Schön, F., & Tompits, H. (2022). PAUL: An Algorithmic Composer for Classical Piano Music Supporting Multiple Complexity Levels. In Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, Proceedings (EPIA 2022) (pp. 415–426). Springer. https://doi.org/10.1007/978-3-031-16474-3_34
E192-03 - Forschungsbereich Knowledge Based Systems
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Published in:
Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, Proceedings (EPIA 2022)
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ISBN:
978-3-031-16474-3
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Volume:
13566
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Date (published):
2022
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Event name:
21st EPIA Conference on Artificial Intelligence (EPIA 2022)
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Event date:
31-Aug-2022 - 2-Sep-2022
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Event place:
Portugal
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Number of Pages:
12
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Publisher:
Springer
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Peer reviewed:
Yes
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Keywords:
Algorithmic composition; Music education; Neural networks
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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.