activity
20192026
most citedAudioLDM: Text-to-Audio Generation with Latent Diffusion Models

82 citations · 167 across the 18 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.SD2024

FlowSep: Language-Queried Sound Separation with Rectified Flow Matching

Yi Yuan, Xubo Liu, Haohe Liu +2

Language-queried audio source separation (LASS) focuses on separating sounds using textual descriptions of the desired sources. Current methods mainly use discriminative approaches…

eess.AS2024

Universal Sound Separation with Self-Supervised Audio Masked Autoencoder

Junqi Zhao, Xubo Liu, Jinzheng Zhao +4

Universal sound separation (USS) is a task of separating mixtures of arbitrary sound sources. Typically, universal separation models are trained from scratch in a supervised manner…

cs.SD2024

Sound-VECaps: Improving Audio Generation with Visual Enhanced Captions

Yi Yuan, Dongya Jia, Xiaobin Zhuang +9

Generative models have shown significant achievements in audio generation tasks. However, existing models struggle with complex and detailed prompts, leading to potential performan…

cs.SD2024★ 36 cited

SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound

Haohe Liu, Xuenan Xu, Yi Yuan +3

Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelli…

cs.SD2024

T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining

Yi Yuan, Zhuo Chen, Xubo Liu +6

Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classificatio…