Noise reduction method and device and computer equipment

Abstract

The invention relates to a noise reduction method and device and computer equipment. The method comprises the following steps: determining a noise estimation signal of a target noise reduction area according to a residual noise signal corresponding to the target noise reduction area and a historical output signal, and determining a target noise reduction algorithm according to the noise estimation signal, thereby obtaining a reference signal corresponding to the medical scanning equipment according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning equipment; and determining the current output signal. Wherein the output signal is used for driving the loudspeaking equipment to generate a sound wave signal. By adopting the method, the noise of the medical scanning equipment can be reduced.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a noise reduction method, device and computer equipment. Background Art

[0002] Medical scanning equipment generates noise during operation. For example, when the medical scanning equipment is a magnetic resonance imaging device, the magnetic resonance imaging device generates high-intensity noise at the center of the aperture when it is in operation.

[0003] Since a medical examination using a medical scanning device may last for a long time, high-intensity noise may have a certain impact on the object of examination. Therefore, how to provide a noise reduction method is a key research topic for people in this field. Summary of the invention

[0004] Based on this, it is necessary to provide a noise reduction method, device and computer equipment that can reduce the noise of medical scanning equipment in order to solve the above technical problems.

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

[0006] Determine the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area; the output signal is used to drive the speaker device to generate a sound wave signal;

[0007] determining a target noise reduction algorithm according to the noise estimation signal;

[0008] The current output signal is determined according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0009] In one embodiment, determining a target noise reduction algorithm according to a noise estimation signal includes:

[0010] determining a relative factor according to the residual noise signal and the noise estimation signal;

[0011] The target noise reduction algorithm is determined based on the relative factor.

[0012] In one embodiment, determining the relative factor based on the residual noise signal and the noise estimation signal includes:

[0013] Determine the first parameter of the current time according to the residual noise signal and the first parameter of the previous time;

[0014] Determine the second parameter of the current time according to the noise estimation signal and the second parameter of the previous time;

[0015] A relative factor is determined according to the first parameter of the current time and the second parameter of the current time.

[0016] In one embodiment, determining a target noise reduction algorithm according to the relative factor includes:

[0017] If the relative factor is greater than the preset switching threshold, determining the first preset algorithm as the target noise reduction algorithm;

[0018] If the relative factor is not greater than the preset switching threshold, determining the second preset algorithm as the target noise reduction algorithm;

[0019] The convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

[0020] In one embodiment, determining the current output signal according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device includes:

[0021] Determine the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal;

[0022] The current output signal is determined according to the current weight parameter and the current reference signal.

[0023] In one embodiment, if the target noise reduction algorithm is the first preset algorithm, the current weight parameter is determined according to the target noise reduction algorithm, the last residual noise signal and the historical reference signal, including:

[0024] Determine the last Kalman gain vector and the last a posteriori estimation error according to the last residual noise signal and the historical reference signal;

[0025] The current weight parameter is determined based on the previous weight parameter, the previous a posteriori estimation error and the previous Kalman gain vector.

[0026] In one embodiment, if the target noise reduction algorithm is the second preset algorithm, the current weight parameter is determined according to the target noise reduction algorithm, the last residual noise signal and the historical reference signal, including:

[0027] Determine the last filtering reference signal according to the historical reference signal and the filter weight coefficient; the filter weight coefficient is a coefficient determined when the loudspeaker device is driven to generate a preset noise signal;

[0028] The current weight parameter is determined according to the previous weight parameter, the previous residual noise signal, the previous filtering reference signal and the preset algorithm step size.

[0029] In one embodiment, determining a noise estimation signal of the target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area includes:

[0030] Determine the analog output signal according to the historical output signal and the filter weight coefficient;

[0031] A noise estimate signal is determined based on the difference between the residual noise signal and the analog output signal.

[0032] In a second aspect, the present application also provides a noise reduction device, comprising:

[0033] A first determination module is used to determine a noise estimation signal of a target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; the output signal is used to drive a loudspeaker device to generate a sound wave signal;

[0034] A second determination module, used to determine a target noise reduction algorithm according to the noise estimation signal;

[0035] The third determination module is used to determine the current output signal according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0036] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0038] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of any of the above methods when executed by a processor.

[0039] The above-mentioned noise reduction method, device and computer equipment determine the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area, and determine the target noise reduction algorithm according to the noise estimation signal, so as to determine the current output signal according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device. Since the output signal is used to drive the speaker device to generate a sound wave signal, the sound wave signal generated by the speaker device can be controlled by controlling the current output signal, so that the sound wave signal can attenuate the current noise signal of the medical scanning device, thereby efficiently controlling the noise of the medical scanning device. In addition, since the target noise reduction algorithm is determined based on the noise estimation signal, the target noise reduction algorithm can be determined more flexibly to improve the noise reduction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. 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 creative work.

[0041] Figure 1 A diagram showing an application environment of a noise reduction method in an embodiment;

[0042] Figure 2 is a schematic flow chart of a noise reduction method in one embodiment;

[0043] Figure 3 A schematic diagram of a flow chart of a target noise reduction algorithm for determining an algorithm in an embodiment;

[0044] Figure 4 A schematic diagram of a process for determining a relative factor in one embodiment;

[0045] Figure 5 A schematic diagram of a flow chart of yet another method for determining a target noise reduction algorithm in an embodiment;

[0046] Figure 6 A schematic diagram of a process for determining a current output signal in one embodiment;

[0047] Figure 7 A schematic diagram of a process for determining a current weight parameter in one embodiment;

[0048] Figure 8 It is a schematic diagram of a process of determining a current weight parameter in another embodiment;

[0049] Fig. 9 A schematic diagram of a process for determining a noise estimation signal in one embodiment;

[0050] Fig.10 A schematic diagram of a noise reduction method according to an embodiment;

[0051] Fig.11 A schematic diagram of a signal transmission process in one embodiment;

[0052] Fig.12 A comparison diagram of noise reduction effects in one embodiment;

[0053] Fig.13 is a structural block diagram of a noise reduction device in one embodiment;

[0054] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] Figure 1 FIG. 1 is an application environment diagram of a noise reduction method in an embodiment. The noise reduction method provided in the embodiment of the present application can be applied to the following Figure 1 In the application environment shown, the detection device 101 is used to obtain the residual noise signal of the target noise reduction area. The detection device 101 may include but is not limited to a microphone.

[0057] The detection device 101 is installed in the target noise reduction area. The target noise reduction area refers to the area where noise reduction is desired, which can be any position on the scanning bed of the medical scanning device 103 or any position on or around the subject. The subject is located on the scanning bed of the medical scanning device 103.

[0058] The speaker device 102 is a device capable of generating sound waves, including but not limited to a speaker. Furthermore, the speaker device 102 is used to generate a sound wave signal according to the output signal. The speaker device 102 can be installed on the medical scanning device 103, or can be installed around the medical scanning device, which is not limited in this embodiment.

[0059] The processing device 104 can communicate with the detection device 101 and the speaker device 102 respectively. The processing device 104 can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The processing device 104 can also be an independent physical server or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0060] In some embodiments, the processing device 104 may also include but is not limited to a central processing unit (CPU), a digital signal processor (DSP), a field programmable gate array (FPGA) or other programmable logic devices.

[0061] Figure 2 FIG. 1 is a flow chart of a noise reduction method in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 2 As shown, a noise reduction method is provided, which is applied to Figure 1 The processing device in is used as an example for description, including the following S201 to S203.

[0062] S201, determining a noise estimation signal of a target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; the output signal is used to drive a speaker device to generate a sound wave signal.

[0063] In this embodiment, the processing device may obtain the residual noise signal through a detection device arranged in a target noise reduction area. The target noise reduction area refers to an area where noise reduction is desired, for example, it may be near the ear of the object under examination.

[0064] Furthermore, the processing device may also determine a historical output signal. The output signal is a signal determined by the processing device based on the reference signal and the residual noise signal. The historical output signal includes at least one output signal before the current output signal. Optionally, the historical output signal may include M output signals before the current output signal, where M is the length of the filter preset during offline identification.

[0065] The reference signal refers to a signal that can cause the medical scanning device to generate noise during operation. Optionally, the processing device can receive the reference signal sent by the medical scanning device, and the processing device can also read the reference signal from the system where the medical scanning device is located.

[0066] Taking the medical scanning device as an example, the gradient sequence signal is a necessary technical means for magnetic resonance examination. In order to meet the inspection requirements of different parts and different lesions, the magnetic resonance apparatus is usually equipped with a large number of different gradient sequence signals. Different gradient sequence signals mean that the change law of the alternating current passing through the gradient coil is different, and the distribution and change law of the Lorentz force on the gradient coil are also different, which leads to the gradient coil generating noise. In other words, the gradient sequence signal will cause the medical scanning device to generate noise. In this case, the reference signal includes the gradient sequence signal. For example, the gradient sequence signal can be used as a reference signal.

[0067] Furthermore, the output signal is used to drive the speaker device to generate a sound wave signal, and the sound wave signal is used to attenuate the current noise signal of the medical scanning device. For example, the processing device can determine the output signal according to the reference signal and the residual noise signal, and drive the speaker device according to the output signal. Under the drive of the output signal, the speaker device generates a sound wave signal to attenuate the current noise signal.

[0068] The current noise signal of the medical scanning device is a noise signal transmitted to the target noise reduction area by the medical scanning device based on the reference signal. Optionally, the current noise signal and the sound wave signal can destructively interfere with each other. Further optionally, the current noise signal and the sound wave signal have the same or similar amplitude and an inverted or nearly inverted phase. For example, the current noise signal and the sound wave signal are of equal amplitude and inverted phase.

[0069] The processing device can periodically obtain the residual noise signal and the reference signal to determine the corresponding output signal. The reference signal for the nth time is , the nth residual noise signal is , the nth output signal is . n is an integer greater than or equal to 1. The nth time can also be understood as the nth moment. Among them, the current time can represent the current moment, and the last time can represent the moment before the current moment. In this way, when the processing device needs to determine the output signal of the current time, it can determine the historical output signal.

[0070] Furthermore, the processing device can determine a noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal, wherein the noise estimation signal represents an estimated value of the current noise signal of the target noise area.

[0071] Optionally, the processing device may be configured to process the residual noise signal of the nth order. and historical output signals to determine the corresponding noise estimation signal For example, the residual noise signal of the nth order can be And the nth output signal , determine the corresponding noise estimation signal , or according to the nth residual noise signal and the nm-th output signal , determine the corresponding noise estimation signal . Where m is 0~M-1.

[0072] Further optionally, the processing device may input the residual noise signal and the historical output signal into a prediction model, and the prediction model outputs the noise estimation signal of the target noise reduction area. The prediction model may be determined based on the residual noise signal sample, the historical output signal sample, and the actual noise signal label of the corresponding target noise reduction area. The prediction model may include, but is not limited to, a Convolutional Neural Network (CNN) model, a Recurrent Neural Network (RNN), a Fully Convolutional Neural Network (FCN) model, a Generative Adversarial Network (GAN) model, a Back-propagation (BP) machine learning model, a Radial Basis Function (RBF) model, a Deep Belief Network (DBN) model, an Elman model, or at least one of a combination thereof.

[0073] S202: Determine a target noise reduction algorithm according to the noise estimation signal.

[0074] In this embodiment, the target noise reduction algorithm is used to determine the output signal each time. The target noise reduction algorithm may include but is not limited to at least two algorithms of the least mean square algorithm, the recursive least squares algorithm, the neural network algorithm, the filtered-x stable fast recursive least squares transversal filter (Filtered-x Stable Fast Recursive Least Squares Transversal Filter, FxSFTF) algorithm and the normalized filtered-x least mean square (Normalized Filtered-x Least Mean Square, NFxLMS).

[0075] Among them, the FxSFTF algorithm is a fast version based on the FxRLS algorithm. The computational complexity of the FxRLS algorithm is , and the NFxLMS algorithm legal person calculation complexity is ,In this way, the use of NFxLMS algorithm can not only use the same order as FxRLS algorithm to update, but also solve the problem that the convergence stage is affected by too low order in FxRLS algorithm, and improve the performance of the target denoising algorithm in the convergence stage.

[0076] Then, the noise estimation signal is determined The processing device can then estimate the signal from the noise Select the corresponding target noise reduction algorithm. Optionally, the processing device can estimate the signal based on the noise The size of the target noise reduction algorithm is determined. For example, the processing device can be in the noise estimation signal When the size of the noise estimation signal is greater than the preset signal threshold, the target noise reduction algorithm is determined to be the first preset algorithm. When the size of is not greater than the preset signal threshold, the target noise reduction algorithm is determined to be the second preset algorithm.

[0077] S203, determining a current output signal according to a target noise reduction algorithm, a residual noise signal and a reference signal corresponding to the medical scanning device.

[0078] In this embodiment, the processing device may periodically acquire the reference signal and the residual noise signal. The periods for acquiring the reference signal and the residual noise signal may be different. The reference signal and the residual noise signal may be signals acquired at the same time or at different times.

[0079] After determining the target noise reduction algorithm, optionally, the processing device may determine the current output signal based on the target noise reduction algorithm according to the current residual noise signal and the current reference signal.

[0080] Optionally, the processing device may also determine the current output signal based on the target noise reduction algorithm, the previous residual noise signal and the historical reference signal. The historical reference signal includes at least one reference signal before the current reference signal. Optionally, the historical reference signal may include M reference signals before the current reference signal. Exemplarily, the output signal (n+1) may be determined based on the target noise reduction algorithm, the nth residual noise signal and the nth reference signal. , or according to the residual noise signal of the nth time and the reference signal of the nmth time , determine the output signal for the n+1th time .

[0081] In the above-mentioned noise reduction method, the noise estimation signal of the target noise reduction area is determined according to the residual noise signal and the historical output signal corresponding to the target noise reduction area, and the target noise reduction algorithm is determined according to the noise estimation signal, so as to determine the current output signal according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device. Since the output signal is used to drive the speaker device to generate a sound wave signal, the sound wave signal generated by the speaker device can be controlled by controlling the current output signal, so that the sound wave signal can attenuate the current noise signal of the medical scanning device, thereby efficiently controlling the noise of the medical scanning device. In addition, since the target noise reduction algorithm is determined based on the noise estimation signal, the target noise reduction algorithm can be determined more flexibly to improve the noise reduction effect.

[0082] Figure 3 FIG. 1 is a flow chart of a method for determining a target noise reduction algorithm in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 3 As shown, S202 includes S301 to S302.

[0083] S301, determining a relative factor according to a residual noise signal and a noise estimation signal.

[0084] In this embodiment, when determining the target noise reduction algorithm, the processing device first determines the residual noise signal of the nth time. and the corresponding noise estimation signal , determine the relative factor , which is the relative factor of the nth time. Residual noise signal Estimating signal with noise The size relationship of can also represent the denoising degree of the target denoising algorithm, thereby reflecting the convergence degree of the target denoising algorithm.

[0085] For example, the processing device may and noise estimation signal The mean square error (MSE) between , or according to the residual noise signal and noise estimation signal The relative factor is determined by the change in the signal-to-noise ratio (SNR) between , this embodiment is not limited to this.

[0086] S302: Determine a target noise reduction algorithm according to the relative factor.

[0087] Then, the processing device determines the relative factor After that, we can use the relative factor Determine the target noise reduction algorithm. Optionally, the processing device can be based on the first corresponding relationship and the relative factor Determine the target noise reduction algorithm. The first correspondence includes the relationship between the ranges of different relative factors and the corresponding noise reduction algorithms. For example, in the first correspondence, when the relative factor is in range A, it corresponds to noise reduction algorithm A; when the relative factor is in range B, it corresponds to noise reduction algorithm B; when the relative factor is in range C, it corresponds to noise reduction algorithm C, and so on. Then, the processing device determines the relative factor Then, if the relative factor If the object is actually in range B, the processing device may use noise reduction algorithm B as the target noise reduction algorithm.

[0088] In the above embodiment, since the relative factor can be determined based on the residual noise signal and the noise estimation signal, the relative factor can represent the magnitude relationship between the residual noise signal and the current noise signal. Furthermore, the target noise reduction algorithm suitable for the current noise situation can be determined based on the relative factor to improve the noise reduction effect.

[0089] Figure 4 FIG. 1 is a flow chart of determining a relative factor in one embodiment. In an exemplary embodiment, as shown in FIG. Figure 4 As shown, S301 includes S401 to S403.

[0090] S401, determining a current first parameter according to a residual noise signal and a previous first parameter.

[0091] In this embodiment, the first parameter is used to represent the noise condition of the target noise reduction area after noise reduction. Optionally, the processing device can determine the current first parameter based on the current residual noise signal and the previous first parameter. The previous first parameter is , the first parameter of the current time is , the processing device can be based on the residual noise signal of the nth and the first parameter of the n-1th , determine the first parameter of the nth time .

[0092] Optionally, the processing device may determine the first preset coefficient and the first parameter of the previous and determine the absolute value of the second preset coefficient and the residual noise signal. The second product between them is used to determine the first parameter of the current time according to the sum of the first product and the second product. . Wherein, the sum of the first preset coefficient and the second preset coefficient is equal to 1.

[0093] Exemplarily, the processing device may determine the first parameter of the current time according to the following formula (1): .

[0094] (1)

[0095] in, Can be a decimal close to 1, the first parameter at the initial moment Can be 0.

[0096] S402, determining the current second parameter according to the noise estimation signal and the previous second parameter.

[0097] In this embodiment, the second parameter is used to represent the noise condition of the target noise reduction area before noise reduction is performed. Optionally, the processing device can determine the current second parameter based on the noise estimation signal corresponding to the current residual noise signal and the previous second parameter. The previous second parameter is recorded as , the second parameter of the current time is , then the processing device can estimate the signal based on the noise and the second parameter of the n-1th , determine the second parameter for the nth time .

[0098] Optionally, the processing device may determine the first preset coefficient and the second parameter of the previous and determine the absolute value of the second preset coefficient and the noise estimation signal. The second product between them is used to determine the second parameter of the current time according to the sum of the first product and the second product. .

[0099] Exemplarily, the processing device may determine the second parameter of the current time according to the following formula (2): .

[0100] (2)

[0101] Among them, the second parameter at the initial time Can be 0.

[0102] S403: Determine a relative factor according to the current first parameter and the current second parameter.

[0103] Furthermore, the processing device can be based on the first parameter of the nth time With the second parameter of the nth , determine the relative factor of the nth time Optionally, the processing device may be configured to process the first parameter of the nth time. With the second parameter of the nth The ratio between them determines the relative factor of the nth time For example, the processing device may determine the relative factor according to the following formula (3): .

[0104] (3)

[0105] In the above embodiment, since the first parameter of the current time is determined based on the residual noise signal and the first parameter of the previous time, and the second parameter of the current time is determined based on the noise estimation signal and the second parameter of the previous time, the relative factor that can represent the magnitude relationship between the residual noise signal and the current noise signal can be accurately determined based on the first parameter of the current time and the second parameter of the current time.

[0106] Figure 5 FIG. 1 is a flow chart of another method for determining a target noise reduction algorithm in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 5 As shown, S302 includes S501 to S502.

[0107] S501: If the relative factor is greater than a preset switching threshold, determining a first preset algorithm as a target noise reduction algorithm.

[0108] S502: If the relative factor is not greater than the preset switching threshold, determine the second preset algorithm as the target noise reduction algorithm.

[0109] The noise of medical scanning equipment is usually colored noise with a wide bandwidth and obvious intermittent characteristics. In addition, the inspection time of medical scanning equipment usually lasts from several minutes to tens of minutes. Therefore, the target noise reduction algorithm needs to run continuously for a long time. The target noise reduction algorithm needs to have a fast convergence speed, low steady-state error and good stability.

[0110] In this embodiment, due to the relative factor is determined based on the residual noise signal and the noise estimation signal, so the relative factor It can indicate the convergence degree of the target denoising algorithm. When the target denoising algorithm is in the convergence stage, the relative factor will decrease sharply, so when the relative factor Not greater than the preset switching threshold When , it can be considered that the target noise reduction algorithm has completed convergence and entered the steady state stage. Among them, the preset switching threshold It can be set according to requirements, for example, it can be a number close to 0.

[0111] Therefore, the relative factor of the processing equipment In the case of, the first preset algorithm is determined as the target noise reduction algorithm, and in the relative factor In the case of, the second preset algorithm is determined as the target noise reduction algorithm. The convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm. In this way, the processing device can use the first preset algorithm with a faster convergence speed in the convergence stage, and use the second preset algorithm with a slower convergence speed but more stable in the steady-state stage.

[0112] Taking the case where the first preset algorithm is the FxSFTF algorithm and the second preset algorithm is the NFxLMS algorithm as an example, the processing device may determine the target noise reduction algorithm according to the following formula (4).

[0113] (4)

[0114] In the above embodiment, when the relative factor is greater than the preset switching threshold, the first preset algorithm is determined as the target noise reduction algorithm, and when the relative factor is not greater than the preset switching threshold, the second preset algorithm is determined as the target noise reduction algorithm. Since the convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm, the processing device can use different noise reduction algorithms in different stages. For example, in the convergence stage, the first preset algorithm with a faster convergence speed is used, and in the steady-state stage, the second preset algorithm with a slower convergence speed but more stability is used to improve the efficiency of noise reduction.

[0115] Figure 6 FIG. 1 is a flow chart of determining the current output signal in one embodiment. In an exemplary embodiment, as shown in FIG. Figure 6 As shown, S203 includes S601 to S602.

[0116] S601, determining the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal.

[0117] In this embodiment, when the processing device needs to determine the current output signal, it first determines the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal.

[0118] Among them, different weight parameters will correspond to different output signals. The weight parameter represents the impulse response of the filter transfer function in the processing device, which can be, for example, a weight coefficient vector corresponding to the filter in the processing device. In other words, the processing device can change the corresponding output signal by adjusting the filter inside it, so different weight parameters will correspond to different output signals. It should be noted that the weight parameter can be represented by a vector, a matrix, or other methods, which is not limited in this embodiment.

[0119] The weight parameter includes multiple elements, and the number of elements in the weight parameter is equal to the order of the filter, that is, the length of the filter. The filter of the processing device includes but is not limited to a finite impulse response filter (FIR filter). Let the length of the filter be L, then the weight parameter for the nth time is . L is an integer greater than 1.

[0120] Exemplarily, the processing device may be configured to generate a residual noise signal according to the target noise reduction algorithm and the nth residual noise signal. and the nth reference signal , determine the weight parameter for the n+1th time , or according to the target noise reduction algorithm, the nth residual noise signal and the nmth reference signal , determine the weight parameter for the n+1th time .

[0121] S602, determining the current output signal according to the current weight parameter and the current reference signal.

[0122] In this embodiment, the processing device may determine the current output signal according to the current weight parameter and the current reference signal. Optionally, the processing device determines the current output signal according to the product of the transposition of the current weight parameter and the current reference signal. For example, the output signal of the nth time is recorded as .

[0123] In the above embodiment, since the current weight parameter is determined based on the target noise reduction algorithm, the previous residual noise signal and the historical reference signal, the weight parameter can be determined more accurately. In this way, the current output signal can be determined based on the current weight parameter and the current reference signal, thereby improving the accuracy of the current output signal.

[0124] In an exemplary embodiment, optionally, the above S601 may be implemented in the following manner:

[0125] Based on the target noise reduction algorithm, the current weight parameter is determined according to the previous residual noise signal, the historical reference signal and the previous weight parameter.

[0126] In this embodiment, the processing device can be based on the target noise reduction algorithm, according to the residual noise signal of the nth time , historical reference signals and the nth weight parameter , determine the weight parameter for the n+1th time For example, the target noise reduction algorithm can be used to calculate the residual noise signal of the nth time. , the nth reference signal and the nth weight parameter , determine the weight parameter for the n+1th time , or based on the target noise reduction algorithm, according to the residual noise signal of the nth , the nmth reference signal and the nth weight parameter , determine the weight parameter for the n+1th time .

[0127] In one embodiment, the weight parameter at the initial moment may take a preset value, for example, the weight parameter at the initial moment is a zero vector.

[0128] In the above embodiment, since the current weight parameters are determined based on the previous residual noise signal, the historical reference signal and the previous weight parameters, the current weight parameters can be iteratively updated using the previous weight parameters, thereby improving the accuracy of the determined weight parameters in the continuous iteration process.

[0129] Figure 7 FIG. 1 is a flow chart of determining the current weight parameter in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 7 As shown, if the target noise reduction algorithm is the first preset algorithm, S601 includes S701 to S703.

[0130] S701, determining the last Kalman gain vector and the last a posteriori estimation error according to the last residual noise signal and the historical reference signal.

[0131] In this embodiment, the processing device can be based on the residual noise signal of the nth time and historical reference signals to determine the nth Kalman gain vector and the nth posterior estimation error .

[0132] Exemplarily, the processing device may determine the nth Kalman gain vector based on the following equations (5) to (22): and the nth posterior estimation error That is, to calculate the Kalman gain vector and the posterior estimation error The processing device can first calculate the a priori estimation error and the a posteriori estimation error of the forward predictor and update the coefficients of the forward prediction filter and least square sum Next, the processing device updates the Kalman gain vector , and update the conversion factor and ,calculate . And, the Kalman gain vector and the posterior estimation error The update involves the backward prediction filter, so after calculating the Kalman gain vector and the posterior estimation error After that, the coefficients of the backward filter need to be iterated according to equations (16) to (22). and the least squares sum .

[0133] (5)

[0134] (6)

[0135] (7)

[0136] (8)

[0137] (9)

[0138] (10)

[0139] (11)

[0140] (12)

[0141] (13)

[0142] (14)

[0143] (15)

[0144] (16)

[0145] (17)

[0146] (18)

[0147] (19)

[0148] (20)

[0149] (twenty one)

[0150] (twenty two)

[0151] in, , each value on the right side of the equation is based on Sure. is the forgetting factor, which is usually very close to 1.

[0152] S702, determining the current weight parameter according to the previous weight parameter, the previous a posteriori estimation error and the previous Kalman gain vector.

[0153] In this embodiment, optionally, the processing device may determine the first product based on the last a posteriori estimation error and the last Kalman gain vector. Further optionally, the processing device may determine the first product between the last a posteriori estimation error and the last Kalman gain vector. Exemplarily, the processing device may determine the last Kalman gain vector and the previous posterior estimation error The first product between .

[0154] Furthermore, the processing device may determine the current weight parameter based on the weight parameter of the previous time and the first product. In other words, the processing device may determine the current weight parameter based on the weight parameter of the nth time. The first product with the nth The sum of the n+1th weight parameters is determined Exemplarily, the processing device determines the current weight parameter according to the following formula (23).

[0155] (twenty three)

[0156] In the above embodiment, since the last Kalman gain vector and the last a posteriori estimation error can be determined based on the last residual noise signal and the historical reference signal, and based on the last weight parameters, the last Kalman gain vector and the last a posteriori estimation error, the current weight parameters can be determined at a faster convergence speed.

[0157] Figure 8 FIG. 1 is a flow chart of another method for determining the current weight parameter in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 8 As shown, if the target noise reduction algorithm is the second preset algorithm, S601 includes S801 to S803.

[0158] S801, determining the last filtering reference signal according to the historical reference signal and the filter weight coefficient; the filter weight coefficient is a coefficient determined when driving the loudspeaker device to generate a preset noise signal.

[0159] In this embodiment, the filter weight coefficient is a coefficient determined when the speaker device generates a preset noise signal. For example, the processing device sends a random signal, and the random signal drives the speaker device so that the speaker device generates a preset noise signal. In this case, the detection device can also obtain the acquisition signal in the target noise reduction area, and then the filter weight coefficient can be determined based on the acquisition signal, the random signal and the adaptive algorithm. Among them, the random signal can be a flat white noise signal. In other words, the filter weight coefficient is the information obtained when the transfer function of the secondary path is offline identified using a preset filter. Similarly, the number of weight coefficients in the filter weight coefficient is equal to the length of the preset filter. The filter weight coefficient is denoted as , M is the length of the filter preset during offline identification.

[0160] Then, the processing device can determine the last filtering reference signal based on the historical reference signal and the filter weight coefficient. For example, the processing device may determine the nth filtering reference signal according to the following equation (24): That is, based on the historical reference signal and the filter weight coefficient , determine the nth filtering reference signal , each value on the right side of the equation is determined based on equation (1).

[0161] (twenty four)

[0162] S802, determining the current weight parameter according to the previous weight parameter, the previous residual noise signal, the previous filtering reference signal and the preset algorithm step size.

[0163] In this embodiment, optionally, the computer device can continuously iterate the last weight parameter based on the last residual noise signal, the last filtering reference signal and the preset algorithm step size to obtain the current weight parameter.

[0164] Optionally, the processing device may also determine the initial result based on the last residual noise signal, the last filtering reference signal, and the preset algorithm step size, and determine the current weight parameter based on the last weight parameter and the initial result. Further optionally, the processing device may also determine the initial result based on the last residual noise signal, the last filtering reference signal, the preset algorithm step size and the preset regularization factor, and determine the current weight parameter based on the difference between the last weight parameter and the initial result.

[0165] For example, the preset algorithm step size is , which can be set as required, for example, it can be the step size of the NFxLMS algorithm. The preset regularization factor is , which is usually a decimal close to 0 to avoid the denominator being zero. In other words, the processing device can calculate the residual noise signal according to the nth order. , the nth filtering reference signal , preset algorithm step size and the preset regularization factor , determine the initial result for the nth time.

[0166] In one embodiment, the processing device may Determine the initial result. For example, the processing equipment will As the initial result for the nth time.

[0167] Furthermore, the processing device may use the difference between the previous weight parameter and the initial result as the current weight parameter. The difference between the initial result of the nth time determines the weight parameter of the n+1th time For example, the processing device may determine the current weight parameter according to the following formula (25).

[0168] (25)

[0169] That is, in the relative factor No more than In the case of, the processing device will switch to the second preset algorithm, and use the second preset algorithm to determine the current weight parameter using S801~S803.

[0170] In the above embodiment, since the filter weight coefficient is a coefficient determined when driving the loudspeaker device to generate a preset noise signal, and then, after determining the last filter reference signal based on the historical reference signal and the filter weight coefficient, the current weight parameter can be determined more accurately based on the last weight parameter, residual noise signal, filter reference signal and preset algorithm step size.

[0171] Fig. 9 FIG. 1 is a flow chart of determining a noise estimation signal in one embodiment. In an exemplary embodiment, as shown in FIG. Fig. 9 As shown, S202 includes S901 to S902.

[0172] S901, determining a simulated output signal according to a historical output signal and a filter weight coefficient.

[0173] In this embodiment, the processing device can Determine an analog output signal. The analog output signal represents the signal of the output signal determined by the processing device after passing through the electronic circuit, the speaker, and the physical sound path and propagating to the error microphone. It can also be understood as the signal of the output signal after passing through the secondary path.

[0174] S902: Determine a noise estimation signal according to the difference between the residual noise signal and the analog output signal.

[0175] Furthermore, the processing device can be used to calculate the residual noise signal With analog output signal The difference between the two determines the noise estimation signal Exemplarily, the processing device may determine the noise estimation signal according to the following equation (26).

[0176] (26)

[0177] In the above embodiment, since the analog output signal can be determined according to the historical output signal and the filter weight coefficient, the noise estimation signal can be determined more accurately according to the difference between the residual noise signal and the analog output signal.

[0178] In order to more clearly introduce the noise reduction method of the present application, Fig.10 and Fig.11 Provide explanation. Fig.10 The figure is a process diagram of a noise reduction method according to an embodiment. The processing device can execute the noise reduction method according to the following process.

[0179] S1001, determining a simulated output signal according to a historical output signal and a filter weight coefficient.

[0180] S1002: Determine a noise estimation signal according to a difference between the residual noise signal and the analog output signal.

[0181] S1003, determining the first parameter of the current time according to the residual noise signal and the first parameter of the previous time.

[0182] S1004, determining the second parameter of the current time according to the noise estimation signal and the second parameter of the previous time.

[0183] S1005: Determine a relative factor according to the current first parameter and the current second parameter, wherein the relative factor represents the magnitude relationship between the residual noise signal and the current noise signal.

[0184] S1006: If the relative factor is greater than the preset switching threshold, determine the first preset algorithm as the target noise reduction algorithm.

[0185] S1007: If the relative factor is not greater than the preset switching threshold, determine the second preset algorithm as the target noise reduction algorithm, wherein the convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

[0186] S1008, determining the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal. Optionally, if the target noise reduction algorithm is the first preset algorithm, the processing device may Figure 7 The method shown in FIG. 1 determines the weight parameter of the current time. If the target noise reduction algorithm is the second preset algorithm, the processing device can Figure 8 The method shown determines the weight parameters for the current time.

[0187] S1009, determining the current output signal according to the current weight parameter and the current reference signal, wherein the output signal is used to drive the speaker device to generate a sound wave signal, and the sound wave signal is used to attenuate the current noise signal of the medical scanning device.

[0188] The processes of S1001 to S1009 can refer to the above-mentioned embodiment and will not be described in detail here.

[0189] Fig.11 FIG. 1 is a schematic diagram of a signal transmission process in an embodiment. Fig.11 As shown, It is the reference signal for medical scanning equipment. yes Transfer function through the primary path The noise signal propagates to the target noise reduction area. That is, the current noise signal refers to the noise signal that needs to be attenuated or offset. Taking the medical scanning device as an example, the reference signal The noise is converted into a current signal passing through the gradient coil, causing the gradient coil to generate noise due to the Lorentz force. This noise is transmitted to the target noise reduction area through the primary path. That is the transfer function of the whole process mentioned above.

[0190] It can be a residual noise signal collected by a detection device in the target noise reduction area. is the transfer function of the filter in the processing device, It is the output signal obtained by the processing device based on the noise reduction method of the present application. It is the transfer function of the secondary path, which refers to the transfer function of the entire process of the output signal undergoing digital-to-analog conversion, reconstruction filtering, power amplification and other signal processing, as well as driving the loudspeaker device to emit a sound wave signal, and the sound wave signal propagating to the target noise reduction area. The estimated value of the transfer function of the secondary path is , which can be determined by offline identification.

[0191] The primary path consists of a reference signal To the current noise signal The secondary path consists of the output signal The transmission path to the target noise reduction area. At a certain sampling time The residual noise signal can be expressed as . is the impulse response of the transfer function of the secondary path. Determined by offline identification The process of determining the filter weight coefficients can be as follows process.

[0192] It can be seen that the present application can be based on the target noise reduction algorithm, according to the reference signal and residual noise signal Calculate the output signal , the speaker device will output a signal The sound wave signal is converted into a corresponding sound wave signal, and the sound wave signal destructively interferes with the current noise signal to attenuate the noise in the target noise reduction area.

[0193] The target noise reduction algorithm includes a first preset algorithm and a second preset algorithm. Fig.11 ① in the figure indicates the first preset algorithm. Fig.11 ② in it represents the second preset algorithm. The present application can achieve reasonable switching between the first preset algorithm and the second preset algorithm by determining whether the target noise reduction algorithm has ended the convergence stage and entered a stable solution. In the convergence stage, the processing device will use the first preset algorithm to quickly converge to the vicinity of the optimal solution to ensure the convergence speed and steady-state noise reduction amount of the target noise reduction algorithm. In the steady-state stage, the processor uses the second preset algorithm to ensure algorithm stability and avoid the possibility of system divergence due to poor numerical stability of the first preset algorithm. In this way, the advantages of the first preset algorithm and the second preset algorithm can be retained, while reducing the adverse effects of the shortcomings of the first preset algorithm and the second preset algorithm on noise control.

[0194] Fig.12 The following is a comparison chart of the noise reduction effect of an embodiment. The simulation is performed by taking the medical scanning device as an MRI as an example. The noise signal collected at the center of the aperture when the MRI performs the T1 sequence is used as the current noise signal. It is assumed that the length of the filter in the processing device is 400, the filter order of the secondary path is 100, and the forgetting factor in the first preset algorithm is , the preset algorithm step size in the second preset algorithm , the regularization factor , , preset switching threshold .

[0195] Afterwards, the average noise reduction (ANR) was used to compare the related technologies with the noise reduction method proposed in this application, and the comparison results were as follows: Fig.12 As shown. Among them, the average noise reduction The calculation method of can refer to the following formula (27). Where, A related technique may be to use only the FxSFTF algorithm to determine the output signal.

[0196] (27)

[0197] Combination Fig.12 It can be seen that starting from 6 minutes, the fluctuation range of the average noise reduction amount in the related technology increases significantly, and there are hidden dangers in stability, and there is a tendency to diverge. However, the noise reduction method provided by this application no longer has the phenomenon of increasing fluctuation range of the average noise reduction amount after 6 minutes, and the stability of the algorithm has been improved. In other words, as time goes by, the noise reduction method used in this application has smaller fluctuations and is more stable than the related technology, converges faster, and has a better noise reduction effect.

[0198] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0199] Based on the same inventive concept, the embodiment of the present application also provides a noise reduction device for implementing the noise reduction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded 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 above, and will not be repeated here.

[0200] Fig.13 FIG. 1 is a block diagram of a noise reduction device in an embodiment. In an exemplary embodiment, Fig.13 As shown, a noise reduction device is provided, including: a first determination module 1301, a second determination module 1302 and a third determination module 1303, wherein:

[0201] The first determination module 1301 is used to determine the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area; the output signal is used to drive the speaker device to generate a sound wave signal.

[0202] The second determination module 1302 is configured to determine a target noise reduction algorithm according to the noise estimation signal.

[0203] The third determination module 1303 is used to determine the current output signal according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0204] In the above-mentioned noise reduction device, the noise estimation signal of the target noise reduction area is determined according to the residual noise signal and the historical output signal corresponding to the target noise reduction area, and the target noise reduction algorithm is determined according to the noise estimation signal, so as to determine the current output signal according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device. Since the output signal is used to drive the speaker device to generate a sound wave signal, the sound wave signal generated by the speaker device can be controlled by controlling the current output signal, so that the sound wave signal can attenuate the current noise signal of the medical scanning device, thereby efficiently controlling the noise of the medical scanning device. In addition, since the target noise reduction algorithm is determined based on the noise estimation signal, the target noise reduction algorithm can be determined more flexibly to improve the noise reduction effect.

[0205] Optionally, the second determining module 1302 includes:

[0206] The first determining unit is used to determine a relative factor according to the residual noise signal and the noise estimation signal.

[0207] The second determining unit is used to determine a target noise reduction algorithm according to the relative factor.

[0208] Optionally, the first determination unit is used to determine that the first preset algorithm is the target noise reduction algorithm if the relative factor is greater than a preset switching threshold; if the relative factor is not greater than the preset switching threshold, determine that the second preset algorithm is the target noise reduction algorithm; the convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

[0209] Optionally, the third determining module 1303 includes:

[0210] The third determination unit is used to determine the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal.

[0211] The fourth determination unit is used to determine the current output signal according to the current weight parameter and the current reference signal.

[0212] Optionally, if the target noise reduction algorithm is the first preset algorithm, the third determination unit is used to determine the current weight parameter based on the last residual noise signal and the historical reference signal, the last Kalman gain vector and the last a posteriori estimation error; determine the current weight parameter based on the last weight parameter, the last a posteriori estimation error and the last Kalman gain vector.

[0213] Optionally, if the target noise reduction algorithm is the second preset algorithm, the third determination unit is used to determine the last filtering reference signal based on the historical reference signal and the filter weight coefficient; and determine the current weight parameter based on the last weight parameter, the last residual noise signal, the last filtering reference signal and the preset algorithm step size.

[0214] Optionally, the first determining module 1301 includes:

[0215] The fifth determination unit is used to determine the analog output signal according to the historical output signal and the filter weight coefficient.

[0216] The sixth determining unit is configured to determine a noise estimation signal according to a difference between the residual noise signal and the analog output signal.

[0217] Each module in the above noise reduction device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0218] Fig.14 FIG. 1 is an internal structure diagram of a computer device in an embodiment. In an exemplary embodiment, a computer device is provided. The computer device may be a server. The internal structure diagram thereof may be as shown in FIG. Fig.14As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data. 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 through a network connection. When the computer program is executed by the processor, a noise reduction method is implemented.

[0219] Those skilled in the art will understand that Fig.14 The 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0220] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0221] Determine the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area; the output signal is used to drive the speaker device to generate a sound wave signal;

[0222] determining a target noise reduction algorithm according to the noise estimation signal;

[0223] The current output signal is determined according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0224] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0225] A relative factor is determined according to the residual noise signal and the noise estimation signal; and a target noise reduction algorithm is determined according to the relative factor.

[0226] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0227] Determine the current first parameter according to the residual noise signal and the previous first parameter; determine the current second parameter according to the noise estimation signal and the previous second parameter; determine the relative factor according to the current first parameter and the current second parameter.

[0228] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0229] If the relative factor is greater than the preset switching threshold, the first preset algorithm is determined as the target noise reduction algorithm; if the relative factor is not greater than the preset switching threshold, the second preset algorithm is determined as the target noise reduction algorithm; the convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

[0230] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0231] According to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal, the current weight parameter is determined; according to the current weight parameter and the current reference signal, the current output signal is determined.

[0232] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0233] According to the last residual noise signal and the historical reference signal, the last Kalman gain vector and the last a posteriori estimation error are determined; according to the last weight parameter, the last a posteriori estimation error and the last Kalman gain vector, the current weight parameter is determined.

[0234] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0235] The last filtering reference signal is determined based on the historical reference signal and the filter weight coefficient; the filter weight coefficient is the coefficient determined when driving the loudspeaker device to generate a preset noise signal; the current weight parameter is determined based on the last weight parameter, the last residual noise signal, the last filtering reference signal and the preset algorithm step size.

[0236] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0237] The analog output signal is determined according to the historical output signal and the filter weight coefficient; and the noise estimation signal is determined according to the difference between the residual noise signal and the analog output signal.

[0238] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0239] Determine the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area; the output signal is used to drive the speaker device to generate a sound wave signal;

[0240] determining a target noise reduction algorithm according to the noise estimation signal;

[0241] The current output signal is determined according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0242] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0243] A relative factor is determined according to the residual noise signal and the noise estimation signal; and a target noise reduction algorithm is determined according to the relative factor.

[0244] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0245] Determine the current first parameter according to the residual noise signal and the previous first parameter; determine the current second parameter according to the noise estimation signal and the previous second parameter; determine the relative factor according to the current first parameter and the current second parameter.

[0246] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0247] If the relative factor is greater than the preset switching threshold, the first preset algorithm is determined as the target noise reduction algorithm; if the relative factor is not greater than the preset switching threshold, the second preset algorithm is determined as the target noise reduction algorithm; the convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

[0248] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0249] According to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal, the current weight parameter is determined; according to the current weight parameter and the current reference signal, the current output signal is determined.

[0250] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0251] According to the last residual noise signal and the historical reference signal, the last Kalman gain vector and the last a posteriori estimation error are determined; according to the last weight parameter, the last a posteriori estimation error and the last Kalman gain vector, the current weight parameter is determined.

[0252] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0253] The last filtering reference signal is determined based on the historical reference signal and the filter weight coefficient; the filter weight coefficient is the coefficient determined when driving the loudspeaker device to generate a preset noise signal; the current weight parameter is determined based on the last weight parameter, the last residual noise signal, the last filtering reference signal and the preset algorithm step size.

[0254] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0255] The analog output signal is determined according to the historical output signal and the filter weight coefficient; and the noise estimation signal is determined according to the difference between the residual noise signal and the analog output signal.

[0256] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0257] Determine the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area; the output signal is used to drive the speaker device to generate a sound wave signal;

[0258] determining a target noise reduction algorithm according to the noise estimation signal;

[0259] The current output signal is determined according to the target noise reduction algorithm, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0260] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0261] A relative factor is determined according to the residual noise signal and the noise estimation signal; and a target noise reduction algorithm is determined according to the relative factor.

[0262] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0263] Determine the current first parameter according to the residual noise signal and the previous first parameter; determine the current second parameter according to the noise estimation signal and the previous second parameter; determine the relative factor according to the current first parameter and the current second parameter.

[0264] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0265] If the relative factor is greater than the preset switching threshold, the first preset algorithm is determined as the target noise reduction algorithm; if the relative factor is not greater than the preset switching threshold, the second preset algorithm is determined as the target noise reduction algorithm; the convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

[0266] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0267] According to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal, the current weight parameter is determined; according to the current weight parameter and the current reference signal, the current output signal is determined.

[0268] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0269] According to the last residual noise signal and the historical reference signal, the last Kalman gain vector and the last a posteriori estimation error are determined; according to the last weight parameter, the last a posteriori estimation error and the last Kalman gain vector, the current weight parameter is determined.

[0270] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0271] The last filtering reference signal is determined based on the historical reference signal and the filter weight coefficient; the filter weight coefficient is the coefficient determined when driving the loudspeaker device to generate a preset noise signal; the current weight parameter is determined based on the last weight parameter, the last residual noise signal, the last filtering reference signal and the preset algorithm step size.

[0272] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0273] The analog output signal is determined according to the historical output signal and the filter weight coefficient; and the noise estimation signal is determined according to the difference between the residual noise signal and the analog output signal.

[0274] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and 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. Among them, any reference to the 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. 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0275] The technical features of the above embodiments may be combined arbitrarily. 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.

[0276] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A noise reduction method, characterized in that: The method comprises: Determine a noise estimation signal of the target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; the output signal is used to drive a loudspeaker device to generate a sound wave signal; determining a target noise reduction algorithm according to the noise estimation signal; The current output signal is determined according to the target noise reduction algorithm, the residual noise signal and a reference signal corresponding to the medical scanning device.

2. The method according to claim 1, characterized in that The step of determining a target noise reduction algorithm according to the noise estimation signal comprises: determining a relative factor based on the residual noise signal and the noise estimation signal; The target noise reduction algorithm is determined according to the relative factor.

3. The method according to claim 2, characterized in that The determining of the relative factor according to the residual noise signal and the noise estimation signal comprises: Determine the first parameter of the current time according to the residual noise signal and the first parameter of the previous time; Determine the second parameter of the current time according to the noise estimation signal and the second parameter of the previous time; The relative factor is determined according to the current first parameter and the current second parameter.

4. The method according to claim 2, characterized in that: The step of determining the target noise reduction algorithm according to the relative factor comprises: If the relative factor is greater than a preset switching threshold, determining the first preset algorithm as the target noise reduction algorithm; If the relative factor is not greater than the preset switching threshold, determining the second preset algorithm as the target noise reduction algorithm; The convergence speed of the first preset algorithm is greater than the convergence speed of the second preset algorithm.

5. The method according to any one of claims 1 to 4, characterized in that: The step of determining the current output signal according to the target noise reduction algorithm, the residual noise signal and a reference signal corresponding to the medical scanning device comprises: Determine the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal; The current output signal is determined according to the current weight parameter and the current reference signal.

6. The method according to claim 5, characterized in that If the target noise reduction algorithm is the first preset algorithm, determining the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal includes: Determining a previous Kalman gain vector and a previous a posteriori estimation error according to the previous residual noise signal and the historical reference signal; The current weight parameter is determined according to the previous weight parameter, the previous a posteriori estimation error and the previous Kalman gain vector.

7. The method according to claim 5, characterized in that If the target noise reduction algorithm is the second preset algorithm, determining the current weight parameter according to the target noise reduction algorithm, the previous residual noise signal and the historical reference signal includes: Determine the last filtering reference signal according to the historical reference signal and the filter weight coefficient; the filter weight coefficient is a coefficient determined when the loudspeaker device is driven to generate a preset noise signal; The current weight parameter is determined according to the previous weight parameter, the previous residual noise signal, the previous filtering reference signal and a preset algorithm step size.

8. The method according to any one of claims 1 to 4, characterized in that: The step of determining the noise estimation signal of the target noise reduction area according to the residual noise signal and the historical output signal corresponding to the target noise reduction area includes: Determining an analog output signal according to the historical output signal and the filter weight coefficient; The noise estimate signal is determined based on a difference between the residual noise signal and the analog output signal.

9. A noise reduction device, characterized in that: The device comprises: A first determination module is used to determine a noise estimation signal of a target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; the output signal is used to drive a speaker device to generate a sound wave signal, and the sound wave signal is used to attenuate a current noise signal of a medical scanning device; A second determination module, configured to determine a target noise reduction algorithm according to the noise estimation signal; The third determination module is used to determine the current output signal according to the target noise reduction algorithm, the residual noise signal and a reference signal corresponding to the medical scanning device.

10. 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, the steps of the method according to any one of claims 1 to 8 are implemented.

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