AI Music Creation mood-based composition using artificial intelligence has becomea novel interdisciplinary field combining signal processing, affective computing, and deep learning. This paper proposes a music-generatingAI based on a user’s emotional context by leveraging a combination of recurrent neural networks (RNNs), transformer architectures, and audio feature embeddings. The system incorporates emotion recognition via audio or text input, followed by real-time music generation aligned with the detected mood (e.g., happy, sad, calm, energetic). A custom The dataset was compiled from openaccess sources annotated with emotion labels. The proposed architecture achieves highmood-classification accuracy and generates harmonically rich, emotionally aligned music sequences. This study explores both performance and interpretability using attention heatmaps and feature saliency analysis to enhance transparency and user trust in generative AI systems.
Sagar et al. (Mon,) studied this question.