MMAE: New Comprehensive Benchmark for Instruction-Based Audio Editing
Researchers have introduced MMAE, a new benchmark for evaluating audio editing systems that can follow natural language instructions across diverse audio types. The benchmark includes 2,000 samples spanning 7 audio modalities and 8 operation types, with a detailed rubric containing 17,741 verifiable criteria. Current leading audio editing models perform poorly on the benchmark, with exact match rates below 5% and 0% on complex mixed-modality tasks, highlighting significant gaps in the field.
MMAE (Massive Multitask Audio Editing) is presented as the first comprehensive evaluation framework for general-purpose, instruction-based audio editing systems. The benchmark addresses a significant gap in evaluation infrastructure, which has remained fragmented and limited to specific audio subdomains. The dataset comprises 2,000 high-fidelity samples covering sound, speech, music, and their mixtures, organized across 6 levels of task complexity ranging from basic modifications to multi-hop reasoning and multi-round editing. The evaluation framework uses a novel rubric-based approach that decomposes tasks into 17,741 verifiable criteria to assess both instruction following and context consistency. Testing of leading models reveals critical limitations: exact match rates consistently fall below 5%, with performance dropping to 0% on complex mixed-modality tasks, indicating substantial challenges in precise execution and structural robustness that the authors hope will guide future development.
What's missing
The paper does not specify which leading models were evaluated or provide detailed performance breakdowns by model. Additionally, the specific methodologies used for human-agent collaboration in dataset curation and the inter-annotator agreement metrics for the rubric-based evaluation are not detailed in the abstract.
What different sources said
- arXiv cs.CLCenter
MMAE: A Massive Multitask Audio Editing Benchmark
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