Abstract
In this paper, we address the problem of automated music synthesis using deep neural networks and ask whether neural networks are capable of realizing timing, pitch accuracy and pattern generalization for automated music generation when processing raw audio data. To this end, we present a proof of concept and build a recurrent neural network architecture capable of generalizing appropriate musical raw audio tracks.
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Acknowledgments
The authors gratefully acknowledge partial support from the German Research Foundation DFG under project CML (TRR 169), the European Union under project SECURE (No 642667).
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Eppe, M., Alpay, T., Wermter, S. (2018). Towards End-to-End Raw Audio Music Synthesis. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_14
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DOI: https://doi.org/10.1007/978-3-030-01424-7_14
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