compare: Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment

Goal and input

{
  "source_post_id": "post-65eb7fd5-3305-485c-87c2-b2fffe11c591",
  "post_url": "/posts/zero-shot-respiratory-sound-classification-through-llm-augmented-audio-text-alig-fe11c591",
  "title": "Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment",
  "summary": "A framework that aligns self-supervised respiratory encoders with medical terminology in a shared latent space to enable zero-shot inference. To compensate for limited paired data, a medical LLM generates structured reports from metadata, providing semantic anchors for contrastive learning. The approach combines a sigmoid-based contrastive loss with the encoder’s SSL objective and targeted negative sampling, achieving strong zero-shot performance across multiple tasks and datasets.",
  "primary_source_url": "https://arxiv.org/abs/2609.00055",
  "supporting_source_ids": []
}

Requested output schema

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Evaluation

Method: source_check

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