RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios
Abstract
While Audio Large Models (ALMs) have achieved remarkable proficiency, their robustness remains brittle in real-world deployment. Existing evaluations largely rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics -- or ``Acoustic Ecology'' -- that characterize authentic physical environments. To bridge this ecological gap, we introduce \textbf{RSA-Bench}, a comprehensive robustness benchmark designed to stress-test ALLMs through high-fidelity auditory scene simulations. Unlike traditional methods, we construct evaluation samples by naturally superimposing diverse environmental soundscapes -- spanning \textit{Pasture}, \textit{Extreme Weather}, \textit{Classroom}, and \textit{Outdoors} -- onto clean speech signals across a spectrum of interference intensities. By evaluating models on six core tasks ranging from fundamental perception to complex reasoning, our study unveils three macro-level insights: \textbf{(I) The Perception-Cognition Gap:} Models maintain relative resilience in low-level recognition but suffer a \textbf{functional collapse} in high-order reasoning tasks under stress; \textbf{(II) Scenario Sensitivity:} ``Vocal-like'' interference (e.g., background laughter) proves significantly more destructive than mechanical noise, challenging the model's auditory attention mechanisms; and \textbf{(III) The Denoising Paradox:} Standard speech enhancement often exacerbates performance degradation, as ALLMs prove highly sensitive to the semantic distortions introduced by denoising artifacts.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces RSA-Bench, a novel benchmark for evaluating the robustness of Audio Large Models in complex acoustic environments, revealing critical insights into model vulnerabilities and the limitations of current denoising strategies. The comprehensive methodology and thorough experimental evaluation contribute significantly to the understanding of ALLMs' performance in real-world scenarios, highlighting the need for further research into improving model robustness.
Methodology
The methodology presented in RSA-Bench is robust and innovative, focusing on creating a comprehensive benchmark for Audio Large Models (ALLMs) that simulates real-world acoustic environments. The authors utilize a multi-source superposition strategy to create realistic soundscapes, which is a significant improvement over traditional benchmarks that rely on simplistic noise models. The systematic approach to categorizing tasks into perception and cognitive reasoning is well-structured, allowing for a nuanced evaluation of model performance across different scenarios. However, the methodology could benefit from a clearer explanation of the acoustic ecology concept and its implications for model evaluation.
Results & evaluation
The experimental evaluation is thorough, with a large dataset of over 100,000 samples and a diverse set of tasks that comprehensively assess model performance. The results reveal critical insights into the vulnerabilities of ALLMs, particularly the perception-cognition gap and the denoising paradox. The use of various models for comparison adds depth to the analysis, although the paper could provide more detailed statistical analyses to support the findings. The experiments effectively highlight the limitations of current models in real-world scenarios, making a significant contribution to the field.
Reproducibility
The authors provide a GitHub repository with code and datasets, which enhances reproducibility. However, the paper lacks detailed implementation specifics, such as the exact configurations used for the models and the parameters for the noise generation process. Including these details would improve the ability of other researchers to replicate the study.
Limitations
One limitation is the focus on only a limited number of environmental scenarios, which may not encompass the full range of real-world acoustic conditions. Additionally, the paper primarily addresses inference-time mitigation strategies, leaving out potential training-time interventions that could enhance model robustness. The findings regarding the ineffectiveness of denoising methods could also benefit from further exploration of alternative strategies.
Broader impact
The findings of this research have significant implications for the deployment of ALLMs in real-world applications, particularly in environments with complex acoustic dynamics. By identifying vulnerabilities in model performance, this work paves the way for future research aimed at improving the robustness of audio processing systems, which could enhance their usability in various fields such as telecommunications, assistive technologies, and interactive AI systems. The paper introduces RSA-Bench, a novel benchmark for evaluating the robustness of Audio Large Models in complex acoustic environments, revealing critical insights into model vulnerabilities and the limitations of current denoising strategies. The comprehensive methodology and thorough experimental evaluation contribute significantly to the understanding of ALLMs' performance in real-world scenarios, highlighting the need for further research into improving model robustness.
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