Abstract
The inherent complexity and structure on long timescales make the automated composition of music a challenging problem. Here we present the Deep Artificial Composer (DAC), a recurrent neural network model of note transitions for the automated composition of melodies. Our model can be trained to produce melodies with compositional structures extracted from large datasets of diverse styles of music, which we exemplify here on a corpus of Irish folk and Klezmer melodies. We assess the creativity of DAC-generated melodies by a new measure, the novelty of musical sequences, showing that melodies imagined by the DAC are as novel as melodies produced by human composers. We further use the novelty measure to show that the DAC creates melodies musically consistent with either of the musical styles it was trained on. This makes the DAC a promising candidate for the automated composition of convincing musical pieces of any provided style.
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Acknowledgments
The authors thank Samuel P. Muscinelli and Johanni Brea for their guidance and helpful comments. This research was partially supported by the Swiss National Science Foundation (200020_147200) and the European Research Council grant no. 268689 (MultiRules).
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Colombo, F., Seeholzer, A., Gerstner, W. (2017). Deep Artificial Composer: A Creative Neural Network Model for Automated Melody Generation. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_6
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