26 citations · 55 across the 8 of their papers we have counts for
8 papers
Shao: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation
Jiafeng Liu, Yuanliang Dong, Hongjia Liu +8
A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first models high-level structure, foll…
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
Shangda Wu, Yashan Wang, Ruibin Yuan +12
Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems. These limitations reduce their effe…
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai +24
This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music so…
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…
CWS-PResUNet: Music Source Separation with Channel-wise Subband Phase-aware ResUNet
Haohe Liu, Qiuqiang Kong, Jiafeng Liu
Music source separation (MSS) shows active progress with deep learning models in recent years. Many MSS models perform separations on spectrograms by estimating bounded ratio masks…
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…
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…
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…