12 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…