PAUL: An Algorithmic Composer for Classical Piano Music Supporting Multiple Complexity Levels

Abstract

Algorithmic composition (AC) refers to the process of creating music by means of algorithms, either for realising music entirely composed by a computer or with the help of a computer. In this paper, we report on the development of the system PAUL, an algorithmic composer for the automatic creation of short pieces of classical piano music, based on a neural-network architecture. The distinguishing feature of PAUL is that it allows to specify the desired complexity of the output piece in terms of an input parameter, which is a central aspect towards the designated future usage of PAUL as being part of a tutoring system teaching piano students how to sight-read music. PAUL employs a long short-term memory (LSTM) neural network to produce the lead track and a sequence-to-sequence neural network for the realisation of the accompanying track. Although PAUL is still work-in-progress, the obtained results are of reasonable to good quality. In a small-scale study, evaluating the specified vs. the perceived complexity of different pieces generated by PAUL, a clear correlation is observable.