Symbolic Music Generation from Graph-Learning-based Preference Modeling and Textual Queries

This paper investigates the domain of automatic music generation (AMG) and its capacity to produce music that is aligned with user preferences. The incorporation of user music preference (UMP) awareness in AMG technology has the potential to reduce reliance on musicians and domain experts while encouraging users to engage in activities that promote human health and potential. Current research in AMG has been limited to the qualitative control of a constrained set of attributes in the generated music such as selecting a genre from a given list. This constraint makes it challenging to develop music that is both aligned with UMP and suitable for practical text query-based applications. To address this challenge, we propose to apply deep-graph-networks on music community data, jointly modeling UMP and music features. Moreover, users' textual descriptions of expected music can be transformed into graphs that are compatible with UMPs. Node embeddings representing user queries' connotation are extracted to condition the music generator. The results on objective and subjective metrics demonstrate a significant improvement in UMP accuracy by 31.3%, UMP-aware AMG by 63.5%, and text-to-music AMG effectiveness by 76.5%. Our detailed analysis indicates that the generated music aligns best with queries comprised of short sentences and commonly used words.