activity
20242026
collaborators

14 papers

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

Smorph: Playable Sound Morphing with Diffusion Models

Annie Chu, Hugo Flores García, Johannes Imort +5

Sound morphing, generating intermediate sounds that transition from one sonic identity to another, can be a powerful tool for musical sound design. Existing diffusion-based morphin…

cs.HC2026

Dramarrator: Object-Based Audio Editing for Audio Drama Production from Books

Karim Benharrak, Oriol Nieto, Bryan Wang +2

Audio dramas weave dialogue, sound effects, and music into immersive stories. Creators often adapt books into audio dramas, but this process remains labor-intensive, requiring them…

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.SD2026

Generative Audio Extension and Morphing

Prem Seetharaman, Oriol Nieto, Justin Salamon

In audio-related creative tasks, sound designers often seek to extend and morph different sounds from their libraries. Generative audio models, capable of creating audio using exam…

cs.SD2026

TAC: Timestamped Audio Captioning

Sonal Kumar, Prem Seetharaman, Ke Chen +8

Large Audio Language Models struggle to disentangle overlapping events in complex acoustic scenes, yielding temporally inconsistent captions and frequent hallucinations. We introdu…

cs.SD2026

Audiocards: Structured Metadata Improves Audio Language Models For Sound Design

Sripathi Sridhar, Prem Seetharaman, Oriol Nieto +2

Sound designers search for sounds in large sound effects libraries using aspects such as sound class or visual context. However, the metadata needed for such search is often missin…

cs.SD2026

Mix2Morph: Learning Sound Morphing from Noisy Mixes

Annie Chu, Hugo Flores García, Oriol Nieto +3

We introduce Mix2Morph, a text-to-audio diffusion model fine-tuned to perform sound morphing without a dedicated dataset of morphs. By finetuning on noisy surrogate mixes at higher…

cs.SD2025

PromptSep: Generative Audio Separation via Multimodal Prompting

Yutong Wen, Ke Chen, Prem Seetharaman +7

Recent breakthroughs in language-queried audio source separation (LASS) have shown that generative models can achieve higher separation audio quality than traditional masking-based…

cs.SD2025

FLAM: Frame-Wise Language-Audio Modeling

Yusong Wu, Christos Tsirigotis, Ke Chen +5

Recent multi-modal audio-language models (ALMs) excel at text-audio retrieval but struggle with frame-wise audio understanding. Prior works use temporal-aware labels or unsupervise…

cs.SD2024

SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation

Sonal Kumar, Prem Seetharaman, Justin Salamon +2

The field of text-to-audio generation has seen significant advancements, and yet the ability to finely control the acoustic characteristics of generated audio remains under-explore…

cs.SD2024

Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitations

Hugo Flores García, Oriol Nieto, Justin Salamon +2

We present Sketch2Sound, a generative audio model capable of creating high-quality sounds from a set of interpretable time-varying control signals: loudness, brightness, and pitch,…

cs.CV2024

Video-Guided Foley Sound Generation with Multimodal Controls

Ziyang Chen, Prem Seetharaman, Bryan Russell +4

Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To ad…

cs.SD2024

Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning

Ilaria Manco, Justin Salamon, Oriol Nieto

Audio-text contrastive models have become a powerful approach in music representation learning. Despite their empirical success, however, little is known about the influence of key…