Deep reinforcement learning for adaptive music emotion recognition and generation

Music emotion recognition and generation play a vital role in creating emotionally resonant experiences in human-computer interaction. Deep reinforcement learning (DRL) offers promising capabilities for dynamically modeling and generating music aligned with human emotional states. However, existing methods often struggle with rigid emotional mapping, a lack of real-time adaptability, and limited generalization across diverse musical contexts. To address these limitations, this study proposes an Emotion-Conditioned Deep Reinforcement Learning (EC-DRL) framework that integrates emotion-aware representations into the reward mechanism of a DRL agent. The system employs deep neural networks to extract high-level audio features and map them onto valence-arousal emotion space, guiding the music generation process through a reinforcement learning policy optimized for emotional congruence. This framework is applied in adaptive soundtrack generation for interactive applications such as video games, where music responds dynamically to emotional cues derived from gameplay and user interactions. Experimental results demonstrate that the EC-DRL framework significantly improves emotional accuracy, coherence, and user satisfaction compared to traditional sequence-based generation models. The proposed system shows the potential for creating emotionally intelligent music systems that can adapt in real-time and produce expressive musical output. EC-DRL achieves the mapping accuracy by 98%, emotional congruence score by 0.9%, real-time responsiveness by 280 ms, reward function optimization by 9.5%, audio feature extraction quality by 86%, policy convergence rate by 0.8%, user satisfaction score by 8.9%, and cross-domain generalization by 88%.