Context-AI Tunes: Context-Aware AI-Generated Music for Stress Reduction

TL;DR

This paper introduces Context-AI Tune (CAT), an AI system that generates personalized music tailored to environmental context and stress levels, effectively reducing stress more than manual music selection.

Contribution

The paper presents a novel AI-based system for context-aware music generation that adapts to individual stress and environment, improving stress reduction outcomes.

Findings

CAT outperforms manual music selection in stress reduction

Personalized music adapts effectively to environmental context

System demonstrates significant stress decrease in experimental settings

Abstract

Music plays a critical role in emotional regulation and stress relief; however, individuals often need different types of music tailored to their unique stress levels or surrounding environment. Choosing the right music can be challenging due to the overwhelming number of options and the time-consuming trial-and-error process. To address this, we propose Context-AI Tune (CAT), a system that generates personalized music based on environmental inputs and the user's self-assessed stress level. A 2x2 within-subject experiment (N=26) was conducted with two independent variables: AI (AI, NoAI) and Environment (Busy Hub, Quiet Library). CAT's effectiveness in reducing stress was evaluated using the Visual Analog Scale for Stress (VAS-S). Results show that CAT is more effective than manually chosen music in reducing stress by adapting to user context.