activity
20242026
most citedFindings of the IWSLT 2024 Evaluation Campaign

5 citations · 8 across the 13 of their papers we have counts for

collaborators
Showing 2026Show all

5 papers · 1 filter

cs.SD2026

CrossEdit: Cross-Modal Training Enables Rich Audio-Visual Editing

William Chen, Prem Seetharaman, Ke Chen +10

Multimedia editing requires generative models to interpret complex, compositional instructions and modify only the desired elements across modalities, a capability largely beyond e…

cs.SD2026

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

Thanapat Trachu, Samuele Cornell, William Chen +1

Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing computational costs. Dynamic frame ra…

cs.SD2026

MulTTiPop: A Multitrack Transcription Dataset for Pop Music

Nathan Pruyne, Benjamin Stoler, William Chen +3

We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcription models. MulTTiPop contains…

cs.SD2026

AudioChat: Unified Audio Storytelling, Editing, and Understanding with Transfusion Forcing

William Chen, Prem Seetharaman, Rithesh Kumar +4

Despite recent breakthroughs, audio foundation models struggle in processing complex multi-source acoustic scenes. We refer to this challenging domain as audio stories, which can h…

cs.CL2026

Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions

Jinchuan Tian, Haoran Wang, Bo-Hao Su +14

Current audio foundation models typically rely on rigid, task-specific supervision (e.g., speech recognition), addressing isolated factors of audio rather than the whole. In contra…