music as data, data as music

unleashing data tools for music theory, analysis and composition

Marco Buongiorno Nardelli is University Distinguished Research Professor at the University of North Texas: composer, flutist, computational materials physicist, and 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, an Associate Fellow of IMéRA, the  Institute for Advanced Studies of Aix-Marseille University, 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, 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 www.musicntwrk.com.  

To learn more on my artistic practice:

M. Buongiorno Nardelli, The hitchhiker’s guide to the all-interval 12-tone rows, submitted (2020), also at https://arxiv.org/abs/2006.05007

M. Buongiorno Nardelli, Tonal harmony, the topology of dynamical score networks and the Chinese postman problem, submitted (2020), also at https://arxiv.org/abs/2006.01033

M. Buongiorno Nardelli, Topology of Networks in Generalized Musical Spaces, Leonardo Music Journal, Just Accepted at https://www.mitpressjournals.org/doi/abs/10.1162/lmj_a_01079 (2020), also at arXiv:1905.01842

M. Buongiorno Nardelli, MUSICNTWRK: data tools for music theory, analysis and composition, arxiv.org/abs/1906.01453, Springer Lecture Notes in Computer Science,  Proceedings of CMMR 2019, October 14,19, 2019, Marseille, France, in press.

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 ermes.unt.edu for more on my scientific research