Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
The unmanned aerial vehicle (UAV) industry is developing rapidly, and the application of UAVs is becoming increasingly widespread. Due to the lowering of the threshold for using UAVs, the random flight of UAVs poses safety hazards. In response to the safety risks associated with the unauthorized ope...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/5/351 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850258068623851520 |
|---|---|
| author | Jilong Zhong Aigen Fan Kuangang Fan Wenjie Pan Lu Zeng |
| author_facet | Jilong Zhong Aigen Fan Kuangang Fan Wenjie Pan Lu Zeng |
| author_sort | Jilong Zhong |
| collection | DOAJ |
| description | The unmanned aerial vehicle (UAV) industry is developing rapidly, and the application of UAVs is becoming increasingly widespread. Due to the lowering of the threshold for using UAVs, the random flight of UAVs poses safety hazards. In response to the safety risks associated with the unauthorized operation of UAVs, research on anti-UAV technology has become imperative. This study proposes an improved sound feature extraction method that utilizes the frequency distribution features of UAV sounds. By analyzing the spectrogram of UAV sounds, it was found that the classic Mel Frequency Cepstral Coefficients (MFCC) feature extraction method does not match the frequency bands of UAV sounds. Based on the MFCC feature extraction algorithm framework, an improved frequency band feature extraction method was proposed. This method replaces the Mel filter in the classic algorithm with a piecewise linear function with the frequency band weight as the slope, which can effectively suppress the influence of low- and high-frequency noise and fully focus on the different frequency band feature data of UAV sounds. In this study, the actual flight sounds of UAVs were collected, and the sound feature matrix of UAVs was extracted using the frequency band feature extraction method. The sound features were classified and recognized using a Convolutional Neural Network (CNN). The experimental results show that the frequency band feature extraction method has a better recognition effect compared to the classic MFCC feature extraction method. |
| format | Article |
| id | doaj-art-d568633c07584ab5a0cb8e8a809f0a63 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-d568633c07584ab5a0cb8e8a809f0a632025-08-20T01:56:16ZengMDPI AGDrones2504-446X2025-05-019535110.3390/drones9050351Research on the UAV Sound Recognition Method Based on Frequency Band Feature ExtractionJilong Zhong0Aigen Fan1Kuangang Fan2Wenjie Pan3Lu Zeng4School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaShenzhen Institute of Radio Testing & Tech, Shenzhen 100041, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaThe unmanned aerial vehicle (UAV) industry is developing rapidly, and the application of UAVs is becoming increasingly widespread. Due to the lowering of the threshold for using UAVs, the random flight of UAVs poses safety hazards. In response to the safety risks associated with the unauthorized operation of UAVs, research on anti-UAV technology has become imperative. This study proposes an improved sound feature extraction method that utilizes the frequency distribution features of UAV sounds. By analyzing the spectrogram of UAV sounds, it was found that the classic Mel Frequency Cepstral Coefficients (MFCC) feature extraction method does not match the frequency bands of UAV sounds. Based on the MFCC feature extraction algorithm framework, an improved frequency band feature extraction method was proposed. This method replaces the Mel filter in the classic algorithm with a piecewise linear function with the frequency band weight as the slope, which can effectively suppress the influence of low- and high-frequency noise and fully focus on the different frequency band feature data of UAV sounds. In this study, the actual flight sounds of UAVs were collected, and the sound feature matrix of UAVs was extracted using the frequency band feature extraction method. The sound features were classified and recognized using a Convolutional Neural Network (CNN). The experimental results show that the frequency band feature extraction method has a better recognition effect compared to the classic MFCC feature extraction method.https://www.mdpi.com/2504-446X/9/5/351UAV sound recognitionfeature extractionfrequency domain analysisCNN |
| spellingShingle | Jilong Zhong Aigen Fan Kuangang Fan Wenjie Pan Lu Zeng Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction Drones UAV sound recognition feature extraction frequency domain analysis CNN |
| title | Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction |
| title_full | Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction |
| title_fullStr | Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction |
| title_full_unstemmed | Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction |
| title_short | Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction |
| title_sort | research on the uav sound recognition method based on frequency band feature extraction |
| topic | UAV sound recognition feature extraction frequency domain analysis CNN |
| url | https://www.mdpi.com/2504-446X/9/5/351 |
| work_keys_str_mv | AT jilongzhong researchontheuavsoundrecognitionmethodbasedonfrequencybandfeatureextraction AT aigenfan researchontheuavsoundrecognitionmethodbasedonfrequencybandfeatureextraction AT kuangangfan researchontheuavsoundrecognitionmethodbasedonfrequencybandfeatureextraction AT wenjiepan researchontheuavsoundrecognitionmethodbasedonfrequencybandfeatureextraction AT luzeng researchontheuavsoundrecognitionmethodbasedonfrequencybandfeatureextraction |