Date of Award
Santa Clara : Santa Clara University, 2022.
Computer Science and Engineering
Music recommendations from apps are traditionally based on attributes like popularity or trendiness. While this method of recommending music may work for finding music in general, it does not address the granularity with which human emotions can change day to day, or even hour to hour. AMBER provides an accessible way to discover and listen to new music that matches one’s emotions in real time using the Spotify music platform and AI. Preliminary test results indicate that users found the music AMBER recommended to be more in line with the emotions they were feeling or wanted to feel. Future developments of AMBER could explore possible improvements on the recommendations it provides by integrating lyrics into the recommendation algorithm, as well as by increasing the amount of user input used in generating recommendations.
Anderhub, Xavi; Bahl, Uma; Hou, Betty; and Krakauer, David, "AMBER: AI Music Based Emotion Regulation" (2022). Computer Science and Engineering Senior Theses. 217.