Methods, electronic devices and software products for generating noise cancellation signals for unmanned aerial vehicles (UAVs)

Abstract

This disclosure provides a method, electronic device, and software product for generating noise-canceling wave signals for unmanned aerial vehicles (UAVs), relating to the field of acoustic noise cancellation technology. The method includes: receiving an audio signal and performing spectral analysis to obtain characteristic frequency information corresponding to rotor noise and ambient noise; based on the characteristic frequency information, extracting signals corresponding to rotor noise and ambient noise from the audio signal to obtain a first rotor noise signal and a first ambient noise signal; based on the first rotor noise signal, predicting using an adaptive filtering algorithm to obtain a second rotor noise signal; based on the second rotor noise signal, generating an inverse acoustic wave signal with a phase opposite to the second rotor noise signal; adding the inverse acoustic wave signal to the first ambient noise signal, and multiplying the sum by an adjustment amount obtained based on a flight state adjustment function to obtain a noise-canceling wave signal.

Description

Technical Field

[0001] This disclosure relates to the field of acoustic noise reduction technology, specifically to a method for generating noise reduction signal for unmanned aerial vehicles (UAVs), electronic equipment, and software products. Background Technology

[0002] With the widespread application of drones, the low-to-mid-frequency noise generated by their high-speed rotor rotation (typically concentrated in the 200Hz–5kHz frequency band) has become an increasingly prominent problem, potentially causing noise interference to urban environments and wildlife. Existing noise reduction methods mainly rely on rotor structure optimization (such as noise-reducing propeller design), motor vibration damping installation, and passive sound insulation materials. However, these methods have limited effectiveness in suppressing low-to-mid-frequency noise during actual flight and are difficult to adapt to complex and changing environmental acoustic conditions. Meanwhile, active noise control (ANC) technology has been maturely applied in noise-canceling headphones, vehicle cabins, and other fields, achieving noise cancellation by generating oppositely phased sound waves. Although recent research has attempted to introduce active noise control technology into drone noise control, it has mostly been limited to local suppression and has not fully integrated with environmental acoustic characteristics for dynamic optimization. Summary of the Invention

[0003] This disclosure provides a method for generating noise cancellation signals for unmanned aerial vehicles (UAVs), an electronic device, and a program product.

[0004] According to one aspect of this disclosure, a method for generating a noise-canceling wave signal for a UAV is provided, comprising: receiving an audio signal acquired by an audio acquisition device mounted on the UAV, the audio signal including rotor noise and ambient noise; performing spectral analysis on the audio signal to obtain first characteristic frequency information corresponding to the rotor noise and second characteristic frequency information corresponding to the ambient noise; extracting the signal corresponding to the rotor noise from the audio signal based on the first characteristic frequency information to obtain a first rotor noise signal; predicting the first rotor noise signal using an adaptive filtering algorithm to obtain a second rotor noise signal; generating an inverse acoustic wave signal with a phase opposite to the second rotor noise signal based on the second rotor noise signal; extracting the signal corresponding to the ambient noise from the audio signal based on the second characteristic frequency information to obtain a first ambient noise signal; and adding the inverse acoustic wave signal to the first ambient noise signal and multiplying the sum by an adjustment amount obtained based on a flight state adjustment function to obtain the noise-canceling wave signal, wherein the flight state adjustment function obtains the adjustment amount based on the state parameters of the UAV.

[0005] According to one technical solution, by extracting real environmental noise and fusing it with the reverse sound wave, and then combining the adjustment amount dynamically generated by the flight state parameters to adaptively adjust the amplitude and phase of the fused signal, the final output noise-canceling wave signal can be highly matched with the current environmental background in terms of energy, timing and spectral characteristics, thereby avoiding "abnormal silence" or artificial noise exposure, and significantly improving the acoustic noise cancellation capability and environmental fusion capability of UAVs in complex scenarios.

[0006] According to at least one embodiment of this disclosure, the audio acquisition device is a microphone array, which includes at least one first microphone and at least one second microphone. The first microphone is disposed on the surface of the drone body near the rotor, and the second microphone is disposed on the surface of the drone body away from the rotor, with the sound wave receiving surface of the second microphone facing outwards from the drone.

[0007] According to the technical solution of this embodiment, the physical separation of rotor noise and environmental noise can be achieved by utilizing spatial position differences. The first microphone prioritizes capturing rotor noise with a high signal-to-noise ratio, providing a reliable basis for accurate modeling and active cancellation. The second microphone effectively picks up the real environmental background sound undisturbed by the aircraft, providing a natural reference for acoustic noise reduction. The two work together to significantly improve the noise separation accuracy, the realism of environmental perception, and the overall performance of subsequent active noise reduction and acoustic noise reduction.

[0008] According to at least one embodiment of this disclosure, before receiving the audio signal acquired by the audio acquisition device mounted on the UAV, the method further includes: fusing the signals acquired by the at least one first microphone and the at least one second microphone to obtain a fused signal; and using the fused signal as the audio signal acquired by the audio acquisition device.

[0009] According to the technical solution of this embodiment, distortion or abnormality caused by the location limitation or local interference of a single microphone can be effectively suppressed, while taking into account both high-fidelity capture of rotor noise and true reproduction of environmental noise.

[0010] According to at least one embodiment of this disclosure, after obtaining the first environmental noise signal, the method further includes: performing first time compensation and first amplitude compensation on the first environmental noise signal to obtain a second environmental noise signal; and adding the reverse sound wave signal to the second environmental noise signal.

[0011] According to the technical solution of this embodiment, the playback timing of ambient noise can be effectively aligned and its sound pressure level corrected. This ensures that the synthesized noise-canceling signal is synchronized with the current real background sound in time and naturally matched with the ambient noise in amplitude. This avoids acoustic inconsistencies or artificial artifacts caused by delay misalignment or volume imbalance, significantly improving the realism and noise cancellation capability of the acoustic output after active noise cancellation.

[0012] According to at least one embodiment of this disclosure, the flight state adjustment function determines the adjustment amount based on the state parameters of the UAV, including: determining an amplitude adjustment component based on the speed, altitude, and elevation angle in the state parameters; determining a phase adjustment component based on the elevation angle in the state parameters; and using the amplitude adjustment component and the phase adjustment component as the adjustment amount.

[0013] According to the technical solution of this embodiment, the noise cancellation signal can be accurately matched in intensity to the actual acoustic radiation level under different flight conditions, and compensated in phase for the propagation path difference caused by attitude change. Thus, it can achieve a high degree of adaptive fusion with the environmental background in both energy and timing dimensions, effectively avoid acoustic anomalies caused by abrupt amplitude or phase mismatch, and significantly improve the acoustic noise cancellation capability and noise cancellation naturalness of UAVs in complex flight conditions.

[0014] According to at least one embodiment of this disclosure, determining an amplitude adjustment component based on the speed, height, and elevation angle in the state parameters includes: determining a first amplitude based on the speed using a speed correlation function, wherein the speed correlation function is the difference between 1 and a first negative exponential decay function, the first negative exponential decay function being an exponential function with a base of a natural constant and an exponent of the negative product of the decay coefficient and the speed; and determining a second amplitude based on the height using a height correlation function, wherein the height correlation function is a second negative exponential decay function, the second negative exponential decay function being an exponent with a base of a natural constant and an exponent of the negative product of the height coefficient and the height; For the aforementioned elevation angle, a third amplitude is determined through a first elevation angle correlation function. The first elevation angle correlation function is a piecewise function. When the elevation angle is within the allowable range, the third amplitude output by the first elevation angle correlation function is 1. When the elevation angle is not within the allowable range, the third amplitude output by the first elevation angle correlation function increases linearly from 1 as the distance between the elevation angle and the boundary of the allowable range increases, and stops increasing and remains constant when it reaches a critical value. The first amplitude, the second amplitude, and the third amplitude are weighted and summed to obtain the amplitude adjustment component.

[0015] According to the technical solution of this embodiment, the output volume of the noise cancellation wave can be dynamically, continuously and realistically matched with the actual perceptible noise intensity in the external environment under the current flight conditions, thereby improving the acoustic noise cancellation capability and environmental adaptability of the UAV in complex mission scenarios.

[0016] According to at least one embodiment of this disclosure, determining a phase adjustment component based on the elevation angle in the state parameters includes: determining the phase adjustment component based on the elevation angle using a second elevation angle correlation function, wherein the second elevation angle correlation function is the sum of a base phase value and an elevation angle compensation phase function, and the elevation angle compensation phase function is a linear function that increases based on the magnitude of the elevation angle.

[0017] According to the technical solution of this embodiment, the influence of flight attitude changes on the sound wave propagation path length can be effectively reflected. It avoids identifiable transient distortion or an "artificial feel" caused by phase mismatch, thereby significantly improving the temporal naturalness of the acoustic noise reduction signal.

[0018] According to at least one embodiment of this disclosure, when multiple drones are located in the same area, the noise cancellation signal generation process of each drone includes: receiving a first ambient noise signal; performing second time compensation and second amplitude compensation on the first ambient noise signal to obtain a target sound field signal; and distributing the target sound field signal to each drone according to the weight of each drone to obtain the noise cancellation signal of each drone.

[0019] According to the technical solution of this embodiment, the spatiotemporal coordination and natural integration of the overall sound field of multiple UAV formations can be achieved.

[0020] According to at least one embodiment of this disclosure, the weight of each drone is determined based on the position, altitude, speed, etc. of each drone.

[0021] According to the technical solution of this embodiment, intelligent allocation of noise cancellation signals can be achieved: UAVs in key positions (such as the center of formation or low observable areas) or with high-value mission capabilities (such as weapon delivery) are assigned lower weights to reduce sound noise.

[0022] According to at least one embodiment of this disclosure, after obtaining the noise-canceling signal, the method further includes: outputting the noise-canceling signal through an audio playback device mounted on a drone, wherein the audio playback device employs a speaker array.

[0023] According to the technical solution of this embodiment, the beamforming and multi-channel collaborative control capabilities of the array can be used to achieve directional radiation, frequency response optimization and precise phase control of sound waves.

[0024] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, causing the processor to perform a method for generating a drone noise cancellation signal according to any embodiment of this disclosure.

[0025] According to another aspect of this disclosure, a readable storage medium is provided, wherein execution instructions are stored therein, which, when executed by a processor, are used to implement the UAV noise cancellation signal generation method of any embodiment of this disclosure.

[0026] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements a method for generating noise-canceling signals for unmanned aerial vehicles according to any embodiment of this disclosure. Attached Figure Description

[0027] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0028] Figure 1 This is a flowchart illustrating a method for generating noise cancellation signals for unmanned aerial vehicles according to one embodiment of the present disclosure.

[0029] Figure 2 This is a flowchart illustrating a method for determining adjustment amount according to one embodiment of the present disclosure.

[0030] Figure 3 This is a flowchart illustrating the method corresponding to step S210 of one embodiment of the present disclosure.

[0031] Figure 4 This is a flowchart illustrating a method for generating noise cancellation signals for unmanned aerial vehicles according to another embodiment of this disclosure.

[0032] Figure 5 This is a flowchart illustrating a method for generating noise cancellation signals for unmanned aerial vehicles according to yet another embodiment of this disclosure.

[0033] Figure 6 This is a schematic block diagram of a drone noise cancellation signal generation device according to one embodiment of the present disclosure.

[0034] Figure 7 This is a schematic structural block diagram of an electronic device employing a processor-based hardware implementation according to one embodiment of the present disclosure. Detailed Implementation

[0035] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0036] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] Existing noise reduction solutions for drones have the following shortcomings: (1) Limited noise reduction effect of structure: Although optimizing the aerodynamic shape of the propeller or adding sound insulation materials can suppress noise to a certain extent, it is difficult to fundamentally eliminate the aerodynamic noise generated by the rotor. At the same time, such measures often increase the weight of the airframe, resulting in increased flight energy consumption and reduced endurance.

[0038] (2) One-sided application of active noise cancellation: Current attempts at active noise control focus on noise cancellation of single machines and local areas, lacking the ability to perceive and integrate the sound field of the surrounding environment.

[0039] (3) Insufficient environmental adaptability: Traditional noise reduction strategies are mostly static designs, which cannot dynamically adjust noise reduction parameters and acoustic output according to actual flight scenarios (such as urban blocks, forests, seas or nighttime environments with different background sound characteristics), making it difficult to match with environmental acoustic characteristics.

[0040] To address this, this disclosure proposes the following technical solution, which abandons passive noise reduction methods relying on propeller modification or sound insulation materials, and instead adopts active noise prediction and cancellation technology based on audio signal analysis and adaptive filtering. This eliminates the need for additional structural weight, avoiding a decrease in endurance, while effectively suppressing rotor aerodynamic noise at the sound source level, achieving deeper noise reduction. By generating inverse sound waves to cancel its own noise, it simultaneously extracts and fuses real-world environmental noise to construct a noise-canceling wave signal containing background sound, rather than simply performing local noise cancellation. By introducing an adjustment function based on UAV flight state parameters, it dynamically adjusts the intensity and spectral characteristics of the noise-canceling wave and adaptively synthesizes it in conjunction with real-time environmental noise characteristics. It can automatically optimize acoustic output strategies according to different flight scenarios (such as urban blocks, forests, sea, nighttime, etc.).

[0041] To facilitate description and make the technical solutions of this disclosure easier to understand, the terminology of this disclosure will be explained before describing the technical solutions of this disclosure.

[0042] Rotor noise refers to the aerodynamic noise generated by the high-speed rotation of the rotor and its interaction with the air during the flight of a drone.

[0043] Environmental noise refers to the sum of all background acoustic signals in the external space where the drone is located, excluding the noise generated by the drone itself.

[0044] Spectrum analysis refers to converting an audio signal represented in the time domain into a frequency domain representation using mathematical methods such as Fourier transform, in order to obtain the energy distribution of the signal at different frequency components.

[0045] Characteristic frequency information refers to the key frequency components and their related parameters (such as center frequency, bandwidth, amplitude, etc.) that can characterize the physical characteristics of a specific sound source (such as a drone rotor or environmental background) in the spectrum analysis results.

[0046] Adaptive filtering algorithms are a class of digital signal processing algorithms that can adjust their filter coefficients in real time based on the input signal and error feedback. Typical examples include the Least Mean Square (LMS) algorithm and the Recursive Least Squares (RLS) algorithm.

[0047] This disclosure can be applied to unmanned aerial vehicle (UAV) mission scenarios with high requirements for sound noise and complex and ever-changing environmental backgrounds, such as wildlife monitoring and ecological protection, urban logistics and low-altitude delivery.

[0048] Figure 1 A schematic diagram illustrating the overall flow of a method for generating noise cancellation signals for unmanned aerial vehicles (UAVs) according to one embodiment of this disclosure is shown. Figure 1 The method shown includes steps S110 to S170.

[0049] In step S110, an audio signal collected by an audio acquisition device mounted on the UAV is received. The audio signal includes rotor noise and ambient noise.

[0050] An audio acquisition device refers to a hardware device installed on a drone for real-time acquisition of ambient acoustic signals. This device converts changes in sound pressure in the air into electrical signals, and further into digital audio data for use in subsequent noise analysis, feature extraction, and active control algorithms. In this disclosure, the audio acquisition device is a key sensor module for realizing environmental noise perception and rotor noise monitoring.

[0051] As one possible implementation, the audio acquisition device is a microphone array. This microphone array includes at least one first microphone and at least one second microphone. The first microphone is positioned on the surface of the UAV near the rotor, while the second microphone is positioned on the surface of the UAV away from the rotor, with the sound wave receiving surface of the second microphone facing outwards from the UAV. Through this implementation, the audio acquisition device can effectively separate rotor noise from ambient noise at the physical level. This provides a high-quality raw signal foundation for subsequent accurate noise feature extraction, high-precision rotor noise prediction, generation of inverse cancelling sound waves, and dynamic noise reduction by fusing ambient sound, significantly improving active noise reduction and acoustic noise cancellation capabilities.

[0052] In one example, the first microphone is positioned directly below, to the side of, or near the rotor hub, within the strong sound field of rotor aerodynamic noise. The audio signal received at this location is primarily composed of aerodynamic noise generated by the rotor, with relatively weak ambient background noise.

[0053] In another example, the second microphone is positioned at the center of the fuselage bottom, the belly fairing, the inside of the landing gear, or the tail section. This location primarily receives background noise from the external environment (such as wind, traffic noise, birdsong, and ocean waves), while rotor noise is significantly reduced due to propagation attenuation and fuselage obstruction.

[0054] As one possible implementation, before receiving the audio signal acquired by the audio acquisition device mounted on the UAV, the method further includes: fusing the signals acquired by at least one first microphone and at least one second microphone to obtain a fused signal. The fused signal is then used as the audio signal acquired by the audio acquisition device.

[0055] For example, signal fusion can employ common signal processing techniques such as frequency band fusion, weighted superposition fusion, and adaptive beamforming. By performing signal fusion, interference that may be introduced by a single microphone (such as false triggering caused by aircraft vibration or localized ambient sound anomalies) can be effectively suppressed. Simultaneously, key features of rotor noise and ambient noise are enhanced. The generated fused signal accurately reflects the comprehensive acoustic state of "UAV's own noise + external ambient noise." It not only possesses high-fidelity rotor noise details and realistic environmental background information, avoiding feature loss due to the limitations of a single microphone position, but also simplifies subsequent processing, overcoming the algorithmic complexity of directly using multi-channel raw signals. This provides a stable, reliable, and comprehensive input foundation for subsequent steps such as spectrum analysis, feature extraction, and adaptive noise modeling.

[0056] In step S120, the audio signal is subjected to spectrum analysis to obtain the first characteristic frequency information corresponding to the rotor noise and the second characteristic frequency information corresponding to the environmental noise.

[0057] Rotor noise consists of two main parts: discrete noise generated by the blade passage frequency and its harmonics, and broadband noise caused by tip vortex shedding and turbulent boundary layer disturbances. The energy of rotor noise is mainly concentrated in the 200Hz to 5kHz frequency band.

[0058] Environmental noise typically includes natural sound sources (such as wind, rain, birdsong, and flowing water) and anthropogenic sound sources (such as traffic noise, human voices, and machinery noise). The spectral characteristics, intensity, and time-varying patterns of environmental noise vary significantly with the flight environment (such as the rustling of forest sounds from 1kHz to 3kHz, and the sound of ocean waves from 50Hz to 200Hz). It usually exhibits a continuous random distribution (without obvious periodic peaks and low time-frequency stability).

[0059] Based on the aforementioned differences in spectral characteristics, the first characteristic frequency information corresponding to the rotor noise and the second characteristic frequency information corresponding to the environmental noise can be accurately identified and extracted from the audio signal that mixes rotor noise and environmental noise through spectral analysis.

[0060] As one possible implementation, the audio signal (time-domain signal) is first divided into continuous or overlapping short frames (e.g., 20–50 ms per frame), and a window function (e.g., Hanning window) is applied to each frame to reduce spectral leakage. Subsequently, a Fast Fourier Transform (FFT) or more sophisticated time-frequency analysis methods (e.g., Short-Time Fourier Transform, Wavelet Transform, etc.) are used to transform each frame signal from the time domain to the frequency domain, obtaining its spectral representation, i.e., the frequency-amplitude distribution map. Then, in the characteristic frequency information extraction: during the process of obtaining the first characteristic frequency information corresponding to the rotor noise, the dominant frequency and its harmonic set of the rotor noise, including parameters such as center frequency, bandwidth, amplitude, and phase, are extracted through peak detection, harmonic structure identification, or by combining known flight parameters (e.g., motor speed obtained from the flight control system) for prior guidance. In obtaining the second characteristic frequency information corresponding to the environmental noise, the spectral profile, main energy concentration frequency band and dynamic change trend of the environmental noise are extracted by performing energy statistics, spectral envelope analysis or background noise modeling (such as minimum tracking method and noise threshold estimation) in the frequency band after excluding the rotor tone component.

[0061] In step S130, based on the first characteristic frequency information, the signal corresponding to the rotor noise is extracted from the audio signal to obtain the first rotor noise signal.

[0062] Based on the first characteristic frequency information, the main energy distribution area of ​​rotor noise in the frequency domain can be determined, including the fundamental frequency (such as the blade passing frequency) and the center frequency and effective bandwidth corresponding to its several harmonics.

[0063] In one possible implementation, the audio signal is first converted to the frequency domain using a Fourier transform. Then, a frequency-domain masking function or bandpass filter bank is constructed to retain the signal components within the frequency band corresponding to the first characteristic frequency information, while attenuating or suppressing other frequency bands, thereby obtaining the frequency-domain signal corresponding to the rotor noise. Finally, the frequency-domain signal is converted back to the time domain using an inverse Fourier transform or other time-frequency reconstruction method to obtain the first rotor noise signal.

[0064] In step S140, based on the first rotor noise signal, the second rotor noise signal is obtained by prediction through an adaptive filtering algorithm.

[0065] Adaptive filtering algorithms can employ commonly used algorithms such as Least Mean Square (LMS) and Recursive Least Squares (RLS). By using adaptive filtering algorithms for prediction, a second rotor noise signal that accurately characterizes the rotor noise properties at future moments can be generated, providing a basis for the subsequent generation of high-precision reverse acoustic waves.

[0066] As one possible implementation, the least mean square algorithm is used in an adaptive filtering algorithm. The prediction process includes: first, discretizing the first rotor noise signal to obtain the discrete-time input noise sequence x(n), where n represents the sampling time. Then, adaptive filtering modeling is performed: the input noise sequence x(n) is used as a reference input signal and substituted into the least mean square algorithm. The least mean square algorithm dynamically updates the filter weight coefficients w(n) iteratively, with the core update formula being: w(n+1) = w(n) + μ. e(n) x(n), where e(n) represents the prediction error at time n, i.e., the deviation between the predicted value of the rotor noise at the next time step (n+1 time) based on the rotor noise at the current time (time n) and the actual value. w(n) represents the weighting coefficient at time n. w(n+1) represents the weighting coefficient at time n+1. μ represents the step size factor, used to control the convergence speed and stability. Through this prediction process, the least mean square algorithm filter gradually learns the periodicity and time-varying characteristics of the rotor noise, establishing an adaptive prediction model specifically for rotor noise. The output of this adaptive prediction model is the second rotor noise signal, which is a high-precision estimate of the actual rotor noise at the next time step.

[0067] In step S150, a reverse acoustic wave signal with the opposite phase to the second rotor noise signal is generated based on the second rotor noise signal.

[0068] The reverse acoustic signal is a signal with the same amplitude but opposite phase to the second rotor noise signal. This reverse acoustic signal is used to achieve active noise cancellation. As one possible implementation, the reverse acoustic signal can be generated by a digital signal processing (DSP) unit onboard the UAV. Specifically, the DSP unit receives the second rotor noise signal obtained in the aforementioned steps (i.e., the high-precision prediction result of the UAV rotor noise) and generates a control signal with the same waveform shape but completely opposite phase as the reverse acoustic signal.

[0069] In step S160, based on the second characteristic frequency information, the signal corresponding to the environmental noise is extracted from the audio signal to obtain the first environmental noise signal.

[0070] Based on the second characteristic frequency information, the main energy distribution region of environmental noise in the frequency domain can be determined. This main energy distribution includes its spectral envelope, energy concentration frequency band, and dynamic variation characteristics, and usually exhibits a continuous or slowly varying broadband spectral shape, rather than a discrete harmonic structure.

[0071] In one possible implementation, the audio signal is first converted to the frequency domain using a Fourier transform. Then, a frequency-domain masking function or band-stop filter bank is constructed to preserve the signal components within the frequency band corresponding to the second characteristic frequency information, while attenuating or suppressing frequency bands dominated by rotor noise (such as the fundamental frequency and its harmonic regions), thereby obtaining the frequency-domain signal corresponding to the environmental noise. Finally, this frequency-domain signal is converted back to the time domain using an inverse Fourier transform or other time-frequency reconstruction method to obtain the first environmental noise signal.

[0072] The first environmental noise signal effectively eliminated the interference of rotor noise and preserved the real background sound characteristics that match the current flight scenario (such as city, forest, sea, etc.), providing a key environmental acoustic reference for the subsequent generation of natural and credible noise cancellation waves.

[0073] In step S170, the reverse acoustic wave signal is added to the first environmental noise signal, and the addition result is multiplied by the adjustment amount obtained based on the flight state adjustment function to obtain the noise-canceling wave signal.

[0074] For example, the process of obtaining the noise-cancelled signal is represented by the following formula: N out (t)=g(v,h,θ) (N c (t)+N env (t)) Where, N out (t) represents the noise-canceling signal at time t; N c (t) represents the reverse acoustic signal at time t; N env(t) represents the first ambient noise signal at time t; t represents time; g(v,h,θ) represents the flight state adjustment function, v represents speed, h represents altitude, and θ represents the pitch angle (i.e., the angle between the longitudinal axis of the aircraft and the horizontal plane).

[0075] The flight status adjustment function obtains the adjustment amount based on the UAV's status parameters. Figure 2 A schematic flowchart illustrating the adjustment amount determination method according to one embodiment of this disclosure is shown. Figure 2 The method shown includes steps S210 to S230.

[0076] In step S210, the amplitude adjustment component is determined based on the speed, altitude, and elevation angle in the state parameters.

[0077] Speed ​​affects rotor load and airflow disturbance intensity; noise energy is stronger at high speeds and significantly weaker at low speeds or when hovering. Altitude determines the sound wave propagation path length and atmospheric attenuation; sound pressure is enhanced at low altitudes due to ground reflection, while sound energy is significantly attenuated at high altitudes due to propagation loss. The pitch angle characterizes flight attitude (such as climb, dive, or level flight), directly affecting the direction and intensity of rotor noise radiation to the ground or surrounding space. By dynamically adjusting the amplitude modulation component based on these three state parameters, the output volume of the noise-canceling wave can be made highly consistent with the current actual noise radiation level. This avoids both excessive volume that could expose the target and excessive volume that could create "abnormal silence," thus achieving adaptive matching with the environmental background at the energy level.

[0078] In step S220, the phase adjustment component is determined based on the elevation angle in the state parameters.

[0079] The elevation angle not only affects the radiation direction of rotor noise but also alters the relative geometric relationship between the sound source (rotor) and the external receiving point (such as a ground-based monitor). This causes changes in the length of the sound wave propagation path, resulting in a phase shift at the receiver. For example, during a climb (positive elevation angle), the rotor position is relatively higher and farther from the ground, lengthening the sound wave propagation path and increasing propagation delay, manifesting as phase lag. Conversely, during a dive (negative elevation angle), the rotor is closer to the ground, shortening the propagation path and advancing the phase accordingly. By dynamically adjusting the phase adjustment component based on the elevation angle, phase pre-compensation can be applied to the noise cancellation wave, ensuring a natural temporal transition between the target listening area and the real environmental background sound. This effectively avoids the "artificial" feel or transient anomalies that can be detected by acoustic detection systems due to abrupt phase changes, thereby significantly improving the temporal continuity and acoustic naturalness of the output sound wave.

[0080] In step S230, the amplitude adjustment component and the phase adjustment component are used as adjustment quantities.

[0081] By designing the adjustment amount as a combination of amplitude and phase dimensions, it breaks through the limitations of traditional methods that only adjust volume, achieving coordinated control of the complete acoustic characteristics of the noise-canceling wave. This adjustment method ensures that the final output noise-canceling wave signal not only "sounds like" the ambient background, but also maintains consistency with the actual flight state in terms of sound field spatial characteristics, dynamic change rhythm, and transient response. This greatly enhances the ability to deceive acoustic reconnaissance and intelligent listening systems, truly achieving "acoustic stealth" rather than simply "noise reduction."

[0082] In steps S210 to S230, the adjustment amount is decomposed into amplitude adjustment components and phase adjustment components, and calculated separately based on different flight state parameters. This allows for a more precise and realistic simulation of the acoustic radiation characteristics of the UAV under different flight attitudes and environments. This structured, parameter-driven adjustment mechanism ensures that the noise cancellation wave not only matches the environment in energy but also conforms to the real flight sound field in spatiotemporal characteristics, significantly improving the realism and noise cancellation capability of acoustic noise cancellation.

[0083] Regarding step S210, in some embodiments of this disclosure, it may include, for example... Figure 3 Steps S2101 to S2104 are shown.

[0084] In step S2101, based on the velocity, a first amplitude is determined using a velocity correlation function. The velocity correlation function is the difference between 1 and a first negative exponential decay function. The first negative exponential decay function is an exponential function with a base of the natural constant and an exponent of the negative product of the decay coefficient and the velocity.

[0085] For example, the velocity correlation function f v The mathematical formula corresponding to (v) is: f v (v)=1-e -av Where e represents the natural constant, v represents velocity, and a represents the attenuation coefficient. The greater the velocity, the greater the attenuation coefficient. v (v) The closer it is to 1, the larger the first amplitude.

[0086] By using the difference between 1 and the first negative exponential decay function as the velocity correlation function to determine the first amplitude, the nonlinear characteristics of UAV noise energy changing with flight speed can be accurately reflected. This nonlinear characteristic is as follows: at low speeds or when hovering, rotor noise is relatively low, and the first amplitude is small. As speed increases, the noise rises rapidly and gradually approaches saturation. This velocity correlation function not only conforms to the physical law that aerodynamic noise is approximately exponential with speed, but also avoids excessive amplitude growth at high speeds, thus ensuring that the output intensity of the noise cancellation wave maintains high consistency with the actual flight noise across the entire speed range, improving the dynamic adaptability of acoustic noise cancellation.

[0087] In step S2102, a second amplitude is determined based on the altitude using a height correlation function. The height correlation function is a second negative exponential decay function. This second negative exponential decay function is an exponential function with a base of the natural constant and an exponent of the negative product of the altitude coefficient and the altitude.

[0088] For example, the highly correlated function f h The mathematical formula corresponding to (h) is: f h (h)=e -bh Where h represents altitude and b represents altitude coefficient. The higher the altitude, the higher the altitude coefficient. h The smaller (h) is, the smaller the second amplitude is.

[0089] By using a negative exponential decay function with a base of the natural constant as the altitude correlation function to determine the second amplitude, the natural attenuation effect of sound waves propagating in the atmosphere with increasing altitude is effectively simulated. This natural attenuation effect is as follows: at low altitudes, sound energy is stronger due to ground reflection and a shorter propagation path, resulting in a second amplitude close to 1. As flight altitude increases, sound pressure rapidly attenuates due to diffusion losses and air absorption, causing the second amplitude to decrease exponentially. This altitude correlation function accurately characterizes the impact of altitude on sound radiation intensity, enabling noise-canceling waves to match the energy levels of the real sound field at different altitudes, thus enhancing environmental integration capabilities.

[0090] In step S2103, based on the elevation angle, the third amplitude is determined using a first elevation angle correlation function. The first elevation angle correlation function is a piecewise function. When the elevation angle is within the allowable range, the third amplitude output by the first elevation angle correlation function is 1. When the elevation angle is outside the allowable range, the third amplitude output by the first elevation angle correlation function increases linearly from 1 as the distance between the elevation angle and the boundary of the allowable range increases, and stops increasing and remains constant when it reaches a critical value.

[0091] For example, the first upward angle correlation function f θ The mathematical formula corresponding to (θ) is: Where θ represents the elevation angle; k represents the linear coefficient; θ1 and θ2 both represent the boundaries within the allowable range of the elevation angle, where θ1 represents the minimum value within the allowable range of the elevation angle, and θ2 represents the maximum value within the allowable range of the elevation angle. For example, k can be 0.01, meaning that for every 1° deviation of the drone from its maximum elevation angle, the third amplitude increases by 0.01 from 1. For example, θ1 and θ2 can be -15° and 15° respectively, meaning that when the drone's elevation angle varies within the range of [-15, 15°], the corresponding third amplitude is always 1.

[0092] In addition, to avoid overcompensation leading to abnormal sound field, this embodiment also sets a threshold value. The threshold value is used to limit the maximum value of the third amplitude. For example, the threshold value can be 1.3 (considering that the maximum increase in the pitch angle does not exceed 30%, which can cover most flight scenarios), indicating that when the value of the third amplitude calculated by the first pitch angle correlation function is greater than 1.3, the third amplitude is set to 1.3.

[0093] The third amplitude is determined by a piecewise first pitch angle correlation function. This ensures that no additional amplification of noise radiation intensity is applied under normal flight attitudes (where the pitch angle is within the allowable range, such as level flight, slight climb, or dive) (the third amplitude is 1). At the same time, when extreme attitudes (such as steep climbs or dives) exceed the allowable range, the amplitude is linearly increased according to the degree of deviation, and then limited after reaching a critical value to avoid overcompensation.

[0094] In step S2104, the first amplitude, the second amplitude, and the third amplitude are weighted and summed to obtain the amplitude adjustment component.

[0095] As one possible implementation, the weighted summation calculation process is as follows: the first amplitude, the second amplitude, and the third amplitude are each multiplied by their corresponding weights, and then the products of the three multiplications are added together. The weights corresponding to the first amplitude, the second amplitude, and the third amplitude can be set according to the specific application scenario, and are not limited here.

[0096] Through steps S2101 to S2104, the output volume of the noise cancellation wave can be dynamically, continuously, and realistically matched to the actual perceptible noise intensity in the external environment under the current flight conditions. This significantly improves the acoustic noise cancellation capability and environmental adaptability of the UAV in complex mission scenarios.

[0097] Regarding step S220, in some embodiments of this disclosure, it includes: determining the phase adjustment component based on the elevation angle using a second elevation angle correlation function. The second elevation angle correlation function is the sum of the base phase value and the elevation angle compensation phase function. The elevation angle compensation phase function is a linear function that increases with the magnitude of the elevation angle.

[0098] For example, the second elevation view correlation function g θ The mathematical formula corresponding to (θ) is: g θ (θ)=φ0+Δφ(θ) Where φ0 represents the reference phase offset, which indicates the inherent phase delay of the sound wave propagating from the sound source to the target listening area under a reference flight attitude (e.g., an elevation angle of 0°, i.e., level flight), measured in radians. Δφ(θ) represents the elevation angle compensation phase function, a linear function of the elevation angle, used to compensate for the phase offset caused by the additional propagation path difference due to changes in flight attitude. The mathematical formula for the elevation angle compensation phase function is: Δφ(θ) = k θ θ, k θ This represents the phase compensation coefficient.

[0099] By employing a second elevation angle correlation function, the impact of flight attitude changes on the sound wave propagation path length can be effectively reflected. Specifically, when the UAV's elevation angle increases (e.g., during climb), the rotor sound source rises relative to the ground listening point, increasing the sound wave propagation distance and causing phase lag. Conversely, during dive, the propagation distance shortens, and the phase advances. This linear compensation mechanism can adjust the phase offset in real time according to the elevation angle, ensuring that the temporal characteristics of the denoised noise wave in the target area remain consistent with the actual flight noise. This avoids identifiable transient distortion or an "artificial" feel caused by phase mismatch, thereby significantly improving the temporal naturalness and noise reduction capability of the acoustic noise reduction signal.

[0100] Figure 4 A schematic diagram illustrating the overall flow of a drone noise cancellation signal generation method according to another embodiment of this disclosure is shown. Figure 4 The method shown includes steps S310 to S380. Steps S310-S360 respectively correspond to... Figure 1 Steps S110-S160 of the embodiment are detailed below. Figure 1 The relevant descriptions of the embodiments are not repeated here.

[0101] In step S370, the first environmental noise signal is subjected to first time compensation and first amplitude compensation to obtain the second environmental noise signal.

[0102] As one possible implementation method, the formula for calculating the second environmental noise signal is expressed as follows: N m (t)=α1 N env (t+τ1) Where, N m (t) represents the second environmental noise signal at time t; α1 represents the first amplitude compensation coefficient; τ1 represents the first time compensation coefficient, corresponding to the total signal delay; N env (t+τ1) represents the first environmental noise signal at time t+τ1.

[0103] In this implementation, by performing first-time compensation, the system delay generated throughout the entire process of environmental noise signal acquisition, processing, and acoustic playback can be aligned. This ensures that the played noise-cancelled ambient sound is synchronized in time with the current real background sound in the target listening area. It avoids sound field misalignment, echoes, or artificial artifacts caused by playback lag or lead, thereby improving the naturalness and continuity of acoustic noise cancellation. First-amplitude compensation corrects volume deviations caused by factors such as microphone sensitivity, speaker output efficiency, propagation attenuation, and installation location. This matches the output ambient noise sound pressure level with the actual background sound. It prevents residual noise from being ineffectively masked after cancellation due to insufficient volume, and also avoids sounding abrupt or revealing the drone's location due to excessive volume.

[0104] In step S380, the reverse acoustic wave signal is added to the second environmental noise signal, and the addition result is multiplied by the adjustment amount obtained based on the flight state adjustment function to obtain the noise-canceling wave signal.

[0105] In steps S310 to S380, both first time compensation and first amplitude compensation are introduced simultaneously, ensuring that the second environmental noise signal is highly consistent with the real environment in both the timing and energy dimensions. This significantly enhances the realism and noise reduction capability of the noise-cancelling wave, providing technical support for achieving "acoustic integration" rather than "simple playback".

[0106] Figure 5 A schematic flowchart illustrating the overall process of a UAV noise cancellation signal generation method according to yet another embodiment of this disclosure is shown, applicable to cooperative acoustic noise cancellation in multi-UAV formations. Figure 5 The method shown includes steps S410 to S430.

[0107] In step S410, a first ambient noise signal is received.

[0108] The first environmental noise signal can be any drone in a multi-drone formation based on... Figure 1 The first environmental noise signal obtained in the illustrated embodiment is extracted from an audio signal acquired by an audio acquisition device mounted on the UAV, based on the second characteristic frequency information corresponding to the environmental noise. For details, please refer to [link / reference]. Figure 1 The relevant descriptions of the embodiments are not repeated here.

[0109] In step S420, the first ambient noise signal undergoes second time compensation and second amplitude compensation to obtain the target sound field signal. The formula for calculating the target sound field signal in this step is similar to the formula in step S370, only the values ​​of the compensation parameters are different. By performing second time compensation and second amplitude compensation on the first ambient noise signal, the obtained target sound field signal can accurately match the spatiotemporal characteristics of the external real sound field of the UAV in the current flight state in terms of timing and sound pressure level.

[0110] In step S430, the target sound field signal is distributed to each UAV according to the weight of each UAV, and the noise cancellation signal of each UAV is obtained.

[0111] As one possible implementation, the weight of each drone is determined based on its position, altitude, speed, etc. For example, a drone formation may contain three drones, with drones A, B, and C having weights of 0.3, 0.5, and 0.2 respectively (the sum of all drone weights is 1). Then, 30% of the target sound field signal is allocated to drone A as its noise-canceling signal. 50% of the target sound field signal is allocated to drone B as its noise-canceling signal. And 20% of the target sound field signal is allocated to drone C as its noise-canceling signal.

[0112] Through steps S410 to S430, different weights are assigned to each UAV based on its role, position, and acoustic capabilities in the formation, and the target sound field signal is distributed to each UAV in a differentiated manner, thereby achieving intelligent acoustic noise reduction through multi-UAV collaboration.

[0113] As a further implementation, after obtaining the noise-cancelled signal, the method further includes: outputting the noise-cancelled signal through an audio playback device mounted on a drone, wherein the audio playback device employs a speaker array. By using a speaker array as the audio playback device, beamforming and multi-channel collaborative control can be used to achieve directional radiation, frequency response optimization, and precise phase control of the noise-cancelled signal, significantly improving the realism, spatial adaptability, and system robustness of acoustic noise cancellation.

[0114] According to any of the above embodiments, this disclosure also provides a drone noise cancellation signal generation device 500. Figure 6 This is a schematic block diagram of a drone noise cancellation signal generation device 500 according to one embodiment of this disclosure. Figure 6As shown, the UAV noise cancellation signal generation device 500 includes an audio signal receiving module 510, a spectrum analysis module 520, a rotor noise signal extraction module 530, an adaptive filtering prediction module 540, a reverse acoustic wave signal generation module 550, an environmental noise signal extraction module 560, and a noise cancellation signal generation module 570. The audio signal receiving module 510 receives audio signals collected by an audio acquisition device mounted on the UAV, which contain rotor noise and environmental noise. The spectrum analysis module 520 performs spectrum analysis on the audio signals to obtain first characteristic frequency information corresponding to rotor noise and second characteristic frequency information corresponding to environmental noise. The rotor noise signal extraction module 530 extracts the rotor noise signal from the audio signals based on the first characteristic frequency information to obtain a first rotor noise signal. The adaptive filtering prediction module 540 predicts the second rotor noise signal based on the first rotor noise signal using an adaptive filtering algorithm. The reverse acoustic wave signal generation module 550 generates a reverse acoustic wave signal with an opposite phase to the second rotor noise signal. The environmental noise signal extraction module 560 extracts the signal corresponding to the environmental noise from the audio signal based on the second characteristic frequency information to obtain the first environmental noise signal. The noise cancellation signal generation module 570 adds the reverse sound wave signal to the first environmental noise signal and multiplies the addition result by an adjustment amount obtained based on the flight state adjustment function to obtain the noise cancellation signal. The flight state adjustment function obtains the adjustment amount based on the UAV's state parameters.

[0115] According to further embodiments of this disclosure, an electronic device is also provided. Figure 7This diagram illustrates a schematic block diagram of an electronic device employing a processor-based hardware implementation according to an embodiment of the present disclosure. The hardware structure of the electronic device of the present disclosure can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application and overall design constraints of the hardware. Bus 1100 connects various circuits including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400 such as peripheral devices, voltage regulators, power management circuits, external antennas, etc. Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, only one connecting line is used in this figure, but this does not imply that there is only one bus or one type of bus. Memory 1300 stores a computer program, and when processor 1200 executes the computer program, processor 1200 is able to perform the following processes. The system receives audio signals collected by an audio acquisition device mounted on the UAV. These audio signals contain rotor noise and ambient noise. Spectral analysis is performed on the audio signals to obtain first characteristic frequency information corresponding to the rotor noise and second characteristic frequency information corresponding to the ambient noise. Based on the first characteristic frequency information, the rotor noise signal is extracted from the audio signals to obtain a first rotor noise signal. Based on the first rotor noise signal, an adaptive filtering algorithm is used to predict and obtain a second rotor noise signal. Based on the second rotor noise signal, an inverse acoustic wave signal with opposite phase to the second rotor noise signal is generated. Based on the second characteristic frequency information, the ambient noise signal is extracted from the audio signals to obtain a first ambient noise signal. The inverse acoustic wave signal is added to the first ambient noise signal, and the result is multiplied by an adjustment amount obtained based on a flight state adjustment function to obtain a noise-canceling signal. The flight state adjustment function is based on the UAV's state parameters.

[0116] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0117] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0118] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0119] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0124] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for generating noise cancellation wave signals for unmanned aerial vehicles (UAVs), characterized in that, include: Receive audio signals collected by an audio acquisition device mounted on a drone, the audio signals including rotor noise and ambient noise; Spectral analysis is performed on the audio signal to obtain the first characteristic frequency information corresponding to the rotor noise and the second characteristic frequency information corresponding to the environmental noise; Based on the first characteristic frequency information, the signal corresponding to the rotor noise is extracted from the audio signal to obtain the first rotor noise signal; Based on the first rotor noise signal, the second rotor noise signal is obtained by prediction using an adaptive filtering algorithm; Based on the second rotor noise signal, an inverse acoustic wave signal with the opposite phase to the second rotor noise signal is generated; Based on the second characteristic frequency information, the signal corresponding to the environmental noise is extracted from the audio signal to obtain a first environmental noise signal; and The reverse acoustic wave signal is added to the first ambient noise signal, and the result is multiplied by an adjustment amount obtained based on the flight state adjustment function to obtain the noise-canceling wave signal. The flight state adjustment function obtains the adjustment amount based on the state parameters of the UAV, including: determining the amplitude adjustment component based on the speed, altitude, and elevation angle in the state parameters; determining the phase adjustment component based on the elevation angle in the state parameters; and using the amplitude adjustment component and the phase adjustment component as the adjustment amount.

2. The method as described in claim 1, characterized in that, The audio acquisition device is a microphone array, which includes at least one first microphone and at least one second microphone. The first microphone is located on the surface of the drone body near the rotor, and the second microphone is located on the surface of the drone body away from the rotor. The sound wave receiving surface of the second microphone faces outward from the drone.

3. The method as described in claim 2, characterized in that, Before receiving the audio signal collected by the audio acquisition device mounted on the drone, the process also includes: The signals collected by the at least one first microphone and the at least one second microphone are fused to obtain a fused signal; and The fused signal is used as the audio signal acquired by the audio acquisition device.

4. The method as described in claim 1, characterized in that, After obtaining the first environmental noise signal, the process further includes: The first ambient noise signal is subjected to first time compensation and first amplitude compensation to obtain a second ambient noise signal; and The reverse acoustic wave signal is added to the second environmental noise signal.

5. The method as described in claim 1, characterized in that, Based on the speed, altitude, and elevation angle in the state parameters, the amplitude adjustment component is determined, including: Based on the speed, a first amplitude is determined by a speed correlation function, which is the difference between 1 and a first negative exponential decay function, which is an exponential function with the natural constant as the base and the negative value of the product of the decay coefficient and the speed as the exponent. Based on the height, a second amplitude is determined through a height correlation function, which is a second negative exponential decay function. The second negative exponential decay function is an exponential function with the natural constant as the base and the negative value of the product of the height coefficient and the height as the exponent. Based on the aforementioned elevation angle, a third amplitude is determined using a first elevation angle correlation function. This first elevation angle correlation function is a piecewise function. When the elevation angle is within the allowable range, the third amplitude output by the first elevation angle correlation function is 1. When the elevation angle is outside the allowable range, the third amplitude output by the first elevation angle correlation function increases linearly from 1 as the distance between the elevation angle and the boundary of the allowable range increases, and stops increasing and remains constant when it reaches a critical value. The first amplitude, the second amplitude, and the third amplitude are weighted and summed to obtain the amplitude adjustment component.

6. The method as described in claim 1, characterized in that, Based on the elevation angle in the state parameters, determine the phase adjustment component, including: Based on the elevation angle, the phase adjustment component is determined by the second elevation angle correlation function. The second elevation angle correlation function is the sum of the basic phase value and the elevation angle compensation phase function. The elevation angle compensation phase function is a linear function that increases with the magnitude of the elevation angle.

7. The method as described in claim 1, characterized in that, In the case of multiple drones in the same area, the noise cancellation signal generation process for each drone includes: Receive the first ambient noise signal; The first ambient noise signal is subjected to second time compensation and second amplitude compensation to obtain the target sound field signal; and Based on the weight assigned to each UAV, the target sound field signal is distributed to each UAV to obtain the noise cancellation signal of each UAV.

8. An electronic device, characterized in that, include: The memory stores execution instructions; as well as A processor that executes the execution instructions stored in the memory, causing the processor to perform the UAV noise cancellation signal generation method according to any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for generating UAV noise cancellation signals according to any one of claims 1 to 7.

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