllable manner, resulting in distributions of
musical attributes similar to those of the training dataset. We also verified
through a subjective listening test that our approach is effective in
controlling the valence of a generated chord progression.
