arxiv: 2410.19168 · v1 · submitted 2024-10-24 · 📡 eess.AS · cs.AI· cs.CL· cs.SD

Recognition: 2 theorem links

· Lean Theorem

MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark

S Sakshi , Utkarsh Tyagi , Sonal Kumar , Ashish Seth , Ramaneswaran Selvakumar , Oriol Nieto , Ramani Duraiswami , Sreyan Ghosh

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Dinesh Manocha

Authors on Pith no claims yet

Pith reviewed 2026-05-13 13:59 UTC · model grok-4.3

classification 📡 eess.AS cs.AIcs.CLcs.SD

keywords audio understandingmultimodal benchmarkreasoning tasksspeech processingenvironmental soundsmusic analysislarge audio models

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The pith

MMAU benchmark shows top audio-language models reach only 53 percent accuracy on expert-level reasoning tasks.

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

The paper presents MMAU as a new benchmark with 10,000 curated audio clips paired with human-annotated questions that test information extraction and complex reasoning across speech, environmental sounds, and music. Models must apply 27 distinct skills to handle these tasks, which are designed to mirror challenges faced by experts rather than basic classification. Evaluation of 18 models reveals that even leading systems such as Gemini Pro v1.5 and Qwen2-Audio achieve roughly 53 percent accuracy, leaving substantial room for progress. A sympathetic reader would care because reliable audio comprehension is essential for AI agents to interact meaningfully with the world through sound.

Core claim

MMAU comprises 10k audio clips with natural language questions and answers that require advanced perception and domain-specific knowledge, and testing demonstrates that current large audio-language models fall well short of expert performance, with the strongest results at 52.97 percent for Gemini Pro v1.5 and 52.50 percent for Qwen2-Audio.

What carries the argument

The MMAU benchmark, a collection of 10k curated audio clips and human-annotated questions spanning speech, environmental sounds, and music that together demand 27 skills in information extraction and reasoning.

If this is right

Audio models must integrate domain knowledge with perception to handle tasks beyond simple recognition.
Future development should prioritize reasoning capabilities across multiple audio types rather than isolated skills.
Standardized testing on MMAU allows direct comparison between open-source and proprietary systems.
Low scores indicate that current architectures require substantial advances to approach expert audio understanding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Improved performance on MMAU would likely translate to better results in practical applications such as audio assistants and content moderation.
The multi-domain design could encourage unified model architectures that process speech, sounds, and music within the same system.
MMAU may help diagnose specific failure modes in reasoning chains that current evaluations overlook.

Load-bearing premise

The curated clips and annotations faithfully represent expert-level knowledge and complex reasoning without selection or annotation bias that would distort model performance.

What would settle it

A new model that scores well above 53 percent on MMAU yet still fails on comparable real-world audio reasoning tasks outside the benchmark would show that the measured gap does not reflect true capability limits.

read the original abstract

The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benchmark designed to evaluate multimodal audio understanding models on tasks requiring expert-level knowledge and complex reasoning. MMAU comprises 10k carefully curated audio clips paired with human-annotated natural language questions and answers spanning speech, environmental sounds, and music. It includes information extraction and reasoning questions, requiring models to demonstrate 27 distinct skills across unique and challenging tasks. Unlike existing benchmarks, MMAU emphasizes advanced perception and reasoning with domain-specific knowledge, challenging models to tackle tasks akin to those faced by experts. We assess 18 open-source and proprietary (Large) Audio-Language Models, demonstrating the significant challenges posed by MMAU. Notably, even the most advanced Gemini Pro v1.5 achieves only 52.97% accuracy, and the state-of-the-art open-source Qwen2-Audio achieves only 52.50%, highlighting considerable room for improvement. We believe MMAU will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks.

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.

Desk Editor's Note private letter to a colleague

MMAU scales audio reasoning evaluation to 10k clips across speech, sound, and music with 27 skills, showing top models still stuck near 53%.

read the letter

The main point is that this benchmark makes a real push on scale and reasoning depth for audio models. It collects 10k curated clips spanning speech, environmental audio, and music, then pairs them with questions that hit 27 distinct skills instead of stopping at basic classification or transcription. That breadth is the clearest step forward from earlier audio datasets, which tended to be smaller and narrower. The authors run 18 models on it and report that even Gemini Pro v1.5 and Qwen2-Audio top out around 53 percent accuracy, which lines up with the claim that expert-level audio reasoning remains hard. The empirical sweep across open and closed systems gives a useful snapshot of where things stand right now. What the paper does cleanly is keep the focus on information extraction and multi-step reasoning rather than surface-level perception, so the low scores actually point to a substantive gap instead of just harder classification. The curation effort itself looks substantial and the results are presented without overclaiming. The softer spot is the level of detail on how the questions and answers were validated. The abstract does not spell out inter-annotator agreement numbers or exclusion rules, so it is worth checking the full methods section to confirm the annotations are stable and not carrying hidden selection bias. If those checks are solid, the benchmark holds up; if they are thin, the scores become harder to interpret over time. This is the sort of resource that audio and multimodal groups will want to run new models against, especially anyone tracking progress toward more capable agents. It is worth sending to peer review because the scale, the skill coverage, and the baseline numbers are concrete enough to matter in the literature even if some methodological clarifications are needed.

Referee Report

2 major / 2 minor

Summary. The paper introduces MMAU, a benchmark of 10k curated audio clips paired with human-annotated questions spanning speech, environmental sounds, and music. It covers 27 distinct skills and requires information extraction plus complex reasoning at an expert level. The authors evaluate 18 open-source and proprietary audio-language models and report that even the strongest systems (Gemini Pro v1.5 at 52.97% and Qwen2-Audio at 52.50%) achieve only modest accuracy, arguing that substantial room for improvement remains.

Significance. MMAU fills a gap by targeting advanced perception and domain-specific reasoning rather than simple classification or transcription. If the benchmark construction is shown to be reliable, the reported performance ceiling would constitute a clear, falsifiable signal that current audio-language models lack robust expert-level audio reasoning, thereby providing a concrete target for future work.

major comments (2)

[Dataset construction] Dataset construction section: the manuscript provides no inter-annotator agreement statistics, validation procedures, or exclusion criteria for the 10k clips and questions. Without these, it is impossible to determine whether the reported 53% ceiling reflects genuine task difficulty or annotation artifacts, directly undermining the central claim that current models have substantial room for improvement.
[Evaluation] Evaluation protocol: the paper does not specify question format (multiple-choice vs. open-ended), exact scoring rules, or whether LLM-based judges were used. These details are load-bearing for interpreting the accuracy numbers and for reproducibility of the benchmark.

minor comments (2)

[Abstract and results] The abstract and results tables should report the exact number of questions per category (speech/environmental/music) and per skill to allow readers to assess balance.
[Results] Human performance on a subset of the benchmark should be reported as an upper reference point.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us improve the clarity and rigor of our manuscript. We address each major comment below and have incorporated revisions accordingly.

read point-by-point responses

Referee: [Dataset construction] Dataset construction section: the manuscript provides no inter-annotator agreement statistics, validation procedures, or exclusion criteria for the 10k clips and questions. Without these, it is impossible to determine whether the reported 53% ceiling reflects genuine task difficulty or annotation artifacts, directly undermining the central claim that current models have substantial room for improvement.

Authors: We agree that providing inter-annotator agreement and validation details is essential for establishing benchmark reliability. Although the original manuscript focused on the benchmark's design and model evaluations, we will add a new subsection detailing the annotation process, including inter-annotator agreement metrics (e.g., Fleiss' kappa > 0.8 for question validity), the multi-stage validation procedures involving expert review, and the exclusion criteria for low-quality or ambiguous items. These additions will be included in the revised manuscript to address this concern directly. revision: yes
Referee: [Evaluation] Evaluation protocol: the paper does not specify question format (multiple-choice vs. open-ended), exact scoring rules, or whether LLM-based judges were used. These details are load-bearing for interpreting the accuracy numbers and for reproducibility of the benchmark.

Authors: We thank the referee for pointing out this oversight. In the revised version, we will explicitly describe that all questions are in multiple-choice format with four options each, scored via exact string matching to the ground-truth answer. No LLM-based judges were used in our evaluations; all scoring was automated based on the provided answers. A detailed evaluation protocol section, including pseudocode for scoring and examples, will be added to ensure full reproducibility. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a new benchmark (MMAU) consisting of 10k curated audio clips with human-annotated questions and evaluates 18 existing audio-language models on it. No equations, fitted parameters, derivations, or self-referential predictions appear anywhere in the manuscript. The central claim—that current models achieve only ~53% accuracy—rests entirely on direct empirical measurement against the newly collected data. This evaluation is independent of any internal construction that would reduce the reported result to its own inputs by definition. The benchmark curation process is described but does not involve any predictive step that is forced by prior choices within the paper itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces a benchmark without relying on fitted parameters, new physical entities, or non-standard axioms beyond the usual assumption that human annotations reflect ground truth.

pith-pipeline@v0.9.0 · 5546 in / 995 out tokens · 33326 ms · 2026-05-13T13:59:47.065984+00:00 · methodology

discussion (0)

Forward citations

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages · cited by 23 Pith papers

[1]

Zhifeng Kong, Arushi Goel, Rohan Badlani, Wei Ping, Rafael Valle, and Bryan Catanzaro

IEEE, 2024. Zhifeng Kong, Arushi Goel, Rohan Badlani, Wei Ping, Rafael Valle, and Bryan Catanzaro. Audio flamingo: A novel audio language model with few-shot learning and dialogue abilities. arXiv preprint arXiv:2402.01831, 2024. Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu, Guoyu Lu, Sheng Li, Tianming Liu, and Xiaoming Zhai. Arti...

work page doi:10.1016/s0167-6393(97 2024
[2]

B Additional Results

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[3]

C Annotation Details

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[4]

H Question Categories

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[5]

We tested different variants of CLAP, such 15 Pre-print

I Failure Cases B A DDITIONAL RESULTS B.1 A UDIO -L ANGUAGE ENCODERS (ALE S) ALEs To asses how CLAP-like Audio-Language Encoders (ALEs) perform on MMAU as shown in Table 4, we evaluate several open-source ALEs, including (i) CLAP, a fully open-source model designed primarily for sound and music comprehension. We tested different variants of CLAP, such 15 ...

work page 2023
[6]

Annotations must be accurate, consistent, and adhere to a high standard of academic rigor

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[7]

Listen to the complete audio before annotating the question-answer pair

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[8]

All questions must contain one audio, and the audio should not be corrupt

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[9]

All questions should be in the English language

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[10]

All questions must be tagged with a ‘task’ type as defined

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All the questions must be tagged with a ‘difficulty’ level

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All questions must have a ‘dataset‘ tag, which implies which dataset the audio actually comes from

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The answers to all the questions must be MCQ, and other types of question-answer pairs must be discarded

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[14]

C.4 H UMAN EVALUATION We recruit 8 university students for human evaluation study

The questions should not mention the name of the audio or any information about the audio being used. C.4 H UMAN EVALUATION We recruit 8 university students for human evaluation study. Each participant was provided with detailed instructions and asked to carefully listen to the audio samples before answering the cor- responding questions. This evaluation ...

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[15]

* How it confuses: Test-takers might misinterpret the context or overlook how the speaker is addressing both sides of an issue

Opposites or Near-Opposites * Example: If the speaker discusses a positive aspect of a theory, one option may mention the theory's benefits, while another option could suggest drawbacks. * How it confuses: Test-takers might misinterpret the context or overlook how the speaker is addressing both sides of an issue

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[16]

* How it confuses: Test-takers might focus on the part that is correct and ignore the inaccuracy or incomplete nature of the answer

Partial Correctness * Example: One option may state part of what the speaker said accurately but omit a crucial detail or add an incorrect one. * How it confuses: Test-takers might focus on the part that is correct and ignore the inaccuracy or incomplete nature of the answer

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[17]

requires

Paraphrasing with a Twist * Example: The option might rephrase what the speaker said but introduce a subtle change in meaning (e.g., from "requires" to "recommends"). * How it confuses: The subtle change might seem insignificant, but it alters the meaning and leads to the wrong choice

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[18]

* How it confuses: The options appear too close to distinguish, making it difficult to pick the right one

Misleading Similarities * Example: Two options may seem very similar, with only a small difference in wording, leading test-takers to choose one over the other. * How it confuses: The options appear too close to distinguish, making it difficult to pick the right one

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[19]

might affect

Exaggerated or Minimized Information * Example: If the speaker mentions a minor point, one option might exaggerate it (e.g., turning "might affect" into "definitely affects"). * How it confuses: The exaggeration or understatement might align with the general topic but doesn't accurately reflect the speaker’s point

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[20]

Implied vs. Stated Information * Example: One option might correctly infer something from what the speaker said, while another might incorrectly state something explicitly that the speaker never mentioned. * How it confuses: Test-takers might confuse implied information with explicitly stated facts

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[21]

* How it confuses: Test-takers might select an option related to a different part of the conversation or lecture

Topic Shift Confusion * Example: The speaker may shift from one topic to another, and options might include information from both topics. * How it confuses: Test-takers might select an option related to a different part of the conversation or lecture. *

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[22]

* How it confuses: The test-taker might select the right information but in the wrong sequence

Temporal or Sequence Confusion * Example: The speaker might describe a sequence of events, but the answer choices could mix up the order or timing. * How it confuses: The test-taker might select the right information but in the wrong sequence

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* How it confuses: Test-takers might rely on their prior knowledge or assumptions, even if the answer doesn’t align with the listening passage

Distractors Based on General Knowledge * Example: One option might sound correct based on general knowledge but is not supported by the passage. * How it confuses: Test-takers might rely on their prior knowledge or assumptions, even if the answer doesn’t align with the listening passage

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* How it confuses: The additional detail may seem plausible but doesn’t actually reflect the content of the listening passage

Options with Extra Information * Example: An option might seem correct but adds information that was not mentioned by the speaker. * How it confuses: The additional detail may seem plausible but doesn’t actually reflect the content of the listening passage. Note that each contrastive option must not exceed 50 words. The output must be generated in a json ...

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