Kockelkorn, S.M.R. (2020) Bayesian Networks of Spotify’s audio features. Bachelor's Thesis, Mathematics.
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Abstract
More than ever before in history, scientists and developers are analyzing music and lookinginto related applications of the analysis. [Luo, 2018] The modern ability to stream musicusing services such as Spotify, Pandora, and Apple music has revolutionized how musicis consumed [Amsterdam, 2019] and has thereby also opened up many import areas ofresearch. “There are researches that focus on underlying technologies, working mechanisms,user experience and other specific topics in music analysis field.” [Luo, 2018]Not only is there more demand for research from the streaming music industry, they alsoprovide new research opportunities. Spotify for instance, created something calledSpotifyAPI for developers, an open database from which for every song on Spotify various audiofeatures can be downloaded. Examples of such features aredanceability,loudness, andinstrumentalness.Various research has been done with their openly accessible audio features. Topics includehistory of music, genre prediction [Luo, 2018], prediction of hit songs [Georgieva et al.],music recommendations and many more.Bayesian Networks, a probabilistic graphical model, can be used to graphically representconditional (in)dependencies between variables learnt from data. [Pearl, 2011] It combineselements fromgraph theory(directed acyclic graphs) andprobability theory(Bayesian statis-tics [Bolstad and Curran, 2016]). Probabilistic models based on directed acyclic graphs(DAGs) such as Bayesian networks have a long and r
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Grzegorczyk, M.A. |
| Degree programme: | Mathematics |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 25 Jun 2021 08:20 |
| Last Modified: | 25 Jun 2021 08:20 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/24600 |
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