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

Abstract

The application relates to a noise reduction method and device, computer equipment, a readable storage medium and a program product. After an initial reference signal is converted from an original noise signal, the initial reference signal is phase-adjusted according to preset delay information to obtain an adjusted reference signal, the adjusted reference signal is input into a target adaptive filter for filtering processing to obtain an anti-noise signal, and a secondary noise signal is output based on the anti-noise signal to superpose and cancel the original noise signal, so that noise reduction is realized. The phase of the input signal is adjusted in the front stage, and the frequency and amplitude of the secondary noise signal are adjusted in the rear stage. The double-stage active noise reduction control method can effectively compensate for system delay, the noise reduction system has faster convergence speed and precision, the secondary noise signal and the original noise signal are subjected to sound wave superposition interference in an expected area, and higher noise reduction efficiency is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a noise reduction method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] With the development of electronic technology, active noise cancellation (ANC) has become an effective means to reduce environmental noise and improve acoustic comfort. The basic principle is to collect environmental noise signals in real time through an electronic system, generate secondary sound waves with opposite phase and equivalent amplitude, and use the sound wave interference cancellation effect to achieve noise suppression in the target area (such as earphone cavities, car cabins, etc.). This technology has broad application prospects in consumer electronics (such as noise-cancelling headphones), home appliances, and automobiles.

[0003] However, according to the theory of sound wave interference cancellation, the secondary noise signal needs to maintain a strict phase relationship with the original noise signal, i.e. the phase difference between the two should not exceed 90° (equivalent to one-fourth of the noise signal period). In current noise reduction systems, not only do microphones and speakers have response delays, but ADC (Analog to Digital Converter) / DAC (Digital to Analog Converter) conversion and DSP (Digital Signal Processing) algorithm processing also have computational delays. How to optimize system delays and reduce costs while ensuring noise reduction performance is a key problem that active noise reduction technology needs to solve. SUMMARY

[0004] Therefore, it is necessary to provide a noise reduction method, device, computer equipment, computer readable storage medium and computer program product to solve the technical problem of system delay in the above active noise reduction technology.

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

[0006] Converting an initial reference signal from the collected original noise signal;

[0007] Phase adjusting the initial reference signal according to preset delay information to obtain an adjusted reference signal;

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

[0009] outputting a secondary noise signal based on the anti-noise signal, and performing noise reduction processing on the original noise signal.

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

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

[0012] inputting the initial reference signal into the target phase adjustment model to perform phase adjustment and obtain an adjusted reference signal.

[0013] In one of the embodiments, the phase adjustment of the initial reference signal according to the preset delay information to obtain an adjusted reference signal comprises:

[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 and obtain an adjusted reference signal;

[0015] otherwise, inputting the initial reference signal into the target phase adjustment model to perform phase advance adjustment and obtain an adjusted reference signal.

[0016] In one of the embodiments, the solving of the preset adjustment coefficient according to the preset delay information comprises:

[0017] calculating according to a 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] solving based on the preset phase difference and the phase frequency characteristic function to obtain a preset adjustment coefficient.

[0020] In one of the embodiments, the obtaining of the target adaptive filter comprises:

[0021] respectively performing weighting processing on the initial reference signal and the anti-noise signal to obtain an expected noise signal and a secondary noise signal;

[0022] performing cancellation according to the secondary noise signal and the expected noise signal to generate an error signal;

[0023] updating a weight coefficient of an initial adaptive filter according to the adjusted reference signal and the error signal until the error signal satisfies a preset convergence condition to obtain the target adaptive filter.

[0024] In one embodiment, the weighting processing of the initial reference signal and the anti-noise signal respectively to obtain the desired noise signal and the secondary noise signal comprises:

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

[0026] weighting processing of the anti-noise signal 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, which comprises:

[0028] a signal conversion module, configured to convert an initial reference signal from an original noise signal collected;

[0029] a phase adjustment module, configured to adjust the phase of the initial reference signal according to preset delay information to obtain an adjusted reference signal;

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

[0031] a noise output module, configured to output a secondary noise signal based on the anti-noise signal to reduce 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, which stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

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

[0035] The noise reduction method, device, computer device, computer readable storage medium and computer program product can first adjust the phase of the initial reference signal according to preset delay information to obtain an adjusted reference signal, then input the adjusted reference signal into a target adaptive filter for filtering processing to obtain an anti-noise signal, and finally output a secondary noise signal based on the anti-noise signal to superimpose and cancel the original noise signal, so as to realize noise reduction. By adjusting the phase of the input signal in the front stage and adjusting the frequency and amplitude of the secondary noise signal in the rear stage, the double-stage active noise reduction control method can effectively compensate for system delay, so that the noise reduction system has faster convergence speed and accuracy, and the secondary noise signal and the original noise signal can be subjected to sound wave cancellation interference in the expected area, so that more efficient noise reduction is realized. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0037] Figure 1 An application environment diagram of the noise reduction method in an embodiment;

[0038] Figure 2 A flowchart of the noise reduction method in an embodiment;

[0039] Figure 3 A system block diagram of the active noise reduction system in an embodiment;

[0040] Figure 4 An algorithm block diagram of the noise reduction method in an embodiment;

[0041] Figure 5 A flowchart of the noise reduction method in another embodiment;

[0042] Figure 6 A flowchart of the noise reduction method in another embodiment;

[0043] Figure 7 A flowchart of the noise reduction method in another embodiment;

[0044] Figure 8 A flowchart of the noise reduction method in another embodiment;

[0045] Figure 9 An input signal diagram of the active noise reduction system in an embodiment;

[0046] Figure 10 Figure 1 is a global schematic diagram of a single-stage active noise reduction according to an embodiment;

[0047] Figure 11 Figure 2 is a local detail diagram of a single-stage active noise reduction according to an embodiment;

[0048] Figure 12 Figure 3 is a global schematic diagram of a two-stage active noise reduction according to an embodiment;

[0049] Figure 13 Figure 4 is a local detail diagram of a two-stage active noise reduction according to an embodiment;

[0050] Figure 14 Figure 5 is a structural block diagram of a noise reduction device according to an embodiment;

[0051] Figure 15 Figure 6 is an internal structure diagram of a computer device according to an embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0053] As described in the background, the existing active noise reduction system still faces significant technical bottlenecks in practical application. According to the theory of sound wave interference, the secondary noise signal needs to maintain a strict phase relationship with the original noise, that is, the phase difference between the two should not exceed 90° (equivalent to one quarter of the noise signal period). For example, for a 500 Hz (Hertz) mid-frequency noise, its period is 2 ms (milliseconds), so the total delay of the system from noise collection to secondary sound wave output needs to be controlled within 500 (μs) otherwise the noise reduction effect will be greatly reduced or even lead to noise enhancement.

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

[0055] 1. Sensor and actuator delay: there is inherent response delay in microphone noise signal collection and loudspeaker secondary sound wave playback;

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

[0057] The strict phase condition puts high low-delay requirements on the hardware (such as circuit design) and software (such as the calculation speed of the master control chip) of the active noise reduction system. The traditional scheme relies on high-performance hardware (such as high-speed ADC / DAC and low-delay DSP chips) to compress the delay, but such devices are costly, which restricts the marketization of noise reduction products. Therefore, how to optimize the system delay and reduce the cost while ensuring the noise reduction performance has become a key problem to be solved in the active noise reduction technology.

[0058] The noise reduction method provided by the embodiments of the present application can be applied to an active noise reduction system as shown in Figure 1 The active noise reduction system includes a first sensor 102, a controller 104 and a secondary loudspeaker 106. The first sensor 102 and the secondary loudspeaker 106 communicate with the controller 104. The data storage system can store the data required to be processed by the controller 104. The data storage system can be integrated on the controller 104, or placed on the cloud or other network servers.

[0059] Specifically, the controller 104 acquires the original noise signal collected by the first sensor 102, and converts the original noise signal to obtain an initial reference signal. The initial reference signal is phase-adjusted according to preset delay information to obtain an adjusted reference signal. The adjusted reference signal is input to a target adaptive filter for filtering processing to obtain an anti-noise signal. Finally, the secondary loudspeaker 106 outputs a secondary noise signal based on the anti-noise signal to superimpose and cancel the original noise signal, thereby realizing noise reduction. The target adaptive filter is updated and iterated using the adjusted reference signal.

[0060] The controller 104 can be a control chip or a control circuit board provided in the active noise reduction system, or an external control system realized based on wireless communication. The external control system can be realized through a terminal or a server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0061] In an exemplary embodiment, as shown in Figure 2 a noise reduction method is provided. The method is applied to Figure 1The controller 104 in the active noise reduction system 100 is taken as an example to illustrate the method, including the following steps S202 to S208. Wherein:

[0062] At step S202, an initial reference signal is converted from the collected original noise signal.

[0063] The original noise signal is the noise subject to be reduced. For example, the original noise signal can be the noise generated by the fan of the range hood. In the embodiment of the present application, the first sensor is arranged at the noise generation position to collect the original noise signal, for example, the first sensor is arranged at the air inlet of the range hood to detect the original noise signal generated by the fan of the range hood.

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

[0065] Specifically, after the controller obtains the original noise signal collected by the first sensor, the original noise signal needs to be acoustoelectrically converted to form an initial reference signal of an electric signal type. For example, referring to Figure 3 The acoustoelectric conversion of the original noise signal can be realized by an analog-to-digital conversion (ADC) circuit. Before analog-to-digital conversion, the original noise signal can be preprocessed by a filter circuit 1, an inverting amplifier circuit and a filter circuit 2, etc. to exclude interference signals.

[0066] At step S204, the initial reference signal is phase adjusted according to preset delay information to obtain an adjusted reference signal.

[0067] The preset delay information is used to represent the total transmission delay generated by each stage in the active noise reduction system. For example, referring to Figure 3 The preset delay information can include a first delay of the front-end transmission and calculation of the controller (DSP) and a second delay of the rear-end transmission and calculation of the controller (DSP). The specific data of the above delays is mainly determined by the device performance and algorithm complexity. The preset delay information can be obtained by detecting the active noise reduction system after actual use.

[0068] Specifically, referring to Figure 4 On the basis of the traditional single-stage active noise reduction system, the embodiment of the present application adds a phase adjustment model H(Z) of the front stage before entering the active noise reduction algorithm, to phase adjust the initial reference signal x(n) according to the preset delay information, compensate for the total transmission delay generated by the system response and calculation, and obtain an adjusted reference signal x2(n).

[0069] Exemplarily, the implementation of the phase adjustment model H(Z) is not unique, and can be implemented by a software algorithm, for example, by designing a digital filter (such as FIR or IIR) to implement. Specifically, it can be a lead compensator design, which generates a phase lead based on the transfer function through zero-pole configuration, or it can be a pre-estimation (such as Smith pre-estimator) to compensate for system delay, or a digital control algorithm (such as the derivative term in PID) to introduce a leading phase. In other embodiments, the phase adjustment model H(Z) can also be implemented by a hardware circuit, for example, a first-order high-pass filter is used to achieve the phase lead effect, by adjusting the resistance R or the capacitance C, or adjusting R and C at the same time, the phase of the input signal can be changed, thereby generating the desired phase lead effect.

[0070] In practical applications, if the preset delay information of the active noise reduction system cannot be completely determined, the structure or coefficient of the phase adjustment model H(Z) can also be adjusted to achieve the effect of optimizing noise reduction.

[0071] Step 206, input the adjusted reference signal into the target adaptive filter for filtering processing to obtain an anti-noise signal, wherein the target adaptive filter is updated iteratively using the adjusted reference signal.

[0072] Step 208, output a secondary noise signal based on the anti-noise signal to reduce the original noise signal.

[0073] The target adaptive filter can be a feedforward adaptive filter and is arranged in the controller (DSP).

[0074] Before practical application, a second sensor needs to be arranged in a target noise reduction area (main area where noise reduction is needed) to collect an electrical signal reaching the target noise reduction area, which is referred to as an error signal and denoted as e(n). Corresponding to the first sensor, the original noise signal is collected at the noise generation position to obtain an initial reference signal x(n).

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

[0076] The above noise reduction method, according to the initial reference signal converted from the collected original noise signal, first adjusts the phase of the initial reference signal according to the preset delay information to obtain an adjusted reference signal, then inputs the adjusted reference signal into a target adaptive filter for filtering processing to obtain an anti-noise signal, and finally outputs a secondary noise signal based on the anti-noise signal to superimpose and cancel the original noise signal, so as to realize noise reduction. By adjusting the phase of the input signal in the front stage, and adjusting the frequency and amplitude of the secondary noise signal in the rear stage, the double-stage active noise reduction control method can effectively compensate for the system delay, so that the noise reduction system has faster convergence speed and accuracy. The secondary noise signal and the original noise signal interfere with each other in the expected area, and higher noise reduction is achieved.

[0077] In one exemplary embodiment, as shown in Figure 5 Step S204 includes steps S302 to S304. Among them:

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

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

[0080] Correspondingly, the preset adjustment coefficient (time constant ) can be solved according to the preset delay information, and then the preset adjustment coefficient is substituted into the initial phase adjustment model, that is, the time constant solved is taken as the time constant of the initial high-pass filter constructed, so that the target phase adjustment model can realize the phase advance effect according to the preset delay information.

[0081] In one exemplary embodiment, as shown in Figure 6 Step S302 includes steps S402 to S406. Among them:

[0082] Step S402, according to the frequency domain transfer function of the initial phase adjustment model, calculate to obtain the corresponding phase frequency characteristic function.

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

[0084]

[0085] wherein, is a time constant, i.e. a preset adjustment coefficient. Ts is a sampling period of the input signal.

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

[0087]

[0088] Further, based on the above high-pass filter, a relationship between a phase and a frequency after the signal passes through the filter, i.e. a phase-frequency characteristic function, is obtained: .

[0089] In step 404, a preset phase difference is obtained by phase conversion of the preset delay information.

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

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

[0092] In step 406, the preset adjustment coefficient is obtained by solving based on the preset phase difference and the phase-frequency characteristic function.

[0093] Specifically, the preset phase difference calculated can be substituted into the phase-frequency characteristic function to obtain the preset adjustment coefficient. Taking the preset phase difference calculated in the above example as an example, the preset adjustment coefficient can be calculated as follows: , and the preset adjustment coefficient ≈0.0056 can be obtained.

[0094] In step 304, the initial reference signal is input into the target phase adjustment model to perform phase adjustment, and an adjusted reference signal is obtained.

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

[0096]

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

[0098] In an exemplary embodiment, the step S304 comprises: 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 the adjusted reference signal; otherwise, inputting the initial reference signal into the target phase adjustment model to perform phase advance adjustment to obtain the adjusted reference signal.

[0099] It can be understood that, in order to compensate for the inherent delay generated by the active noise system, the initial reference signal x(n) generally needs to be phase-advanced by the phase adjustment model H(Z) to avoid noise reduction failure caused by lag adjustment. If the original noise signal is determined to be a periodic signal, phase advance adjustment or phase lag adjustment can be dynamically selected to fine-tune the phase delay in a specific frequency band (such as a low-frequency resonance peak) and optimize the interference cancellation effect.

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

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

[0102] Step S502, the initial reference signal and the anti-noise signal are respectively weighted to obtain the expected noise signal and the secondary noise signal.

[0103] Specifically, referring to Figure 3 , the primary channel P(Z) represents the physical path from the first sensor (noise generation position) to the second sensor, and the secondary channel S(Z) corresponds to the physical path from the secondary loudspeaker to the second sensor. In general, the two physical paths can be regarded as channels with filtering characteristics.

[0104] Correspondingly, the desired noise signal d(n) can be generated by obtaining the weight coefficients of the primary channel corresponding to the propagation path of the initial reference signal x(n) and performing weight processing on the initial reference signal x(n) according to the weight coefficients of the primary channel. Similarly, the weight-processed secondary noise signal y2(n) can be generated by obtaining the weight coefficients of the secondary channel corresponding to the propagation path of the anti-noise signal y(n) and performing weight processing on the anti-noise signal y(n) according to the weight coefficients of the secondary channel.

[0105] In an exemplary embodiment, as shown in FIG. 5, step S502 includes steps S602-S604. In step S602, the initial reference signal is weight-processed based on the weight coefficients of the primary channel filter to obtain the desired noise signal. In step S604, the anti-noise signal is weight-processed based on the weight coefficients of the secondary channel filter to obtain the secondary noise signal. Figure 8

[0106] Specifically, assuming that the primary channel and the secondary channel are filter structures, the data of the first sensor (reference microphone), the secondary loudspeaker (speaker end), and the second sensor (error microphone) can be collected before the active noise reduction algorithm is run, and then the initial reference signal x(n) picked up by the first sensor, the sound wave signal emitted by the secondary loudspeaker based on the anti-noise signal y(n), and the secondary noise signal y2(n) picked up by the second sensor are used as the desired noise signal d(n) to estimate the filter weight coefficients of the primary channel and the secondary channel by using a system identification method (such as the least mean square error method, the least square method, and the neural network method, etc.). On this basis, the identification accuracy is measured in combination with a mathematical index (such as cross-entropy loss, mean square error, and determination coefficient, etc.), and when the accuracy reaches a set threshold, the estimated filter weight coefficients can be used as the primary channel and the secondary channel.

[0107] It can be understood that in actual applications, the original noise signal is usually collected in the target noise reduction area to perform system identification in the above manner due to the difficulty in placing the second sensor (error microphone). Once the forms of the primary channel and the secondary channel are determined by the system identification method, the second sensor (error microphone) can be removed, and only the reference microphone is used to capture the reference noise signal, and the active noise reduction system is run to achieve noise reduction.

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

[0109]

[0110]

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

[0112] Further, the manner of performing the weighting processing on the initial reference signal x(n) based on the weight coefficients of the primary channel filter P(Z) to obtain the expected noise signal d(n) can be obtained according to the following formula:

[0113]

[0114] The manner of performing the weighting processing on the anti-noise signal y(n) based on the weight coefficients of the secondary channel filter S(Z) to obtain the secondary noise signal y2(n) can be obtained according to the following formula:

[0115]

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

[0117] Step S504, according to 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 expected noise signal, since the phases of the secondary noise signal and the expected noise signal are opposite, the superimposed secondary noise signal and the expected noise signal can generate a corresponding residual noise, i.e. the error signal.

[0119] Step S506, according to the adjustment reference signal and the error signal to update the weight coefficients of the initial adaptive filter until the error signal meets the preset convergence condition, and obtain a target adaptive filter.

[0120] Specifically, the least mean square error algorithm can be used to update the weight coefficients of the initial adaptive filter. The least mean square algorithm is based on the input signal obtained by superimposing the adjustment reference signal x2(n) and the error signal e(n). Then determine the negative gradient of the input signal, and adjust and update the filter weight coefficients of the initial adaptive filter according to the determined negative gradient of the input signal to generate the adjusted filter weight coefficients.

[0121] In this embodiment, the following formula is used to adjust the filter weight coefficients:

[0122]

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

[0124] In one specific embodiment, in order to verify the effectiveness of the noise reduction method proposed in the present application, the present embodiment is simulated based on the system framework diagram of the noise reduction method shown in the figure. Figure 4

[0125] Specifically, the active noise reduction method of the present application increases a front-stage phase regulator H(Z), which usually produces a phase advance effect, for compensating system response delay and calculation delay. In the system, Z -N1 represents the total delay of DSP front-end transmission and calculation, Z -N2 represents the DSP back-end transmission delay, which is mainly determined by device performance and algorithm complexity. The adaptive filter W(Z) and the least mean square error (LMS) algorithm constitute the core part of the back-stage active noise reduction algorithm.

[0126] Wherein, the expected noise signal d(n) is calculated as follows:

[0127]

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

[0129]

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

[0131]

[0132] The adaptive filter output signal y1(n) can be represented as:

[0133]

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

[0135]

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

[0137]

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

[0139] The least mean square error algorithm is used to update the adaptive filter coefficient, and the calculation formula is as follows:​

[0140]

[0141] wherein w(n) is the adaptive filter weight coefficient, w(n+1) is the adjusted filter weight coefficient, μ is a normal number, which can be set in advance or modified.

[0142] The error signal calculation formula is as follows:

[0143]

[0144] In one simulation example, the following parameters are set, the sampling frequency fs = 100 kHz, the sampling period Ts = 10 , the front-end delay N1 = 15, the back-end delay N2 = 5, that is, the front-end delay is 150 , the back-end delay is 50 , and the total system delay is 200 . The order of the adaptive filter L = 4, the step size = 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] wherein f = 150, which is assumed to be the frequency of the original noise signal, for example, the noise generated by the rotation of the motor of the automobile engine or the extractor hood. In order to facilitate computer processing, the initial reference signal x(t) after analog-to-digital conversion can be rewritten as:

[0152]

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

[0154] Based on the foregoing parameter settings, a simulation experiment is carried out in matlab, and the experimental results are shown as Figures 9-11 . Figure 9The changes of the system input signals are shown, including the initial reference signal x(n), the front-end delay signal x1(n), and the adjusted reference signal x2(n). The changes of the system input signals are shown, including the initial reference signal x(n), the front-end delay signal x1(n), and the adjusted reference signal x2(n). Figure 9 It can be seen that the phase of x2(n) is obviously ahead of that of x(n) and x1(n). Figures 10-11 The simulation effect of the single-stage active noise reduction system (i.e., without the front-stage phase adjuster H(Z)) is shown. Figure 10 is a global schematic diagram of the single-stage active noise reduction system, Figure 11 is a local detail diagram of the time period [3.9, 4]. From Figure 10 It can be seen from the error signal e(n) that the single-stage active noise reduction system can achieve effective noise reduction effect. Figure 12 and Figure 13 The simulation effect of the double-stage active noise reduction system provided by the present application is shown. Figure 12 is a global schematic diagram of the double-stage active noise reduction system, Figure 13 is a local detail diagram of the time period [3.9, 4]. From Figure 12 It can be seen from the error signal e(n) that the single-stage active noise reduction system can achieve effective noise reduction effect.

[0155] In the present embodiment, the double-stage active noise reduction control method proposed has a front-stage phase adjuster and a rear-stage active noise reduction algorithm. The phase adjuster adjusts the phase of the input signal, and the active noise reduction algorithm controls the system to generate noise with the same frequency and amplitude as the original noise, i.e., the front-stage adjusts the phase, and the rear-stage adjusts the frequency and amplitude. This double-stage adjustment mechanism can more flexibly generate secondary noise, which can interfere with the original noise in the desired area to effectively reduce the environmental noise in the long term.

[0156] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0157] Based on the same inventive concept, the embodiments of the present application also provide a noise reduction device for implementing the noise reduction method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more noise reduction device embodiments provided below can refer to the limitations of the noise reduction method described above, which will not be described here.

[0158] In one exemplary embodiment, as shown in Figure 14 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 configured to convert an initial reference signal from an original noise signal collected;

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

[0161] The active noise reduction module 30 is configured to input the adjusted reference signal into a target adaptive filter for filtering processing to obtain an anti-noise signal, and the target adaptive filter is updated iteratively using the adjusted reference signal;

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

[0163] In one exemplary embodiment, the phase adjustment module 20 is further configured to solve a preset adjustment coefficient according to the preset delay information, and substitute the preset adjustment coefficient into an initial phase adjustment model to obtain a target phase adjustment model; and input the initial reference signal into the target phase adjustment model for phase adjustment to obtain the adjusted reference signal.

[0164] In one exemplary embodiment, the phase adjustment module 20 is further configured to, if the original noise signal belongs to a periodic signal, input the initial reference signal into the target phase adjustment model for phase lead adjustment or phase lag adjustment to obtain the adjusted reference signal; otherwise, input the initial reference signal into the target phase adjustment model for phase lead adjustment to obtain the adjusted reference signal.

[0165] In one exemplary embodiment, the phase adjustment module 20 is further configured to calculate a corresponding phase-frequency characteristic function according to a frequency domain transfer function of the initial phase adjustment model; perform phase conversion on the preset delay information to obtain a preset phase difference; and solve the preset adjustment coefficient based on the preset phase difference and the phase-frequency characteristic function.

[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 the desired noise signal and the secondary noise signal; cancel 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 meets the preset convergence condition to obtain the 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 coefficients of the primary channel filter to obtain the desired noise signal;

[0168] The secondary noise signal is obtained by weighting the anti-noise signal based on the weighting coefficients of the secondary channel filter.

[0169] Each module in the aforementioned noise reduction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0170] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 15 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a noise reduction method.

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

[0172] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0173] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0174] In an embodiment, a computer program product is provided, comprising a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0176] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0177] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A noise reduction method, characterized in that, The method includes: The initial reference signal is obtained by converting the acquired raw noise signal. The initial reference signal is phase-advanced adjusted according to the preset delay information to obtain the adjusted reference signal; the preset delay information represents the total transmission delay generated at each stage in the active noise cancellation system, including the first delay of transmission and calculation at the front end of the controller, and the second delay of transmission and calculation at the back end of the controller. The adjustment reference signal is input to the target adaptive filter for filtering to obtain an anti-noise signal. The target adaptive filter is updated iteratively using the adjustment reference signal. The original noise signal is then processed by outputting a secondary noise signal based on the anti-noise signal.

2. The method according to claim 1, characterized in that, The step of adjusting the phase of the initial reference signal according to preset delay information to obtain an adjusted reference signal includes: The preset adjustment coefficient is calculated based on the preset delay information, and the preset adjustment coefficient is substituted into the initial phase adjustment model to obtain the target phase adjustment model. The initial reference signal is input into the target phase adjustment model for phase adjustment to obtain the adjustment reference signal.

3. The method according to claim 2, characterized in that, The phase adjustment model is constructed using a first-order high-pass filter, and the preset adjustment coefficient is the time constant in the first-order high-pass filter.

4. The method according to claim 2, characterized in that, The step of calculating the preset adjustment coefficient based on the preset delay information includes: The corresponding phase frequency characteristic function is obtained by calculating based on the frequency domain transfer function of the initial phase adjustment model; The preset delay information is converted into a phase to obtain a preset phase difference; The 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 steps for obtaining the target adaptive filter include: The initial reference signal and the anti-noise signal are weighted and processed respectively to obtain the desired noise signal and the secondary noise signal; An error signal is generated by canceling the secondary noise signal with the desired 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 the preset convergence condition, thereby obtaining the target adaptive filter.

6. The method according to claim 5, characterized in that, The step of weighting the initial reference signal and the anti-noise signal to obtain the desired noise signal and the secondary noise signal includes: The initial reference signal is weighted based on the weight coefficients of the primary channel filter to obtain the desired noise signal; The anti-noise signal is weighted based on the weight coefficients of the secondary channel filter to obtain the secondary noise signal.

7. A noise reduction device, characterized in that, The device includes: The signal conversion module is used to convert the acquired raw noise signal into an initial reference signal; The phase adjustment module is used to perform phase advance adjustment on the initial reference signal according to the preset delay information to obtain the adjusted reference signal; the preset delay information represents the total transmission delay generated by each stage in the active noise cancellation system, including the first delay of transmission and calculation at the front end of the controller, and the second delay of transmission and calculation at the back end of the controller. An active noise reduction module is used to input the adjustment reference signal into a target adaptive filter for filtering to obtain an anti-noise signal. The target adaptive filter is updated iteratively using the adjustment reference signal. The noise output module is used to output a secondary noise signal based on the anti-noise signal to 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, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

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