musicntwrk 2.1.0

music as data, data as music

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
• What's included
• Documentation
• Author
• Citation
• Thanks

Quick start

Get started with musicntwrk:

NEW!!! version 2.1.0 now on PyPi

pip install musicntwrk

or

pip install musicntwrk[with_MPI]

(if there is a pre-existing installation of MPI, pip will automatically install the mpi4pi wrapper)

• OR download the latest release and the Example notebook and support files

• Clone the repo: git clone https://github.com/marcobn/musicntwrk.git

• cd musicntwrk-2.0

• pip install .

The following documentation is for the old release

NEW DOCUMENTATION COMING SOON

In the meantime, the old documentation is still usable (main routines are still the same)

What's included

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

• 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.
• A jupyter notebook with selected examples is provided in the TESTS directory.

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,powerlaw
4. Music modules: music21,librosa,pyo
5. Visualization modules: matplotlib, vpython
6. Parallel processing: mpi4py

Documentation for the individual modules:

• pcsPy
• rhythmPy
• timbrePy
• sonifiPy
• examples

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:

           

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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
[e-mail]