From Sounds to Scenes: A Benchmark for Evaluating Context-Aware Auditory Scene Understanding in Large Audio Language Models

Researchers introduce CASU, a new benchmark for evaluating Large Audio Language Models' ability to understand complex auditory scenes by integrating multiple acoustic layers—speech, sound events, and background environments—rather than processing them in isolation. The benchmark reveals that current LALMs struggle with holistic scene comprehension and require integration across all audio layers for effective real-world audio understanding.

The emergence of CASU addresses a critical gap in how Large Audio Language Models are currently evaluated. Existing benchmarks treat acoustic elements—speech, sound, and music—as separate tasks, failing to capture the complexity of real-world listening where these layers coexist and interact. This research demonstrates that achieving sophisticated audio understanding requires models to reason about contextual relationships between simultaneous acoustic sources, not just recognize individual components. The benchmark's design is sophisticated, using a semi-synthetic pipeline that combines real environmental sounds with synthetic speech to create time-accurate auditory scenes, then evaluating models through contextual question-answering, entity extraction, speaker role inference, and counterfactual reasoning tasks. Testing across multiple LALMs reveals a consistent weakness: models trained primarily on speech or sound components fail when required to integrate all layers holistically. This finding has significant implications for developers building audio AI applications. Real-world deployment scenarios—such as voice assistants in noisy environments, accessibility tools, or security monitoring—demand exactly this kind of integrated understanding. Companies investing in LALM development must now prioritize training approaches that develop cross-layer reasoning capabilities rather than optimizing individual acoustic tasks in isolation. The CASU benchmark will likely influence how future models are trained and evaluated, potentially driving architectural innovations. Organizations developing audio AI should monitor whether their systems can handle these integration tasks before deploying in complex acoustic environments.

• →Current Large Audio Language Models excel at isolated audio tasks but fail at understanding holistic auditory scenes with multiple overlapping acoustic sources.
• →The CASU benchmark introduces four evaluation tasks designed to measure context-aware audio understanding and cross-layer reasoning capabilities.
• →Real-world audio interpretation requires models to integrate speech, acoustic events, and environmental context simultaneously rather than processing each component independently.
• →Experimental results across multiple LALMs demonstrate that effective scene understanding cannot rely on single-layer analysis, necessitating architectural and training improvements.
• →This benchmark will likely influence LALM development priorities toward integrated audio understanding rather than optimized isolated task performance.