MUSICNTWRK: data tools for music theory, analysis and composition
This work addresses the need for accessible computational tools in music research and creation, though it is incremental as it builds on existing concepts in music informatics.
The authors introduced MUSICNTWRK, a Python library providing tools for music theory, analysis, and composition, including pitch class set classification, rhythmic sequence manipulation, network generation, deep learning for timbre recognition, and data sonification, with the software being freely available under GPL 3.0.
We present the API for 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. The software is freely available under GPL 3.0 and can be downloaded at www.musicntwrk.com or installed as a PyPi project (pip install musicntwrk).