Abstract
We present the API for MUSICNTWRK, a python library for pitch class set and rhythmic sequences classification and manipulation, the generation of networks in generalized music and sound spaces, deep learning algorithms for timbre recognition, and the sonification of arbitrary data. The software is freely available under GPL 3.0 and can be downloaded at www.musicntwrk.com or installed as a PyPi project (pip install musicntwrk).
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Notes
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A full treatment of the mathematical properties of VL operators will be the subject of a forthcoming publication.
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This step might be unnecessary if running on a cloud service like Google Colaboratory.
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References
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Acknowledgments
We acknowledge the support of Aix-Marseille University, IMéRA, and of Labex RFIEA+. It must be understood that MUSICNTWRK is a continuously evolving library, so it is likely that at the time of publication of this paper more functionalities will be available. We invite the reader to explore the GitHub distribution that will always provide the most recent version of the software. Finally, we thank Richard Kronland-Martinet, Sølvi Ystad, Mitsuko Aramaki, Jon Nelson, Joseph Klein, Scot Gresham-Lancaster, David Bard-Schwarz, Roger Malina and Alexander Veremyer for useful discussions.
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Buongiorno Nardelli, M. (2021). MUSICNTWRK: Data Tools for Music Theory, Analysis and Composition. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_14
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DOI: https://doi.org/10.1007/978-3-030-70210-6_14
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