52 citations · 100 across the 5 of their papers we have counts for
6 papers · 1 filter
Improving Choral Music Separation through Expressive Synthesized Data from Sampled Instruments
Ke Chen, Hao-Wen Dong, Yi Luo +4
Choral music separation refers to the task of extracting tracks of voice parts (e.g., soprano, alto, tenor, and bass) from mixed audio. The lack of datasets has impeded research on…
Learning Audio Embeddings with User Listening Data for Content-based Music Recommendation
Ke Chen, Beici Liang, Xiaoshuan Ma +1
Personalized recommendation on new track releases has always been a challenging problem in the music industry. To combat this problem, we first explore user listening history and d…
POP909: A Pop-song Dataset for Music Arrangement Generation
Ziyu Wang, Ke Chen, Junyan Jiang +5
Music arrangement generation is a subtask of automatic music generation, which involves reconstructing and re-conceptualizing a piece with new compositional techniques. Such a gene…
MusPy: A Toolkit for Symbolic Music Generation
Hao-Wen Dong, Ke Chen, Julian McAuley +1
In this paper, we present MusPy, an open source Python library for symbolic music generation. MusPy provides easy-to-use tools for essential components in a music generation system…
Continuous Melody Generation via Disentangled Short-Term Representations and Structural Conditions
Ke Chen, Gus Xia, Shlomo Dubnov
Automatic music generation is an interdisciplinary research topic that combines computational creativity and semantic analysis of music to create automatic machine improvisations.…
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
Ke Chen, Weilin Zhang, Shlomo Dubnov +2
With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progr…