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arxiv: 2604.18360 · v1 · submitted 2026-04-20 · 💻 cs.SD · cs.CL

Recognition: unknown

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

HaeJun Yoo , Yongseop Shin , Insung Lee , Myoung-Wan Koo , Du-Seong Chang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:19 UTC · model grok-4.3

classification 💻 cs.SD cs.CL
keywords audio-text retrievalmultimodal LLMsuser intent querieshard negative discriminationCLAPsemantic understandingrobust audio search
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The pith

Multimodal LLM audio encoders match state-of-the-art retrieval while excelling at complex user queries and hard negatives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that Omni-Embed-Audio (OEA), built on multimodal LLMs with native audio understanding, reaches text-to-audio retrieval levels comparable to the leading M2D-CLAP model across AudioCaps, Clotho, and MECAT. It tests this with five User-Intent Query types that mimic real search behavior instead of caption-only inputs, plus a hard-negative mining setup that measures discrimination against similar-sounding distractors. The LLM approach delivers a 22 percent relative gain in text-to-text retrieval and clear improvements in suppressing hard negatives, indicating stronger semantic grasp of varied and exclusionary queries.

Core claim

OEA achieves comparable text-to-audio retrieval performance to state-of-the-art M2D-CLAP on AudioCaps, Clotho, and MECAT, 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.

What carries the argument

Omni-Embed-Audio (OEA) retrieval-oriented encoder based on multimodal LLMs with native audio understanding, evaluated via User-Intent Queries (questions, commands, keyword tags, paraphrases, exclusion negatives) and a hard negative mining pipeline that computes HNSR and TFR discrimination metrics.

Load-bearing premise

The observed gains in query understanding and negative discrimination arise from the multimodal LLM backbone rather than differences in training data, model scale, or other unmeasured factors.

What would settle it

A controlled retraining of a non-LLM backbone model on the exact same data and showing it matches or exceeds OEA on UIQ text-to-text and hard-negative metrics would falsify the claim that LLM semantics drive the advantages.

Figures

Figures reproduced from arXiv: 2604.18360 by Du-Seong Chang, HaeJun Yoo, Insung Lee, Myoung-Wan Koo, Yongseop Shin.

Figure 1
Figure 1. Figure 1: Performance comparison using OEA-Qwen7B (+Cl) as representative model (mean R@5 across Audio￾Caps, Clotho, and MECAT). (a) Baseline performance: While M2D-CLAP leads T2A (47.9%), OEA achieves competitive results (46.4%) and substantially outper￾forms all baselines on T2T (64.8% vs. M2D-CLAP 59.3%, +5.5). (b–d) UIQ analysis: M2D-CLAP shows strong UIQ retrieval; however, OEA achieves best Im￾perative query p… view at source ↗
Figure 2
Figure 2. Figure 2: OEA architecture overview. A shared multimodal LLM backbone processes both text and audio inputs. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Evaluation metrics for exclusionary queries. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces Omni-Embed-Audio (OEA), a retrieval encoder built on multimodal LLMs with native audio support. It proposes User-Intent Queries (UIQs) in five formulations (questions, commands, tags, paraphrases, exclusion negatives) to move beyond caption-style benchmarks, along with a hard-negative mining pipeline and new discrimination metrics (HNSR, TFR). On AudioCaps, Clotho, and MECAT, OEA matches state-of-the-art M2D-CLAP on text-to-audio retrieval while reporting +22% relative text-to-text gains and +4.3%p HNSR@10 / +34.7% relative TFR@10 on hard-negative UIQs, which the authors attribute to superior semantic understanding from the LLM backbone.

Significance. If the performance deltas can be isolated to the multimodal LLM component, the work would provide a concrete demonstration that LLM-scale text encoders improve robustness on complex, real-world-style queries in audio retrieval. The introduction of UIQs and the associated hard-negative metrics also supplies a reusable evaluation framework that could shift future benchmarking away from caption-only protocols.

major comments (3)
  1. [§4 (Experiments) and abstract] The central attribution—that LLM backbones drive the observed gains in text-to-text retrieval and hard-negative discrimination—rests on a single external baseline comparison (M2D-CLAP) without an ablation that holds pretraining corpus, parameter count, audio encoder, and contrastive objective fixed while varying only the text backbone. This is load-bearing for the claim in the abstract and §4.
  2. [§3.2 (User-Intent Queries)] The five UIQ formulations are presented as capturing natural search behavior, yet no user study, log analysis, or external validation is reported to confirm they reflect real-world query distributions; the performance advantage could therefore reflect interaction between the LLM's instruction-tuning style and the particular UIQ templates rather than general semantic superiority.
  3. [§3.3 (Hard Negative Metrics) and Table 2] HNSR@10 and TFR@10 are introduced as new metrics for hard-negative discrimination, but the manuscript provides neither statistical significance tests across multiple seeds nor comparison against established hard-negative metrics (e.g., recall@K with mined negatives) to establish that the reported +4.3%p and +34.7% relative gains are robust rather than metric-specific artifacts.
minor comments (2)
  1. [Abstract] The abstract states specific percentage improvements without accompanying standard deviations or run counts; these details should appear in the main results tables.
  2. [§3.3] Notation for HNSR and TFR is defined only in prose; a compact mathematical definition (e.g., Eq. form) would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's insightful comments on the attribution of performance gains, the validation of User-Intent Queries, and the robustness of the proposed metrics. Below, we respond to each major comment in turn, indicating planned revisions to the manuscript where appropriate.

read point-by-point responses

  1. Referee: [§4 (Experiments) and abstract] The central attribution—that LLM backbones drive the observed gains in text-to-text retrieval and hard-negative discrimination—rests on a single external baseline comparison (M2D-CLAP) without an ablation that holds pretraining corpus, parameter count, audio encoder, and contrastive objective fixed while varying only the text backbone. This is load-bearing for the claim in the abstract and §4.

    Authors: We agree that a controlled ablation isolating only the text backbone would provide stronger causal evidence. Our evaluation uses M2D-CLAP as the primary baseline, which employs a distinct audio encoder and pretraining corpus, precluding full isolation. The gains in text-to-text retrieval and hard-negative tasks are consistent with LLM semantic strengths, but we acknowledge the attribution is not fully isolated. In the revised manuscript we will add an explicit limitations paragraph in §4 discussing this gap and the practical barriers to such an ablation across heterogeneous architectures. revision: partial

  2. Referee: [§3.2 (User-Intent Queries)] The five UIQ formulations are presented as capturing natural search behavior, yet no user study, log analysis, or external validation is reported to confirm they reflect real-world query distributions; the performance advantage could therefore reflect interaction between the LLM's instruction-tuning style and the particular UIQ templates rather than general semantic superiority.

    Authors: The UIQ templates were motivated by common query patterns in retrieval literature and audio search scenarios rather than direct empirical validation. We did not conduct a user study or log analysis. We will revise §3.2 to present the five formulations as illustrative templates of natural intent types, supported by references to prior work on query formulation, instead of asserting they represent validated real-world distributions. This clarifies the scope of the evaluation framework. revision: yes

  3. Referee: [§3.3 (Hard Negative Metrics) and Table 2] HNSR@10 and TFR@10 are introduced as new metrics for hard-negative discrimination, but the manuscript provides neither statistical significance tests across multiple seeds nor comparison against established hard-negative metrics (e.g., recall@K with mined negatives) to establish that the reported +4.3%p and +34.7% relative gains are robust rather than metric-specific artifacts.

    Authors: We concur that statistical tests and comparisons to established metrics would strengthen the new measures. The reported results are from single runs. In revision we will add direct comparisons of HNSR@10 and TFR@10 against recall@K on the identical hard-negative sets to show consistency with conventional metrics. We will also note the effect sizes and the desirability of multi-seed evaluation for future work, as additional seed runs fall outside the current revision scope. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical comparisons rely on external baselines and new metrics

full rationale

The paper's core contribution is an empirical model (OEA) evaluated on standard benchmarks (AudioCaps, Clotho, MECAT) against an external baseline (M2D-CLAP) using newly introduced User-Intent Queries and discrimination metrics (HNSR, TFR). No equations, fitted parameters, or self-referential definitions are present that would reduce reported gains (+22% text-to-text, +4.3%p HNSR@10) to quantities defined by the model itself. Claims about LLM backbone advantages rest on direct experimental deltas rather than self-citation chains, uniqueness theorems, or ansatz smuggling. The derivation chain consists of model architecture description, UIQ formulation, hard-negative mining, and benchmarking; these steps are self-contained against external data and do not collapse by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The work rests on the standard assumption that contrastive alignment between audio and text embeddings captures semantic similarity, plus the new but unvalidated premise that the five UIQ styles represent natural user behavior.

axioms (1)
  • domain assumption Contrastive pretraining on audio-text pairs produces embeddings that reflect semantic similarity.
    Core assumption inherited from CLAP-style models and invoked to justify the retrieval setup.
invented entities (2)
  • User-Intent Queries (UIQs) no independent evidence
    purpose: Five query formulations to test retrieval beyond caption style.
    Newly defined in the paper to address benchmark limitations.
  • HNSR and TFR metrics no independent evidence
    purpose: Quantify ability to reject acoustically similar distractors.
    Proposed specifically for the negative-query evaluation.

pith-pipeline@v0.9.0 · 5542 in / 1454 out tokens · 52491 ms · 2026-05-10T03:19:17.745809+00:00 · methodology

discussion (0)

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Reference graph

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