2020/12/07 by Ruibin Yuan, Yuan, Ruibin, Ge Zhang +5
Computer Science · #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Multimedia (cs.MM) #Music and Audio Processing #Sound (cs.SD) #Topic Modeling #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2012.03805
openalex publication_date 2020/12/07 · openalex created_date 2020/12/21 · openalex updated_date 2026/07/28
In this paper, we propose to adapt the method of mutual information maximization into the task of Chinese lyrics conditioned melody generation to improve the generation quality and diversity. We employ scheduled sampling and force decoding techniques to improve the alignment between lyrics and melodies. With our method, which we called Diverse Melody Generation (DMG), a sequence-to-sequence model learns to generate diverse melodies heavily depending on the input style ids, while keeping the tonality and improving the alignment. The experimental results of subjective tests show that DMG can generate more pleasing and coherent tunes than baseline methods.