Music personalization system where AI compete in boosting/maintaining a person's mood by playing them the right music
Image credit: https://blog.j-labs.pl/how-does-ai-create-music
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- Create a super personalized music recommendation algorithm by making AI get better at playing a "game" with a given set of rules.
- Instead of tweaking music recommendation algorithms from time to time to improve them, use an alternative way that lets maximally automatize and personalize the process.
- Subjective evaluation by the person him/herself. This is the simplest approach where the user simply presses feedback buttons while the music is playing, once the feedback is below a certain threshold, the system changes the song/melody into something different and takes it from there. If the feedback is rather good, it tries to include more similar songs/melodies.
- Physiological parameters. The data about the person's brain activity (especially that of the limbic system), heartbeat, breathing, electrodermal activity, skin temperature, etc. is sent to the AI while the music is playing. Those are continuous and more objective measures if you know how to convert the data into mood scores. Currently, wireless sensor systems are well developed and don't require bulky wires or for the user to sit in one place, devices similar to the Muse headband and Empatica wristband can be used to build the system.
- Combination of the two. I think the smartest way to go about this would be to first correlate an individual's physiological parameters with their subjective mood evaluations and then create an "atlas" for AI to convert continuously monitored physiological parameters into mood scores. This could be done in a preparatory phase and then the system would keep refining the connections between physiological data and subjective mood evaluation as the user gives subjective feedback while listening to the music.