Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
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 from real-world search behavior, limiting their assessment of practical retrieval robustness. We present Omni-Embed-Audio (OEA), a retrieval-oriented encoder leveraging multimodal LLMs with native audio understanding. To systematically evaluate robustness beyond caption-style queries, we introduce User-Intent Queries (UIQs) - five formulations reflecting natural search behaviors: questions, commands, keyword tags, paraphrases, and exclusion-based negative queries. For nega...
| Authors | HaeJun Yoo, Yongseop Shin, Insung Lee et al. |
|---|---|
| Primary category | cs.SD |
| Published | 20 April 2026 |
| Last updated at source | 14 October 2026 |
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