Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
Abstract
:1. Introduction
2. Method and Model Establishment
2.1. Sound Field Model of UAV Sound Signal
2.2. Frequency Band Feature Extraction Method of UAV Sound
2.2.1. Pre-Processing of Sound Signal
2.2.2. Short Time Fourier Transform of Sound Signal
2.2.3. Frequency Band Feature Extraction Algorithm
2.2.4. Cepstral Domain Analysis
2.3. Convolutional Neural Network for Feature Recognition of UAV Sound
3. Experiment and Analysis
3.1. Experimental Data Collection
3.2. Experimental Data Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Index of band | Frequency Band (Hz) | Weight |
---|---|---|
0 | [0, 100] | 0.1 |
1 | (100, 3000] | 4 |
2 | (3000, 6000] | 2 |
3 | (6000, 12,000] | 1 |
4 | (12,000, 24,000] | 0.15 |
Layer Level | Layer Name | Layer Type |
---|---|---|
1 | input | 2-D data input layer |
2 | conv1 | 2-D data convolutional layer |
3 | bn1 | Batch normalization layer |
4 | relu1 | Activation layer |
5 | maxpool1 | 2-D data max pooling layer |
6 | conv2 | 2-D data convolutional layer |
7 | bn2 | Batch normalization layer |
8 | relu2 | Activation layer |
9 | fc1 | Full connected layer |
10 | relu3 | Activation layer |
11 | fc2 | Full connected layer |
12 | softmax | Softmax function |
13 | classification | Output of classification |
Sound Type | Sample Volume (s) | Distance (m) | Feature Samples |
---|---|---|---|
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 |
Feature | UAV | Accuracy | Precision | Recall |
---|---|---|---|---|
mfcc | air2s | 95.9% | 96.1% | 96.1% |
spark | 96.5% | 96.4% | 96.5% | |
phantom4 | 93.9% | 94.1% | 94.0% | |
gfcc | air2s | 93.3% | 93.5% | 91.4% |
spark | 95.1% | 95.5% | 94.8% | |
phantom4 | 93.9% | 94.2% | 93.8% | |
ufbf | air2s | 97.1% | 97.2% | 97.2% |
spark | 99.3% | 99.4% | 99.2% | |
phantom4 | 95.1% | 95.7% | 95.0% |
Feature | UAV | Accuracy | Precision | Recall |
---|---|---|---|---|
mfcc | air2s | 89.5% | 98.1% | 85.0% |
spark | 92.7% | 85.3% | 96.3% | |
phantom4 | 84.7% | 76.6% | 98.3% | |
gfcc | air2s | 85.5% | 94.4% | 81.7% |
spark | 87.7% | 82.6% | 83.3% | |
phantom4 | 91.1% | 85.5% | 98.3% | |
ufbf | air2s | 91.2% | 91.7% | 94.6% |
spark | 94.7% | 89.0% | 97.2% | |
phantom4 | 96.8% | 95.2% | 98.3% |
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Zhong, J.; Fan, A.; Fan, K.; Pan, W.; Zeng, L. Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction. Drones 2025, 9, 351. https://doi.org/10.3390/drones9050351
Zhong J, Fan A, Fan K, Pan W, Zeng L. Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction. Drones. 2025; 9(5):351. https://doi.org/10.3390/drones9050351
Chicago/Turabian StyleZhong, Jilong, Aigen Fan, Kuangang Fan, Wenjie Pan, and Lu Zeng. 2025. "Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction" Drones 9, no. 5: 351. https://doi.org/10.3390/drones9050351
APA StyleZhong, J., Fan, A., Fan, K., Pan, W., & Zeng, L. (2025). Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction. Drones, 9(5), 351. https://doi.org/10.3390/drones9050351