Date of Award
6-2016
Document Type
Open Access
Degree Name
Bachelor of Science
Department
Computer Science
First Advisor
Matthew Anderson
Language
English
Keywords
similarity, song, end
Abstract
We blend two existing automatic playlist generation algorithms. One algorithm is built to smoothly transition between a start song and an end song (Start-End). The other infers song similarity based on adjacent occurrences in expertly authored streams (EAS). First, we seek to establish the effectiveness of the Start-End algorithm using the EAS algorithm to determine song similarity, then we propose two playlist generation algorithms of our own: the Unbiased Random Walk (URW) and the Biased Random Walk (BRW). Like the Start-End algorithm, both the URW algorithm and BRW algorithm transition between a start song and an end song; however, issues inherent to the Start-End algorithm lead us to believe that our algorithms may create playlists with smoother transitions between songs.
Recommended Citation
Curbow, James, "Blending Two Automatic Playlist Generation Algorithms" (2016). Honors Theses. 138.
https://digitalworks.union.edu/theses/138