Mood Based Music Composition with Transformers and Fuzzy Logic

Music is a Universal Language that influences human emotions, mood and even physiological responses, making it a powerful tool in media, therapy, and entertainment. The increasing interest in AI-generated music that targets specific emotions faces challenges due to current technology's inability to control emotional expression effectively. As a result, AI struggles to create music that truly resonates with listeners on an emotional level. This research addresses this gap by introducing a method that combines transformer models with fuzzy logic to enhance AI's emotional expressiveness in music composition. This approach involves training a transformer model on mood-labeled music to capture correlations between musical features and emotions. Integrating fuzzy logic enables fine-tuning of elements like tempo and dynamics, allowing for precise adjustments that enhance the models ability for composing music that exactly relates the given mood. The result is a system that generates music with given mood. The outcomes show that this model successfully produces emotionally resonant music, refining mood accuracy with each iteration. This project combines automated music generation with mood-based composition allowing for personalized, emotionally attuned music experiences. This project demonstrates how AI can support musicians and creators by helping produce music that matches specific moods, that relates to individual preferences. Beyond art, it makes personalized music possible in areas like entertainment, and therapy, offering people music that fits their emotional needs.