# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 3 Experiment and Analysis
### Experimental Data Analysis  
In the experimental part, this paper uses the above three kinds of experimental UAV audio, which is divided into 3 s segments to preserve the time continuity of the UAV spectrum. The frequency band feature extraction method, the GFCC feature extraction method, and the MFCC feature extraction method were used to extract the UAV sound, and  
Figure 4: Hovering flight sound waveform and spectrogram of UAVs. (**a**) Sound waveform of DJI Air2S. (**b**) Sound waveform of DJI Spark. (**c**) Sound waveform of DJI Phantom4 Pro. (**d**) Sound spectrogram of DJI Air2S. (**e**) Sound spectrogram of DJI Spark. (**f**) Sound spectrogram of DJI Phantom4 Pro. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sound Type** & **Sample Volume (s)** & **Distance (m)** & **Feature Samples** \\ \hline DJI AIR 2S & 1167 & 5–20, 40–100 & 389 \\ DJI Spark & 1002 & 30–70 & 334 \\ DJI Phantom4 & 591 & 70–120 & 197 \\ Ambient noise & 1350 & Not applicable & 450 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: UAV audio data volume. the UAV sound feature dataset was composed, respectively. The sample number of sound features and noise features of each UAV in the dataset is shown in the column \"Feature Samples\" of Table 3.  
The feature datasets of two different feature extraction methods were combined with the CNN model of the same structure to carry out the network training of UAV sound feature recognition. The dataset was randomly divided into training set, verification set and test set according to the ratio of 6:2:2. The training set and validation set are used for model training and small batch validation. The test set is used to evaluate the recognition effect of the model. The experimental results are shown in Table 4, in which ubf represents the UAV Frequency Band Feature. It can be seen from Table 4 that the training results using the three features show high accuracy, precision, and recall in the test set, and the UAV frequency band feature has slightly higher accuracy than the MFCC feature and GFCC feature in the recognition models of the three UAV sounds. Since the sounds in the dataset have similar recording environment and ambient background noise, the flight environment of the same audio is the same, and the test set is randomly selected from the dataset, it may have correlation with the feature data participating in the training, thus affecting the accuracy. After completion of the network training, the continuous audio of three types of UAV flying randomly respectively is used to carry out feature extraction and data labeling using the same method as that used to make the feature dataset. The generalization of the training results is tested using this audio. The results of the recognition effect are shown in Figure 5.  
Figure 5 represents the prediction results of the model and the spectrogram plot corresponding to the predicted audio. The recognition effects of the three UAV sounds in three types of feature are shown in Table 5.  
According to Table 5. The CNN recognition effect using frequency band feature method, the sound recognition accuracy of DJI Air2s was 91.2%, the sound recognition accuracy of DJI spark was 94.7%, and the sound recognition accuracy of the DJI Phantom4 Pro reached 96.8%. By comparing the frequency band feature method with the MFCC feature method on the recognition rates of different types of UAV sound. Using the proposed method, the DJI Air2S recognition accuracy is improved by 1.7%, the DJI Spark recognition accuracy is improved by 2.0%, and the DJI Phantom4 Pro recognition accuracy is improved by 12.1%. By comparing the frequency band feature method with the GFCC feature method, the DJI Air2S recognition accuracy is improved by 5.1%, the DJI Spark recognition accuracy is improved by 7.0%, and the DJI Phantom4 Pro recognition accuracy is improved by 5.1%.