Adaptive Discovery of Interpretable Audio Attributes with Multimodal LLMs for Low-Resource Classification
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 classif...
| Authors | Kosuke Yoshimura, Hisashi Kashima |
|---|---|
| Primary category | cs.SD |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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