The Unbearable Weight: Scaling Models and Methods for UAV Audio Classification
As unmanned aerial vehicles (UAVs) become increasingly prevalent in consumer and defense settings, classifying them reliably from limited, modality-specific data is an urgent challenge. The dominant approach, large pretrained networks fully fine-tuned on task data, carries a substantial computational and memory weight that is hard to bear in resource-constrained UAV deployments, where edge inference and rapid retraining for emerging platforms are both required. This paper systematically scales across both model architectures and fine-tuning methods for UAV audio classification, asking when that weight is justified and when lighter alternatives prevail. Using a custom dataset of 3,100 audi...
| Authors | Andrew P. Berg, Qian Zhang, Mia Y. Wang |
|---|---|
| Primary category | cs.SD |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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