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 power parameter corresponding to a noise estimation signal in a target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area, determining a target signal gain according to the power parameter, and determining a reference signal corresponding to the medical scanning equipment according to the target signal gain, 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 scene applicability can be improved.

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, a high-intensity noise is generated at the center of the aperture when the magnetic resonance imaging device is in operation. Since the corresponding medical examination using the medical scanning equipment lasts for a long time, noise reduction is a necessary task.

[0003] In the related art, it is usually necessary to set appropriate algorithm parameters and perform noise reduction through the algorithm. However, the current noise reduction method has the problem of poor scene applicability. Summary of the invention

[0004] Based on this, it is necessary to provide a noise reduction method, device and computer equipment that can improve scene applicability in response to the above technical problems.

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

[0006] Determine the power parameter corresponding to the noise estimation signal in 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 signal gain according to a power parameter;

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

[0009] In one embodiment, determining a power parameter corresponding to a noise estimation signal in a target noise reduction area according to a residual noise signal and a historical output signal corresponding to the target noise reduction area includes:

[0010] 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;

[0011] A power parameter is determined according to the noise estimation signal and the tap length corresponding to the noise estimation signal.

[0012] In one embodiment, determining a power parameter according to a noise estimation signal and a tap length corresponding to the noise estimation signal includes:

[0013] Determine a first result based on the noise estimation signal and the tap length;

[0014] determining a difference between the noise estimate signal and the first result;

[0015] Determining a second result according to a quotient between the difference value and the tap length;

[0016] The power parameter is determined according to the sum of the last noise estimation signal and the second result.

[0017] 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:

[0018] Determine the analog output signal according to the historical output 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;

[0019] A noise estimate signal is determined based on the difference between the residual noise signal and the analog output signal.

[0020] In one embodiment, determining a target signal gain according to a power parameter includes:

[0021] The target signal gain is determined based on the preset coefficients and power parameters.

[0022] In one embodiment, determining the current output signal according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device includes:

[0023] Determine a corrected reference signal according to the target signal gain and the reference signal;

[0024] Determine a corrected residual signal according to the target signal gain and the residual noise signal;

[0025] The current output signal is determined according to the corrected reference signal and the corrected residual signal.

[0026] In one embodiment, determining the current output signal according to the modified reference signal and the modified residual signal includes:

[0027] Determine the current weight parameter based on the historical correction reference signal and the last correction residual signal;

[0028] The current output signal is determined according to the current weight parameter and the current corrected reference signal.

[0029] In one embodiment, determining the current weight parameter according to the historical corrected reference signal and the last corrected residual signal includes:

[0030] The current weight parameter is determined based on the historical correction reference signal, the last correction residual signal and the last weight parameter.

[0031] In one embodiment, determining the current weight parameter according to the historical corrected reference signal, the last corrected residual signal and the last weight parameter includes:

[0032] Determine the last Kalman gain vector and the last a posteriori estimation error according to the last corrected residual signal and the historical corrected reference signal;

[0033] The current weight parameter is determined based on the previous weight parameter, the previous a posteriori estimation error and the previous Kalman gain vector.

[0034] In a second aspect, the present application also provides a noise reduction device, comprising:

[0035] A first determination module is used to determine a power parameter corresponding to a noise estimation signal in 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;

[0036] A second determination module, used to determine the target signal gain according to the power parameter;

[0037] The third determination module is used to determine the current output signal according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] The above-mentioned noise reduction method, device and computer equipment determine the power parameter corresponding to the noise estimation signal in 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 signal gain according to the power parameter. Since the current output signal can be determined according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device, and 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, there is no need to manually modify the algorithm parameters according to the noise conditions of different scenes. The appropriate output signal can be determined based on the power parameters corresponding to the noise estimation signal, thereby improving the scene applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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.

[0043] Figure 1 A diagram showing an application environment of a noise reduction method in an embodiment;

[0044] Figure 2 is a schematic flow chart of a noise reduction method in one embodiment;

[0045] Figure 3 A schematic diagram of a process for determining a power parameter in an embodiment;

[0046] Figure 4 A schematic diagram of a process for determining a power parameter in another embodiment;

[0047] Figure 5 A schematic diagram of a process for determining a noise estimation signal in one embodiment;

[0048] Figure 6 A schematic diagram of a process of determining a current output signal in an embodiment;

[0049] Figure 7 It is a schematic diagram of a process of determining a current output signal in another embodiment;

[0050] Figure 8 It is a schematic diagram of a process of determining a current output signal in another embodiment;

[0051] Fig. 9A schematic diagram of a noise reduction method according to an embodiment of the present invention;

[0052] Fig.10 A schematic diagram of a signal transmission process in one embodiment;

[0053] Fig.11 is a structural block diagram of a noise reduction device in one embodiment;

[0054] Fig.12 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] In related technologies, it is usually necessary to set appropriate algorithm parameters and perform noise reduction through the algorithm. The noise reduction performance depends on the algorithm used, and the performance indicators such as the convergence speed, steady-state noise reduction amount, and stability of the algorithm are related to the set algorithm parameters. Therefore, only by setting appropriate parameters can the algorithm achieve a good noise reduction effect while maintaining stability.

[0057] Different scenarios may require different algorithm parameters. For example, a medical scanning device is a magnetic resonance imaging device. There are many sequences that can be used for magnetic resonance examinations, and the magnetic resonance noise under different sequences corresponds to different algorithm parameters. If you want to achieve a good noise reduction effect in different scenarios, you currently need to manually adjust the algorithm parameters according to the current sequence, which is less convenient and practical. Based on this, it is necessary to provide a noise reduction method, which will be introduced below.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] S201, determining a power parameter corresponding to a noise estimation signal in 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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 based on 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.

[0070] 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.

[0071] 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.

[0072] Furthermore, after obtaining the residual noise signal and the historical output signal, the processing device can determine the power parameter corresponding to the noise estimation signal in the target noise reduction area according to the residual noise signal and the historical output signal. The noise estimation signal represents the estimated value of the current noise signal in the target noise area, which is recorded as The power parameter corresponding to the noise estimation signal may be used to indicate the power situation corresponding to the noise estimation signal, which may include but is not limited to at least one of a root mean square value (RMS), a power spectrum density, or a rectified average value.

[0073] Optionally, the processing device may determine the corresponding power parameter according to the corresponding relationship among the residual noise signal, the historical output signal and the power parameter after acquiring the actual residual noise signal and the historical output signal.

[0074] Optionally, the processing device may also determine the corresponding noise estimation signal according to the nth residual noise signal and the historical output signal. 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.

[0075] S202: Determine a target signal gain according to a power parameter.

[0076] After the processing device determines the power parameter corresponding to the noise estimation signal, it can determine the target signal gain according to the power parameter. Optionally, the processing device may determine the target signal gain based on the relationship between different power parameters and signal gain and the power parameter determined in S201. For example, assuming that the power parameter is in range A, it corresponds to signal gain A, and the power parameter is in range B, it corresponds to signal gain B. If the power parameter determined in S201 is actually in range B, then signal gain B is determined to be the target signal gain.

[0077] Furthermore, since the target signal gain is determined based on the power parameter, there is a correlation between the target signal gain and the power of the current noise signal. The target signal gain and the power of the current noise signal may be positively correlated or negatively correlated, which is not limited in this embodiment.

[0078] S203, determining a current output signal according to the target signal gain, the residual noise signal and a reference signal corresponding to the medical scanning device.

[0079] Furthermore, the processing device can be based on the target signal gain , residual noise signal Reference signals corresponding to medical scanning equipment , determine the current output signal .

[0080] Optionally, the processing device may be configured to process the reference signal and residual noise signal Determine the initial signal and adjust the gain of the initial signal and target signal Determine the output signal The processing device can also increase the target signal gain , reference signal and residual noise signal Input the trained output model, and the output model determines the output signal The output model may be determined based on the reference signal samples, the residual noise signal samples corresponding to the reference signal samples, the signal gain samples, and the output signal samples, but the present embodiment is not limited thereto.

[0081] In the above noise reduction method, the power parameter corresponding to the noise estimation signal in 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 signal gain is determined according to the power parameter. Since the current output signal can be determined according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device, and 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, there is no need to manually modify the algorithm parameters according to the noise conditions of different scenes. The appropriate output signal can be determined based on the power parameters corresponding to the noise estimation signal, thereby improving the scene applicability.

[0082] Figure 3 FIG. 1 is a flow chart of determining a power parameter in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 3 As shown, S201 includes S301 to S302.

[0083] S301, 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.

[0084] In this embodiment, the processing device can input the residual noise signal and the historical output signal into the prediction model, and the prediction model outputs the noise estimation signal of the target noise reduction area. The prediction model can 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.

[0085] Among them, 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 Networks (DBN) model, an Elman model, or at least one of their combined models.

[0086] S302: Determine a power parameter according to the noise estimation signal and the tap length corresponding to the noise estimation signal.

[0087] In this embodiment, the tap length corresponding to the noise estimation signal is the order corresponding to the noise estimation signal plus 1, which is recorded as . The processing device can then estimate the signal from the noise The tap length corresponding to the noise estimation signal , determine the power parameters.

[0088] Optionally, the power parameter includes a rectified average value, and the processing device can estimate the signal based on the noise and tap length , determine the noise estimation signal The corresponding rectified average . Rectified mean value represents the rectified noise estimation signal The average value of .

[0089] Exemplarily, the processing device may determine the power parameter according to the following formula (1): .

[0090] (1)

[0091] In the above embodiment, since 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, the power parameter can be efficiently determined according to the noise estimation signal and the tap length corresponding to the noise estimation signal.

[0092] Figure 4 FIG. 1 is a flow chart of another method for determining a power parameter in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 4 As shown, S302 includes S401 to S404.

[0093] S401, determining a first result according to a noise estimation signal and a tap length.

[0094] In this embodiment, the processing device can Determine a first result. For example, the processing device will As the first result.

[0095] S402: Determine a difference between the noise estimation signal and the first result.

[0096] In this embodiment, the processing device determines the noise estimation signal With the first result The difference between .

[0097] S403, determining a second result according to a quotient between the difference value and the tap length.

[0098] Afterwards, the processing device With tap length The quotient between determines the second result. For example, the processing device As a second result.

[0099] S404: Determine a power parameter according to the sum of the last noise estimation signal and the second result.

[0100] Furthermore, the processing device estimates the signal based on the last noise and the second result The power parameter is determined by summing the power of the processor. Exemplarily, the processing device may determine the power parameter according to the following formula (2).

[0101] (2)

[0102] In the above embodiment, the first result is determined based on the noise estimation signal and the tap length, and the difference between the noise estimation signal and the first result is determined, and then the second result is determined based on the quotient between the difference and the tap length. In this way, the power parameter can be determined efficiently and accurately based on the sum of the previous noise estimation signal and the second result.

[0103] Figure 5 FIG. 1 is a flow chart of determining a noise estimation signal in one embodiment. In an exemplary embodiment, as shown in FIG. Figure 5 As shown, S301 includes S501 to S502.

[0104] S501, determining a simulated output signal according to a historical output signal and a filter weight coefficient; the filter weight coefficient is a coefficient determined when driving a loudspeaker device to generate a preset noise signal.

[0105] 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 .

[0106] Optionally, the processing device can determine the analog output signal based on the historical output signal and the filter weight coefficient. 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.

[0107] S502: Determine a noise estimation signal according to the difference between the residual noise signal and the analog output signal.

[0108] 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 (3).

[0109] (3)

[0110] In the above embodiment, since the filter weight coefficient is a coefficient determined when driving the speaker device to generate a preset noise signal, after determining the analog output signal based on the historical output signal and the filter weight coefficient, the noise estimation signal can be determined based on the difference between the residual noise signal and the analog output signal.

[0111] In an exemplary embodiment, optionally, the above S202 may be implemented in the following manner:

[0112] The target signal gain is determined based on the preset coefficients and power parameters.

[0113] In this embodiment, the preset coefficient is a fixed coefficient that can be set as required. Optionally, after the optimal parameters of the algorithm are determined under a certain noise condition, Set to the inverse of the power parameter of the noise at this time, for example, Set to the inverse of the rectified average value corresponding to the noise at this time.

[0114] Then, the processing device can Power parameters , determine the target signal gain Optionally, the processing device can be based on a preset coefficient Power parameters The ratio between them determines the target signal gain For example, the processing device may determine the target signal gain according to the following formula (4): .

[0115] (4)

[0116] In the above embodiment, since the target signal gain can be determined according to the preset coefficient and the power parameter, the target signal gain can indicate the power parameter corresponding to the noise estimation signal. In this way, a suitable output signal can be determined based on the target signal gain.

[0117] Figure 6 FIG. 1 is a flow chart of determining a current output signal in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 6 As shown, S203 includes S601 to S603.

[0118] S601, determining a modified reference signal according to a target signal gain and a reference signal.

[0119] In this embodiment, the processing device can be based on the target signal gain and reference signal , determine the corrected reference signal In other words, the processing device can be based on the target signal gain of the nth and the nth reference signal , determine the nth correction reference signal .

[0120] Optionally, the processing device may be adapted to increase the target signal and reference signal The product between them determines the corrected reference signal Exemplarily, the processing device may determine the modified reference signal according to the following equation (5): .

[0121] (5)

[0122] S602: Determine a modified residual signal according to the target signal gain and the residual noise signal.

[0123] Likewise, the processing device can be adjusted based on the target signal gain and residual noise signal , determine the corrected residual signal That is, the processing device can be based on the target signal gain of the nth time and the nth residual noise signal , determine the nth corrected residual signal .

[0124] Optionally, the processing device may be adapted to increase the target signal and residual noise signal The product between them determines the modified residual signal Exemplarily, the processing device may determine the modified residual signal according to the following equation (6): .

[0125] (6)

[0126] S603, determining a current output signal according to the corrected reference signal and the corrected residual signal.

[0127] Furthermore, the processing device can modify the reference signal and the corrected residual signal Determine the current output signal .

[0128] Optionally, the processing device may be based on a preset active control algorithm, according to the correction reference signal and the corrected residual signal Determine the current output signal The active control algorithm may include but is not limited to at least one of a least mean square algorithm, a recursive least square algorithm, a neural network algorithm, a filtered-x stable fast recursive least squares transversal filter (FxSFTF) algorithm, and a normalized filtered-x least mean square (NFxLMS) algorithm.

[0129] In some embodiments, the processing device may also modify the reference signal and the corrected residual signal Input the trained output model, and the output model determines the current output signal . The output model can be determined based on the corrected reference signal samples, the corrected residual signal samples corresponding to the corrected reference signal, and the output signal samples corresponding to the corrected reference signal samples. The output model may also 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 Networks (DBN) model, an Elman model, or at least one of their combined models, and the present embodiment is not limited thereto.

[0130] In the above embodiment, since the corrected reference signal can be determined based on the target signal gain and the reference signal, and the corrected residual signal can be determined based on the target signal gain and the residual noise signal, the current output signal determined based on the corrected reference signal and the corrected residual signal takes into account the power of the noise in the current scene, thereby improving the scene applicability.

[0131] Figure 7 FIG. 1 is a flow chart of another method for determining the current output signal in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 7As shown, S603 includes S701 to S702.

[0132] S701, determining a current weight parameter according to a historical corrected reference signal and a last corrected residual signal.

[0133] In this embodiment, 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, the weight parameter corresponding to the filter in the processing device. In other words, the processing device needs to 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.

[0134] 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.

[0135] Further, when the processing device needs to determine the output signal, it can determine the current weight parameter according to the historical correction reference signal and the last correction residual signal. The historical correction reference signal includes at least one correction reference signal before the current correction reference signal. Optionally, the historical correction reference signal can include M correction reference signals before the current correction reference signal.

[0136] That is, the processing device can correct the reference signal and the nth corrected residual signal based on the historical correction , determine the weight parameter for the n+1th time For example, the nth modified residual signal can be and the nth corrected reference signal , determine the weight parameter for the n+1th time , or according to the nth modified residual signal and the nmth corrected reference signal , determine the weight parameter for the n+1th time .

[0137] S702, determining the current output signal according to the current weight parameter and the current corrected reference signal.

[0138] Furthermore, after the processing device determines the current weight parameter, it can determine the current output signal according to the current weight parameter and the current corrected reference signal. Optionally, the processing device can determine the output signal according to the product of the transpose of the current weight parameter and the current corrected reference signal. Exemplarily, the processing device can determine the current output signal according to the following formula (7): .

[0139] . (7)

[0140] In the above embodiment, since the current weight parameter is determined based on the historical corrected reference signal and the last corrected residual signal, and the current output signal is determined based on the current weight parameter and the current corrected reference signal, the accuracy of the output signal can be improved by using the weight parameter.

[0141] In an exemplary embodiment, optionally, the above S701 may be implemented in the following manner:

[0142] The current weight parameter is determined based on the historical correction reference signal, the last correction residual signal and the last weight parameter.

[0143] In this embodiment, the processing device can be based on the nth modified residual signal , historical correction reference signal and the nth weight parameter , determine the weight parameter for the n+1th time For example, the nth modified residual signal can be , the nth corrected reference signal and the nth weight parameter , determine the weight parameter for the n+1th time , or according to the nth residual noise signal , the nmth corrected reference signal and the nth weight parameter , determine the weight parameter for the n+1th time .

[0144] 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.

[0145] In the above embodiment, since the current weight parameters are determined based on the historical correction reference signal, the previous correction residual 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.

[0146] Figure 8 FIG. 1 is a flow chart of another method for determining the current output signal in an embodiment. In an exemplary embodiment, as shown in FIG. Figure 8 As shown, the above-mentioned "determining the current weight parameter according to the historical correction reference signal, the last correction residual signal and the last weight parameter" includes S801 to S803.

[0147] S801, determining the last Kalman gain vector and the last a posteriori estimation error according to the last corrected residual signal and the historical corrected reference signal.

[0148] In this embodiment, the processing device can be based on the residual noise signal of the nth time And the historical corrected reference signal, determine the nth Kalman gain vector and the nth posterior estimation error .

[0149] Exemplarily, the processing device may determine the nth Kalman gain vector based on the following equations (8) to (25): 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 Then, the coefficients of the backward filter need to be iterated according to equations (19) to (25). and the least squares sum .

[0150] (8)

[0151] (9)

[0152] (10)

[0153] (11)

[0154] (12)

[0155] (13)

[0156] (14)

[0157] (15)

[0158] (16)

[0159] (17)

[0160] (18)

[0161] (19)

[0162] (20)

[0163] (twenty one)

[0164] (twenty two)

[0165] (twenty three)

[0166] (twenty four)

[0167] (25)

[0168] in, , each value on the right side of the equation is a basis Sure. is the forgetting factor, which is usually very close to 1.

[0169] S802, determining the current weight parameter according to the previous weight parameter, the previous a posteriori estimation error and the previous Kalman gain vector.

[0170] 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 .

[0171] 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 .

[0172] Exemplarily, the processing device determines the current weight parameter according to the following formula (26).

[0173] (26)

[0174] Combining the above, we can see that the Kalman gain vector The size of is proportional to the power of the reference signal, and Proportional to the size of the error signal. When the optimal algorithm parameters are determined under certain noise conditions, the weight coefficient update amount at each iteration of the algorithm The optimal value of is determined. In this case, if the power of the current noise signal increases, it means and The value of increases, resulting in an increase in the weight coefficient update amount at each step, which reduces the stability of the algorithm and even leads to the final divergence of the algorithm. Therefore, it is necessary to readjust the algorithm parameters so that it can work in a stable state. On the contrary, if the power of the current noise signal becomes smaller, then and The value of decreases, and the weight coefficient update amount of each step decreases, which reduces the convergence speed of the algorithm and fails to achieve the optimal performance, so the algorithm parameters need to be adjusted. In this way, using the time-varying target signal gain , reference signal With the residual noise signal It will always be scaled to a numerical level close to the original optimal condition, ensuring that the weight coefficient update amount remains basically unchanged, thereby maintaining the stable operation of the algorithm without having to manually adjust the algorithm parameters, thereby enhancing the algorithm's ease of use and practicality.

[0175] In the above embodiment, since the last Kalman gain vector and the last a posteriori estimation error are determined based on the last corrected residual signal and the historical corrected reference signal, and based on the last weight parameters, the last a posteriori estimation error and the last Kalman gain vector, the current weight parameters can be determined at a faster convergence speed.

[0176] In some embodiments, the processing device can also determine the last filtering reference signal based on the historical correction reference signal and the filter weight coefficient, and determine the current weight parameter based on the last weight parameter, the last correction residual signal, the last filtering reference signal and the preset algorithm step size.

[0177] Among them, the nth corrected reference signal is recorded as . Exemplarily, the processing device may determine the filtering reference signal according to the following equation (27). 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 (27).

[0178] (27)

[0179] Further optionally, the computer device can continuously iterate the last weight parameter based on the last corrected residual signal, the last filtered reference signal and the preset algorithm step size to obtain the current weight parameter.

[0180] In one embodiment, the processing device may also determine the initial result based on the last corrected residual signal, the last filtered 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 corrected residual signal, the last filtered 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.

[0181] 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 be based on the nth modified residual signal , the nth filtering reference signal , preset algorithm step size and the preset regularization factor , determine the initial result for the nth time.

[0182] Optionally, the processing device may be based on Determine the initial result. For example, the processing equipment will As the initial result for the nth time.

[0183] Furthermore, the processing device can 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 (28).

[0184] (28)

[0185] In order to more clearly introduce the noise reduction method of the present application, Fig. 9 and Fig.10 Provide explanation. Fig. 9 FIG. 1 is a process diagram of a noise reduction method in an embodiment. Fig. 9 As shown, the processing device can execute the noise reduction method according to the following steps.

[0186] S901, determining a simulated output signal according to a historical output signal and a filter weight coefficient.

[0187] S902: Determine a noise estimation signal according to the difference between the residual noise signal and the analog output signal.

[0188] S903: Determine a first result according to the noise estimation signal and the tap length.

[0189] S904: Determine a difference between the noise estimation signal and the first result.

[0190] S905, determining a second result according to a quotient between the difference value and the tap length.

[0191] S906: Determine a power parameter according to the sum of the last noise estimation signal and the second result.

[0192] S907: Determine a target signal gain according to a preset coefficient and a power parameter.

[0193] S908: Determine a modified reference signal according to the target signal gain and the reference signal.

[0194] S909: Determine a modified residual signal according to the target signal gain and the residual noise signal.

[0195] S910, determining the current weight parameter according to the historical corrected reference signal, the last corrected residual signal and the last weight parameter. Figure 8 The method shown determines the weight parameters for the current time.

[0196] S911, determining the current output signal according to the current weight parameter and the current modified 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.

[0197] The processes of S901 to S911 may refer to the above embodiments and will not be described in detail here.

[0198] Fig.10 FIG. 1 is a schematic diagram of a signal transmission process in an embodiment. Fig.10 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.

[0199] is the residual noise signal collected by the detection device 101 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.

[0200] 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.

[0201] It can be seen that the present application can determine the target signal gain , and then according to the reference signal and target signal gain Determine the correction reference signal , and according to the target signal gain and residual noise signal , determine the corrected residual signal After that, we can correct the reference signal and the corrected residual 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.

[0202] Thus, by using the time-varying target signal gain , for the reference signal With the residual noise signal The algorithm can be scaled to always match the set algorithm parameters, and good noise reduction effects can be achieved for different sequences without manual adjustment of parameters, thus enhancing the ease of use and practicality of the algorithm.

[0203] 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.

[0204] 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.

[0205] Fig.11 FIG. 1 is a block diagram of a noise reduction device in an embodiment. In an exemplary embodiment, Fig.11 As shown, a noise reduction device is provided, including: a first determination module 1101, a second determination module 1102 and a third determination module 1103, wherein:

[0206] The first determination module 1101 is used to determine the power parameter corresponding to the noise estimation signal in 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.

[0207] The second determination module 1102 is configured to determine a target signal gain according to a power parameter.

[0208] The third determination module 1103 is used to determine the current output signal according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device.

[0209] In the above-mentioned noise reduction device, the power parameter corresponding to the noise estimation signal in 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 signal gain is determined according to the power parameter. Since the current output signal can be determined according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device, and 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, there is no need to manually modify the algorithm parameters according to the noise conditions of different scenes. The appropriate output signal can be determined based on the power parameters corresponding to the noise estimation signal, thereby improving the scene applicability.

[0210] Optionally, the first determining module 1101 includes:

[0211] The first determination unit is used to 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.

[0212] The second determining unit is used to determine the power parameter according to the noise estimation signal and the tap length corresponding to the noise estimation signal.

[0213] Optionally, a second determination unit is used to determine a first result based on a noise estimation signal and a tap length; determine a difference between the noise estimation signal and the first result; determine a second result based on a quotient between the difference and the tap length; and determine a power parameter based on the sum of a previous noise estimation signal and the second result.

[0214] Optionally, the first determination unit is used to determine the analog output signal based on the historical output signal and the filter weight coefficient; the filter weight coefficient is a coefficient determined when driving the speaker device to generate a preset noise signal; and determine the noise estimation signal based on the difference between the residual noise signal and the analog output signal.

[0215] Optionally, the second determination module 1102 is used to determine the target signal gain according to a preset coefficient and a power parameter.

[0216] Optionally, the third determining module 1103 includes:

[0217] The third determination unit is used to determine a modified reference signal according to the target signal gain and the reference signal.

[0218] The fourth determining unit is used to determine the modified residual signal according to the target signal gain and the residual noise signal.

[0219] The fifth determining unit is used to determine the current output signal according to the corrected reference signal and the corrected residual signal.

[0220] Optionally, the fifth determining unit includes:

[0221] The first determination subunit is used to determine the current weight parameter according to the historical correction reference signal and the last correction residual signal.

[0222] The second determination subunit is used to determine the current output signal according to the current weight parameter and the current corrected reference signal.

[0223] Optionally, the first determination subunit is used to determine the current weight parameter based on the historical correction reference signal, the last correction residual signal and the last weight parameter.

[0224] Optionally, the first determination subunit is used to determine the last Kalman gain vector and the last a posteriori estimation error based on the last corrected residual signal and the historical corrected reference signal; and determine the current weight parameter based on the last weight parameter, the last a posteriori estimation error and the last Kalman gain vector.

[0225] 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.

[0226] Fig.12 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.12As 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.

[0227] Those skilled in the art will understand that Fig.12 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.

[0228] 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:

[0229] Determine the power parameter corresponding to the noise estimation signal in 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;

[0230] determining a target signal gain according to a power parameter;

[0231] The current output signal is determined according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device.

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

[0233] A noise estimation signal of the target noise reduction area is determined according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; and a power parameter is determined according to the noise estimation signal and a tap length corresponding to the noise estimation signal.

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

[0235] Determine a first result based on the noise estimation signal and the tap length; determine a difference between the noise estimation signal and the first result; determine a second result based on a quotient between the difference and the tap length; and determine a power parameter based on the sum of the last noise estimation signal and the second result.

[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 based on the historical output signal and the filter weight coefficient; the filter weight coefficient is the coefficient determined when the speaker device is driven to generate a preset noise signal; the noise estimation signal is determined based on the difference between the residual noise signal and the analog output signal.

[0238] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0239] The target signal gain is determined based on the preset coefficients and power parameters.

[0240] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0241] According to the target signal gain and the reference signal, a modified reference signal is determined; according to the target signal gain and the residual noise signal, a modified residual signal is determined; and according to the modified reference signal and the modified residual signal, a current output signal is determined.

[0242] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0243] The current weight parameter is determined according to the historical correction reference signal and the last correction residual signal; the current output signal is determined according to the current weight parameter and the current correction reference signal.

[0244] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0245] The current weight parameter is determined based on the historical correction reference signal, the last correction residual signal and the last weight parameter.

[0246] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0247] According to the last corrected residual signal and the historical corrected 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.

[0248] 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:

[0249] Determine the power parameter corresponding to the noise estimation signal in 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;

[0250] determining a target signal gain according to a power parameter;

[0251] The current output signal is determined according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device.

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

[0253] A noise estimation signal of the target noise reduction area is determined according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; and a power parameter is determined according to the noise estimation signal and a tap length corresponding to the noise estimation signal.

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

[0255] Determine a first result based on the noise estimation signal and the tap length; determine a difference between the noise estimation signal and the first result; determine a second result based on a quotient between the difference and the tap length; and determine a power parameter based on the sum of the last noise estimation signal and the second result.

[0256] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0257] The analog output signal is determined based on the historical output signal and the filter weight coefficient; the filter weight coefficient is the coefficient determined when the speaker device is driven to generate a preset noise signal; the noise estimation signal is determined based on the difference between the residual noise signal and the analog output signal.

[0258] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0259] The target signal gain is determined based on the preset coefficients and power parameters.

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

[0261] According to the target signal gain and the reference signal, a modified reference signal is determined; according to the target signal gain and the residual noise signal, a modified residual signal is determined; and according to the modified reference signal and the modified residual signal, a current output signal is determined.

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

[0263] The current weight parameter is determined according to the historical correction reference signal and the last correction residual signal; the current output signal is determined according to the current weight parameter and the current correction reference signal.

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

[0265] The current weight parameter is determined based on the historical correction reference signal, the last correction residual signal and the last weight parameter.

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

[0267] According to the last corrected residual signal and the historical corrected 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.

[0268] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0269] Determine the power parameter corresponding to the noise estimation signal in 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;

[0270] determining a target signal gain according to a power parameter;

[0271] The current output signal is determined according to the target signal gain, the residual noise signal and the reference signal corresponding to the medical scanning device.

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

[0273] A noise estimation signal of the target noise reduction area is determined according to a residual noise signal and a historical output signal corresponding to the target noise reduction area; and a power parameter is determined according to the noise estimation signal and a tap length corresponding to the noise estimation signal.

[0274] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0275] Determine a first result based on the noise estimation signal and the tap length; determine a difference between the noise estimation signal and the first result; determine a second result based on a quotient between the difference and the tap length; and determine a power parameter based on the sum of the last noise estimation signal and the second result.

[0276] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0277] The analog output signal is determined based on the historical output signal and the filter weight coefficient; the filter weight coefficient is the coefficient determined when the speaker device is driven to generate a preset noise signal; the noise estimation signal is determined based on the difference between the residual noise signal and the analog output signal.

[0278] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0279] The target signal gain is determined based on the preset coefficients and power parameters.

[0280] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0281] According to the target signal gain and the reference signal, a modified reference signal is determined; according to the target signal gain and the residual noise signal, a modified residual signal is determined; and according to the modified reference signal and the modified residual signal, a current output signal is determined.

[0282] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0283] The current weight parameter is determined according to the historical correction reference signal and the last correction residual signal; the current output signal is determined according to the current weight parameter and the current correction reference signal.

[0284] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0285] The current weight parameter is determined based on the historical correction reference signal, the last correction residual signal and the last weight parameter.

[0286] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0287] According to the last corrected residual signal and the historical corrected 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.

[0288] 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.

[0289] 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.

[0290] 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 understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations 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 power parameter corresponding to a noise estimation signal in 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; determining a target signal gain according to the power parameter; The current output signal is determined according to the target signal gain, 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 determining, according to the residual noise signal and the historical output signal corresponding to the target noise reduction area, a power parameter corresponding to the noise estimation signal in the target noise reduction area comprises: 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; The power parameter is determined according to the noise estimation signal and a tap length corresponding to the noise estimation signal.

3. The method according to claim 2, characterized in that The determining the power parameter according to the noise estimation signal and the tap length corresponding to the noise estimation signal comprises: Determining a first result according to the noise estimation signal and the tap length; determining a difference between the noise estimate signal and the first result; Determining a second result according to a quotient between the difference and the tap length; The power parameter is determined according to the sum of the last noise estimation signal and the second result.

4. The method according to claim 2, 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: Determine the analog output signal according to the historical output 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 noise estimate signal is determined based on a difference between the residual noise signal and the analog output signal.

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 signal gain, the residual noise signal and a reference signal corresponding to the medical scanning device comprises: Determining a corrected reference signal according to the target signal gain and the reference signal; Determining a modified residual signal according to the target signal gain and the residual noise signal; The current output signal is determined according to the modified reference signal and the modified residual signal.

6. The method according to claim 5, characterized in that The determining the current output signal according to the corrected reference signal and the corrected residual signal comprises: Determine the current weight parameter based on the historical correction reference signal and the last correction residual signal; The current output signal is determined according to the current weight parameter and the current corrected reference signal.

7. The method according to claim 6, characterized in that The step of determining the current weight parameter according to the historical corrected reference signal and the last corrected residual signal includes: The current weight parameter is determined according to the historical corrected reference signal, the last corrected residual signal and the last weight parameter.

8. The method according to claim 7, characterized in that The determining the current weight parameter according to the historical corrected reference signal, the last corrected residual signal and the last weight parameter comprises: Determining a previous Kalman gain vector and a previous a posteriori estimation error according to the previous corrected residual signal and the historical corrected 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.

9. A noise reduction device, characterized in that: The device comprises: A first determination module is used to determine a power parameter corresponding to a noise estimation signal in 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; A second determination module, used to determine a target signal gain according to the power parameter; The third determination module is used to determine the current output signal according to the target signal gain, 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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