  Considering music as a sequence of events with multiple complex dependencies,
the Long Short-Term Memory (LSTM) architecture has proven very efficient in
learning and reproducing musical styles. However, the generation of rhythms
requires additional information regarding musical structure and accompanying
instruments. In this paper we present DeepDrum, an adaptive Neural Network
capable of generating drum rhythms under constraints imposed by Feed-Forward
(Conditional) Layers which contain musical parameters along with given
instrumentation information (e.g. bass and guitar notes). Results on generated
drum sequences are presented indicating that DeepDrum is effective in producing
rhythms that resemble the learned style, while at the same time conforming to
given constraints that were unknown during the training process.
