arXiv:2606.07229v1 Announce Type: cross Abstract: We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing. Spurred by the shift toward intelligent creation, interactive editing has rapidly expanded from visual domains, pioneered by models like Nano-banana 2 for images and Gemini-Omni for video, into audio. However, the current evaluation infrastructure lags severely, remaining highly fragmented and restricted to specific subdomains or basic operations. Unlike existing benc
The proliferation of advanced AI models in visual domains like Nano-banana 2 and Gemini-Omni has created a clear precedent and demand for similar sophistication in audio editing, leading to the development of dedicated benchmarks.
This benchmark signifies a crucial step towards generalized, instruction-based audio AI, enabling more powerful and accessible audio content creation tools for various industries.
The introduction of a comprehensive benchmark for multitask audio editing will accelerate R&D by providing a standardized, rigorous evaluation method, moving audio AI beyond niche applications.
- · AI audio model developers
- · Creative industries relying on audio
- · Content creators
- · Audio software companies
- · Manual audio engineers (for routine tasks)
The benchmark will drive rapid improvements and convergence in general-purpose audio AI capabilities, fostering competition among developers.
Advanced AI audio editing will democratize high-quality sound design, allowing complex audio tasks to be performed by non-experts through natural language instructions.
This could lead to new forms of audio-centric media and entertainment, with AI co-creating or even autonomously generating vast quantities of personalized audio experiences.
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