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学术报告:Neural Melody Generation from Lyrics
来源:  点击次数: 次 发布时间:2019-11-08   编辑:统计数学学院

时间:20191111(星期1400-1500

地点:沙河校区7号楼113

报告人:Yi Yu,日本情报学研究所

报告摘要:Generating melody from lyrics to compose a song has been a very interesting research topic in the area of artificial intelligence and music, which tries to predict generative music relationship between lyrics and melody. Unfortunately, the limited availability of paired lyrics-melody corpus with alignment information has hindered the research progress. To address this problem, a large corpus consisting of 12,197 MIDI songs each with paired English lyrics and melody alignment is created by our group in National Institute of Informatics (NII), Tokyo. In this talk, I will introduce a novel deep generative model, conditional Long Short-Term Memory - Generative Adversarial Network (LSTM-GAN) for melody generation from lyrics, which is developed based on this large corpus. Moreover, some interesting experiment and demonstration will confirm that plausible and tuneful melody can be inferred from lyrics.

报告人简介:Dr. Yi Yu is an assistant professor with National Institute of Informatics (NII). Before joining NII, she was a senior research fellow at School of Computing, National University of Singapore. Her research interests include generative modeling and embedding learning, and its application in data mining, vision, music, and multimedia. She has published more than 60 scientific publications, and has supervised more than 30 students including Ph.D students, Master students, and project researchers. She and her team received best poster award from IEEE ISM 2018 and best paper runner up in APWeb-WAIM 2017, were recognized as finalist of the World’s FIRST 10K best paper award in ICME17, won the 2nd prize in Yahoo Flickr grand challenge 2015, were in the top winners (out of 29 teams) awarded by ACM SIGSPATIAL GIS Cup 2013, and received a best paper award from IEEE ISM 2012. More details about her research can be found at http://research.nii.ac.jp/~yiyu/ .

本次活动受中央财经大学2019专题学术讲座项目资助。

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