2021/11/03 by Dylan R. Ashley, Vincent Herrmann, Ashley, Dylan R. +7
Computer Science · Engineering · #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #H.5.5 #I.2.6 #J.5 #Machine Learning (cs.LG) #Multimedia (cs.MM) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Topic Modeling #cs.CL #cs.LG #cs.MM #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.02216
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
arxiv created 2021/11/03 · openalex publication_date 2021/11/03 · arxiv updated 2021/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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