musicntwrk

Network Analysis of Generalized Musical Spaces

View the Project on GitHub marcobn/musicntwrk

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music as data, data as music

unleashing data tools for music theory, analysis and composition

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

Table of contents

Quick start

Get started with musicntwrk:

What’s included

musicntwrk is a software written for python 3 and comprises of four modules, pcsPy, rhythmPy, timbrePy and sonifiPy:

Documentation

musicntwrk requires the installation of the following modules via the “pip install” (or equivalent, depending on individual environments) command:

  1. System modules: sys, re, time, os,urllib,wget,bs4,warnings
  2. Math modules: numpy, scipy, itertools, fractions, gcd, functools
  3. Data modules: pandas, sklearn, networkx, community,tensorflow
  4. Music modules: music21,librosa
  5. Visualization modules: matplotlib, vpython
  6. Parallel processing: mpi4py

Documentation for the individual modules:

The most computationally intensive parts of the modules can be run on parallel processors using the MPI (Message Passing Interface) protocol. Communications are handled by two additional modules: communications and load_balancing. Since the user will never have to interact with these modules, we omit here a detailed description of their functions.

Author

Marco Buongiorno Nardelli

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, and a Parma Recordings artist. See here for a longer bio-sketch.

Citation

Marco Buongiorno Nardelli, “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”, www.musicntwrk.com (2019).

Thanks

This project has been made possible by contributions from the following institutions:

UNT logo     CEMI logo     PRISM logo     IMeRA logo


musicntwrk is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Copyright (C) 2019 Marco Buongiorno Nardelli
www.materialssoundmusic.com
www.musicntwrk.com
mbn@unt.edu
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