# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 5 Conclusions  
To address the demand for UAV detection and recognition in anti-UAV technology, microphones were used as sensors to collect UAV sounds in the environment for the recognition of UAV sounds. A novel frequency band feature extraction method, which defines a UAV-specific filter bank using a new frequency scaling function, is proposed. This  
Figure 5: Results of the recognition effect of the ubf model on UAVs sound: (**a**) DJI Air2S, (**b**) DJI Spark, (**c**) DJI Phantom4 Pro.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **UAV** & **Accuracy** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{mfcc} & air2s & 89.5\% & 98.1\% & 85.0\% \\  & spark & 92.7\% & 85.3\% & 96.3\% \\  & phantom4 & 84.7\% & 76.6\% & 98.3\% \\ \hline \multirow{3}{*}{gfcc} & air2s & 85.5\% & 94.4\% & 81.7\% \\  & spark & 87.7\% & 82.6\% & 83.3\% \\  & phantom4 & 91.1\% & 85.5\% & 98.3\% \\ \hline \multirow{3}{*}{ufbf} & air2s & 91.2\% & 91.7\% & 94.6\% \\  & spark & 94.7\% & 89.0\% & 97.2\% \\ \cline{1-1}  & phantom4 & 96.8\% & 95.2\% & 98.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The CNN recognition result of UAV sound.  
method extracts UAV acoustic feature vectors by segmenting continuous 3 s audio data into frames, extracting features from each frame, and assembling them into a 2-D feature matrix. A CNN was used for UAV sound recognition. Experimental results demonstrate that the proposed frequency band feature extraction method effectively captures features from various types of UAVs at varying distances and improves recognition accuracy. Future research on the recognition of UAV sounds may include the following:  
(1) Experiments can be conducted using more types of UAV to verify the universality of the proposed method. More sounds can be collected to ensure the balance of training samples. Experiments can include more data as well as optimize the weight parameters, sample segmentation parameters, and framing parameters in the method.  
(2) By applying sound signal filtering algorithms or using microphone arrays for sound source separation and enhancement, the sound is processed [21; 22; 23]. Combined with feature extraction algorithms, recognition is achieved in environments with strong noise interference, forming a more complete and unified system.  
(3) By utilizing wireless communication technology to connect multiple devices [24; 25; 26; 27]. Information such as sound, image, and radar can be integrated for information fusion. The identification, location, tracking, and countermeasures of the UAV can be determined.  
Conceptualization, J.Z. and K.F.; methodology, J.Z. and W.P.; software, J.Z. and A.F.; validation, K.F., W.P., A.F. and L.Z.; writing--original draft preparation, J.Z. and A.F.; writing--review and editing, J.Z., K.F., L.Z. and A.F.; funding acquisition, K.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.62363014, No.61763018), the Program of Qingjiang Excellent Young Talents in Jiangxi University of Science and Technology (JXUSTQJBJ2019004), and the Key Plan Project of Science and Technology of Ganzhou (GZ2024 ZDZ008).  
Data Availability Statement: Data are available upon request.