MRMAD: A Multi-Round Multi-Audio Benchmark for Evaluating Acoustic Degradation Perception in Large Audio-Language Models

2026-08-23

AI Summary

研究通过引入MRMAD基准来评估大型音频-语言模型对音频降质感知的理解能力，采用多轮多音频对话形式测试模型识别与解释降质现象的能力。

Abstract

Large audio-language models (LALMs) have shown promising progress in understanding speech, music, and general sound events, yet their ability to reason about how audio signals are degraded remains underexplored. Existing benchmarks primarily evaluate semantic understanding, event recognition, or high-level audio reasoning, leaving a basic question unanswered: Do LALMs understand the differences in audio quality? We introduce MRMAD, a Multi-Round Multi-Audio Degradation benchmark for evaluating audio degradation perception and understanding in LALMs. MRMAD spans speech, music, and sound, and frames evaluation as multi-turn dialogues over multiple audio inputs, requiring models to identify degradation types, compare severity, and perceive corruption changes across turns. Unlike current single-turn audio-language benchmarks, MRMAD evaluates whether LALMs can maintain consistent degradation hypotheses with new evidence and explain low-level acoustic phenomena in natural language. Through a systematic evaluation of 18 representative LALMs from non-thinking to reasoning and Omni models, we find that current models often recognize coarse content while failing to diagnose, compare, or reason about degradations reliably. MRMAD reveals an important yet overlooked aspect of audio-language understanding and provides a diagnostic foundation for building future LALMs that are robust to real-world acoustic conditions.

Problem

• audio degradation
• large audio-language models
• audio quality

Innovation

• Multi-Round Multi-Audio Degradation
• Acoustic Degradation Perception
• Large Audio-Language Models
• Consistent Degradation Hypotheses