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music as data, data as music

unleashing data tools for music theory, analysis and composition

Marco Buongiorno Nardelli is Regents Professor at the University of North Texas and External Faculty at the Santa Fe Institute: composer, flutist, computational materials physicist, he holds a joint appointment between Physics and Composition Studies, he is a member of CEMI, the Center for Experimental Music and Intermedia, and iARTA, the Initiative for Advanced Research in Technology and the Arts. He is a Fellow of the American Physical Society and of the Institute of Physics, and a Parma Recordings artist. 

Full CV here.

My Art/Science practice orbits around the duality "music as data, data as music": translation of complex events in sonic material, and object for musical poiesis. On one hand, data are raw elements for a compositional process that transcends the materiality of the original information in a post-sonification praxis: the data stream is open for elaboration as principal element of a data-driven compositional environment. I use these data as a sculptor would use clay (the raw data) to mold any object or create any design (the music). On the other, I generalize the concept of musical spaces as big data networks and derive functional principles of compositional design by the direct analysis of the network topology. With the materialssoundmusic project I want to exploit the dual aspect of data in music, the analytic and the artistic, within the general framework of "music as a complex adaptive system", and provide open source software tools for a novel approach to the dialogue between perceptualization of data and artistic creation. To this purpose I have developed a new computer-aided data-driven composition environment for the sonification, remix and artistic reinterpretation of large data spaces as expressive media: MUSICNTWRKMUSICNTWRK is 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  

Keywords: music computation (Python, Csound), algorithmic and data-centered composition, interactivity and live installations, micro-computers (RPi) and micro-controllers (Arduino), performance practices and spatial audio, machine learning and AI, sound design.

Official bio: 

To learn more on my artistic practice:

M. Buongiorno Nardelli, G. Culbreth and M. Fuentes, Evolution of harmonic complexity in western classical music, Advances in Complex Systems, vol. 25, No. 05n06 (2022)
M. Buongiorno Nardelli, The hitchhiker’s guide to the all-interval 12-tone rows, Perspectives of New Music, Volume 60, Number 1, Winter 2022, pp.225-237 (, also at

M. Buongiorno Nardelli, Tonal harmony and the topology of dynamical score networks, Journal of Mathematics and Music, DOI: (2021), also at
M. Buongiorno Nardelli, Topology of Networks in Generalized Musical Spaces, Leonardo Music Journal, 30, 38-43 (2020), also at arXiv:1905.01842
M. Buongiorno Nardelli, MUSICNTWRK: data tools for music theory, analysis and composition,, Springer Lecture Notes in Computer Science, vol. 12631, p. 190 (2021) Proceedings of Computer Music Multidisciplinary Research, Oct 2019, Marseille, France
M. Buongiorno Nardelli, Beautiful data: Reflections for a sonification and post-sonificatio aesthetics, in Leonardo Gallery: Scientific Delirium Madness 4.0, Leonardo | Volume 51 | Issue 3 | June 2018 - p.227-238.
M. Buongiorno Nardelli, materialssoundmusic: a computer-aided data-driven composition environment for the sonification and dramatization of scientific data streams, International Computer Music Conference Proceedings, 2015, 356 (2015).
M. Buongiorno Nardelli, Compositional design and pitch class set operations: integrated tools for composition and analysis, unpublished (2009).

Visit for more on my scientific research

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