# Automatic Embedding of Stories Into Collections of Independent Media

arXiv: https://arxiv.org/abs/2111.02216 · HTML: https://mlsanity.com/paper/2111.02216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters](https://mlsanity.com/paper/1903.10839/) (2019)

## Abstract (arXiv)

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