Automatic Embedding of Stories Into Collections of Independent Media
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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
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.
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References · 4
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2018Learning deep representations by mutual information estimation and maximizationcited by 1,273
- 2016The emotional arcs of stories are dominated by six basic shapescited by 71
- 2019Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filterscited by 4
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