activity
20132023
most citedBioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

1.2k citations · 9.1k across the 205 of their papers we have counts for

collaborators
Showing cs.SDShow all

11 papers · 1 filter

cs.SD2022★ 20 cited

Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation

Botao Yu, Peiling Lu, Rui Wang +6

Symbolic music generation aims to generate music scores automatically. A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, beca…

cs.SD2022★ 4 cited

MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks

Peiling Lu, Xu Tan, Botao Yu +3

Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do…

cs.SD2022★ 3 cited

Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation

Ang Lv, Xu Tan, Tao Qin +2

Lyric-to-melody generation is an important task in songwriting, and is also quite challenging due to its unique characteristics: the generated melodies should not only follow good…

cs.SD2022★ 13 cited

ReLyMe: Improving Lyric-to-Melody Generation by Incorporating Lyric-Melody Relationships

Chen Zhang, Luchin Chang, Songruoyao Wu +4

Lyric-to-melody generation, which generates melody according to given lyrics, is one of the most important automatic music composition tasks. With the rapid development of deep lea…

cs.SD2021★ 7 cited

TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method

Zeqian Ju, Peiling Lu, Xu Tan +7

Lyric-to-melody generation is an important task in automatic songwriting. Previous lyric-to-melody generation systems usually adopt end-to-end models that directly generate melodie…

cs.SD2021★ 9 cited

AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style

Yuzi Yan, Xu Tan, Bohan Li +6

While recent text to speech (TTS) models perform very well in synthesizing reading-style (e.g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e…

cs.SD2021★ 3 cited

DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling

Lanqing Xue, Kaitao Song, Duocai Wu +5

Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics bu…

cs.SD2021★ 10 cited

MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training

Mingliang Zeng, Xu Tan, Rui Wang +3

Symbolic music understanding, which refers to the understanding of music from the symbolic data (e.g., MIDI format, but not audio), covers many music applications such as genre cla…

cs.SD2021

AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data

Yuzi Yan, Xu Tan, Bohan Li +4

Text to speech (TTS) is widely used to synthesize personal voice for a target speaker, where a well-trained source TTS model is fine-tuned with few paired adaptation data (speech a…

cs.SD2021★ 4 cited

LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search

Renqian Luo, Xu Tan, Rui Wang +5

Text to speech (TTS) has been broadly used to synthesize natural and intelligible speech in different scenarios. Deploying TTS in various end devices such as mobile phones or embed…

cs.SD2020★ 6 cited

PopMAG: Pop Music Accompaniment Generation

Yi Ren, Jinzheng He, Xu Tan +3

In pop music, accompaniments are usually played by multiple instruments (tracks) such as drum, bass, string and guitar, and can make a song more expressive and contagious by arrang…