An Adaptive Acoustic Recognition System for Low-Altitude Airspace Security Based on Decoupled Dynamic Frequency Bands and Random Sound Sources

The acoustic frequency fingerprint (AFF) for drone recognition has emerged as a flexible and practical approach to address low-altitude airspace security concerns. However, three problems restrict its real-world application: (1) efficient AFF features are distributed across a wide range of frequencies, making them hard to extract accurately; (2) environmental noise originating from diverse sources, across different bands, cannot be denoised by a general method and limits monitoring accuracy; and (3) multi-band noise greatly increases the labeling workload and computational resources, limiting the flexibility in practical monitoring. This paper proposes a lightweight but efficient acoustic recognition system. We adopt a DE-filter and HLDWT to adaptively extract multi-band AFF features and limit other bands’ noises; a D-MPGAN to decouple and denoise motor and propeller characteristics, avoiding noisy sample labeling; and a lightweight MP-ResNet for motor and propeller feature handling, masking and comprehensive recognition decisions. Furthermore, we build a real dataset. Trained on clean 10 dB data and validated on −30 dB to 10 dB data, our system achieves 85% recognition accuracy at −30 dB with an inference time of 0.058 s/2000, outperforming mainstream methods trained on mixed noisy signals in anti-noise capability and efficiency, thus enabling reliable practical monitoring for low-altitude security.