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Sidd Srinivasan

2 accepted papers

2024

In-Context Prompt Editing for Conditional Audio Generation

ICASSP 2024accepted

Distributional shift is a central challenge in the deployment of machine learning models as they can be ill-equipped for real-world data. This is particularly evident in text-to-audio generation where the encoded representations are easily undermined by unseen prompts, which leads to the degradation…

Cited by 0Source ↗Scholar ↗
2024

On the Open Prompt Challenge in Conditional Audio Generation

ICASSP 2024accepted

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 challenging as user-input prompts are often under-specified when compared to text descriptions used to train TTA models. I…

Cited by 0Source ↗Scholar ↗