14 papers
Taming Audio VAEs via Target-KL Regularization
Prem Seetharaman, Rithesh Kumar
Latent diffusion models have emerged as the dominant paradigm for many generation tasks including audio generation such as text-to-audio, text-to-music and text-to-speech. A key co…
Audio Hallucination Attacks: Probing the Reliability of Large Audio Language Models
Ashish Seth, Sonal Kumar, Ramaneswaran Selvakumar +5
Large Audio Language Models (LALMs) achieve strong performance on audio-language tasks; however, their reliability in real-world settings remains underexplored. We introduce Audio…
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…