Automatic Embedding of Stories Into Collections of Independent Media
This work addresses a niche problem for creators or users of music playlists, but it is incremental as it applies existing pre-trained models to a new application.
The paper tackles the problem of automatically embedding stories into collections of independent media by using machine learning to extract tempo from songs and create playlists that follow a narrative arc, resulting in an open-source tool available for use.
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