activity
20232026
most citedSCNet: Sparse Compression Network for Music Source Separation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SD2026

Diff-Symbo: Text-Controlled Long-Duration Symbolic Music Generation Using Autoregressive Latent Diffusion Model

Zhiwei Lin, Jun Chen, Boshi Tang +5

Text-controlled symbolic music generation has recently gained research attention due to its versatile, flexible and straightforward approach to music composition. However, previous…

eess.AS20241 cited

SCNet: Sparse Compression Network for Music Source Separation

Weinan Tong, Jiaxu Zhu, Jun Chen +5

Deep learning-based methods have made significant achievements in music source separation. However, obtaining good results while maintaining a low model complexity remains challeng…

cs.SD2024

Multi-view MidiVAE: Fusing Track- and Bar-view Representations for Long Multi-track Symbolic Music Generation

Zhiwei Lin, Jun Chen, Boshi Tang +7

Variational Autoencoders (VAEs) constitute a crucial component of neural symbolic music generation, among which some works have yielded outstanding results and attracted considerab…

cs.LG2023

SimCalib: Graph Neural Network Calibration based on Similarity between Nodes

Boshi Tang, Zhiyong Wu, Xixin Wu +4

Graph neural networks (GNNs) have exhibited impressive performance in modeling graph data as exemplified in various applications. Recently, the GNN calibration problem has attracte…

cs.HC2023

Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations

Zilin Wang, Haolin Zhuang, Lu Li +6

This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation…