Abstract
In predictive modeling for low-resource audio classification, extracting high-accuracy and interpretable attributes is critical. Particularly in high-reliability applications, interpretable audio attributes are indispensable. While human-driven attribute discovery is effective, its low throughput becomes a bottleneck. We propose a method for adaptively discovering interpretable audio attributes using Multimodal Large Language Models (MLLMs). By replacing humans in the AdaFlock framework with MLLMs, our method achieves significantly faster attribute discovery. Our method dynamically identifies salient acoustic characteristics via prompting and constructs an attribute-based ensemble classifier. Experimental results across various audio tasks demonstrate that our method outperforms direct MLLM prediction in the majority of evaluated cases. The entire training completes within 11 minutes, proving it a practical, adaptive solution that surpasses conventional human-reliant approaches.
Abstract (translated)
在低资源音频分类的预测建模中，提取高准确性和可解释性的属性至关重要。特别是在需要高度可靠的应用场景下，可解释性音频属性不可或缺。虽然人工驱动的属性发现方法非常有效，但其低吞吐量成为了瓶颈问题。我们提出了一种使用多模态大型语言模型（MLLM）自适应地发现可解释音频属性的方法。通过在AdaFlock框架中用MLLM替代人类，我们的方法实现了显著更快的属性发现速度。我们的方法能够动态识别关键声学特征并通过提示构建基于属性的集成分类器。实验结果表明，在各种音频任务上，我们的方法在大多数评估案例中的表现都优于直接使用MLLM进行预测的方法。整个训练过程仅需11分钟即可完成，证明了这种方法是一种实用且自适应的解决方案，超越了传统依赖人工的方法。
URL
https://arxiv.org/abs/2603.06991
