Indoor noise active control method in urban traffic environment based on Wiener algorithm and related device

Abstract

The invention discloses an indoor noise active control method in an urban traffic environment based on a Wiener algorithm and a related device, and relates to the technical field of indoor noise control, and the method comprises the steps: collecting a plurality of groups of indoor steady-state noise data mainly containing traffic noise through a reference microphone and an error microphone; relying on the steady-state characteristic of the noise, adopting a Wiener algorithm to carry out offline solving to obtain an optimal filter parameter; and finally, taking the optimal parameter as a filter parameter of an indoor noise controller, and applying the optimal parameter to an active control system to reduce the noise of an area to be subjected to noise reduction. According to the scheme, the problem that conventional hardware is difficult to update coefficients in real time in a 1-3ms low-delay scene of adaptive algorithms such as FxLMS and the like is effectively solved, and adaptation can be realized without upgrading the hardware; according to the method, the overall noise reduction of 2-2.5 dB (A) can be realized in the frequency band of 50-1000 Hz, the noise reduction amount of a peak frequency point exceeds 10 dB (A), parameters are adaptive to noise in different time periods, the generalization is good, and the indoor traffic noise control requirement is practically met.

Description

Technical Field

[0001] This application relates to the field of indoor noise control technology, and in particular to an active indoor noise control method and related device based on Wiener algorithm in urban traffic environments. Background Technology

[0002] With the development of urban transportation, residential buildings near urban expressways are easily affected by traffic noise, impacting residents' quality of life. Active noise control (ANC) technology is an effective means to solve indoor noise problems, among which the FxLMS algorithm has become the mainstream ANC algorithm due to its good stability and fast convergence speed.

[0003] However, in indoor environments dominated by traffic noise, the acoustic propagation delay between the collected noise signal and the noise signal in the target noise reduction area is extremely small. In this case, adaptive algorithms such as FxLMS require real-time updates of the controller coefficients, placing extremely high demands on the hardware system's computational speed and responsiveness. Conventional hardware struggles to complete coefficient updates within 1-3ms, causing the controller to fail to converge to the optimal state, resulting in poor noise reduction performance. Noise reduction in the 50-1000Hz frequency band is only 0.5-0.6dB(A), which fails to meet indoor noise control requirements. Summary of the Invention

[0004] The purpose of this application is to provide an active indoor noise control method and related device based on Wiener algorithm in urban traffic environments, which can achieve good indoor noise control effect under conventional hardware.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for active indoor noise control in urban traffic environments based on the Wiener algorithm. The method employs an active indoor noise control system, which includes: several reference microphones, two error microphones, several secondary loudspeakers, and an indoor noise controller. The reference microphones are positioned near the noise source indoors, while the two error microphones are positioned on the left and right sides of the target noise reduction area, respectively. The reference and error microphones are used to jointly acquire real-time steady-state indoor noise data. The indoor noise controller filters the steady-state noise data and outputs an anti-noise signal. The secondary loudspeakers receive the anti-noise signal and generate a noise cancellation wave, thereby achieving noise suppression in the target noise reduction area. The active indoor noise control method based on the Wiener algorithm in urban traffic environments includes the following steps: Multiple sets of indoor steady-state noise data, mainly traffic noise, were collected using a reference microphone and an error microphone.

[0006] Based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data, the optimal filter parameters are obtained offline using the Wiener algorithm.

[0007] The optimal filter parameters are used as the filter parameters of the indoor noise controller and applied to the indoor noise active control system to reduce noise in the area to be reduced.

[0008] Optionally, the indoor steady-state noise data includes reference signals collected by each reference microphone and desired signals collected by each error microphone; the indoor steady-state noise data satisfies the following: the time delay between the reference signal and the desired signal is 1-3ms, and within the 50-1000Hz frequency band, the multicoherence of the reference signal and the desired signal of the left error microphone is ≥0.804, and the multicoherence of the reference signal and the desired signal of the right error microphone is ≥0.826.

[0009] Optionally, based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data, the Wiener algorithm is used to solve for the optimal filter parameters offline, specifically including: For multiple sets of indoor steady-state noise data, calculate the filter parameters corresponding to each set of indoor steady-state noise data.

[0010] For any set of filter parameters, calculate the spectral similarity between the filter parameters and each other set of filter parameters, and calculate the mean value to obtain the mean spectral similarity of the filter parameters.

[0011] The filter parameters with the highest mean similarity in spectral characteristics are selected as candidate optimal filter parameters.

[0012] Simulation experiments were conducted on the indoor steady-state noise data of each group using candidate optimal filter parameters. If the simulation noise reduction effect is stable, the candidate optimal filter parameters are taken as the optimal filter parameters. The simulation noise reduction effect is stable when the noise reduction amount in the 50-1000Hz frequency band is ≥2.0dB(A).

[0013] Alternatively, the Wiener algorithm solves for the optimal filter parameters according to the following formula: .

[0014] in, w opt This represents the optimal filter parameters. E Indicates the expectation. R ( n ) represents the filter reference signal matrix, and the superscript is... T Represents the transpose of a matrix. d ( n ) represents the noise expected signal matrix, , d 1( n () represents the desired noise signal from the first error microphone. The filter reference signal matrix is ​​shown in the following equation: .

[0015] in, r ( n ) represents the reference signal x ( n The filtered reference signal after secondary path filtering, I This indicates the filter order of the indoor noise controller. L Indicates the number of error microphones.

[0016] Optionally, n Time of the first m Control signals for each secondary speaker As shown in the following formula: .

[0017] in, y m ( n )express n Time of the first m Control signals for each secondary speaker, K Indicates the number of reference microphones. k The label representing the reference microphone. i Indicates the filter order label. w mki ( n )express n Time of the first k The reference signal to the first m The filter of the room noise controller between the secondary speakers. i Order coefficient, x k ( ni )express n - i Time of the first k One reference signal.

[0018] The control signal output by the indoor noise controller y m ( n After the secondary path, the first l The signal received by each error microphone is shown in the following formula: .

[0019] in, e l ( n ) indicates the first l The signal received by the error microphone G lmj Indicates the first m Control signals for each secondary speakery m ( n ) to the l The second path traversed by the error sensor j Order coefficient, M Indicates the number of secondary speakers. m The designation indicating the secondary speaker. J Indicates the order of the secondary path. j Indicates the order label of the secondary path. d l ( n ) indicates the first l The expected noise signal of an error microphone.

[0020] Alternatively, the objective function of the indoor noise controller is shown in the following equation: .

[0021] in, J Represents the objective function value. e ( n ) represents the error signal matrix, ; w Represents the control coefficient matrix. , .

[0022] Secondly, this application provides an active indoor noise control system for urban traffic environments based on the Wiener algorithm, comprising: several reference microphones, two error microphones, several secondary loudspeakers, and an indoor noise controller; the reference microphones are arranged indoors near the noise source, and the two error microphones are respectively arranged on the left and right sides of the target noise reduction area; the reference microphones and error microphones are used to jointly collect real-time indoor steady-state noise data; the indoor noise controller is used to filter the indoor steady-state noise data and output an anti-noise signal; the secondary loudspeakers are used to receive the anti-noise signal and generate a noise cancellation wave to achieve noise suppression in the target noise reduction area; it also includes the following functional modules: The indoor steady-state noise data acquisition module is used to collect multiple sets of indoor steady-state noise data, mainly traffic noise, through a reference microphone and an error microphone.

[0023] The optimal filter algorithm solution module is used to obtain the optimal filter parameters offline by using the Wiener algorithm based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data.

[0024] The indoor noise active control module is used to use the optimal filter parameters as the filter parameters of the indoor noise controller. It is applied to the indoor noise active control system to reduce noise in the area to be reduced.

[0025] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the active indoor noise control method based on the Wiener algorithm in urban traffic environments described above.

[0026] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned active indoor noise control method based on the Wiener algorithm in an urban traffic environment.

[0027] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aforementioned active indoor noise control method based on the Wiener algorithm in an urban traffic environment.

[0028] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides an active indoor noise control method and related device based on the Wiener algorithm in urban traffic environments. The method addresses the steady-state characteristics of indoor noise in urban traffic environments, such as stable spectrum and high coherence between the reference signal and the target area noise. Multiple sets of data are collected, covering noise characteristics at different time periods, providing a data foundation for solving universally optimal parameters. This avoids the problem of parameters being only applicable to a single time period, ensuring the algorithm's adaptability to noise at different times. Then, the Wiener algorithm is used to solve for the optimal filter parameters offline (without real-time processing), avoiding the computational requirements of adaptive algorithms like FxLMS, ​​which require real-time coefficient updates within a low latency of 1-3ms. This allows conventional hardware to support parameter solving. Furthermore, multiple sets of data are used to... By comparing and selecting the optimal parameters, the noise reduction stability of the parameters in the 50-1000Hz frequency band can be ensured, laying the foundation for efficient noise reduction. Finally, the optimal filter parameters are used as the filter parameters of the indoor noise controller and applied to the indoor noise active control system to reduce noise in the area to be reduced. The fixed filter parameters do not need to be updated in real time, which completely solves the problem that conventional hardware cannot complete the coefficient update within 1-3ms. This allows the controller to stably output an anti-noise signal with the same amplitude and opposite phase as the original noise. In practical applications, an overall noise reduction of 2-2.5dB(A) can be achieved in the 50-1000Hz frequency band, and the peak frequency noise reduction exceeds 10dB(A), which is significantly better than the existing adaptive algorithm and effectively meets the needs of indoor noise control. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the principle of a traditional indoor active noise control system.

[0031] Figure 2 The flowchart illustrates an active indoor noise control method based on the Wiener algorithm in an urban traffic environment, as provided in one embodiment of this application.

[0032] Figure 3 This is a flowchart of step A2 in an active indoor noise control method based on Wiener algorithm in an urban traffic environment, provided as an embodiment of this application.

[0033] Figure 4 This is an indoor-outdoor top-down view of an active indoor noise control method based on Wiener algorithm in an urban traffic environment, provided as an embodiment of this application.

[0034] Figure 5 This is a schematic diagram of the layout of the noise data acquisition site in an active indoor noise control method based on the Wiener algorithm in an urban traffic environment, provided as an embodiment of this application.

[0035] Figure 6 This is a schematic diagram comparing the spectral characteristics of various locations in an active indoor noise control method based on the Wiener algorithm in an urban traffic environment, as provided in another embodiment of this application.

[0036] Figure 7 This is a schematic diagram illustrating the coherence between collected data in an active indoor noise control method for urban traffic environments based on the Wiener algorithm, provided as another embodiment of this application.

[0037] Figure 8 This is a schematic diagram of the arrangement of secondary loudspeakers and error microphones in an active indoor noise control method for urban traffic environments based on the Wiener algorithm, provided in another embodiment of this application.

[0038] Figure 9 This is a schematic diagram of the secondary path identification results in an active indoor noise control method based on the Wiener algorithm in an urban traffic environment, provided as another embodiment of this application.

[0039] Figure 10 This is a schematic diagram comparing the amplitude-frequency response of an active indoor noise control method based on Wiener's algorithm in an urban traffic environment, provided as another embodiment of this application.

[0040] Figure 11 This is a schematic diagram comparing the phase frequency response in an active indoor noise control method based on the Wiener algorithm in an urban traffic environment, provided as another embodiment of this application.

[0041] Figure 12 This is a schematic diagram of the simulation results of the No. 4 data fixed filter algorithm in an active indoor noise control method based on Wiener algorithm in an urban traffic environment, provided as another embodiment of this application.

[0042] Figure 13 This is a schematic diagram of the simulation results of the No. 4 data adaptive iterative algorithm in an active indoor noise control method based on Wiener algorithm in an urban traffic environment, which is provided as another embodiment of this application.

[0043] Figure 14 This is a schematic diagram of the functional modules of an active indoor noise control system based on Wiener's algorithm in an urban traffic environment, provided as an embodiment of this application.

[0044] Figure 15 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] Active noise control (ANC) technology is an effective means to solve indoor noise problems. Among them, the FxLMS algorithm has become the mainstream ANC algorithm due to its good stability and fast convergence speed. The FxLMS algorithm has the advantages of stability and fast convergence speed, and is the current mainstream algorithm. Figure 1 This is a schematic diagram of an active indoor noise control system. Considering the performance requirements of the active indoor noise control system, a multi-channel control algorithm is adopted, wherein the number of reference microphones is K, the number of secondary speakers is M, and the number of error microphones is L. For the time-domain signals detected by K reference microphones, Let P(z) be the control signal for M secondary loudspeakers, and P(z) be the primary path, which is the path of noise from the reference signal to the human ear. Let L be the expected noise signals at L locations. There are L error signals. The filtered reference signal is the reference signal after passing through the secondary path filter. Let M be the secondary paths between the M secondary speakers and the L error microphones. The secondary path is estimated using a J-order FIR filter. (Controller) There are K*M FIR filters of length I.

[0047] However, in indoor environments dominated by traffic noise, the acoustic propagation delay between the reference signal (the collected noise signal) and the desired signal (the noise signal in the target noise reduction area) is extremely small (only 1-3 ms). In this case, adaptive algorithms such as FxLMS require real-time updates of the controller coefficients, placing extremely high demands on the computing speed and responsiveness of the hardware system. Existing conventional hardware (such as the ADSP-21565 controller with 5.0 GFLOPS of computing power) struggles to complete coefficient updates within 1-3 ms, causing the controller to fail to converge to the optimal state. Ultimately, this results in poor noise reduction performance, with only 0.5-0.6 dB(A) noise reduction in the 50-1000 Hz frequency band, which fails to meet indoor noise control requirements.

[0048] Therefore, in order to make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] This application provides an active indoor noise control method based on Wiener's algorithm in an urban traffic environment. In one exemplary embodiment, such as... Figure 2 As shown, it includes the following steps: A1. Multiple sets of indoor steady-state noise data, mainly traffic noise, were collected using a reference microphone and an error microphone.

[0050] A2. Based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data, the optimal filter parameters are obtained offline using the Wiener algorithm. In this embodiment, as shown... Figure 3 As shown, step A2 specifically includes the following steps: A21. For multiple sets of indoor steady-state noise data, calculate the filter parameters corresponding to each set of indoor steady-state noise data.

[0051] A22. For any set of filter parameters, calculate the spectral similarity between the filter parameters and each other set of filter parameters, and calculate the mean value to obtain the mean spectral similarity of the filter parameters.

[0052] A23. Select the filter parameters with the highest average spectral similarity as candidate optimal filter parameters.

[0053] A24. Conduct simulation experiments on the indoor steady-state noise data of each group using the candidate optimal filter parameters; if the simulation noise reduction effect is stable, proceed to step A25; the simulation noise reduction effect is stable when the noise reduction amount in the 50-1000Hz frequency band is ≥2.0dB(A).

[0054] A25. Use the candidate optimal filter parameters as the optimal filter parameters.

[0055] A3. Use the optimal filter parameters as the filter parameters of the indoor noise controller and apply them to the indoor noise active control system to reduce noise in the area to be reduced.

[0056] This method employs an active indoor noise control system, comprising: several reference microphones, two error microphones, several secondary loudspeakers, and an indoor noise controller; the reference microphones are positioned indoors near the noise source, and the two error microphones are positioned on the left and right sides of the target noise reduction area, respectively. The reference microphones and error microphones are used to jointly acquire real-time indoor steady-state noise data. The indoor noise controller is used to filter the indoor steady-state noise data and output an anti-noise signal. The secondary loudspeakers are used to receive the anti-noise signal and generate a noise cancellation wave, thereby achieving noise suppression in the target noise reduction area.

[0057] Specifically, the indoor steady-state noise data includes reference signals collected by each reference microphone and expected signals collected by each error microphone; the indoor steady-state noise data meets the following requirements: the time delay between the reference signal and the expected signal is 1-3ms, and within the 50-1000Hz frequency band, the multicoherence between the reference signal and the expected signal of the left error microphone is ≥0.804, and the multicoherence between the reference signal and the expected signal of the right error microphone is ≥0.826.

[0058] In one exemplary embodiment, n Time of the first m Control signals for each secondary speaker As shown in equation (1): (1).

[0059] in, y m ( n )express n Time of the first m Control signals for each secondary speaker, K Indicates the number of reference microphones. k The label representing the reference microphone. i Indicates the filter order label. w mki ( n )express n Time of the firstk The reference signal to the first m The filter of the room noise controller between the secondary speakers. i Order coefficient, x k ( ni )express n - i Time of the first k One reference signal.

[0060] The control signal output by the indoor noise controller y m ( n After the secondary path, the first l The signal received by the error microphone is shown in equation (2) below: (2).

[0061] in, e l ( n ) indicates the first l The signal received by the error microphone G lmj Indicates the first m Control signals for each secondary speaker y m ( n ) to the l The second path traversed by the error sensor j Order coefficient, M Indicates the number of secondary speakers. m The designation indicating the secondary speaker. J Indicates the order of the secondary path. j Indicates the order label of the secondary path. d l ( n ) indicates the first l The expected noise signal of an error microphone.

[0062] Combining equations (1) and (2) yields equation (3): (3).

[0063] To simplify the expression, the filtered reference signal is represented by equation (4): (4).

[0064] Therefore, equation (3) above can be transformed into equation (5): (5).

[0065] The objective function of an indoor noise controller is generally taken as minimizing the sum of squares of the sound pressure amplitude at the error microphone, i.e., the objective function is shown in equation (6): (6).

[0066] When calculating the optimal solution for the filter weight vector, the least mean square (LMS) algorithm is generally used to update the coefficients of the adaptive filter. n Time of the first i The first order k The first reference sensor to the first m The iterative formula for the filter weight coefficients of each controller is shown in equation (7): (7).

[0067] The FxLMS algorithm essentially uses the steepest descent method to iteratively approximate the optimal solution, which is the Wiener solution. Its calculation process includes: Equation (5) can be expressed in vector form as shown in equation (8): (8).

[0068] in: The vector of L error signals is shown in equation (9) below: (9).

[0069] The vector form of the desired noise signal is: Equation (8) can then be written as Equation (10): (10).

[0070] in, w Represents the control coefficient matrix. Filtered reference signal matrix R ( n As shown in equation (11): (11).

[0071] in, r ( n ) represents the reference signal x ( n The filtered reference signal after secondary path filtering, I This indicates the filter order of the indoor noise controller. L Indicates the number of error microphones.

[0072] The objective function is represented in vector form, as shown in equation (12): (12).

[0073] in, J Represents the objective function value. e ( n ) represents the error signal matrix. From equation (12), it can be seen that the objective function is a quadratic function of the controller coefficients w. It has a local minimum. When the objective function is extremely small, the control filter coefficients are its Wiener solution, as shown in equation (13): (13).

[0074] in, w opt This represents the optimal filter parameters. E Indicates the expectation. R ( n ) represents the filter reference signal matrix, and the superscript is... T Represents the transpose of a matrix. d ( n ) represents the noise expected signal matrix, , d 1( n Let be the desired noise signal from the first error microphone. When the spectral characteristics of the noise are stable, the Wiener algorithm can be used to solve for the optimal controller parameters of the control system offline, and then applied as a fixed controller to the system.

[0075] In another exemplary embodiment of this application, in order to analyze the noise characteristics of an indoor space with the windows closed, noise data was collected from the 28th floor of a high-rise building in a residential complex near an urban expressway in a certain city. Figure 4 This is an aerial view from inside the building, where traffic noise is the primary source of noise. Noise data was collected in the lobby with windows closed. The data collection setup is as follows. Figure 5 As shown, four reference microphones were positioned near the window, while two secondary speakers and two error microphones were positioned at the rear, one on the left and one on the right of the chair. All microphones were GM26-V063 from JUANST, with a sampling frequency of 2000Hz and a sampling duration of 10 seconds, collecting data from multiple time periods throughout the day.

[0076] In one specific embodiment, the main hardware devices and quantities used in this active indoor noise control system are shown in Table 1. The controller core is an ADSP-21565 with a computing power of 5.0 GFLOPS; the reference and error microphones are selected from the JUANTECH A2B digital microphone GM26-V063, with a maximum sampling frequency of 48 kHz; the secondary speakers are headrest speakers with good low-frequency response. The headrest speakers are installed behind the seats, approximately 1.2 meters above the ground, while the error microphone is placed approximately 7 centimeters in front of the headrest speakers to measure the noise in the controlled area.

[0077] Table 1. Description of Main Hardware Equipment

[0078] Based on multiple sets of collected indoor noise data, the energy distribution characteristics of noise in the 50-1000 Hz range were studied through spectral analysis, with a focus on analyzing its peak characteristics and spectral stability. The positions of the four reference microphones, from left to right, are labeled as: Position 1, Position 2, Position 3, and Position 4.

[0079] Spectral analysis was performed on the multiple sets of noise data collected. Figure 6 The comparison results of five sets of noise data are presented. The analysis results show that the noise peaks are mainly concentrated in 100–200 Hz and 700–800 Hz, and the noise spectrum characteristics at the same location have high consistency, indicating that the noise has good stability under the experimental conditions.

[0080] To verify the rationality of the reference microphone arrangement, it is necessary to perform multicoherence analysis on the acquired signals. This is because the maximum noise reduction that the active noise cancellation system can achieve at a single frequency point is determined by the multicoherence between the signals, and its calculation formula is shown in Equation (14): (14).

[0081] Wherein, NR represents multiple correlation. The frequency domain multicoherence coefficient between signals is calculated using the formula shown in equation (15): (15).

[0082] in, The cross-power spectral density between the reference signal and the desired signal; for The transpose and conjugate of; , Let be the autopower spectral density of the desired signal and the reference signal. The coherence function ranges from 0 to 1; the closer its value is to 1, the stronger the linear correlation between the reference signal and the desired signal at that frequency.

[0083] The coherence of the collected data in this embodiment is as follows: Figure 7 As shown, within the 50–1000 Hz frequency band, the multicoherence of the four reference signals with the desired signal on the left is 0.804, and the multicoherence with the desired signal on the right is 0.826. This indicates that the reference microphones are reasonably positioned, and the signals collected by the reference microphones can be used as inputs for an active indoor noise control system.

[0084] Although coherence analysis shows that the reference signal can effectively characterize the desired signal, in active noise control systems, if the time delay between the reference and desired signals is too small, the controller will struggle to achieve real-time updates, thus affecting noise reduction performance. Therefore, it is necessary to analyze the time delay characteristics of both signals. Cross-correlation analysis is used to estimate the time delay between signals, particularly for discrete signals. and The cross-correlation function is defined as shown in equation (16): (16).

[0085] in, The delay is the time when the cross-correlation coefficient reaches its maximum value. This refers to the time delay between signals.

[0086] Table 1. Signal Delay Between Reference Signal and Desired Signal

[0087] The reference signals R1, R2, R3, and R4 in Table 1 correspond to... Figure 5 The reference microphones, arranged from left to right, collect signals. As shown in Table 1, the time delay between the reference signal and the desired signal is relatively small. This indicates that if an adaptive algorithm for real-time updating of filter coefficients is used in the active noise control system, the hardware system must have extremely high computing speed and response capability; otherwise, it will be difficult to complete the calculation and output of the control signal within a finite time delay range.

[0088] In another exemplary embodiment of this application, based on the foregoing analysis results, the performance of the indoor noise active control system can be verified through simulation. To maintain the causal relationship in the active noise cancellation system and reduce the impact of secondary path delay, secondary paths need to be identified in advance. Since the target of active noise control is 50-1000Hz, during identification, the two secondary speakers sequentially emit sweep signals with a frequency of 50-1000Hz at a sweep rate of 100Hz / s and a magnitude of 1Pa. Simultaneously, the microphone's sampling frequency is set to 2000Hz. The on-site equipment arrangement is as follows... Figure 8 As shown.

[0089] Offline identification was performed using the LMS algorithm, and four secondary paths were estimated using a 128th-order finite filter. The frequency response of the secondary path from the headrest speaker to the error microphone obtained by the offline identification method is as follows: Figure 9 As shown.

[0090] from Figure 9It can be seen that the impulse response of the secondary path between the speaker and each error microphone is mainly related to distance. The closer the distance, the smaller the amplitude attenuation and the smaller the time delay of the signal received by the microphone. The amplitude frequency attenuation of the headrest speaker and the microphones that are close to it is basically in the range of -20 dB, and the phase frequency attenuation is basically in the range of -40 radians.

[0091] The Wiener algorithm was used to calculate the optimal filter coefficients, which were then used as the fixed controller for the control system in experiments. Specifically, the optimal 128th-order filter was solved for each set of measurement data. Five sets of measured data were selected, and their optimal filter coefficients were calculated for each set. The frequency response characteristics of each filter were then compared. The control filter system contains eight channels, and the frequency response comparison results are as follows: Figure 10 and Figure 11 As shown.

[0092] Frequency response analysis was performed on five sets of collected data. The results showed that the spectral characteristics of each filter group were basically consistent in the 50-1000 Hz range, proving that when the noise spectral characteristics are similar, the corresponding optimal filter coefficients tend to be consistent. Based on the frequency response comparison, the optimal filter parameters calculated from the fifth set of data were selected as the fixed filter for use in the active noise control system.

[0093] Next, based on the noise data collected on-site, an indoor active noise control simulation verification was conducted. A fixed filter algorithm was adopted, which was calculated using the fifth set of data selected earlier. The change in total sound pressure level in the 50-1000 Hz frequency band before and after noise reduction was used as an indicator to measure the noise reduction effect, and the results are shown in Table 2.

[0094] Table 2 Noise Reduction Simulation Results

[0095] The simulation results of data number 4 were selected for spectrum analysis, such as... Figure 12 As shown in the figure, the noise reduction results indicate that the fixed filter-based noise reduction method is feasible, and the fixed filter parameters calculated by the Wiener algorithm have good generalization ability. The noise reduction effect in the 50-1000 Hz range is basically above 2 dB(A), and the noise is effectively suppressed in the noise-prone frequency bands (100-200 Hz, 700-800 Hz).

[0096] After verifying the performance of the fixed filter algorithm, the FxLMS adaptive iterative algorithm was simulated and verified on data No. 4. The iteration step size was adopted as 0.03 through trial and error. The simulation results are as follows. Figure 13 As shown, the noise reduction level of the adaptive iterative algorithm is basically the same as that of the fixed filter algorithm, indicating that under the condition of stable noise spectrum characteristics, the fixed filter can achieve noise reduction performance comparable to the adaptive algorithm.

[0097] Based on the same inventive concept, this application also provides a system for implementing the above-described active indoor noise control method based on Wiener's algorithm in urban traffic environments. The solution provided by this system is similar to the implementation described in the above method. In an exemplary embodiment, such as... Figure 14 As shown, an active indoor noise control system based on Wiener's algorithm in an urban traffic environment is provided, including: several reference microphones, two error microphones, several secondary loudspeakers, and an indoor noise controller; the reference microphones are arranged indoors near the noise source, and the two error microphones are respectively arranged on the left and right sides of the target noise reduction area. The reference microphones and error microphones are used to jointly collect real-time indoor steady-state noise data. The indoor noise controller is used to filter the indoor steady-state noise data and output an anti-noise signal. The secondary loudspeakers are used to receive the anti-noise signal and generate a noise cancellation wave to achieve noise suppression in the target noise reduction area; it also includes the following functional modules: The indoor steady-state noise data acquisition module is used to collect multiple sets of indoor steady-state noise data, mainly traffic noise, through a reference microphone and an error microphone.

[0098] The optimal filter algorithm solution module is used to obtain the optimal filter parameters offline by using the Wiener algorithm based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data.

[0099] The indoor noise active control module is used to use the optimal filter parameters as the filter parameters of the indoor noise controller. It is applied to the indoor noise active control system to reduce noise in the area to be reduced.

[0100] certainly, Figure 14 The architecture shown is merely exemplary; it can be omitted as needed when implementing different functionalities. Figure 14 One or at least two components of the system shown.

[0101] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 15As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it can implement the active indoor noise control method based on the Wiener algorithm in urban traffic environments provided in the previous embodiment.

[0102] Those skilled in the art will understand that Figure 15 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0103] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0104] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0105] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0108] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for active indoor noise control in urban traffic environments based on Wiener's algorithm, comprising an active indoor noise control system, wherein the active indoor noise control system includes: Several reference microphones, two error microphones, several secondary speakers, and an indoor noise controller; Several reference microphones are arranged indoors near the noise source, and two error microphones are respectively arranged on the left and right sides of the target noise reduction area. The reference microphones and error microphones are used to jointly collect real-time indoor steady-state noise data. The indoor noise controller is used to filter the indoor steady-state noise data and output an anti-noise signal. The secondary loudspeaker is used to receive the anti-noise signal and generate a noise cancellation wave to achieve noise suppression in the target noise reduction area. The method for active indoor noise control in urban traffic environments based on the Wiener algorithm includes: Multiple sets of indoor steady-state noise data, mainly traffic noise, were collected using the reference microphone and the error microphone. Based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data, the optimal filter parameters are obtained offline using the Wiener algorithm. The optimal filter parameters are used as the filter parameters of the indoor noise controller and applied to the indoor noise active control system to reduce noise in the area to be reduced.

2. The method for active indoor noise control in urban traffic environments based on Wiener's algorithm according to claim 1, characterized in that, The indoor steady-state noise data includes reference signals collected by each of the reference microphones and desired signals collected by each of the error microphones; the indoor steady-state noise data satisfies the following: the time delay between the reference signal and the desired signal is 1-3ms, and within the 50-1000Hz frequency band, the multicoherence of the reference signal and the desired signal of the left error microphone is ≥0.804, and the multicoherence of the reference signal and the desired signal of the right error microphone is ≥0.

826.

3. The method for active indoor noise control in urban traffic environments based on Wiener's algorithm according to claim 1, characterized in that, Based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data, the optimal filter parameters are obtained offline using the Wiener algorithm, specifically including: For each set of indoor steady-state noise data, the filter parameters corresponding to each set of indoor steady-state noise data are calculated. For any set of filter parameters, calculate the spectral similarity between the filter parameters and each other set of filter parameters, and calculate the mean value to obtain the mean spectral similarity of the filter parameters. The filter parameter with the highest mean spectral characteristic similarity is selected as the candidate optimal filter parameter. The candidate optimal filter parameters are used to conduct simulation experiments on each group of indoor steady-state noise data. If the simulation noise reduction effect is stable, the candidate optimal filter parameters are used as the optimal filter parameters. The simulation noise reduction effect is stable when the noise reduction amount in the 50-1000Hz frequency band is ≥2.0dB(A).

4. The method for active indoor noise control in urban traffic environments based on Wiener's algorithm according to claim 1, characterized in that, Wiener's algorithm solves for the optimal filter parameters according to the following formula: ； in, w opt This represents the optimal filter parameters. E Indicates the expectation. R ( n ) represents the filter reference signal matrix, and the superscript is... T Represents the transpose of a matrix. d ( n ) represents the noise expected signal matrix, , d 1( n Let be the desired noise signal of the first error microphone; the filter reference signal matrix is ​​shown in the following equation: ； in, r ( n ) represents the reference signal x ( n The filtered reference signal after secondary path filtering, I This indicates the filter order of the indoor noise controller. L Indicates the number of error microphones.

5. The method for active indoor noise control in urban traffic environments based on Wiener's algorithm according to claim 4, characterized in that, n Time of the first m Control signals for each secondary speaker As shown in the following formula: ； in, y m ( n )express n Time of the first m Control signals for each secondary speaker, K Indicates the number of reference microphones. k The label representing the reference microphone. i Indicates the filter order label. w mki ( n )express n Time of the first k The reference signal to the first m The filter of the room noise controller between the secondary speakers. i Order coefficient, x k ( ni )express n - i Time of the first k One reference signal; The control signal output by the indoor noise controller y m ( n After the secondary path, the first l The signal received by each error microphone is shown in the following formula: ； in, e l ( n ) indicates the first l The signal received by the error microphone G lmj Indicates the first m Control signals for each secondary speaker y m ( n ) to the l The second path traversed by the error sensor j Order coefficient, M Indicates the number of secondary speakers. m The designation indicating the secondary speaker. J Indicates the order of the secondary path. j Indicates the order label of the secondary path. d l ( n ) indicates the first l The expected noise signal of an error microphone.

6. The method for active indoor noise control in urban traffic environments based on Wiener's algorithm according to claim 5, characterized in that, The objective function of the indoor noise controller is shown in the following equation: ； in, J Represents the objective function value. e ( n ) represents the error signal matrix, ; w Represents the control coefficient matrix. , .

7. An active indoor noise control system based on Wiener's algorithm in an urban traffic environment, comprising: Several reference microphones, two error microphones, several secondary speakers, and an indoor noise controller; Several reference microphones are arranged indoors near the noise source, and two error microphones are respectively arranged on the left and right sides of the target noise reduction area. The reference microphones and error microphones are used to jointly collect real-time indoor steady-state noise data. The indoor noise controller is used to filter the indoor steady-state noise data and output an anti-noise signal. The secondary loudspeaker is used to receive the anti-noise signal and generate a noise cancellation wave to achieve noise suppression in the target noise reduction area. The indoor noise active control system based on the Wiener algorithm in an urban traffic environment further includes: The indoor steady-state noise data acquisition module is used to acquire multiple sets of indoor steady-state noise data, mainly traffic noise, through the reference microphone and the error microphone. The optimal filter algorithm solution module is used to obtain the optimal filter parameters offline by using the Wiener algorithm based on the noise steady-state characteristics of multiple sets of indoor steady-state noise data. An indoor noise active control module is used to use the optimal filter parameters as the filter parameters of the indoor noise controller, and applied to the indoor noise active control system to reduce noise in the area to be reduced.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the active indoor noise control method based on the Wiener algorithm in an urban traffic environment according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the active indoor noise control method based on Wiener algorithm in urban traffic environments as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the active indoor noise control method based on Wiener algorithm in urban traffic environments as described in any one of claims 1-6.

Images