activity
20232026
most citedOn The Open Prompt Challenge In Conditional Audio Generation

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SD2026

SongCraft: Unified Song Generation and Editing with Reconstructive Learning

Haohe Liu, Varun Nagaraja, Gael Le Lan +5

Song generation and editing have mostly been treated as separate tasks. Existing editing methods often require noise injection and regeneration or curated paired training data. We…

cs.CV2026

Exploring Audio Hallucination in Egocentric Video Understanding

Ashish Seth, Xinhao Mei, Changsheng Zhao +9

Egocentric videos provide a distinctive setting in which sound serves as crucial cues to understand user activities and surroundings, particularly when visual information is unstab…

cs.CV2026

EgoAVU: Egocentric Audio-Visual Understanding

Ashish Seth, Xinhao Mei, Changsheng Zhao +9

Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due…

eess.AS2026

SLAP: Scalable Language-Audio Pretraining with Variable-Duration Audio and Multi-Objective Training

Xinhao Mei, Gael Le Lan, Haohe Liu +5

Contrastive language-audio pretraining (CLAP) has achieved notable success in learning semantically rich audio representations and is widely adopted for various audio-related tasks…

cs.MM2024

SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Text

Haohe Liu, Gael Le Lan, Xinhao Mei +7

Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, produc…

eess.AS2024★ 1 cited

High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching

Gael Le Lan, Bowen Shi, Zhaoheng Ni +9

We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48…

cs.SD2023★ 1 cited

On The Open Prompt Challenge In Conditional Audio Generation

Ernie Chang, Sidd Srinivasan, Mahi Luthra +8

Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text. However, commercializing audio generation is ch…