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20212023
most citedSymphony Generation with Permutation Invariant Language Model

10 citations · 21 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2023★ 1 cited

TunesFormer: Forming Irish Tunes with Control Codes by Bar Patching

Shangda Wu, Xiaobing Li, Feng Yu +1

This paper introduces TunesFormer, an efficient Transformer-based dual-decoder model specifically designed for the generation of melodies that adhere to user-defined musical forms.…

cs.MM2022★ 6 cited

CCOM-HuQin: an Annotated Multimodal Chinese Fiddle Performance Dataset

Yu Zhang, Ziya Zhou, Xiaobing Li +2

HuQin is a family of traditional Chinese bowed string instruments. Playing techniques(PTs) embodied in various playing styles add abundant emotional coloring and aesthetic feelings…

cs.SD2022★ 10 cited

Symphony Generation with Permutation Invariant Language Model

Jiafeng Liu, Yuanliang Dong, Zehua Cheng +4

In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation. We propose a novel Multi-track Multi-instrument…

cs.CL2021★ 1 cited

Lingxi: A Diversity-aware Chinese Modern Poetry Generation System

Xinran Zhang, Maosong Sun, Jiafeng Liu +1

Poetry generation has been a difficult task in natural language processing. Unlike plain neural text generation tasks, poetry has a high requirement for novelty, since an easily-un…

cs.SD2021★ 1 cited

Optimal Embedding Calibration for Symbolic Music Similarity

Xinran Zhang, Maosong Sun, Jiafeng Liu +1

In natural language processing (NLP), the semantic similarity task requires large-scale, high-quality human-annotated labels for fine-tuning or evaluation. By contrast, in cases of…

cs.CL2021★ 2 cited

Improving Diversity of Neural Text Generation via Inverse Probability Weighting

Xinran Zhang, Maosong Sun, Jiafeng Liu +1

The neural text generation suffers from the text degeneration issue such as repetition. Traditional stochastic sampling methods only focus on truncating the unreliable "tail" of th…