# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 1 Introduction  
With the continuous maturation of key technologies such as automatic control and communication systems in the UAV industry, its development has accelerated significantly. UAVs have found extensive applications across multiple domains, including remote sensing mapping, environmental monitoring, and logistics transportation, and the sector holds promising prospects for market expansion. Many surveys suggest that the proliferation of consumer-grade UAV products presents dual challenges [1, 2]. On one hand, non-professionals without specialized training can easily acquire and operate UAVs. Lacking safety awareness and legal knowledge, they may unintentionally engage in de facto illegal flight operations, creating substantial security risks. On the other hand, malicious actors can readily obtain, utilize, or even modify UAVs for unlawful activities. Notable examples include cross-border smuggling via UAVs, deploying UAV-mounted high-resolution cameras to surveil military vessels, and disruptingairport operations through unauthorized UAV flights that interfere with aircraft takeoffs and landings. Facing the security challenges posed by UAV \"illegal flights\", the demand for UAV regulation continues to grow. In response, the Chinese government has recently implemented multiple UAV regulatory policies and laws. Developing intelligent and efficient counter-UAV technologies has become imperative [3]. UAV recognition technologies primarily include radar detection [4], radio frequency signal [5,6], control signal recognition [7,8], video/image recognition [9], infrared imaging [10], remote sensing image [11], and acoustic recognition, with some researchers achieving UAV detection through multi-sensor data fusion techniques [12]. This article focuses on acoustic identification technology. Existing research on UAV sound recognition algorithms involves two aspects: acoustic feature extraction algorithms, and audio feature classification and recognition models [13]. In current UAV sound processing research, four acoustic feature extraction algorithms are commonly used: MFCC, Gammatone Frequency Cepstral Coefficients (GFCC), Linear Predictive Cepstral Coefficients (LPCC), and Short-Time Fourier Transform (STFT). The first three algorithms adopt nonlinear frequency perception features of human auditory systems in their filter bank frequency band distributions, typically applied in speech recognition, while STFT features describe full-band spectral components of sound signals over time but the overall data volume is huge. Many scholars have contributed to the study of different methods. [PERSON] et al. used MFCC and LPCC features with a multi-kernel Support Vector Machine (SVM) for sound classification [14]. [PERSON] et al. fused MFCC and STFT features for UAV flight posture recognition via an ADS-CNN model [15]. [PERSON] et al. used a sensor fusion approach combining visual, radar, and acoustic data with polynomial logistic regression-CNN integration [16], [PERSON] et al. investigated UAV sound propagation characteristics using low-frequency acoustic sensors and sparse Mayer filters with deep residual networks [17]. [PERSON] et al. used a neural network architecture incorporating global-local attention mechanisms for raw audio processing [18]. [PERSON] et al. proposed an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect UAVs in a practical scenario [19]. [PERSON] et al. proposed an acoustic UAV detection method based on blind source separation (BSS) framework to solve the UAV sound detection problem with multi-source interference [20]. Most existing studies focus on short-range UAV audio data for detection and model identification, predominantly through complex modifications of deep neural networks. In this research, we recorded the UAV audio data of medium-distance flight in the environment of normal level noise as the dataset. We studied the limitations of the traditional MFCC method on the frequency distribution.