Music streaming services such as SPOTIFY offer their users not only a broad music selection but also personalised playlists and individual music suggestions. Complex algorithms enable these services and, according to recent trends, also seem to replace the established criteria of ones music selection.

This bachelor thesis data visualisation allows SPOTIFY users to analyse their own musical behaviour based on simplified algorithmically calculated song attributes and values. The user should be able to establish a relation between already familiar parameters such as songs and genres and the abstract song values. Metaphorically speaking the observer transforms into an algorithm to analyse his /her own music profile. 

For the realisation of the project current frameworks are used such as Node.js as well as programming paradigms REST for the server sided programming. The data visualisation is implemented with Webpack and the JavaScript library P5.js which offeres great interaction possibilities for the user. The visualisation uses behavioural metaphors around the topic of music which strengthens the user experience. 

This project was highly honored by SPOTIFY and can be seen in their showcase:

Try it: www.klangspektrum.digital