Document Type
Article
Publication Date
5-2023
Publication Title
The Journal of Open Source Software (JOSS)
Abstract
We introduce repytah, a Python package that constructs the aligned hierarchies representation that contains all possible structure-based hierarchical decompositions for a finite length piece of sequential data aligned on a common time axis. In particular, this representation–introduced by Kinnaird (2016) with music-based data (like musical recordings or scores) as the primary motivation–is intended for sequential data where repetitions have particular meaning (such as a verse, chorus, motif, or theme). Although the original motivation for the aligned hierarchies representation was finding structure for music-based data streams, there is nothing inherent in the construction of these representations that limits repytah to only being used on sequential data that is music-based.
The repytah package builds these aligned hierarchies by first extracting repeated structures (of all meaningful lengths) from the self-dissimilarity matrix (SDM) for a piece of sequential data. Intentionally repytah uses the SDM as the starting point for constructing the aligned hierarchies, as an SDM cannot be reversed-engineered back to the original signal and allows for researchers to collaborate with signals that are protected either by copyright or under privacy considerations. This package is a Python translation of the original MATLAB code by Kinnaird (2014) with additional documentation, and the code has been updated to leverage efficiencies in Python.
DOI
10.21105/joss.05213
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
© The authors
Recommended Citation
Jia, Chenhui; Carpenter, Lizette; Tran, Thu; Liu, Amanda Y.; Yeutseyva, Sasha; Tapal, Mariun; Wang, Yingke; Zhou, Zoie Kexin; Moody, Jordan; Nava, Denise; Donaher, Eleanor; Jiang, Lillian Yusha; Bruncati, Ben; and Kinnaird, Katherine M., "repytah: An Open-Source Python Package for Building Aligned Hierarchies for Sequential Data" (2023). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/362
Comments
Archived as published.