DeepDrum: An Adaptive Conditional Neural Network
This addresses the problem of generating structured drum rhythms for music composition, but it is incremental as it builds on existing LSTM methods by adding conditional layers.
The paper tackled generating drum rhythms with musical constraints by introducing DeepDrum, an adaptive neural network that uses conditional layers to incorporate parameters and instrumentation information, resulting in effective rhythm generation that resembles learned styles while adhering to unseen constraints.
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.