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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 0SourceScholar
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 6SourceScholar