Noise reduction method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a noise reduction method and device, computer equipment, a readable storage medium and a program product, and the method comprises the steps: converting a collected original noise signal to obtain an initial reference signal, carrying out the phase adjustment of the initial reference signal according to preset delay information, and obtaining an adjusted reference signal; and inputting the adjusted reference signal into a target adaptive filter for filtering processing to obtain an anti-noise signal, and outputting a secondary noise signal based on the anti-noise signal to carry out superposition cancellation on the original noise signal to realize noise reduction. According to the two-stage active noise reduction control method, the phase of an input signal is adjusted through the front stage, the frequency and the amplitude of a secondary noise signal are adjusted through the rear stage, the system delay can be effectively compensated, the noise reduction system has the higher convergence speed and precision, and the generated secondary noise signal and an original noise signal have the higher convergence speed and precision. Sound wave destructive interference occurs in an expected area, and more efficient noise reduction is achieved.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a noise reduction method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the advancement of electronic technology, active noise cancellation (ANC) has become an effective means of reducing ambient noise and improving acoustic comfort. Its basic principle is to use an electronic system to collect ambient noise signals in real time, generate secondary sound waves with opposite phase and comparable amplitude, and then utilize the destructive interference of these waves to suppress noise in target areas (such as the headphone cavity and vehicle cabin). This technology has broad application prospects in consumer electronics (such as noise-canceling headphones), home appliances, and automotive applications.

[0003] However, according to the theory of acoustic interference, the secondary noise signal must maintain a strict phase relationship with the original noise signal. That is, the phase difference between the two must not exceed 90° (equivalent to one-quarter of the noise signal period). Current noise reduction systems not only experience response delays in the microphone and speaker, but also computational delays in the ADC (Analog to Digital Converter) / DAC (Digital to Analog Converter) conversion and DSP (Digital Signal Processing) algorithm processing. Optimizing system latency and reducing costs while maintaining noise reduction performance have become key challenges in active noise reduction technology. Summary of the Invention

[0004] Based on this, it is necessary to provide a noise reduction method, apparatus, computer device, computer-readable storage medium and computer program product to address the technical problem of system delay in the above-mentioned active noise reduction technology.

[0005] In a first aspect, the present application provides a noise reduction method, the method comprising:

[0006] An initial reference signal is obtained by converting the collected original noise signal;

[0007] Performing phase adjustment on the initial reference signal according to the preset delay information to obtain an adjusted reference signal;

[0008] Inputting the adjustment reference signal into a target adaptive filter for filtering to obtain an anti-noise signal, wherein the target adaptive filter is updated and iterated using the adjustment reference signal;

[0009] A secondary noise signal is output based on the anti-noise signal, and noise reduction processing is performed on the original noise signal.

[0010] In one embodiment, the phase adjustment of the initial reference signal according to the preset delay information to obtain the adjusted reference signal includes:

[0011] Solving a preset adjustment coefficient according to the preset delay information, and substituting the preset adjustment coefficient into the initial phase adjustment model to obtain a target phase adjustment model;

[0012] The initial reference signal is input into the target phase adjustment model for phase adjustment to obtain an adjustment reference signal.

[0013] In one embodiment, inputting the initial reference signal into the target phase adjustment model for phase adjustment to obtain an adjustment reference signal includes:

[0014] If the original noise signal is a periodic signal, inputting the initial reference signal into the target phase adjustment model to perform phase advance adjustment or phase lag adjustment to obtain an adjustment reference signal;

[0015] Otherwise, the initial reference signal is input into the target phase adjustment model to perform phase advance adjustment to obtain an adjustment reference signal.

[0016] In one embodiment, solving a preset adjustment coefficient according to the preset delay information includes:

[0017] Calculating the frequency domain transfer function of the initial phase adjustment model to obtain a corresponding phase-frequency characteristic function;

[0018] Performing phase conversion on the preset delay information to obtain a preset phase difference;

[0019] A preset adjustment coefficient is obtained by solving the preset phase difference and the phase-frequency characteristic function.

[0020] In one embodiment, the step of acquiring the target adaptive filter includes:

[0021] Performing weighted processing on the initial reference signal and the anti-noise signal respectively to obtain a desired noise signal and a secondary noise signal;

[0022] generating an error signal by canceling the secondary noise signal and the expected noise signal;

[0023] The weight coefficients of the initial adaptive filter are updated according to the adjustment reference signal and the error signal until the error signal meets a preset convergence condition, thereby obtaining the target adaptive filter.

[0024] In one embodiment, the weighted processing of the initial reference signal and the anti-noise signal to obtain the expected noise signal and the secondary noise signal includes:

[0025] Performing weighted processing on the initial reference signal based on the weight coefficient of the primary channel filter to obtain the expected noise signal;

[0026] The anti-noise signal is weighted based on the weight coefficient of the secondary channel filter to obtain the secondary noise signal.

[0027] In a second aspect, the present application provides a noise reduction device, comprising:

[0028] A signal conversion module is used to convert the collected original noise signal into an initial reference signal;

[0029] A phase adjustment module, configured to perform phase adjustment on the initial reference signal according to preset delay information to obtain an adjustment reference signal;

[0030] An active noise reduction module, configured to input the adjustment reference signal into a target adaptive filter for filtering to obtain an anti-noise signal, wherein the target adaptive filter is updated and iterated using the adjustment reference signal;

[0031] The noise output module is used to output a secondary noise signal based on the anti-noise signal and perform noise reduction processing on the original noise signal.

[0032] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0034] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0035] The above-mentioned noise reduction method, apparatus, computer device, computer-readable storage medium, and computer program product, after converting the collected original noise signal to obtain an initial reference signal, first adjusts the phase of the initial reference signal according to preset delay information to obtain an adjusted reference signal, and then inputs the adjusted reference signal into a target adaptive filter for filtering processing to obtain an anti-noise signal. Based on the anti-noise signal, a secondary noise signal is output to superimpose and cancel the original noise signal to achieve noise reduction. By adjusting the phase of the input signal in the front stage and then adjusting the frequency and amplitude of the secondary noise signal in the back stage, this two-stage active noise reduction control method can effectively compensate for system delays, making the noise reduction system have faster convergence speed and accuracy. The generated secondary noise signal and the original noise signal undergo acoustic destructive interference in the desired area, achieving more efficient noise reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A diagram showing an application environment of a noise reduction method according to an embodiment;

[0038] Figure 2 1 is a flow chart of a noise reduction method according to an embodiment;

[0039] Figure 3 Schematic diagram of a system block diagram of an active noise reduction system in one embodiment;

[0040] Figure 4 Schematic diagram of an algorithm block diagram of a noise reduction method in one embodiment;

[0041] Figure 5 is a flow chart of a noise reduction method according to another embodiment;

[0042] Figure 6 is a flow chart of a noise reduction method according to another embodiment;

[0043] Figure 7 is a flow chart of a noise reduction method according to another embodiment;

[0044] Figure 8 is a flow chart of a noise reduction method according to another embodiment;

[0045] Figure 9 A schematic diagram of input signals of an active noise reduction system according to an embodiment;

[0046] Figure 10 A global schematic diagram of single-stage active noise reduction in one embodiment;

[0047] Figure 11 A local detail diagram of a single-stage active noise reduction in one embodiment;

[0048] Figure 12 A global schematic diagram of dual-stage active noise reduction in one embodiment;

[0049] Figure 13 A partial detail diagram of dual-stage active noise reduction in one embodiment;

[0050] Figure 14 is a structural block diagram of a noise reduction device in one embodiment;

[0051] Figure 15 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] As mentioned in the background technology, existing active noise reduction systems still face significant technical bottlenecks in practical applications. According to the theory of acoustic wave interference, the secondary noise signal must maintain a strict phase relationship with the original noise, that is, the phase difference between the two must not exceed 90 degrees (equivalent to one-quarter of the noise signal period). For example, for a 500Hz (hertz) intermediate frequency noise, its period is 2ms (milliseconds), so the total delay from noise acquisition to secondary sound wave output must be controlled within 500 (microseconds), otherwise the noise reduction effect will be greatly reduced or even lead to noise enhancement.

[0054] Currently, the main factors affecting system latency include:

[0055] 1. Sensor and actuator delay: There is an inherent response delay when the microphone collects noise signals and the speaker plays secondary sound waves;

[0056] 2. Signal processing delay: There is computational delay in analog-to-digital conversion (ADC), digital-to-analog conversion (DAC), and digital signal processing (DSP) algorithm processing.

[0057] Strict phase conditions place extremely high latency demands on both the hardware (e.g., circuit design) and software (e.g., computing speed of the main control chip) of active noise cancellation systems. Traditional solutions rely on high-performance hardware (e.g., high-speed ADC / DAC, low-latency DSP chips) to reduce latency, but the high cost of such components hinders the market adoption of noise cancellation products. Therefore, optimizing system latency and reducing costs while maintaining noise cancellation performance has become a key challenge facing active noise cancellation technology.

[0058] The noise reduction method provided in the embodiment of the present application can be applied to Figure 1 The active noise reduction system shown in FIG. The active noise reduction system includes a first sensor 102, a controller 104, and a secondary speaker 106. The first sensor 102 and the secondary speaker 106 communicate with the controller 104. A data storage system can store data that the controller 104 needs to process. The data storage system can be integrated with the controller 104 or stored in the cloud or other network servers.

[0059] Specifically, the controller 104 obtains the original noise signal collected by the first sensor 102, and converts the original noise signal to obtain an initial reference signal, adjusts the phase of the initial reference signal according to the preset delay information to obtain an adjusted reference signal, and then inputs the adjusted reference signal into the target adaptive filter for filtering to obtain an anti-noise signal. Finally, by controlling the secondary speaker 106 to output a secondary noise signal based on the anti-noise signal, the original noise signal is superimposed and cancelled to achieve noise reduction, wherein the target adaptive filter is updated and iterated using the adjusted reference signal.

[0060] Controller 104 can be a control chip or control circuit board within the active noise reduction system, or an external control system implemented via wireless communication. The external control system can be implemented via devices such as a terminal or server. Terminals can include, but are not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices can include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. A server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0061] In an exemplary embodiment, Figure 2 As shown, a noise reduction method is provided, which is applied to Figure 1The controller 104 in FIG. 1 is taken as an example to illustrate the process, which includes the following steps S202 to S208. In which:

[0062] Step S202: Obtain an initial reference signal by converting the collected original noise signal.

[0063] The original noise signal is the main noise subject to be reduced. For example, in the case of noise reduction of a household appliance, such as a range hood, the original noise signal may be the noise generated by the range hood's fan. In this embodiment, a first sensor is positioned at the noise-generating location to collect the original noise signal. For example, for noise generated by the range hood's fan, the first sensor may be positioned at the range hood's air outlet to detect the original noise signal generated by the range hood's fan.

[0064] In some examples, the first sensor may be a microphone device, and the signal type of the original noise signal collected by the first sensor may be a sound wave signal.

[0065] Specifically, after the controller obtains the original noise signal collected by the first sensor, it needs to perform acoustic-electrical conversion on the original noise signal to form an initial reference signal of the electrical signal type. Figure 3 The acoustic-to-electrical conversion of the original noise signal can be achieved through an analog-to-digital conversion (ADC) circuit. Before the analog-to-digital conversion, the original noise signal can be pre-processed by circuits such as filter circuit 1, an inverting amplifier circuit, and filter circuit 2 to eliminate interference signals.

[0066] Step S204: performing phase adjustment on the initial reference signal according to the preset delay information to obtain an adjusted reference signal.

[0067] The preset delay information is used to characterize the total transmission delay generated by each level in the active noise reduction system. Figure 3 The controller (DSP) in the example is the boundary. The preset delay information can include a first delay for DSP front-end transmission and calculation, and a second delay for DSP back-end transmission and calculation. The specific data for these delays is primarily determined by device performance and algorithm complexity. The corresponding preset delay information can be obtained by testing an actual active noise reduction system after operation.

[0068] Specifically, refer to Figure 4 Based on the traditional single-stage active noise reduction system, this application adds a front-stage phase adjustment model H(Z) before entering the active noise reduction algorithm to perform phase adjustment on the initial reference signal x(n) according to the preset delay information, compensate for the total transmission delay caused by the system response and calculation, and obtain the adjusted reference signal x2(n).

[0069] For example, the phase adjustment model H(Z) can be implemented in a variety of ways. It can be implemented using a software algorithm, such as by designing a digital filter (e.g., FIR or IIR). Specifically, a lead compensator can be designed to generate a phase lead through a zero-pole configuration based on a transfer function. Alternatively, a predictor (e.g., a Smith predictor) can be used to compensate for system delays, or a digital control algorithm (e.g., the differential step in a PID controller) can be used to introduce a phase lead. In other embodiments, the phase adjustment model H(Z) can also be implemented using a hardware circuit, such as a first-order high-pass filter to achieve a phase lead effect. By adjusting the resistor R or the capacitor C, or both R and C, the phase of the input signal can be changed to produce the desired phase lead effect.

[0070] In practical applications, if the preset delay information of the active noise reduction system cannot be fully determined, the noise reduction effect can be optimized by adjusting the structure or coefficients of the phase adjustment model H(Z).

[0071] Step 206 : Input the adjusted reference signal into the target adaptive filter for filtering to obtain an anti-noise signal, wherein the target adaptive filter is updated and iterated using the adjusted reference signal.

[0072] Step 208: Output a secondary noise signal based on the anti-noise signal, and perform noise reduction processing on the original noise signal.

[0073] The target adaptive filter may be a feedforward adaptive filter, which is arranged in the controller (DSP).

[0074] Before practical application, a second sensor must be placed in the target noise reduction area (the primary area to be reduced). This sensor collects the electrical signal reaching the target noise reduction area, hereafter referred to as the error signal, denoted as e(n). The corresponding first sensor collects the original noise signal at the noise source and converts it into the initial reference signal x(n).

[0075] Specifically, the target adaptive filter can process and generate an anti-noise signal y(n) based on the initial reference signal x(n) and the error signal e(n) collected by the first sensor. The anti-noise signal y(n) is an electrical signal, which can be converted into a sound wave signal by a secondary speaker and emitted. The sound wave signal and the initial reference signal x(n) arrive at the second sensor at the same time. Among them, the signal of the sound wave signal reaching the second sensor is the secondary noise signal y2(n), and the signal of the initial reference signal x(n) reaching the second sensor is the expected noise signal d(n). The expected noise signal d(n) and the secondary noise signal y2(n) are signals with opposite phases and the same frequency and amplitude. They can meet and cancel each other out in the target noise reduction area, thereby achieving the expected noise reduction effect.

[0076] The above-mentioned noise reduction method, after converting the collected original noise signal into an initial reference signal, first adjusts the phase of the initial reference signal according to preset delay information to obtain an adjusted reference signal. The adjusted reference signal is then input into the target adaptive filter for filtering to obtain an anti-noise signal. The anti-noise signal is then used to output a secondary noise signal that superimposes and cancels the original noise signal to achieve noise reduction. By adjusting the phase of the input signal in the front stage and then adjusting the frequency and amplitude of the secondary noise signal in the back stage, this two-stage active noise reduction control method can effectively compensate for system delays, enabling the noise reduction system to have faster convergence speed and accuracy. The generated secondary noise signal and the original noise signal undergo destructive interference in the desired area, achieving more efficient noise reduction.

[0077] In an exemplary embodiment, Figure 5 As shown, step S204 includes the following steps S302 to S304. Among them:

[0078] Step S302 : Calculate a preset adjustment coefficient according to the preset delay information, and substitute the preset adjustment coefficient into the initial phase adjustment model to obtain a target phase adjustment model.

[0079] Specifically, this embodiment uses a first-order high-pass filter to implement the phase adjustment model. Correspondingly, the preset adjustment coefficient can refer to the time constant in the high-pass filter. , which is determined by the circuit parameters (R, C) or filter design, and directly determines the cutoff frequency and phase change rate.

[0080] Correspondingly, the preset adjustment coefficient (time constant ), and then substitute the preset adjustment coefficient into the initial phase adjustment model, that is, the time constant obtained by the solution As the time constant of the initial high-pass filter constructed, the target phase adjustment model can achieve a phase advance effect according to the preset delay information.

[0081] In an exemplary embodiment, Figure 6 As shown, the step S302 of solving the preset adjustment coefficient according to the preset delay information includes the following steps S402 to S406.

[0082] Step S402 : performing calculations based on the frequency domain transfer function of the initial phase adjustment model to obtain a corresponding phase-frequency characteristic function.

[0083] Specifically, in order to simplify the system implementation and description, this embodiment uses a simple first-order high-pass filter to construct the phase adjustment model H(Z), which is specifically constructed as follows:

[0084]

[0085] in, is the time constant, also known as the preset adjustment coefficient. Ts is the sampling period of the input signal.

[0086] The above formula is the transfer function of the constructed first-order high-pass filter in the discrete time domain (Z domain). Correspondingly, the frequency domain transfer function in the continuous time domain (S domain) can be obtained through some transformation (such as bilinear transformation) as H(s), which is expressed as:

[0087]

[0088] Furthermore, based on the above high-pass filter, the relationship between the phase and frequency of the signal after passing through the filter is obtained, that is, the phase-frequency characteristic function: .

[0089] Step 404: Perform phase conversion on the preset delay information to obtain a preset phase difference.

[0090] Specifically, the preset delay information is time data, and a preset phase difference corresponding to the preset delay information, i.e., the magnitude of the phase difference, can be calculated based on the period of the initial reference signal x(n). This conversion can be based on the principle that the total phase corresponding to a total period is 360°, and the preset phase difference corresponding to the preset delay information can be calculated based on the ratio of the preset delay information to the total period being equal to the ratio of the preset phase difference to the total phase.

[0091] The period of the initial reference signal x(n) is T=1 / 150≈6666.67 , the front-end delay is 150 , the backend delay is 50 , the total system delay is 200 Taking (preset delay information) as an example, the preset phase difference can be calculated as 360°*200 / 6666.67≈10.8°.

[0092] Step 406 : performing a solution based on the preset phase difference and the phase-frequency characteristic function to obtain a preset adjustment coefficient.

[0093] Specifically, the calculated preset phase difference can be substituted into the phase-frequency characteristic function to obtain the preset adjustment coefficient. , the preset adjustment coefficient can be obtained by solving ≈0.0056.

[0094] Step 304: Input the initial reference signal into the target phase adjustment model to perform phase adjustment to obtain an adjustment reference signal.

[0095] Specifically, the initial reference signal x(n) is filtered using the simple first-order high-pass filter to construct the phase adjustment model H(Z) to obtain the adjustment reference signal x2(n). The adjustment reference signal x2(n) can be obtained according to the following formula:

[0096]

[0097] in, M1 is the filter order of the primary channel filter.

[0098] In an exemplary embodiment, step S304 includes: if the original noise signal is a periodic signal, inputting the initial reference signal into the target phase adjustment model for phase advance adjustment or phase lag adjustment to obtain an adjustment reference signal; otherwise, inputting the initial reference signal into the target phase adjustment model for phase advance adjustment to obtain an adjustment reference signal.

[0099] Understandably, to compensate for the inherent delay of active noise systems, a phase adjustment model H(Z) is generally required to apply a phase advance to the initial reference signal x(n), preventing noise reduction failure due to lag adjustment. If the original noise signal is determined to be periodic, dynamic phase advance or lag adjustment can be selected to fine-tune the phase delay in specific frequency bands (such as low-frequency resonance peaks) to optimize the interference cancellation effect.

[0100] Specifically, the phase advance adjustment and phase lag adjustment can be achieved by adjusting the structure or coefficient of the phase adjustment model H(Z), which can be determined according to the actual construction method of the phase adjustment model H(Z) and is not limited.

[0101] In an exemplary embodiment, Figure 7 As shown, the step of obtaining the target adaptive filter in step S206 includes the following steps S502 to S506.

[0102] Step S502 : performing weighted processing on the initial reference signal and the anti-noise signal to obtain a desired noise signal and a secondary noise signal.

[0103] Specifically, refer to Figure 3 The primary channel P(Z) represents the physical path from the first sensor (where the noise is generated) to the second sensor, while the secondary channel S(Z) corresponds to the physical path from the secondary speaker to the second sensor. Generally, these two physical paths can be considered channels with filtering characteristics.

[0104] Correspondingly, the desired noise signal d(n) can be generated by obtaining the weight coefficient of the primary channel corresponding to the propagation path of the initial reference signal x(n) and weighting the initial reference signal x(n) according to the weight coefficient of the primary channel. Similarly, the weight coefficient of the secondary channel corresponding to the propagation path of the anti-noise signal y(n) can be obtained and weighted according to the weight coefficient of the secondary channel to generate the weighted secondary noise signal y2(n).

[0105] In an exemplary embodiment, Figure 8 As shown, step S502 includes the following steps S602 to S604. In step S602, the initial reference signal is weighted based on the weight coefficient of the primary channel filter to obtain the desired noise signal. In step S604, the anti-noise signal is weighted based on the weight coefficient of the secondary channel filter to obtain the secondary noise signal.

[0106] Specifically, assuming the primary and secondary channels are filter structures, data from the first sensor (reference microphone), secondary speaker (speaker end), and second sensor (error microphone) can be collected before running the active noise reduction algorithm. The initial reference signal x(n) picked up by the first sensor, the acoustic signal emitted by the secondary speaker based on the anti-noise signal y(n), and the secondary noise signal y2(n) picked up by the second sensor, along with the desired noise signal d(n), are then used to estimate the filter weights for the primary and secondary channels using system identification methods (such as minimum mean square error, least squares, and neural network methods). This is then combined with mathematical metrics (such as cross-entropy loss, mean square error, and coefficient of determination) to measure identification accuracy. Once the accuracy reaches a set threshold, the estimated filter weights can be used as the primary and secondary channels.

[0107] Understandably, in practical applications, since the second sensor (error microphone) is difficult to place, the original noise signal is typically collected in the target noise reduction area for system identification using the aforementioned method. Once the primary and secondary channels are determined through system identification, the second sensor (error microphone) can be removed, leaving only the reference microphone to capture the reference noise signal, and the active noise reduction system can be run to achieve noise reduction.

[0108] For example, in one example, the primary channel filter P(Z) and the secondary channel filter S(Z) can be determined by a system identification method as follows:

[0109]

[0110]

[0111] Among them, 0.4, 0.2, and 0.1 in P(Z) are assumed to be the primary channel filter coefficients, and 0.25, 0.14, and 0.06 in S(Z) are assumed to be the secondary channel filter coefficients.

[0112] Furthermore, the initial reference signal x(n) is weighted based on the weight coefficient of the primary channel filter P(Z) to obtain the desired noise signal d(n), which can be obtained according to the following formula:

[0113]

[0114] The anti-noise signal y(n) is weighted based on the weight coefficient of the secondary channel filter S(Z) to obtain the secondary noise signal y2(n), which can be obtained according to the following formula:

[0115]

[0116] in, , M1 is the filter order of the primary channel filter. , M2 is the filter order of the secondary channel filter.

[0117] Step S504 : canceling the secondary noise signal and the expected noise signal to generate an error signal.

[0118] Specifically, by superimposing the weighted secondary noise signal and the desired noise signal, since the secondary noise signal and the desired noise signal have opposite phases, the superposition and cancellation of the two can generate corresponding residual noise, ie, an error signal.

[0119] Step S506 , updating the weight coefficients of the initial adaptive filter according to the adjustment reference signal and the error signal until the error signal satisfies a preset convergence condition, thereby obtaining a target adaptive filter.

[0120] Specifically, a minimum mean square error algorithm may be used to update the weight coefficients of the initial adaptive filter. The minimum mean square error algorithm is based on an input signal obtained by superimposing an adjustment reference signal x2(n) and an error signal e(n). The negative gradient of the input signal is then determined, and the filter weight coefficients of the initial adaptive filter are adjusted and updated based on the determined negative gradient of the input signal to generate adjusted filter weight coefficients.

[0121] In this embodiment, the filter weight coefficient is adjusted using the following formula:

[0122]

[0123] Among them, w(n) is the adaptive filter weight coefficient, w(n+1) is the filter weight coefficient after adjustment, and μ is the step size, which can be pre-set or modified.

[0124] In a specific embodiment, in order to verify the effectiveness of the noise reduction method proposed in this application, this embodiment is based on Figure 4 The system framework diagram of the noise reduction method shown is simulated and verified.

[0125] Specifically, the active noise reduction method of the present application adds a pre-stage phase regulator H(Z), which usually produces a phase advance effect to compensate for the system response delay and calculation delay. -N1 Represents the total delay of DSP front-end transmission and calculation, Z -N2 = ∑ ...

[0126] The expected noise signal d(n) is calculated as follows:

[0127]

[0128] The front-end delay signal x1(n) can be expressed as:

[0129]

[0130] The adjusted reference signal x2(n) can be expressed as:

[0131]

[0132] The adaptive filtering output signal y1(n) can be expressed as:

[0133]

[0134] The back-end delay signal y(n) can be expressed as:

[0135]

[0136] The secondary noise signal y2(n) is calculated as follows:

[0137]

[0138] in, , M1 is the filter order of the primary channel filter. , M2 is the filter order of the secondary channel filter.

[0139] The minimum mean square error algorithm is used to update the adaptive filter coefficients. The calculation formula is as follows:

[0140]

[0141] Wherein, w(n) is the weight coefficient of the adaptive filter, w(n+1) is the weight coefficient of the filter after adjustment, and μ is a positive constant, which can be preset or modified.

[0142] The error signal is calculated as follows:

[0143]

[0144] In a simulation example, the following parameters are set: sampling frequency fs = 100 kHz, sampling period Ts = 10 Front-end delay N1 = 15, back-end delay N2 = 5, that is, the front-end delay is 150 , the backend delay is 50 , the total system delay is 200 The order of the adaptive filter is L=4, and the step size is =0.2, which can be expressed as:

[0145]

[0146] The primary channel filter and the secondary channel filter are constructed as:

[0147]

[0148]

[0149] Correspondingly, the initial reference signal x(t) of the active noise reduction system is as follows:

[0150]

[0151] Here, f = 150, assuming it is the frequency of the original noise signal, such as the noise generated by the rotation of a motor such as a car engine or a range hood. To facilitate computer processing, the initial reference signal x(t) after analog-to-digital conversion can be rewritten as:

[0152]

[0153] Where n=0,1,2,..., the period of the reference signal is T=1 / 150≈6666.67 , T / 4≈1666.67 .

[0154] Based on the above parameter settings, a simulation experiment was carried out in Matlab. The experimental results are as follows: Figures 9-11 shown. Figure 9It shows the changes of the system input signal, including the initial reference signal x(n), the front-end delayed signal x1(n), and the adjustment reference signal x2(n). Figure 9 It can be seen that the phase of x2(n) is significantly ahead of x(n) and x1(n). Figure 10-11 The simulation results of a single-stage active noise reduction system are shown (i.e., without the preceding phase adjuster H(Z)). Figure 10 is a global schematic diagram of a single-stage active noise reduction system. Figure 11 It is to intercept the local details in the time period [3.9, 4]. Figure 10 It can be seen that the error signal e(n) converges gradually, indicating that the single-stage active noise reduction system can achieve effective noise reduction effect. Figure 12 and Figure 13 The simulation effect of the dual-stage active noise reduction system provided by this application is demonstrated. Figure 12 This is a global schematic diagram of the dual-stage active noise reduction system. Figure 13 It is to intercept the local details in the time period [3.9, 4]. Figure 12 It can be seen that the error signal e(n) also converges gradually, but its convergence speed and accuracy are obviously better than those of the single-stage active noise reduction system.

[0155] In this embodiment, a dual-stage active noise reduction control method is proposed, with a phase adjuster as the front stage and an active noise reduction algorithm as the back stage. The phase adjuster adjusts the phase of the input signal, while the active noise reduction algorithm controls the system to generate noise with the same frequency and amplitude as the original noise. In other words, the front stage adjusts the phase, while the back stage adjusts the frequency and amplitude. This dual-stage adjustment mechanism allows for more flexible generation of secondary noise, which destructively interferes with the original noise in the desired area, effectively reducing ambient noise over the long term.

[0156] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0157] Based on the same inventive concept, embodiments of the present application also provide a noise reduction device for implementing the aforementioned noise reduction method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more noise reduction device embodiments provided below can be found in the above-described limitations of the noise reduction method and will not be further elaborated here.

[0158] In an exemplary embodiment, Figure 14 As shown, a noise reduction device is provided, comprising: a signal conversion module 10, a phase adjustment module 20, an active noise reduction module 30 and a noise output module 40, wherein:

[0159] The signal conversion module 10 is used to convert the collected original noise signal into an initial reference signal;

[0160] The phase adjustment module 20 is used to adjust the phase of the initial reference signal according to the preset delay information to obtain an adjusted reference signal;

[0161] Active noise reduction module 30, used to input the adjustment reference signal into the target adaptive filter for filtering to obtain the anti-noise signal, and the target adaptive filter is updated and iterated using the adjustment reference signal;

[0162] The noise output module 40 is configured to output a secondary noise signal based on the anti-noise signal and perform noise reduction processing on the original noise signal.

[0163] In an exemplary embodiment, the phase adjustment module 20 is also used to solve a preset adjustment coefficient based on preset delay information, and substitute the preset adjustment coefficient into the initial phase adjustment model to obtain a target phase adjustment model; the initial reference signal is input into the target phase adjustment model for phase adjustment to obtain an adjustment reference signal.

[0164] In an exemplary embodiment, the phase adjustment module 20 is also used to input the initial reference signal into the target phase adjustment model for phase advance adjustment or phase lag adjustment to obtain an adjustment reference signal if the original noise signal is a periodic signal; otherwise, input the initial reference signal into the target phase adjustment model for phase advance adjustment to obtain an adjustment reference signal.

[0165] In an exemplary embodiment, the phase adjustment module 20 is also used to perform calculations based on the frequency domain transfer function of the initial phase adjustment model to obtain a corresponding phase-frequency characteristic function; perform phase conversion on the preset delay information to obtain a preset phase difference; and perform a solution based on the preset phase difference and the phase-frequency characteristic function to obtain a preset adjustment coefficient.

[0166] In an exemplary embodiment, the active noise reduction module 30 is further configured to perform weighted processing on the initial reference signal and the anti-noise signal respectively to obtain a desired noise signal and a secondary noise signal; offset the secondary noise signal and the desired noise signal to generate an error signal; and update the weight coefficients of the initial adaptive filter according to the adjusted reference signal and the error signal until the error signal satisfies a preset convergence condition, thereby obtaining a target adaptive filter.

[0167] In an exemplary embodiment, the active noise reduction module 30 is further configured to perform weighted processing on the initial reference signal based on the weight coefficient of the primary channel filter to obtain a desired noise signal;

[0168] The anti-noise signal is weighted based on the weight coefficient of the secondary channel filter to obtain a secondary noise signal.

[0169] Each module in the noise reduction device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0170] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 15 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a noise reduction method is implemented.

[0171] Those skilled in the art will understand that Figure 15The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0172] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0173] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0174] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0175] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0176] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, 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 application.

[0177] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A noise reduction method, characterized in that: The method comprises: An initial reference signal is obtained by converting the collected original noise signal; Performing phase adjustment on the initial reference signal according to the preset delay information to obtain an adjusted reference signal; Inputting the adjustment reference signal into a target adaptive filter for filtering to obtain an anti-noise signal, wherein the target adaptive filter is updated and iterated using the adjustment reference signal; A secondary noise signal is output based on the anti-noise signal, and noise reduction processing is performed on the original noise signal.

2. The method according to claim 1, characterized in that The phase adjustment of the initial reference signal according to the preset delay information to obtain the adjusted reference signal includes: Solving a preset adjustment coefficient according to the preset delay information, and substituting the preset adjustment coefficient into the initial phase adjustment model to obtain a target phase adjustment model; The initial reference signal is input into the target phase adjustment model for phase adjustment to obtain an adjustment reference signal.

3. The method according to claim 2, characterized in that Inputting the initial reference signal into the target phase adjustment model for phase adjustment to obtain an adjustment reference signal includes: If the original noise signal is a periodic signal, inputting the initial reference signal into the target phase adjustment model to perform phase advance adjustment or phase lag adjustment to obtain an adjustment reference signal; Otherwise, the initial reference signal is input into the target phase adjustment model to perform phase advance adjustment to obtain an adjustment reference signal.

4. The method according to claim 2, characterized in that The step of solving a preset adjustment coefficient according to the preset delay information includes: Calculating the frequency domain transfer function of the initial phase adjustment model to obtain a corresponding phase-frequency characteristic function; Performing phase conversion on the preset delay information to obtain a preset phase difference; A preset adjustment coefficient is obtained by solving the preset phase difference and the phase-frequency characteristic function.

5. The method according to any one of claims 1 to 4, characterized in that The step of acquiring the target adaptive filter comprises: Performing weighted processing on the initial reference signal and the anti-noise signal respectively to obtain a desired noise signal and a secondary noise signal; generating an error signal by canceling the secondary noise signal and the expected noise signal; The weight coefficients of the initial adaptive filter are updated according to the adjustment reference signal and the error signal until the error signal meets a preset convergence condition, thereby obtaining the target adaptive filter.

6. The method according to claim 5, characterized in that The performing weighted processing on the initial reference signal and the anti-noise signal respectively to obtain the expected noise signal and the secondary noise signal includes: Performing weighted processing on the initial reference signal based on the weight coefficient of the primary channel filter to obtain the expected noise signal; The anti-noise signal is weighted based on the weight coefficient of the secondary channel filter to obtain the secondary noise signal.

7. A noise reduction device, characterized in that: The device comprises: A signal conversion module is used to convert the collected original noise signal into an initial reference signal; A phase adjustment module, configured to perform phase adjustment on the initial reference signal according to preset delay information to obtain an adjustment reference signal; An active noise reduction module, configured to input the adjustment reference signal into a target adaptive filter for filtering to obtain an anti-noise signal, wherein the target adaptive filter is updated and iterated using the adjustment reference signal; The noise output module is used to output a secondary noise signal based on the anti-noise signal and perform noise reduction processing on the original noise signal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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