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 Dinesh Manocha
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 2.0 Flash achieves only 59.93% 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.
Benchmark, Audio Language Models, Complex Reasoning
We introduce MMAU, a comprehensive benchmark evaluating multimodal audio understanding across speech, sound, and music, challenging models with 27 distinct skills requiring advanced perception, reasoning, and domain-specific knowledge.