  Monaural Speech enhancement on drones is challenging because the ego-noise
from the rotating motors and propellers leads to extremely low signal-to-noise
ratios at onboard microphones. Although recent masking-based deep neural
network methods excel in monaural speech enhancement, they struggle in the
challenging drone noise scenario. Furthermore, existing drone noise datasets
are limited, causing models to overfit. Considering the harmonic nature of
drone noise, this paper proposes a frequency domain bottleneck adapter to
enable transfer learning. Specifically, the adapter's parameters are trained on
drone noise while retaining the parameters of the pre-trained Frequency
Recurrent Convolutional Recurrent Network (FRCRN) fixed. Evaluation results
demonstrate the proposed method can effectively enhance speech quality.
Moreover, it is a more efficient alternative to fine-tuning models for various
drone types, which typically requires substantial computational resources.
