JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models. (arXiv:2308.04729v1 [cs.SD])

Music generation has attracted growing interest with the advancement of deep
generative models. However, generating music conditioned on textual
descriptions, known as text-to-music, remains challenging due to the complexity
of musical structures and high sampling rate requirements. Despite the task's
significance, prevailing generative models exhibit limitations in music
quality, computational efficiency, and generalization. This paper introduces
JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a
diffusion model incorporating both autoregressive and non-autoregressive
training. Through in-context learning, JEN-1 performs …

arxiv complexity deep generative models diffusion diffusion models generative generative models limitations music music generation quality rate requirements sampling significance text textual universal music