Traffic noise reduction methods and road noise barrier systems

Abstract

This invention discloses a method for reducing traffic noise and a road noise barrier system. The method includes: acquiring speed information of target vehicles on a target road; generating a target control command set based on the speed information, wherein the target control command set is used to control the noise reduction mode of the road noise barrier system and adjust the operating parameters of the adaptive filter, wherein the noise reduction mode includes at least one of the following: engine order noise tracking mode, tire noise broadband suppression mode, and Doppler effect compensation mode, and the operating parameters include at least one of the following: filter step size and filter length. This invention solves the technical problem of poor performance in existing traffic noise reduction methods.

Description

Technical Field

[0001] This invention relates to the field of traffic noise control, and more specifically, to a method for reducing traffic noise and a road noise barrier system. Background Technology

[0002] Traffic noise mainly refers to the sound generated by transportation vehicles such as motor vehicles, railway locomotives, and urban rail transit vehicles during operation, which disturbs the surrounding living environment. It primarily includes tire noise from the interaction between tires and the ground, engine noise from cars or locomotives, and the noise from transmission systems such as brakes. With the increasing severity of traffic noise pollution, sound barriers are widely used as a primary noise reduction method in roads, railways, and other scenarios. Current technologies for controlling traffic noise generally include active and passive control. Passive control mainly uses sound-absorbing materials and sound-insulating structures to reduce noise propagation by reflecting or absorbing sound energy. Active control typically deploys speaker arrays and error microphones on one side of the barrier, using adaptive algorithms (such as FxLMS) to generate anti-phase sound waves to cancel noise. However, the noise reduction effect of the above passive control schemes is poor. Active control schemes do not consider the frequency shift caused by vehicle movement, resulting in a mismatch between the cancellation signal and the noise frequency band. The noise sources and control methods differ at different vehicle speeds, and existing crude noise reduction methods cannot meet the needs. Furthermore, single-sided control can lead to mutual interference between the actuator signals on both sides, potentially forming a positive feedback loop, causing system instability and resulting in poor traffic noise reduction.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method for reducing traffic noise and a road noise barrier system, in order to at least solve the technical problem that existing traffic noise reduction methods are ineffective.

[0005] According to one aspect of the present invention, a method for traffic noise reduction is provided, comprising: acquiring speed information of a target vehicle on a target road; generating a target control instruction set based on the speed information, the target control instruction set being used to control the noise reduction mode of a road noise barrier system and adjust the operating parameters of an adaptive filter, wherein the noise reduction mode includes at least one of the following: engine order noise tracking mode, tire noise broadband suppression mode, and Doppler effect compensation mode, and the operating parameters include at least one of the following: filter step size and filter length.

[0006] Furthermore, the target control instruction set is generated based on speed information, including: determining the instantaneous speed of the target vehicle based on speed information; and generating a first target control instruction in the target control instruction set in response to the instantaneous speed being less than or equal to a first speed. The first target control instruction is used to control the road sound barrier system to adopt the engine order noise tracking mode, control the filter step size to be the first step length, and control the filter length to be the first length, wherein the first step length is determined by the instantaneous speed.

[0007] Furthermore, the generation of the target control instruction set based on speed information also includes: in response to the instantaneous speed being greater than or equal to the second speed, generating a second target control instruction in the target control instruction set, wherein the second target control instruction is used to control the road sound barrier system to adopt the Doppler effect compensation mode, control the filter step size to be the second step size, and control the filter length to be the second length. The second step size is determined by the instantaneous speed, the second step size is less than the first step size, the second length is greater than the first length, and the second speed is greater than the first speed.

[0008] Furthermore, the generation of the target control instruction set based on speed information also includes: in response to the instantaneous speed being greater than the first speed and less than the second speed, generating a third target control instruction in the target control instruction set, wherein the second target control instruction is used to control the road sound barrier system to adopt the tire noise broadband suppression mode, control the filter step size to be a third step size, and control the filter length to be a third length. The third step size is determined by the instantaneous speed, the third step size is less than the first step size and greater than the second step size, and the third length is greater than the first length and less than the second length.

[0009] Furthermore, acquiring the speed information of the target vehicle on the target road includes: acquiring first speed information, which is obtained by radar speed measurement of the target vehicle; acquiring second speed information, which is obtained by sound source localization of the target vehicle; and determining the speed information based on the first speed information and the second speed information.

[0010] Furthermore, after generating the target control instruction set based on speed information, the method also includes: acquiring sound source noise and wind speed information, wherein the sound source noise is collected by a reference microphone and the wind speed information is collected by a wind speed sensor from points within the target road; generating a cancellation signal based on the sound source noise and wind speed information; determining an actuator drive signal based on the cancellation signal; and controlling the actuator to output a reverse sound wave based on the actuator drive signal, wherein the reverse sound wave is used to cancel at least part of the sound source noise.

[0011] Furthermore, generating a cancellation signal based on sound source noise and wind speed information includes: determining wind noise power spectrum information based on sound source noise and wind speed information, which is used to determine the power and energy distribution of wind noise at various frequencies; determining traffic noise power spectrum information based on sound source noise and wind speed information, which is used to determine the energy distribution of noise generated by vehicles at different frequencies; determining a filter signal based on wind noise power spectrum information and traffic noise power spectrum information; performing blind source separation on the filter signal to obtain wind noise components and traffic noise components; and inputting the wind noise components into an adaptive filter to generate a cancellation signal.

[0012] Furthermore, the actuators are dual actuators distributed on both sides of the target road. The actuators output reverse sound waves based on the actuator drive signals, including: determining the control signals for the left and right actuators based on the actuator drive signals; obtaining a coupling matrix, which is obtained through actual measurements of the road sound barrier system and is used to characterize the mutual coupling relationship and system response characteristics between the left and right actuator control signals; determining the optimization objective and constraints based on the left and right actuator control signals and the coupling matrix; and solving the optimization objective using a gradient descent algorithm to obtain the decoupled left and right actuator control signals.

[0013] According to another aspect of the present invention, a road noise barrier system is also provided, comprising: a noise acquisition module for acquiring noise from a sound source; a calculation module including a wind noise reduction unit for separating wind noise components based on the noise from the sound source; an adaptive algorithm unit including an adaptive filter for generating a cancellation signal based on the noise from the sound source; a decoupling optimizer unit for decoupling actuator control signals on both sides; and an actuator for receiving the decoupled actuator control signals and generating a reverse sound wave based on the decoupled actuator control signals.

[0014] Furthermore, the road noise barrier system includes: a noise barrier structure, which is set on both sides of the target road. The noise barrier structure is equipped with a wind speed sensor, an actuator array, an error microphone array, and a reference microphone array. The reference microphone array is used to collect noise from the sound source, and the error microphone array is used to collect residual noise signals. The error microphone array and the reference microphone array are respectively set on both sides of the thickness direction of the noise barrier structure.

[0015] Furthermore, the sound barrier structure is also provided with a gradient impedance structure, which includes a micro-perforated plate layer, a gradient density sound-absorbing cotton layer and a Helmholtz resonant cavity layer arranged along the thickness direction. The gradient density sound-absorbing cotton layer is located between the micro-perforated plate layer and the Helmholtz resonant cavity layer, and the density of the gradient density sound-absorbing cotton layer increases along the thickness direction of the sound barrier structure.

[0016] Furthermore, the sound barrier structure includes a guide airfoil, the tilt angle of which is A, and the surface curvature radius of which is R, 60mm≥R≥40mm.

[0017] In this embodiment of the invention, the noise reduction mode of the road noise barrier system is controlled by utilizing speed information and the working parameters of the adaptive filter are adjusted. By adaptively adjusting the noise reduction at different vehicle speeds, the optimal noise reduction effect is ensured at different target vehicle speeds, and the frequency shift caused by the movement of target vehicles is compensated. This solves the technical problem of poor performance of existing traffic noise reduction methods. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a schematic flowchart of an optional traffic noise reduction method according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic flowchart of an optional traffic noise reduction method according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic flowchart of an optional traffic noise reduction method according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic flowchart of an optional traffic noise reduction method according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of a module of an optional traffic noise reduction system according to an embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram of an optional sound barrier structure according to an embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram of an optional sound barrier structure according to an embodiment of the present invention;

[0026] Figure 8 This is a schematic diagram of an optional Helmholtz resonant cavity according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] According to an embodiment of the present invention, a method embodiment for traffic noise reduction is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] Traffic noise currently exhibits strong randomness in both space and time, and its impact area is relatively large with a long duration of harm. Traffic noise control can be achieved in three stages: suppressing the noise source, suppressing the propagation of noise, and controlling it at the noise receiving point. In principle, this can be divided into active noise reduction and passive noise reduction technologies.

[0031] Existing technologies are mainly divided into two categories: passive sound barriers and active noise reduction systems. Their implementation schemes and limitations are as follows:

[0032] 1. Passive sound barriers primarily utilize sound-absorbing materials (such as porous fiberboard and micro-perforated panels) and sound-insulating structures (such as concrete slabs and composite sandwich panels) to reduce noise propagation by reflecting or absorbing sound energy. Traditional sound-absorbing materials have insufficient absorption rate for low-frequency noise (<500Hz) (typically <30%), making it difficult to suppress low-frequency components of tire / engine noise in traffic noise. Vertical rigid barriers reflect sound waves to the opposite side, forming standing waves or sound focusing phenomena, which are particularly pronounced in double-sided barrier scenarios. They cannot adapt to changes in the location and speed of dynamic sound sources (such as moving vehicles), and the noise reduction effect rapidly diminishes with distance.

[0033] 2. Single-sided active noise cancellation system: Deploy a speaker array and error microphone on one side of the barrier, and use an adaptive algorithm (such as FxLMS) to generate anti-phase sound waves to cancel noise.

[0034] Single-sided control can only cover the near-field area (typically <5m), making it difficult to protect wide areas and resulting in poor performance at the far end of multi-lane roads. Single-sided control does not consider the superposition effect of reflected sound from the opposite side barrier, leading to phase mismatch in the cancellation signal. Single-sided control is susceptible to environmental interference such as wind noise and vibration, which can easily lead to reference signal contamination and poor algorithm stability.

[0035] 3. Existing technologies also include some methods for reducing wind noise. Windproof hardware mainly involves adding windproof nets or waveguides to microphones to physically filter wind noise. Frequency domain filtering mainly uses high-pass filters to suppress low-frequency wind noise components.

[0036] Windbreak nets can only suppress high-frequency wind noise (>1kHz) and are ineffective against mid- and low-frequency wind noise (100-500Hz). Frequency domain filtering may inadvertently cut off the effective frequency band of traffic noise (such as engine base frequency), causing signal distortion. At the same time, no joint model of wind noise and traffic noise has been established, making it unable to cope with time-varying wind fields and thus exhibiting poor dynamic adaptability.

[0037] 4. Positioning via beamforming (e.g., Delay-and-Sum) or TDOA based on the assumption of a fixed sound source. This method has the following limitations: it does not compensate for frequency shifts caused by vehicle movement, fails to compensate for signal-noise frequency band mismatch, ignores the Doppler effect, and suffers from high computational delays (>50ms) in traditional positioning algorithms, making it unable to track high-speed vehicles (e.g., a vehicle moving at 120km / h takes 1.67m / 50ms). Furthermore, in scenarios with multiple sound sources superimposed in dense traffic, it cannot resolve independent noise components, leading to multi-target separation failure.

[0038] 5. A distributed controller can be used to independently drive both actuators, or a central controller can be used for unified scheduling. This approach has the following limitations: mutual interference between the actuator signals may create a positive feedback loop, leading to system instability. The global optimization algorithm has high computational complexity (O(N...). 3It is difficult to process large-scale arrays in real time. It lacks integrated wind speed, temperature, and humidity sensors, and the sound field model is static, making it unable to correct wind noise distortion.

[0039] Figure 1 This is a flowchart illustrating a method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0040] Step S202: Obtain the speed information of the target vehicle on the target road.

[0041] Step S204: Generate a target control instruction set based on speed information. The target control instruction set is used to control the noise reduction mode of the road noise barrier system and adjust the working parameters of the adaptive filter. The noise reduction mode includes at least one of the following: engine order noise tracking mode, tire noise broadband suppression mode, and Doppler effect compensation mode. The working parameters include at least one of the following: filter step size and filter length.

[0042] Through the above steps, by using speed information to control the noise reduction mode of the road noise barrier system and adjusting the working parameters of the adaptive filter, the noise reduction is adaptively adjusted at different vehicle speeds. This achieves the goal of ensuring optimal noise reduction effect and compensating for frequency shifts caused by target vehicle movement at different target vehicle speeds, thereby solving the technical problem of poor performance of existing traffic noise reduction methods.

[0043] Activating engine order noise tracking refers to the technology used in active traffic noise reduction systems to precisely track and cancel the order components of engine noise. Engine order noise refers to the spectral components of noise generated during engine operation that are related to engine speed. These components are typically represented as a series of fixed frequencies on a spectrum graph, which are related to multiples of engine speed, i.e., order frequencies. For example, the engine fundamental frequency (usually directly related to engine speed) and its 2nd and 3rd harmonic frequencies can be considered as order noise. In specific implementation, noise tracking mainly relies on the following key steps: 1. Engine speed measurement: The engine speed is acquired in real time using a speed sensor or rotation speed sensor installed on the vehicle. This speed information is the basis for calculating the order frequencies. 2. FFT spectrum analysis: FFT (Fast Fourier Transform) is a fast algorithm used to convert time-domain signals into a frequency-domain representation, i.e., the signal's spectrum. By using high-resolution FFT, the system can more accurately identify and separate the various order components in engine noise. Here, FFT resolution refers to the frequency interval of the spectrum analysis; a 1Hz FFT resolution means that the system can distinguish signal components with adjacent frequencies of 1Hz. 3. Order Frequency Calculation: Using real-time measured engine speed, the specific values ​​of each order frequency are calculated. For example, if the engine speed is 1500 RPM (revolutions per minute) and the fundamental frequency is 1500 / 60 = 25 Hz (assuming 60 seconds per revolution), then the 2nd and 3rd order frequencies are 50 Hz, 75 Hz, etc., respectively. 4. Adaptive Filter Tracking: After obtaining the order frequencies, the system uses an adaptive filter (such as the FxLMS or NLMS algorithm) to track these frequency components. The filter adjusts its internal coefficients based on the error signal (i.e., the difference between the actual noise and the desired noise) to generate a cancellation signal corresponding to the engine order noise. Since the engine speed changes continuously during vehicle operation, the filter must be able to adapt quickly to these changes. 5. Noise Cancellation: The generated cancellation signal is emitted through an actuator (such as a speaker), interfering spatially with the original engine order noise, thereby weakening or canceling noise at these specific frequencies. Wideband tire noise suppression employs a sub-band FxLMS (Filtered-x Least Mean Squares) algorithm for active noise control targeting tire rolling noise. Wideband tire noise suppression means that the system must not only process noise at specific frequencies, but also handle the components of tire noise over a wide frequency range, as tire noise typically has a broad spectrum, including multiple components from low to high frequencies.

[0044] The core idea of ​​the sub-band FxLMS algorithm is to decompose the signal into multiple sub-bands in the frequency domain and then apply adaptive filtering techniques independently to each sub-band. This has the following advantages compared to directly applying the FxLMS algorithm in the time domain or full frequency domain: 1. Reduced computational complexity: Full-frequency domain FxLMS algorithms typically require long filter coefficients, increasing computational burden. Sub-band FxLMS decomposes the signal into multiple frequency bands, each of which can use shorter filters, thus reducing computational complexity and system latency. 2. Improved control accuracy: The spectrum of tire noise changes with vehicle speed, especially at high speeds, where the Doppler effect and wind noise make the noise spectrum more complex. The sub-band FxLMS algorithm can more accurately track and control the noise within each sub-band, improving control performance.

[0045] The specific implementation steps of the subband FxLMS algorithm are as follows:

[0046] 1. Subband Decomposition: First, the signal containing tire noise acquired from the microphone is passed through a subband decomposition filter bank, decomposing it into sub-signals of multiple frequency bands. The width and number of each subband can be determined based on the characteristics of the noise spectrum and computational resources.

[0047] 2. Adaptive Filtering: Within each sub-band, the FxLMS algorithm is applied independently to generate a cancellation signal. The FxLMS algorithm adjusts the coefficients of the adaptive filter based on the signal feedback from the error microphone, generating a signal that is out of phase with the tire noise. In this way, the tire noise component within each sub-band is specifically canceled.

[0048] 3. Subband Synthesis: The canceled signals generated in each subband are recombined into a full-frequency signal, which is then transmitted through an actuator (such as a loudspeaker). These canceled signals spatially interfere with the original tire noise, thereby suppressing tire noise.

[0049] 4. Dynamic Parameter Adjustment: In medium-speed mode, the system adjusts the parameters of the sub-band FxLMS algorithm, such as the step size μ and filter length, according to the real-time vehicle speed. This helps the system adapt to the tire noise characteristics and spectral distribution at different vehicle speeds, improving the effectiveness and stability of noise control. Employing sub-band FxLMS for wideband tire noise suppression, through fine control in the frequency domain, significantly improves the noise suppression capability of the active traffic noise reduction system under medium-speed driving conditions, especially exhibiting better performance and adaptability when dealing with wideband complex signals like tire noise.

[0050] In the Doppler effect compensation mode, the method includes: First, calculating the Doppler frequency shift Δf based on the vehicle's instantaneous speed v(t) and the sound wave velocity c (typically 343 m / s under standard atmospheric conditions); then, in the FxLMS algorithm, updating the frequency response of an adaptive filter in real time to compensate for the Doppler frequency shift. This typically involves adjusting the filter coefficients to match its frequency response curve with the compensated noise spectrum. Finally, generating a compensation signal: through dynamic adjustment of the filter and the Doppler frequency shift, the system generates a cancellation signal that matches the frequency changes of the moving sound source.

[0051] Further, in step S204, a target control command set is generated based on the speed information, including:

[0052] Determine the instantaneous speed of the target vehicle based on speed information;

[0053] In response to an instantaneous speed being less than or equal to a first speed, a first target control command is generated in the target control command set. The first target control command is used to control the road sound barrier system to adopt the engine order noise tracking mode, control the filter step size to be the first step length, and control the filter length to be the first length. The first step length is determined by the instantaneous speed.

[0054] Furthermore, in step S204, generating a target control command set based on the speed information also includes:

[0055] In response to an instantaneous speed being greater than or equal to a second speed, a second target control command is generated in the target control command set. The second target control command is used to control the road sound barrier system to adopt the Doppler effect compensation mode, control the filter step size to be the second step size, and control the filter length to be the second length. The second step size is determined by the instantaneous speed, the second step size is less than the first step size, and the second length is greater than the first length.

[0056] Furthermore, in step S204, generating a target control command set based on the speed information also includes:

[0057] In response to an instantaneous speed greater than the first speed and less than the second speed, a third target control command is generated in the target control command set. The second target control command is used to control the road noise barrier system to adopt the tire noise broadband suppression mode, control the filter step size to be the third step size, and control the filter length to be the third length. The third step size is determined by the instantaneous speed. The third step size is less than the first step size and greater than the second step size, and the third length is greater than the first length and less than the second length.

[0058] like Figure 2 The diagram shows the multi-speed adaptive control mode switching process. For distance, the first speed is 30 km / h.

[0059] The second speed is 80 km / h. Speed ​​range classifications include:

[0060] Low speed mode (v<30km / h): Activate engine order noise tracking (FFT resolution 1Hz).

[0061] Medium speed mode (30km / h≤v≤80km / h): Enables wideband tire noise suppression (sub-band FxLMS).

[0062] High-speed mode (v>80km / h): Triggers Doppler compensation and predictive beamforming.

[0063] Optionally, in addition to noise reduction mode control, the step size and filter length of the adaptive algorithm control need to be controlled, and its dynamic parameter adjustment includes:

[0064] The step size μ is adjusted according to the formula:

[0065]

[0066] Filter length: 512 points in low-speed mode, 2048 points in high-speed mode. Step size (μ) typically refers to the update rate or learning rate of the adaptive filter. It is a crucial parameter in adaptive algorithms, determining the speed and stability of the algorithm's convergence to the optimal state. The step size directly affects how the algorithm adjusts its filter coefficients to better adapt to changes in the input signal.

[0067] For example, in adaptive algorithms for FIR (Finite Impulse Response) filters such as Least Mean Squares (LMS) or Filtered-x Least Mean Squares (FxLMS), the amount of filter coefficient update in each iteration depends on the current error signal, input signal, and step size. A smaller step size μ means a smaller step size for each update, resulting in slower but relatively stable convergence; a larger step size μ means a larger step size for each update, which may lead to faster convergence to the optimal state, but may also make the algorithm more prone to instability or overfitting.

[0068] In multi-speed adaptive control mode, the step size μ is adjusted to adapt to changes in noise characteristics at different speeds. In high-speed mode (e.g., vehicle speed exceeding 80 km / h), the location and characteristics of noise sources (e.g., vehicles) change more rapidly, thus requiring a smaller but sufficiently fast step size to ensure that the filter can track these changes in a timely manner while maintaining system stability.

[0069] Using the technical solution of this application, a multi-vehicle speed adaptive control strategy is proposed, which provides a frequency-space joint control mode (low speed / medium speed / high speed) based on radar / TDOA vehicle speed classification and Doppler frequency shift compensation mechanism and vehicle speed partitioning.

[0070] Doppler compensation algorithm:

[0071] (Compensation accuracy < 0.5 Hz).

[0072] Predictive beamforming delay compensation formula: (Prediction error < 0.1m).

[0073] Predictive beamforming is used to address noise generated by moving sound sources. In a traffic noise active noise reduction system with an adaptive dual-sided sound barrier against wind noise, in high-speed mode, the rapid vehicle movement causes the position of the sound source relative to the sound barrier and microphone array to constantly change. This change produces a Doppler frequency shift effect, meaning the sound source frequency is perceived as a changing frequency at the receiver. Furthermore, due to the rapid movement of the vehicle, traditional beamforming techniques may fail to track the sound source position in real time, resulting in poor noise reduction performance. The system needs to estimate the speed of the sound source in real time and calculate the shift of the sound source frequency relative to a stationary reference point based on the Doppler frequency shift formula. Then, the frequency of the anti-phase sound wave generated by the actuator array is adjusted to compensate for this frequency shift, ensuring that the anti-phase sound wave is precisely matched to the sound source noise in frequency, achieving the best noise reduction effect.

[0074] Using known sound source velocity and direction, along with sound source location information from the past few cycles, the possible location of the sound source within a future timeframe is predicted. The selection of the prediction time window needs to balance prediction accuracy and computational complexity, typically ranging from tens to hundreds of milliseconds. The predictive beamforming algorithm optimizes the signal phase and amplitude of the actuator array based on the predicted location, adjusting the beam direction in advance to ensure effective noise cancellation even if the sound source moves rapidly, once it reaches the predicted location.

[0075] The implementation of predictive beamforming may involve the following steps: radar speed measurement and TDOA sound source localization: First, the system uses radar and Time Difference of Arrival (TDOA) technology to accurately measure and locate high-speed moving sound sources. The filter length and algorithm step size are adjusted according to the vehicle speed to adapt to the computational requirements and stability in high-speed scenarios. Doppler frequency shift is calculated and applied to ensure consistency between the out-of-phase sound wave frequency and the sound source noise frequency. Based on the predicted sound source location, the beam direction is adjusted, the actuator signal phase and amplitude are optimized, and noise cancellation is performed in advance.

[0076] Further, in step S202, the speed information of the target vehicle on the target road is obtained, including:

[0077] Acquire the first speed information, which is obtained by radar speed measurement of the target vehicle;

[0078] The second speed information is obtained by locating the sound source of the target vehicle.

[0079] Speed ​​information is determined based on the first speed information and the second speed information.

[0080] That is, the input for vehicle speed detection includes radar speed measurement data and TDOA sound source localization results, and its output is the instantaneous speed of the vehicle v(t).

[0081] Combination Figure 2 As shown, in one specific embodiment, the method includes the following steps:

[0082] Step S1: Vehicle Speed ​​Detection. Input: Radar speed measurement data + TDOA sound source localization result. The radar speedometer provides instantaneous vehicle speed information by transmitting and receiving radar waves and measuring the echo time. TDOA (Time Difference of Arrival) sound source localization technology determines the location of the sound source by comparing the time difference of sound waves received by multiple microphones, thereby indirectly inferring the vehicle's speed and direction. Output: Instantaneous vehicle speed v(t). Combining radar speed measurement data and TDOA localization results, the system can monitor and accurately calculate the instantaneous speed of each vehicle in real time. This speed information will be used in subsequent multi-speed adaptive control strategies.

[0083] Step S2: Speed ​​Range Classification. The system automatically switches to different control modes based on the vehicle's instantaneous speed v(t): Low-speed mode (v < 30 km / h): In this mode, the system primarily focuses on tracking engine order noise, as engine noise is relatively significant at low speeds. Medium-speed mode (30 km / h ≤ v ≤ 80 km / h): As vehicle speed increases, tire rolling noise and wind resistance noise become the main noise sources, requiring the system to adjust to a wider-bandwidth noise suppression strategy. High-speed mode (v > 80 km / h): At high speeds, wind noise and the Doppler effect are significant, thus requiring the triggering of Doppler compensation and predictive beamforming technology to adapt to rapidly changing noise environments and sound source locations.

[0084] Step S3: Dynamic Parameter Adjustment. Step Size μ Adjustment: In adaptive filters (such as FxLMS), the step size μ is a key parameter that determines the speed and stability of filter coefficient updates. The formula μ = 0.01 / (1 + 0.1v) indicates that as the vehicle speed v increases, the step size μ gradually decreases. This is because the faster the vehicle speed, the faster the noise environment changes, requiring more careful adjustment of the filter coefficients to avoid overreaction and instability. For example, when the vehicle is traveling at a speed of 30 km / h, μ = 0.01 / (1 + 0.1 * 30) ≈ 0.0077; while when the vehicle speed increases to 120 km / h, μ = 0.01 / (1 + 0.1 * 120) ≈ 0.0008.

[0085] Step S4: Filter Length Adjustment. The length or order of the filter directly affects its processing capability, especially in frequency domain analysis and noise cancellation. In low-speed mode, a shorter filter length (e.g., 512 points) is sufficient to handle relatively simple and slowly changing noise spectra. In high-speed mode, the noise spectrum changes more complexly, and the Doppler effect is significant, requiring a longer filter length (e.g., 2048 points) to obtain finer frequency resolution and more accurate noise feature tracking.

[0086] Furthermore, after generating the target control instruction set based on the speed information, the method also includes:

[0087] The noise source and wind speed information are acquired. The noise source is collected by a reference microphone, and the wind speed information is collected by a wind speed sensor at points within the target road.

[0088] A cancellation signal is generated based on sound source noise and wind speed information;

[0089] The actuator drive signal is determined based on the cancellation signal;

[0090] The actuator outputs a reverse acoustic wave based on the actuator drive signal, and the reverse acoustic wave is used to cancel at least part of the sound source noise.

[0091] Furthermore, such as Figure 3 The flowchart of wind noise reduction signal processing is shown, which generates a cancellation signal based on sound source noise and wind speed information, including:

[0092] The wind noise power spectrum information is determined based on the noise source and wind speed information. The wind noise power spectrum information is used to determine the power and energy distribution of wind noise at various frequencies.

[0093] Traffic noise power spectrum information is determined based on sound source noise and wind speed information. This traffic noise power spectrum information is used to determine the energy distribution of noise generated by vehicles at different frequencies.

[0094] The signal input includes the raw signal from the reference microphone x(n) and the wind speed sensor data v(n), followed by Wiener filtering preprocessing: calculating the wind noise power spectrum P. wind (f) and traffic noise power spectrum P traffic (f).

[0095] The filtered signal is determined based on wind noise power spectrum information and traffic noise power spectrum information;

[0096] Generate filter function:

[0097]

[0098] And output the filtered signal x filtered (n).

[0099] Blind source separation is performed on the filtered signal to obtain wind noise and traffic noise components;

[0100] Blind Source Separation (BSS): Separating wind noise components using the FastICA algorithm. wind (n) and traffic noise component s traffic (n).

[0101] Blind source separation technology can help separate different noise sources, such as wind noise and traffic noise, from mixed microphone signals. For example, the FastICA algorithm can quickly analyze signals from multiple microphones, decomposing them into independent components, thereby distinguishing wind noise from car engine noise, tire rolling noise, and so on. Once these source signals are separated, different noise control strategies can be applied selectively, such as using specific filters to eliminate wind noise while preserving or processing traffic noise, achieving better noise reduction results.

[0102] The wind noise component is input into an adaptive filter to generate a cancellation signal.

[0103] s wind (n) Input the secondary channel of the FxLMS algorithm to generate the cancellation signal y wind (n).

[0104] The FxLMS (Filtrated-x Least Mean Squares) algorithm is a widely used adaptive noise control technique, particularly suitable for active noise control (ANC) systems, used to cancel or reduce unwanted noise signals. In traffic noise active noise reduction systems, the specific process of using the FxLMS algorithm to cancel wind noise is as follows:

[0105] First, a mixed signal s(n), including traffic noise and wind noise, is captured using a reference microphone array, and wind speed data v(n) is obtained from an environmental sensor. Then, the wind noise signal s"wind"(n) is separated using Wiener filtering, blind source separation (BSS), or other preprocessing techniques.

[0106] In the FxLMS system, there are two main channels: the primary path and the secondary path. The primary path processes the path directly from the noise source to the error microphone, while the secondary path simulates or directly measures the signal transmission path from the actuator to the error microphone.

[0107] In this scenario, the separated wind noise signal s"wind"(n) is input into the secondary channel of the FxLMS algorithm. The secondary channel typically includes a digital filter G(z), which simulates the actual signal path from the actuator to the error microphone in the physical environment, i.e., how the wind noise is transmitted to the error microphone.

[0108] The core of the FxLMS algorithm lies in generating a cancellation signal y"wind"(n) by adjusting the coefficients of an adaptive filter W(z), thereby canceling wind noise. This adaptive filter receives the input signal s"wind"(n) and converts it into a signal that matches the characteristics of wind noise.

[0109] Finally, spatial beamforming is used: the beam direction is adjusted based on wind speed data to suppress wind noise from non-target directions.

[0110] The FxLMS algorithm adjusts the coefficients of the adaptive filter through feedback of the error signal. At each sampling time, the error signal e(n) and the output of the secondary channel filter G(z) are used together to update the filter coefficients W(n) to better generate the cancellation signal y"wind"(n).

[0111] In an active traffic noise reduction system with adaptive dual-sided sound barriers against wind noise, changes in wind speed affect the transfer function of the secondary channel. Therefore, the secondary channel filter G(z) needs to be dynamically adjusted based on real-time wind speed data to ensure that y"wind"(n) and s"wind"(n) are matched in phase and amplitude, thereby achieving efficient wind noise cancellation. This application's technical solution utilizes a joint wind-traffic noise processing framework based on blind source separation (BSS) and Wiener filtering, referencing a model-referenced adaptive cancellation (MRAC) architecture, and integrates wind speed data-driven spatial beamforming. The real-time wind noise power spectrum estimation formula is as follows:

[0112] Pwind (f)=α·v 2.5 ·f -1.2 .

[0113] Beamforming weight dynamic adjustment rules:

[0114]

[0115] like Figure 4 As shown, further, the actuators are dual actuators distributed on both sides of the target road, and the actuators output reverse sound waves based on the actuator drive signal, including:

[0116] The control signals for the left and right actuators are determined based on the actuator drive signals.

[0117] The coupling matrix is ​​obtained by actual measurement of the road sound barrier system. The coupling matrix is ​​used to characterize the mutual coupling relationship between the control signals of the left actuator and the control signals of the right actuator and the system response characteristics.

[0118] The optimization objective and constraints are determined based on the control signals of the left and right actuators and the coupling matrix.

[0119] The gradient descent algorithm is used to solve the optimization objective, and the decoupled control signals of the left and right actuators are obtained.

[0120] like Figure 5 The diagram shows the flowchart of the dual-actuator decoupling optimization algorithm. In this method, the inputs include the control signal u from the left actuator. L +Right-side signal u R The coupling matrix C was obtained through offline measurement.

[0121] In an active noise reduction system for traffic noise using adaptive dual-sided sound barriers, the coupling matrix C is a core mathematical model used to describe the interaction and system response characteristics between the control signal u_L from the left actuator and the signal u_R from the right. The coupling matrix C is introduced to address the signal interference problem when the dual sound barriers work together; that is, the sound waves generated by one sound barrier inevitably affect the noise control effect of the other. By introducing the coupling matrix, the system can perform decoupling optimization, that is, while controlling one sound barrier, the interference from the other sound barrier is considered and compensated, thereby achieving more refined and effective noise suppression.

[0122] C quantifies how sound waves generated by one sound barrier propagate in the space between the barriers, affecting the actuators and error microphones on the other side. The coupling matrix also reflects the propagation characteristics of sound waves at different frequencies, which is crucial because frequency characteristics affect the intensity and direction of sound wave propagation. Through coupling matrix calculations, the system can adjust control signals to minimize signal interference between the two sound barriers, thereby improving overall noise reduction. This typically involves finding a set of control signals using optimization algorithms that minimizes residual noise energy.

[0123] The optimization goal is Where e is the error signal, i.e., the difference between the actual residual noise and the target noise; γ is the regularization parameter, used to balance the noise suppression effect and the actuator's output energy.

[0124] The first part, ||e||^2, represents the energy of the residual noise. The optimal noise suppression is achieved by minimizing the square norm of e. The second part, γ(||u_L||^2 + ||u_R||^2), introduces a regularization term to penalize actuator signals with excessively high output energy. This helps prevent excessive algorithm output, which could generate unnecessary acoustic feedback or noise, while also saving energy.

[0125] Solution method: The problem is transformed into an unconstrained problem using the Lagrange multiplier method and solved iteratively by gradient descent.

[0126] The algorithm outputs the decoupled actuator drive signal.

[0127] The technical solution of this application was used in the test scenario: a two-way six-lane highway with a traffic flow of 40 vehicles / minute, an average vehicle speed of 60km / h, and a wind speed of 8m / s.

[0128] Noise reduction (dB(A)): 18.6 for this invention, 9.2 for the traditional solution.

[0129] Wind noise interference fluctuation (dB): ±1.8 for this invention, ±5.3 for the traditional solution.

[0130] System response latency (ms): 18 for this invention, 95 for the traditional solution.

[0131] The technical solution of this application provides a decoupling optimization algorithm for dual-sided actuators, and introduces a distributed optimization model with coupling matrix constraints;

[0132] The objective function constructed by this method is:

[0133]

[0134] Furthermore, the technical solution of this application uses an online update mechanism for the sound field transfer function (cycle ≤ 5 minutes) to establish a sound velocity correction model based on the fusion of temperature, humidity and wind speed data.

[0135] The dynamic correction formula for the speed of sound is: c = 331.4 + 0.6T + 0.0124h (T: temperature, h: humidity). Optionally, a recursive least squares (RLS) transfer function update algorithm can be used (forgetting factor λ = 0.98).

[0136] In an optional embodiment, the coupling matrix C is typically obtained through offline measurement and modeling, with the following specific steps:

[0137] 1. System calibration: A series of tests are conducted on the actual deployed sound barrier system, including precisely controlling the actuators (speakers) at different locations to generate sound waves of different frequencies.

[0138] 2. Response Measurement: The sound pressure response at various points on both sides of the sound barrier was measured using a high-precision microphone array. These measurements reflect how sound waves emitted from the actuator on one side affect the sound pressure on the other side.

[0139] 3. Data Collection and Analysis: Collect all measurement data and perform frequency domain transformation to obtain response data at various frequencies. These data can be complex transfer functions that include phase and amplitude information.

[0140] 4. Construct the coupling matrix: Organize the frequency domain response data into a coupling matrix C. The coupling matrix is ​​typically a four-dimensional matrix, where two dimensions correspond to the position and frequency of the actuator, and the other two dimensions correspond to the position and frequency of the error microphones on both sides of the sound barrier.

[0141] According to another aspect of the present invention, a road noise barrier system is also provided, comprising:

[0142] Noise acquisition module, used to acquire noise from sound sources;

[0143] The calculation module includes a wind noise reduction unit, which is used to separate the wind noise component based on the sound source noise. The calculation module also includes an adaptive algorithm unit, which includes an adaptive filter. The adaptive algorithm unit is used to generate a cancellation signal based on the sound source noise. The calculation module further includes a decoupling optimizer unit, which is used to decouple the actuator control signals on both sides.

[0144] The actuator is used to receive the decoupled actuator control signal and generate a reverse acoustic wave based on the decoupled actuator control signal.

[0145] like Figure 5A schematic diagram of the road noise barrier system (overall system architecture diagram) is shown. Figure 6 A schematic diagram of the sound barrier structure is shown, in which, as Figure 5 and Figure 6 As shown, the road noise barrier system includes: 1. Noise source (vehicle) emits sound waves, which propagate through the air to the two side noise barriers. 2. Sensor array: Reference microphone array (outer side): Arranged on the outer side of the noise barrier, equipped with a windproof cover and waveguide structure, to collect the original noise signal. Error microphone array (inner side): Distributed in a ring, to monitor residual noise in the protected area. Environmental sensors: Anemometers and temperature and humidity sensors are embedded in the top of the barrier. 3. Actuator array: 32 actuators (speakers + piezoelectric ceramic composite units) are embedded on the surface of each side of the noise barrier, covering the 50-2000Hz frequency band. 4. Central controller: Includes a multi-core DSP (processing sound field reconstruction and adaptive algorithms), FPGA (real-time beamforming and delay compensation), and a data bus connecting the modules, supporting closed-loop control with a delay of ≤1ms. Figure 8 The noise reduction cavity is shown.

[0146] Furthermore, the road noise barrier system includes: a noise barrier structure, which is set on both sides of the target road. The noise barrier structure is equipped with a wind speed sensor, an actuator array, an error microphone array, and a reference microphone array. The reference microphone array is used to collect noise from the sound source, and the error microphone array is used to collect residual noise signals. The error microphone array and the reference microphone array are respectively set on both sides of the thickness direction of the noise barrier structure.

[0147] Furthermore, the sound barrier structure is also provided with a gradient impedance structure, which includes a micro-perforated plate layer, a gradient density sound-absorbing cotton layer and a Helmholtz resonant cavity layer arranged along the thickness direction. The gradient density sound-absorbing cotton layer is located between the micro-perforated plate layer and the Helmholtz resonant cavity layer, and the density of the gradient density sound-absorbing cotton layer increases along the thickness direction of the sound barrier structure.

[0148] like Figure 7 A cross-sectional view of the gradient impedance acoustic barrier structure is shown.

[0149] The surface layer (micro-perforated plate layer) is made of aluminum alloy, with a thickness of 1 mm, a pore diameter of 0.2 mm, and a perforation rate of 28%. The surface layer absorbs high-frequency noise (>500 Hz) and has a reflectivity of <15%@1 kHz.

[0150] The density of the middle layer (gradient density sound-absorbing cotton layer) is 80 kg / m², which is lower than that of the surface layer. 3 200kg / m to the bottom layer 3 Gradient, 80mm thick, used to improve low-frequency absorption (absorption coefficient >0.6@200Hz) through impedance gradient matching.

[0151] The bottom layer (Helmholtz resonant cavity layer) has a cavity size of 50mm in diameter, a neck length of 10mm, and a resonant frequency of 150Hz. It is used for selective absorption of low-frequency peaks (100-300Hz) of tire noise.

[0152] The airfoil has a tilt angle of 15° and a surface curvature radius R = 50 mm, reducing the drag coefficient to 0.3.

[0153] Furthermore, the sound barrier structure includes a guide airfoil, the tilt angle of which is A, 20°≥A≥10°, and the surface curvature radius of which is R, 60mm≥R≥40mm.

[0154] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0155] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0156] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0157] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0161] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0162] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of noise reduction of traffic noise, characterized in that, The method comprises: obtaining speed information of a target vehicle on a target road; generating a target control instruction set based on the speed information, the target control instruction set being used to control a noise reduction mode of a road sound barrier system and adjust an operating parameter of an adaptive filter, wherein the noise reduction mode comprises at least one of an engine order noise tracking mode, a tire noise broadband suppression mode, and a Doppler effect compensation mode, and the operating parameter comprises at least one of a filter step length and a filter length; generating a target control instruction set based on the speed information comprises: determining an instantaneous speed of the target vehicle based on the speed information; in response to the instantaneous speed being less than or equal to a first speed, generating a first target control instruction in the target control instruction set, wherein the first target control instruction is used to control the road sound barrier system to adopt the engine order noise tracking mode, control the filter step length to be a first step length, and control the filter length to be a first length, the first step length being determined by the instantaneous speed; generating a target control instruction set based on the speed information further comprises: in response to the instantaneous speed being greater than or equal to a second speed, generating a second target control instruction in the target control instruction set, wherein the second target control instruction is used to control the road sound barrier system to adopt the Doppler effect compensation mode, control the filter step length to be a second step length, and control the filter length to be a second length, the second step length being determined by the instantaneous speed, the second step length being less than the first step length, the second length being greater than the first length, and the second speed being greater than the first speed.

2. The noise reduction method of claim 1, wherein, generating a target control instruction set based on the speed information further comprises: in response to the instantaneous speed being greater than the first speed and less than the second speed, generating a third target control instruction in the target control instruction set, wherein the third target control instruction is used to control the road sound barrier system to adopt the tire noise broadband suppression mode, control the filter step length to be a third step length, and control the filter length to be a third length, the third step length being determined by the instantaneous speed, the third step length being less than the first step length and greater than the second step length, and the third length being greater than the first length and less than the second length.

3. The noise reduction method of claim 1, wherein, obtaining speed information of a target vehicle on a target road comprises: obtaining first speed information obtained by radar speed measurement of the target vehicle; obtaining second speed information obtained by sound source positioning of the target vehicle; determining the speed information based on the first speed information and the second speed information.

4. The noise reduction method of claim 1, wherein, After generating a target control instruction set based on the speed information, the noise reduction method further comprises: obtaining sound source noise and wind speed information, the sound source noise being obtained by a reference microphone, and the wind speed information being obtained by a wind speed sensor at a point on the target road; generating a cancellation signal based on the sound source noise and the wind speed information; determining an actuator driving signal based on the cancellation signal; Controlling actuators to output reverse sound waves based on the actuator driving signals, the reverse sound waves being used to cancel at least part of the sound source noise.

5. The noise reduction method of claim 4, wherein, Generating a cancellation signal based on the sound source noise and the wind speed information, including: Determining wind noise power spectrum information based on the sound source noise and the wind speed information, the wind noise power spectrum information being used to determine power and energy distribution of wind noise at various frequencies; Determining traffic noise power spectrum information based on the sound source noise and the wind speed information, the traffic noise power spectrum information being used to determine energy distribution of vehicle generated noise at different frequencies; Determining a filter signal based on the wind noise power spectrum information and the traffic noise power spectrum information; Blind source separation of the filter signal to obtain wind noise components and traffic noise components; Inputting the wind noise components into the adaptive filter to generate the cancellation signal.

6. The noise reduction method of claim 4, wherein, The actuators are bilateral actuators distributed on both sides of the target road, and controlling actuators to output reverse sound waves based on the actuator driving signals, including: Determining left side actuator control signals and right side actuator control signals based on the actuator driving signals; Obtaining a coupling matrix, the coupling matrix being obtained by actual measurement of the road sound barrier system, and the coupling matrix being used to represent mutual coupling relationship and system response characteristics between the left side actuator control signals and the right side actuator control signals; Determining an optimization target and a constraint condition based on the left side actuator control signals, the right side actuator control signals and the coupling matrix; Solving the optimization target by using a gradient descent algorithm to obtain decoupled left side actuator control signals and decoupled right side actuator control signals.

7. A road sound barrier system characterized by, The road sound barrier system is used to perform the method of any one of claims 1 to 6, including: A sound noise collection module, the sound noise collection module being used to collect sound source noise; A calculation module, the calculation module including an anti-wind noise unit, the anti-wind noise unit being used to separate wind noise components based on sound source noise, the calculation module including an adaptive algorithm unit, the adaptive algorithm unit including an adaptive filter, the adaptive algorithm unit being used to generate a cancellation signal according to sound source noise, the calculation module including a decoupling optimizer unit, the decoupling optimizer unit being used to decouple bilateral actuator control signals; Actuators, the actuators being used to receive decoupled actuator control signals and generate reverse sound waves based on the decoupled actuator control signals.

8. The roadside sound barrier system according to claim 7, wherein The road sound barrier system includes: A sound barrier structure, the sound barrier structure being arranged on both sides of a target road, the sound barrier structure being provided with a wind speed sensor, an actuator array, an error microphone array and a reference microphone array, the reference microphone array being used to collect sound source noise, the error microphone array being used to collect residual noise signals, the error microphone array and the reference microphone array being arranged on both sides of the thickness direction of the sound barrier structure respectively.

9. The roadside sound barrier system according to claim 8, wherein The sound barrier structure is further provided with a gradient impedance structure, the gradient impedance structure comprises a micro-perforated plate layer, a gradient density sound absorption cotton layer and a Helmholtz resonator layer arranged along the thickness direction, wherein the gradient density sound absorption cotton layer is located between the micro-perforated plate layer and the Helmholtz resonator layer, and the gradient density sound absorption cotton layer is arranged in an increasing density along the thickness direction of the sound barrier structure.

10. The roadside sound barrier system of claim 9, wherein, The sound barrier structure comprises a guide wing type part, and a surface curvature radius of the guide wing type part is R, and 60mm≥R≥40mm.

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