A contrastive multimodal framework augments satellite-audio datasets with vision-language model sound descriptions to learn shared soundscape concepts for zero-shot retrieval and synthesis.
Revis- iting multimodal representation in contrastive learning: from patch and token embeddings to finite discrete tokens
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Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping
A contrastive multimodal framework augments satellite-audio datasets with vision-language model sound descriptions to learn shared soundscape concepts for zero-shot retrieval and synthesis.