Combining LSTM and Feed Forward Neural Networks for Conditional Rhythm Composition

Dimos Makris, Maximos A. Kaliakatsos-Papakostas, Ioannis Karydis, Katia Lida Kermanidis

Abstract: Algorithmic music composition has long been in the spotlight of music information research and Long Short-Term Memory (LSTM) neural networks have been extensively used for this task. However, despite LSTM networks having proven useful in learning sequences, no methodology has been proposed for learning sequences conditional to constraints, such as given metrical structure or a given bass line. In this paper we examine the task of conditional rhythm generation of drum sequences with Neural Networks. The proposed network architecture is a combination of LSTM and feed forward (conditional) layers capable of learning long drum sequences, under constraints imposed by metrical rhythm information and a given bass sequence. The results indicate that the role of the conditional layer in the proposed architecture is crucial for creating diverse drum sequences under conditions concerning given metrical information and bass lines.