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

arXiv: https://arxiv.org/abs/2604.18360 · HTML: https://mlsanity.com/paper/2604.18360/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 12 of 12)

- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [Audio Retrieval with Natural Language Queries: A Benchmark Study](https://mlsanity.com/paper/2112.09418/) (2021)
- [ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning](https://mlsanity.com/paper/2101.10803/) (2021)
- [Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators](https://mlsanity.com/paper/2111.13057/) (2021)
- [NevIR: Negation in Neural Information Retrieval](https://mlsanity.com/paper/2305.07614/) (2023)
- [IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance](https://mlsanity.com/paper/2311.09820/) (2023)
- [Advancing Multi-grained Alignment for Contrastive Language-Audio Pre-training](https://mlsanity.com/paper/2408.07919/) (2024)
- [M2D-CLAP: Exploring General-purpose Audio-Language Representations Beyond CLAP](https://mlsanity.com/paper/2503.22104/) (2025)
- [Do Audio-Language Models Understand Linguistic Variations?](https://mlsanity.com/paper/2410.16505/) (2024)
- [The language of sound search: Examining User Queries in Audio Search Engines](https://mlsanity.com/paper/2410.08324/) (2024)

## Built on by (most cited first, 3 of 3)

- [Fusion Embedding: A Unified Embedding Space for Text, Image, Video, and Audio](https://mlsanity.com/paper/2607.18666/) (2026)
- [Modality-Gated Deep Adapters: Adding a Modality to a Frozen Embedding Model with Exact Preservation](https://mlsanity.com/paper/2609.26182/) (2026)
- [CORA: A Protocol for Diagnosing Boundary Robustness in Text-to-Audio Retrieval under Query Reformulations](https://mlsanity.com/paper/2609.33433/) (2026)

## Abstract (arXiv)

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 negative queries, we develop a hard negative mining pipeline and propose discrimination metrics (HNSR, TFR) assessing models' ability to suppress acoustically similar distractors. Experiments on AudioCaps, Clotho, and MECAT show that OEA achieves comparable text-to-audio retrieval performance to state-of-the-art M2D-CLAP, while demonstrating clear advantages in two critical areas: (1) dominant text-to-text retrieval (+22% relative improvement), and (2) substantially superior hard negative discrimination (+4.3%p HNSR@10, +34.7% relative TFR@10), revealing that LLM backbones provide superior semantic understanding of complex queries.