MMAE: New Comprehensive Benchmark for Instruction-Based Audio Editing
A team of researchers has released MMAE (Massive Multitask Audio Editing), the first large-scale benchmark designed to evaluate general-purpose, instruction-based AI audio editing systems. The benchmark covers 7 audio modalities, 6 levels of task complexity, and 2,000 curated samples assessed via a rubric-based framework decomposed into 17,741 verifiable criteria. Evaluations of leading models show critical performance failures, with Exact Match Rates below 5% overall and 0% on complex mixed-modality tasks, highlighting how far current systems are from reliable audio editing.
MMAE (Massive Multitask Audio Editing) is introduced as the first comprehensive evaluation testbed for general-purpose, instruction-based audio editing, addressing a significant gap in the field as interactive audio editing has grown rapidly alongside advances in image and video generation. The benchmark spans 7 audio modalities—including sound, speech, music, and their mixtures—and establishes a taxonomy covering 6 levels of task complexity ranging from basic modifications to multi-hop reasoning and multi-round editing, 2 levels of granularity, and 8 distinct operation types. Its 2,000 high-fidelity samples were curated through human-agent collaboration and are evaluated using a pioneering rubric-based framework that decomposes free-form tasks into 17,741 verifiable criteria, enabling precise, multi-dimensional assessment of instruction following and contextual consistency. Evaluation of leading AI models reveals that current systems perform poorly across the board: the Exact Match Rate (EMR) remains below 5% in general tasks and drops to an absolute 0% in complex, mixed-modality scenarios. These results expose critical bottlenecks in precise execution and structural robustness in state-of-the-art audio editing systems. The authors position MMAE as a diagnostic roadmap and standardized evaluation paradigm intended to catalyze future progress in intelligent audio creation. The benchmark is open-sourced to facilitate community-wide adoption and reproducibility.
What's missing
The paper does not detail which specific leading models were evaluated or provide a breakdown of their individual scores, making it difficult to assess relative model performance.
What different sources said
- arXiv cs.CLCenter
MMAE: A Massive Multitask Audio Editing Benchmark
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