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When Counterpoint Meets Chinese Folk Melodies
Nan Jiang · Sheng Jin · Zhiyao Duan · Changshui Zhang

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #980

Counterpoint is an important concept in Western music theory. In the past century, there have been significant interests in incorporating counterpoint into Chinese folk music composition. In this paper, we propose a reinforcement learning-based system, named FolkDuet, towards the online countermelody generation for Chinese folk melodies. With no existing data of Chinese folk duets, FolkDuet employs two reward models based on out-of-domain data, i.e. Bach chorales, and monophonic Chinese folk melodies. An interaction reward model is trained on the duets formed from outer parts of Bach chorales to model counterpoint interaction, while a style reward model is trained on monophonic melodies of Chinese folk songs to model melodic patterns. With both rewards, the generator of FolkDuet is trained to generate countermelodies while maintaining the Chinese folk style. The entire generation process is performed in an online fashion, allowing real-time interactive human-machine duet improvisation. Experiments show that the proposed algorithm achieves better subjective and objective results than the baselines.

Author Information

Nan Jiang (Tsinghua University)
Sheng Jin (Tsinghua University)
Zhiyao Duan (Unversity of Rochester)

Zhiyao Duan is an associate professor in Electrical and Computer Engineering, Computer Science and Data Science at the University of Rochester. He received his B.S. in Automation and M.S. in Control Science and Engineering from Tsinghua University, China, in 2004 and 2008, respectively, and received his Ph.D. in Computer Science from Northwestern University in 2013. His research interest is in the broad area of computer audition, i.e., designing computational systems that are capable of understanding sounds, including music, speech, and environmental sounds. He is also interested in the connections between computer audition and computer vision, natural language processing, and augmented and virtual reality. He received a best paper award at the 2017 Sound and Music Computing (SMC) conference, a best paper nomination at the 2017 International Society for Music Information Retrieval (ISMIR) conference, a BIGDATA award and a CAREER award from the National Science Foundation (NSF). His research is funded by NSF, NIH, New York State Center of Excellence on Data Science, and University of Rochester internal awards on AR/VR, data science and health analytics.

Changshui Zhang (Tsinghua University)

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