Multitrack Emotion-Based Music Generation Network Using Continuous Symbolic Features

Musicians need an efficient composition process to yield a multitude of musical pieces. To enhance the artistry and emotional relevance of AI-generated music, a novel Multi-Track Emotion Music Generation model (MEMG) is proposed. MEMG minimizes reliance on emotion-labeled datasets, instead employing musical features such as conditional pitch histograms to signify music emotion attributes. By analyzing music features and emotional responses, MEMG facilitates controlled music generation with diverse emotions, where specific emotions are mapped based on Russell’s psychological model. A novel hybrid feedback reward network is constructed, integrating music theory rules, discriminator, and musical emotion interpretation. Incorporating music theory and embedding music emotion as input parameters during generation. Objective music theory evaluation and subjective auditory testing validate MEMG’s competitiveness in composing varied emotional music, coupled with exemplary adherence to musical rules.