MMAE: A Massive Multitask Audio Editing Benchmark
Summary
MMAE, a Massive Multitask Audio Editing benchmark, is introduced as the first comprehensive evaluation testbed for general-purpose instruction-based audio editing. Submitted on June 5, 2026, this open-source benchmark addresses the fragmented and limited scope of existing audio editing evaluations. MMAE encompasses 7 distinct audio modalities, including sound, speech, music, and their mixtures, and features a comprehensive taxonomy spanning 6 levels of task complexity, from basic modifications to multi-hop reasoning and multi-round editing, alongside 2 levels of granularity and 8 operation types. It consists of 2,000 high-fidelity samples and employs a pioneering rubric-based evaluation framework that breaks down free-form tasks into 17,741 verifiable criteria. Initial evaluations of leading models reveal significant limitations, with Exact Match Rates consistently below 5% and reaching 0% in complex, mixed-modality scenarios, highlighting critical bottlenecks in precise execution and structural robustness.
Key takeaway
For AI Scientists and Machine Learning Engineers developing instruction-based audio editing systems, you must recognize the severe limitations of current models. Your systems likely achieve Exact Match Rates below 5%, dropping to 0% in complex, mixed-modality tasks. You should integrate the MMAE benchmark into your development and evaluation pipelines to diagnose bottlenecks and prioritize improving precise execution and structural robustness for next-generation audio editing.
Key insights
The audio editing evaluation landscape is fragmented, and current models perform poorly on a new comprehensive benchmark.
Principles
- Comprehensive benchmarks are crucial for advancing complex AI tasks.
- Multimodal and multitask evaluation reveals critical model limitations.
- Rubric-based assessment offers precise, multi-dimensional task evaluation.
Method
MMAE constructs a benchmark with 7 audio modalities, 6 complexity levels, 2 granularity levels, and 8 operation types, using 2,000 samples and a rubric-based framework with 17,741 criteria.
In practice
- Use MMAE to benchmark instruction-based audio editing models.
- Decompose free-form tasks into verifiable criteria for evaluation.
- Focus model development on mixed-modality audio editing.
Topics
- MMAE Benchmark
- Audio Editing
- Multitask Learning
- Instruction-based AI
- Benchmark Evaluation
- Mixed-Modality Audio
Code references
Best for: Research Scientist, AI Scientist, Machine Learning Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.