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New Omni-Embed-Audio model enhances audio-text retrieval with LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Omni-Embed-Audio (OEA), a new retrieval-oriented encoder that utilizes multimodal large language models for improved audio-text retrieval. Unlike previous systems that relied on caption-style queries, OEA is designed to handle more natural search behaviors, including questions, commands, and negative queries. Experiments show OEA performs comparably to existing state-of-the-art models in text-to-audio retrieval while significantly outperforming them in text-to-text retrieval and the ability to distinguish between similar-sounding audio clips. AI

IMPACT Introduces a more robust method for audio-text retrieval, potentially improving search capabilities in multimodal AI applications.

RANK_REASON This is a research paper describing a new model and evaluation methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Omni-Embed-Audio model enhances audio-text retrieval with LLMs

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This is a research paper describing a new model and evaluation methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · HaeJun Yoo, Yongseop Shin, Insung Lee, Myoung-Wan Koo, Du-Seong Chang ·

    Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval

    arXiv:2604.18360v2 Announce Type: replace-cross Abstract: Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-style queries that differ substantially fr…

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