Automatic Embedding of Stories Into Collections of Independent Media
Abstract
We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections. To do so, we use models that extract the tempo of songs to make a music playlist follow a narrative arc. Our work specifies an open-source tool that uses pre-trained neural network models to extract the global tempo of a set of raw audio files and applies these measures to create a narrative-following playlist. This tool is available at https://github.com/dylanashley/playlist-story-builder/releases/tag/v1.0.0
Keywords
Cite
@article{arxiv.2111.02216,
title = {Automatic Embedding of Stories Into Collections of Independent Media},
author = {Dylan R. Ashley and Vincent Herrmann and Zachary Friggstad and Kory W. Mathewson and Jürgen Schmidhuber},
journal= {arXiv preprint arXiv:2111.02216},
year = {2021}
}
Comments
2 pages in main text + 1 page of references + 6 pages of appendices, 2 figures in main text + 3 figures in appendices, 1 algorithm in appendices; source code available at https://gist.github.com/dylanashley/1387a99deb85bfc0bce11286810cd98b