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
Get started with musicntwrk:
git clone https://github.com/marcobn/musicntwrk.git
musicntwrk is a software written for python 3 and comprises of four modules,
pcsPy- a module for pitch class set classification and manipulation in any arbitrary temperament; the construction of generalized pitch class set networks using distances between common descriptors (interval vectors, voice leadings); the analysis of scores and the generation of compositional frameworks.
rhythmPy- a module for rhythmic sequence classification and manipulation; and the construction of rhythmic sequence networks using various definitions of rhythmic distance.
timbrePy- comprises of two sections: the first deals with orchestration color and it is the natural extension of the score analyzer in
pscPy; the second deals with analysis and characterization of timbre from a (psycho-)acoustical point of view. In particular, it provides: the characterization of sound using, among others, Mel Frequency or Power Spectrum Cepstrum Coefficients (MFCC or PSCC); the construction of timbral networks using descriptors based on MF- or PS-CCs; and machine learning models for timbre recognition through the TensorFlow Keras framework.
sonifiPy- a module for the sonification of arbitrary data structures, including automatic score (musicxml) and MIDI generation.
musicntwrk requires the installation of the following modules via the “pip install” (or equivalent, depending on individual environments) command:
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:
load_balancing. Since the user will never have to interact with these modules, we omit here a detailed description of their functions.
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.
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).
This project has been made possible by contributions from the following institutions:
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/.