CN113539228B

CN113539228B - Noise reduction parameter determining method and device, active noise reduction method and device

Info

Publication number: CN113539228B
Application number: CN202110873136.3A
Authority: CN
Inventors: 刘益帆; 徐银海
Original assignee: Beijing Ansheng Haolang Technology Co ltd
Current assignee: Beijing Ansheng Haolang Technology Co ltd
Priority date: 2021-07-30
Filing date: 2021-07-30
Publication date: 2023-10-31
Anticipated expiration: 2041-07-30
Also published as: CN113539228A

Abstract

The application provides a noise reduction parameter determining method, which comprises the following steps: acquiring a noise reduction signal sample output by a loudspeaker and an error signal sample acquired by an error microphone; determining an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample; respectively carrying out fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector; determining a fuzzy control mapping relation between the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector; the noise reduction parameters are determined based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation, so that the system design cost can be reduced, the active noise reduction effect can be improved, and the probability of overshoot can be effectively reduced.

Description

Noise reduction parameter determining method and device, active noise reduction method and device

Technical Field

The application relates to the technical field of active noise reduction, in particular to a noise reduction parameter determining method and device, an active noise reduction method and device.

Background

In recent years, products with active noise reduction function have been attracting attention. Existing active noise reduction (ANC (Active Noise Cancellation, ANC) controllers have noise reduction parameters designed based on an accurate solution to the acoustic path, i.e., the acoustic path is considered a "system", and the system response is determined from the relationship between the input and output acoustic signals.

However, objective calculation of the acoustic path is complex, and the acoustic path may also vary with the usage state of the product, which easily results in inaccurate calculation of the acoustic path. Inaccuracy in the calculation of the acoustic path severely affects the noise reduction effect of the target area and even results in inability to reduce noise. In addition, in the active noise reduction process, an overshoot phenomenon is very easy to occur.

Disclosure of Invention

In view of the above, the embodiments of the present application provide a method and an apparatus for determining noise reduction parameters, and an active noise reduction method and an apparatus thereof, which can reduce the design cost of a system, improve the active noise reduction effect of a product, and effectively reduce the probability of overshoot in the active noise reduction process.

According to a first aspect of an embodiment of the present application, there is provided a noise reduction parameter determining method, including: acquiring a noise reduction signal sample output by a loudspeaker and an error signal sample acquired by an error microphone; determining an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample, wherein the error signal trend parameter sample is used for representing the subsequent operation trend of the error signal sample; respectively carrying out fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector; determining a fuzzy control mapping relation between the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector; and determining noise reduction parameters based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation.

In one embodiment of the present application, performing fuzzy quantization processing on an error signal sample, an error signal trend parameter sample, and a noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector, and a noise reduction signal sample fuzzy vector, respectively, including: taking the error signal sample and the error signal trend parameter sample as observed quantity of fuzzy control, and respectively determining error signal fuzzy dividing information and error signal trend parameter fuzzy dividing information; based on the error signal fuzzy partition information, fuzzily quantizing the error signal samples into error signal sample fuzzy vectors; based on the fuzzy dividing information of the error signal trend parameter, the fuzzy quantization of the error signal trend parameter sample is carried out to obtain a fuzzy vector of the error signal trend parameter sample; the noise reduction signal sample is used as the control quantity of fuzzy control, and the fuzzy division information of the noise reduction signal is determined; based on the noise reduction signal fuzzy dividing information, the noise reduction signal sample fuzzy is quantized into noise reduction signal sample fuzzy vectors.

In one embodiment of the present application, determining the error signal fuzzy partition information and the error signal trend parameter fuzzy partition information respectively by using the error signal sample and the error signal trend parameter sample as the observed quantity of the fuzzy control includes: taking the error signal sample and the error signal trend parameter sample as observed quantity of fuzzy control, respectively determining an error signal quantization level and an error signal domain, and determining an error signal trend parameter quantization level and an error signal trend parameter domain; determining an error signal membership degree based on the error signal quantization level and the error signal domain, wherein the error signal membership degree is used for representing the degree that the accurate value of the error signal domain belongs to the error signal quantization level; and determining the membership degree of the error signal trend parameter based on the error signal trend parameter quantization level and the error signal trend parameter domain, wherein the membership degree of the error signal trend parameter is used for representing the degree of the accurate value of the error signal trend parameter domain membership degree of the error signal trend parameter quantization level. And/or, determining noise reduction signal fuzzy division information by taking noise reduction signal samples as control quantity of fuzzy control, including: the noise reduction signal sample is used as the control quantity of fuzzy control, and the quantization level of the noise reduction signal and the domain of the noise reduction signal are determined; and determining the noise reduction signal membership degree based on the noise reduction signal quantization level and the noise reduction signal domain, wherein the noise reduction signal membership degree is used for representing the degree that the accurate value of the noise reduction signal domain belongs to the noise reduction signal quantization level.

In one embodiment of the present application, the fuzzy control mapping is used to eliminate the amplitude of the error signal sample, and at the same time suppress the amplitude overshoot, and finally make the amplitude in the minimum convergence state.

In one embodiment of the present application, determining a fuzzy control mapping relationship between an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector, and a noise reduction signal sample fuzzy vector comprises: if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative large or negative small, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative large or negative small, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive large; if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative big or negative little, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive little; if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is zero, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is zero.

In one embodiment of the present application, determining noise reduction parameters based on an error signal sample blur vector, an error signal trend parameter sample blur vector, a noise reduction signal sample blur vector, and a blur control mapping relationship comprises: for each fuzzy control mapping relation in a plurality of fuzzy control mapping relations, carrying out Cartesian multiplication operation on the mutually mapped error signal sample fuzzy vector and the noise reduction signal sample fuzzy vector corresponding to the fuzzy control mapping relation to obtain a first intermediate value matrix corresponding to the fuzzy control mapping relation, carrying out Cartesian multiplication operation on the mutually mapped error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector corresponding to the fuzzy control mapping relation to obtain a second intermediate value matrix corresponding to the fuzzy control mapping relation, and carrying out minimum operation on elements in the first intermediate value matrix and the second intermediate value matrix to obtain a third intermediate value matrix corresponding to the fuzzy control mapping relation; and performing maximum operation on elements of a third intermediate value matrix corresponding to each of the fuzzy control mapping relations to obtain noise reduction parameters.

According to a second aspect of an embodiment of the present application, there is provided an active noise reduction method, including: determining a current error signal trend parameter corresponding to the current error signal based on the current error signal acquired by the error microphone, wherein the current error signal trend parameter is used for representing a subsequent operation trend of the current error signal; performing fuzzy quantization processing on the current error signal and the current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector; determining a current noise reduction signal fuzzy vector corresponding to the current noise reduction signal based on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector and the noise reduction parameter, wherein the noise reduction parameter is determined based on the noise reduction parameter determining method in the first aspect; and performing de-blurring quantization processing on the fuzzy vector of the current noise reduction signal to obtain the current noise reduction signal so that the loudspeaker plays the current noise reduction signal.

In one embodiment of the present application, performing a defuzzification quantization process on a current error signal blur vector to obtain a current noise reduction signal, including: determining a current noise reduction signal domain to which a current noise reduction signal fuzzy vector belongs by searching the maximum membership; and determining the current noise reduction signal corresponding to the current noise reduction signal domain based on the corresponding relation between the noise reduction signal domain and the noise reduction signal.

According to a third aspect of the embodiment of the present application, there is provided a noise reduction parameter determining apparatus, including: the acquisition module is configured to acquire noise reduction signal samples output by the loudspeaker and error signal samples acquired by the error microphone; the first determining module is configured to determine an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample, wherein the error signal trend parameter sample is used for representing a subsequent operation trend of the error signal sample; the second determining module is configured to perform fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample respectively to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector; the third determining module is configured to determine a fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector; and a fourth determining module configured to determine the noise reduction parameter based on the error signal sample blur vector, the error signal trend parameter sample blur vector, the noise reduction signal sample blur vector, and the fuzzy control mapping relationship.

According to a fourth aspect of an embodiment of the present application, there is provided an active noise reduction device, including: a fifth determining module configured to determine a current error signal trend parameter corresponding to the current error signal based on the current error signal collected by the error microphone, wherein the current error signal trend parameter is used for representing a subsequent running trend of the current error signal; the fuzzy quantization module is configured to perform fuzzy quantization processing on the current error signal and the current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector; the fuzzy reasoning module is configured to determine a current noise reduction signal fuzzy vector corresponding to the current noise reduction signal based on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector and the noise reduction parameter, wherein the noise reduction parameter is determined based on the noise reduction parameter determination method in the first aspect; the de-blurring quantization module is configured to perform de-blurring quantization processing on the fuzzy vector of the current noise reduction signal to obtain the current noise reduction signal so that the loudspeaker plays the current noise reduction signal.

According to the noise reduction parameter determining method provided by the embodiment of the application, the error signal trend parameter sample used for representing the subsequent operation trend of the error signal sample is determined based on the error signal sample, the noise reduction signal sample and the error signal trend parameter sample are subjected to fuzzy quantization processing respectively to obtain an error signal sample fuzzy vector, a noise reduction signal sample fuzzy vector and an error signal trend parameter sample fuzzy vector, a fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector is determined, noise reduction parameters are determined based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation, and the noise reduction parameters can be determined without calculating an acoustic path. Because the mathematical model with accurate acoustic path is not required to be established, the design cost of the active noise reduction system can be obviously reduced, and the active noise reduction process combined with the fuzzy control has the intelligence and the high efficiency of processing the nonlinear time-varying process based on the characteristics of the fuzzy control, and the active noise reduction effect can be obviously improved. In addition, as the subsequent operation trend of the error signal sample is considered when the noise reduction parameters are determined, the probability of overshoot in the active noise reduction process can be effectively reduced.

Drawings

Fig. 1 is a block diagram of an active noise reduction system in the prior art that determines noise reduction parameters by establishing an acoustic path.

Fig. 2 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application.

Fig. 3 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application.

Fig. 4 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application.

Fig. 5 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application.

Fig. 6 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application.

Fig. 7 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application.

Fig. 8 is a flowchart illustrating an active noise reduction method according to an embodiment of the application.

Fig. 9 is a block diagram of an active noise reduction system corresponding to the active noise reduction method provided in fig. 8.

Fig. 10 is a schematic structural diagram of a noise reduction parameter determining apparatus according to an embodiment of the present application.

Fig. 11 is a schematic structural diagram of a noise reduction parameter determining apparatus according to an embodiment of the present application.

Fig. 12 is a schematic structural diagram of an active noise reduction device according to an embodiment of the application.

Fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The error microphone 120 is disposed near the target noise reduction area, and is configured to collect an error signal e, that is, a noise signal obtained in the target noise reduction area after noise reduction, and the calculation module 130 is configured to determine a noise reduction signal y according to the error signal e, and the speaker 110 is configured to output the noise reduction signal y to reduce noise of the noise signal.

The space between the original noise signal x and the error microphone 120 forms a primary path, and the speaker 110 itself and the space between the speaker 110 and the error microphone 120 together form a secondary path. The primary path and the secondary path have respective transfer functions, wherein the transfer function of the primary path is the transfer function of the original noise signal x to the space between the error microphone 120, denoted as P, and the transfer function of the secondary path is the transfer function of the electroacoustic conversion of the loudspeaker 110 and the transfer function of the diaphragm surface of the loudspeaker 110 to the space between the error microphone 120, denoted as G. And determining a noise reduction parameter W through calculation G to establish a feedback active noise reduction system.

However, objective calculation of the acoustic path is complex, and the acoustic path may also vary with the usage state of the product, which easily results in inaccurate calculation of the acoustic path. Inaccuracy in the calculation of the acoustic path severely affects the noise reduction effect of the target area and even results in inability to reduce noise. In addition, in the active noise reduction process, an overshoot phenomenon is very easy to occur. Therefore, the embodiment of the application provides a noise reduction parameter determining method, which can determine noise reduction parameters without calculating an acoustic path and can effectively reduce the probability of overshoot in the active noise reduction process.

Specifically, an error signal trend parameter sample for representing a subsequent operation trend of the error signal sample is determined based on the error signal sample, fuzzy quantization processing is performed on the error signal sample, the noise reduction signal sample and the error signal trend parameter sample respectively to obtain an error signal sample fuzzy vector, a noise reduction signal sample fuzzy vector and an error signal trend parameter sample fuzzy vector, a fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector is determined, and noise reduction parameters are determined based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation, so that the noise reduction parameters can be determined without calculating an acoustic path. Because the mathematical model with accurate acoustic path is not required to be established, the design cost of the active noise reduction system can be obviously reduced, and the active noise reduction process combined with the fuzzy control has the intelligence and the high efficiency of processing the nonlinear time-varying process based on the characteristics of the fuzzy control, and the active noise reduction effect can be obviously improved. In addition, as the subsequent operation trend of the error signal sample is considered when the noise reduction parameters are determined, the probability of overshoot in the active noise reduction process can be effectively reduced.

The noise reduction parameter determination method, the active noise reduction method, the noise reduction parameter determination device, the active noise reduction device and the electronic device mentioned in the present application are further exemplified below with reference to fig. 2 to 13.

Exemplary noise reduction parameter determination methods

Fig. 2 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application. As shown in fig. 2, the noise reduction parameter determination method includes the following steps.

Step 201: and acquiring a noise reduction signal sample output by the loudspeaker and an error signal sample acquired by the error microphone.

Specifically, the noise reduction signal sample output by the speaker is transferred to the target noise reduction area to reduce noise, and the noise signal after noise reduction in the target noise reduction area is collected by the error microphone (i.e., the error signal sample), so that the noise reduction signal sample corresponds to the error signal sample.

Step 202: and determining an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample.

In one embodiment, the error signal trend parameter samples are used to characterize subsequent operational trends of the error signal samples.

Specifically, the overshoot phenomenon easily occurs in the active noise reduction process, and the overshoot phenomenon is overshootAdjustment of the error signal should be over an adjustment range, such as: the current error signal has a sound pressure value of m Pa, and the purpose of active noise reduction is to suppress the sound pressure to an absolute value (2×10 ^-5 Pa), however, noise reduction according to the noise reduction signal may reduce the sound pressure value of the error signal by n Pa (n is greater than m), and at this time, an overshoot phenomenon occurs. The overshoot phenomenon occurs because only the error signal sample (absolute sound pressure value) is considered, and the subsequent operation trend (relative sound pressure value) of the error signal sample is not considered. Therefore, the application introduces the error signal trend parameter sample which can represent the subsequent operation trend of the error signal sample in the process of determining the noise reduction parameter, so that the error signal sample and the error signal trend parameter sample are considered in the process of determining the noise reduction parameter, thereby reducing the probability of overshoot phenomenon in the process of actively reducing the noise.

It should be noted that the error signal trend parameter samples include error signal sample variations (i.e., first derivatives of sound pressure values of the error signal samples). When the error signal trend parameter sample is the error signal sample change, the method for determining the error signal trend parameter sample based on the error signal sample is to calculate 1 st derivative of the sound pressure value of the error signal sample.

Step 203: and respectively carrying out fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector.

Specifically, fuzzy control is a control method formed based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic reasoning, and imitates the fuzzy reasoning and decision making process of people. In the process of fuzzy control, an operator or expert experience is compiled into a fuzzy control rule, then real-time signals are subjected to fuzzy quantization, the signals after fuzzy quantization are used as input of the fuzzy control rule, fuzzy reasoning is completed, and the output quantity obtained after the reasoning is added to an actuator.

In the active noise reduction process combined with fuzzy control, an error signal sample, an error signal trend parameter sample and a noise reduction signal sample are required to be converted into fuzzy language variables so as to perform fuzzy logic reasoning. Accordingly, it is desirable to quantize the error signal samples, error signal trend parameter samples, and noise reduction signal sample ambiguities into error signal sample ambiguities vectors, error signal trend parameter sample ambiguities vectors, and noise reduction signal sample ambiguities vectors.

Step 204: and determining a fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector.

Specifically, for fuzzy control, the fuzzy control rule needs to be established by means of experience of an operator or expert, and the fuzzy control rule is the core for subsequent fuzzy reasoning and decision. Based on the above, in the active noise reduction process combined with the fuzzy control, the fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector, namely the fuzzy control rule, is determined by combining the characteristics and experience of the feedback active noise reduction.

It should be noted that, the establishment principle of the fuzzy control mapping relationship is as follows: the method is used for eliminating the amplitude of the error signal sample, simultaneously inhibiting the amplitude overshoot, and finally enabling the amplitude to be in a minimized convergence state, namely the fuzzy control rule comprises a quick response rule for eliminating the large error signal sample, a damping rule for preventing the error signal overshoot and a steady-state rule for maintaining the convergence of the error signal sample.

Step 205: and determining noise reduction parameters based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation.

Specifically, the process of determining the noise reduction parameters is a process of simulating fuzzy logic reasoning and decision, and the fuzzy reasoning and decision need fuzzy linguistic variables and fuzzy control rules. Thus, the noise reduction parameters are determined by combining the error signal sample blur vector, the error signal trend parameter sample blur vector, the noise reduction signal sample blur vector, and the blur control mapping relationship.

In the embodiment of the application, the error signal trend parameter sample used for representing the subsequent operation trend of the error signal sample is determined based on the error signal sample, the noise reduction signal sample and the error signal trend parameter sample are subjected to fuzzy quantization processing respectively to obtain an error signal sample fuzzy vector, a noise reduction signal sample fuzzy vector and an error signal trend parameter sample fuzzy vector, a fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector is determined, and noise reduction parameters are determined based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation, so that the noise reduction parameters can be determined without calculating an acoustic path. Because the mathematical model with accurate acoustic path is not required to be established, the design cost of the active noise reduction system can be obviously reduced, and the active noise reduction process combined with the fuzzy control has the intelligence and the high efficiency of processing the nonlinear time-varying process based on the characteristics of the fuzzy control, and the active noise reduction effect can be obviously improved. In addition, as the subsequent operation trend of the error signal sample is considered when the noise reduction parameters are determined, the probability of overshoot in the active noise reduction process can be effectively reduced.

Fig. 3 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application. As shown in fig. 3, the steps of performing fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector respectively include the following steps.

Step 301: and respectively determining the fuzzy dividing information of the error signal and the fuzzy dividing information of the trend parameter of the error signal by taking the error signal sample and the trend parameter sample of the error signal as observed quantity of fuzzy control.

Specifically, the feedback type active noise reduction system adjusts the noise reduction signal sample output by the loudspeaker through the error signal sample of the target noise reduction area acquired by the error microphone, so as to realize active noise reduction, therefore, in order to reduce the probability of overshoot, the fuzzy control needs to take the error signal sample as an observed quantity, and meanwhile, the follow-up operation trend of the error signal sample needs to be considered, so that the fuzzy control also needs to take the error signal trend parameter sample as an observed quantity, and then the error signal sample and the error signal trend parameter sample are taken as the observed quantity of the fuzzy control together, namely, the fuzzy controller is a bivariate fuzzy controller in the active noise reduction process. The error signal fuzzy partition information is information which is established by combining the characteristic of active noise reduction and experience and is used for carrying out fuzzy quantization on error signal samples, and is usually shown in the form of an error signal fuzzy partition table. The fuzzy dividing information of the error signal trend parameter is information which is established by combining the characteristic of active noise reduction and experience and is used for carrying out fuzzy quantization on the error signal trend parameter sample, and is usually displayed in the form of a fuzzy dividing table of the error signal trend parameter.

Step 302: based on the error signal ambiguity partition information, the error signal sample ambiguity is quantized into an error signal sample ambiguity vector.

Specifically, the error signal sample blur is quantized into an error signal sample blur vector by looking up in the error signal blur division information.

Step 303: and based on the fuzzy dividing information of the error signal trend parameter, the fuzzy quantization of the error signal trend parameter sample is carried out to obtain a fuzzy vector of the error signal trend parameter sample.

Specifically, the error signal trend parameter sample blur is quantized into an error signal trend parameter sample blur vector by querying in the error signal trend parameter blur partition information.

Step 304: and determining the fuzzy partition information of the noise reduction signal by taking the noise reduction signal sample as the control quantity of fuzzy control.

Specifically, the feedback type active noise reduction system realizes active noise reduction through noise reduction signal samples output by a loudspeaker. Therefore, the fuzzy control takes the noise reduction signal sample as a control quantity. The noise reduction signal fuzzy division information is information which is established by combining the characteristics of active noise reduction and experience and is used for carrying out fuzzy quantization on noise reduction signal samples, and is usually displayed in the form of a noise reduction signal fuzzy division table.

Step 305: based on the noise reduction signal fuzzy dividing information, the noise reduction signal sample fuzzy is quantized into noise reduction signal sample fuzzy vectors.

Specifically, noise reduction signal sample blur is quantized into noise reduction signal sample blur vectors by querying in noise reduction signal blur division information.

In the embodiment of the application, error signal fuzzy dividing information, error signal trend parameter fuzzy dividing information and noise reduction signal fuzzy dividing information are worked out by combining the characteristics and experience of active noise reduction, the error signal fuzzy dividing information is inquired, the error signal sample fuzzy is quantized into an error signal sample fuzzy vector, the error signal trend parameter fuzzy dividing information is inquired, the error signal trend parameter sample fuzzy is quantized into an error signal trend parameter sample fuzzy vector, the noise reduction signal fuzzy dividing information is inquired, the noise reduction signal sample fuzzy is quantized into a noise reduction signal sample fuzzy vector, and therefore the error signal sample, the error signal trend parameter sample and the noise reduction signal sample are converted into fuzzy language variables, and fuzzy logic reasoning is carried out.

Fig. 4 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application. As shown in fig. 4, the steps of determining the fuzzy division information of the error signal and the fuzzy division information of the trend parameter of the error signal respectively by taking the error signal sample and the trend parameter sample of the error signal as the observed quantity of the fuzzy control comprise the following steps.

Step 401: and respectively determining an error signal quantization level and an error signal domain, and an error signal trend parameter quantization level and an error signal trend parameter domain by taking the error signal sample and the error signal trend parameter sample as observed quantity of fuzzy control.

Specifically, the error signal quantization level is the size of the error signal described in a fuzzy language. The quantization level of the trend parameter of the error signal is the magnitude of the trend parameter of the error signal described in a fuzzy language, i.e. the magnitude of the first derivative of the sound pressure value of the error signal described in a fuzzy language. The quantization level is usually a fuzzy language composed of large, medium and small, plus or minus zero, etc. The error signal argument is a set of discrete values, the elements of which are determined based on the magnitude of the physical quantity of the error signal, each of which corresponds to an accurate error signal. The error signal trend parameter argument is a set of discrete values, the elements in the set being determined based on the magnitude of the physical quantity of the error signal change, i.e. the elements in the set being discrete values determined based on the magnitude of the first derivative value of the sound pressure value of the error signal, each discrete value corresponding to an accurate first derivative value of the sound pressure value of the error signal. The domains are generally represented by Arabic numerals and "positive and negative".

For example, the error signal quantization level and the error signal trend parameter quantization level are { negative, zero, positive }, { negative large, negative small, zero, positive small, positive large }, { negative large, negative medium, negative small, zero, positive small, medium, positive large } or { negative large, negative medium, negative small, negative zero, positive small, medium, positive large } or the like, abbreviated with english terms { N, O, P }, { NB, NS, O, PS }, { NB, NM, NS, O, PS, PM, PB } or { NB, NM, NS, NO, PO, PS, PM, PB }, or the like. The error signal domains are { -2, -1,0, +1, +2}, { -3, -2, -1,0, +1, +2, +3} or { -4, -3, -2, -1,0, +1, +2, +3, +4} and the like.

Step 402: and determining the degree of error signal membership based on the error signal quantization level and the error signal domain, wherein the degree of error signal membership is used for representing the degree of the accurate value of the error signal domain membership to the error signal quantization level.

In particular, in order to implement fuzzy quantization, a relationship is established between the domain of error signals composed of the above-mentioned precise values and the quantization levels of error signals described in fuzzy language, that is, the degree of membership of the precise values in the domain of error signals to the quantization levels of error signals is determined, and the degree of membership of error signals is used to characterize the degree to which the precise values in the domain of error signals are affiliated to the quantization levels of error signals.

Step 403: and determining the membership degree of the error signal trend parameter based on the error signal trend parameter quantization level and the error signal trend parameter domain, wherein the membership degree of the error signal trend parameter is used for representing the degree of the accurate value of the error signal trend parameter domain membership degree of the error signal trend parameter quantization level.

In particular, in order to implement fuzzy quantization, a relationship is established between the error signal trend parameter domain composed of the above-mentioned precise values and the error signal trend parameter quantization levels described in the fuzzy language, that is, the degree of membership of the precise value in the error signal trend parameter domain to each error signal trend parameter quantization level is determined, and the degree of membership of the error signal trend parameter to the precise value in the error signal trend parameter domain is used to characterize the degree of membership of the precise value in the error signal trend parameter domain to the error signal trend parameter quantization level.

For example, the error signal blur division information (error signal blur division table) is shown in table 1.

TABLE 1

The error signal trend parameter fuzzy partition information (error signal trend parameter fuzzy partition table) is shown in table 2.

TABLE 2

In the embodiment of the application, an error signal sample and an error signal trend parameter sample are taken as observed quantity of fuzzy control, an error signal quantization level and an error signal domain as well as an error signal trend parameter quantization level and an error signal trend parameter domain are determined, an error signal membership degree and an error signal trend parameter membership degree are determined, an error signal fuzzy dividing table and an error signal trend parameter fuzzy dividing table are formed, and the error signal sample and the error signal trend parameter sample are respectively quantized into an error signal sample fuzzy vector and an error signal trend parameter sample fuzzy vector based on the error signal fuzzy dividing table and the error signal trend parameter fuzzy dividing table.

It should be noted that the quantization level of the trend parameter of the error signal may be the same as or different from the quantization level of the error signal, and the trend parameter of the error signal may be the same as or different from the domain of the error signal.

Fig. 5 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application. As shown in fig. 5, the step of determining the noise reduction signal fuzzy division information using the noise reduction signal sample as the control amount of fuzzy control includes the following steps.

Step 501: and determining the quantization level of the noise reduction signal and the domain of the noise reduction signal by taking the noise reduction signal sample as the control quantity of fuzzy control.

Step 502: and determining the noise reduction signal membership degree based on the noise reduction signal quantization level and the noise reduction signal domain, wherein the noise reduction signal membership degree is used for representing the degree that the accurate value of the noise reduction signal domain belongs to the noise reduction signal quantization level.

Specifically, the determining method of the noise reduction signal quantization level, the noise reduction signal domain and the noise reduction signal membership degree is the same as the determining method of the error signal quantization level, the error signal domain and the error signal membership degree, and will not be described in detail herein.

For example, noise reduction signal blur division information (noise reduction signal blur division table) is shown in table 3.

TABLE 3 Table 3

In the embodiment of the application, a noise reduction signal sample is taken as a control quantity of fuzzy control, a noise reduction signal quantization level and a noise reduction signal domain are determined, a noise reduction signal membership degree is determined, a noise reduction signal fuzzy dividing table is formed, and noise reduction signal sample information is quantized into noise reduction signal sample fuzzy vectors based on the noise reduction signal fuzzy dividing table.

It should be noted that the division of the noise reduction signal quantization level and the error signal quantization level may be the same or different, and the division of the noise reduction signal domain and the error signal domain may be the same or different.

Fig. 6 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application. As shown in fig. 6, the step of determining the fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector comprises the following steps.

Step 601: if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative large or negative small, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative large or negative small, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive large.

Step 602: if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative large or negative small, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive small.

Step 603: if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is zero, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is zero.

Specifically, the fuzzy control map is typically made up of a set of fuzzy conditional statements of if-then structure. The error signal sample is represented by symbol e, and the fuzzy vector of the error signal sample is represented by symbolThe error signal trend parameter sample is represented by symbol ec, and the error signal trend parameter sample fuzzy vector is represented by symbol +.>The noise reduction signal sample is represented by the symbol y, and the error signal sample fuzzy vector is represented by the symbol +.>The fuzzy control map is represented by an if-then structure.

The above-described fuzzy control map should be expressed as follows:

if...，then...

In the embodiment of the application, the fuzzy control mapping relation is expressed by a group of multiple conditional sentences by combining the characteristics and experience of active noise reduction, so that the noise reduction parameters obtained by the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation are equivalent to noise reduction parameters obtained by acoustic path calculation based on the error signal sample fuzzy vector and the error signal trend parameter sample fuzzy vector.

Note that, the fuzzy control map relationship includes not only the relationship represented by the above expression but also the relationship represented by other expressions.

Fig. 7 is a flowchart illustrating a method for determining noise reduction parameters according to an embodiment of the application. As shown in fig. 7, the step of determining noise reduction parameters based on the error signal sample blur vector, the error signal trend parameter sample blur vector, the noise reduction signal sample blur vector, and the blur control mapping relation includes the following steps.

Step 701: for each fuzzy control mapping relation in the plurality of fuzzy control mapping relations, carrying out Cartesian multiplication operation on the mutually mapped error signal sample fuzzy vector and the noise reduction signal sample fuzzy vector corresponding to the fuzzy control mapping relation to obtain a first intermediate value matrix corresponding to the fuzzy control mapping relation, carrying out Cartesian multiplication operation on the mutually mapped error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector corresponding to the fuzzy control mapping relation to obtain a second intermediate value matrix corresponding to the fuzzy control mapping relation, and carrying out minimum operation on elements in the first intermediate value matrix and the second intermediate value matrix to obtain a third intermediate value matrix corresponding to the fuzzy control mapping relation.

Step 702: and performing maximum operation on elements of a third intermediate value matrix corresponding to each of the fuzzy control mapping relations to obtain noise reduction parameters.

Specifically, noise reduction parametersThe specific mathematical calculation method refers to step 701 and step 702, which are based on the fuzzy relation matrix determined by the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation.

For example, when the fuzzy control mapping relation is the expression of step 601-step 603, the noise reduction parameterThe calculation formula of (2) is as follows:

wherein "+" is the element maximum operation; "X" represents the Cartesian product of the vectors; ". "take small operation for elements in ordered pair in Cartesian product.

In the embodiment of the application, based on the fuzzy control mapping relation, the noise reduction parameters are obtained by calculating the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector through the formula.

It should be noted that, when the quantization level of the error signal sample, the error signal trend parameter sample, the noise reduction signal sample and the domain division are sufficiently fine, and the fuzzy control mapping relationship is sufficient to meet the active noise reduction requirement, the noise reduction parameters are obtained based on the fuzzy control Linear filter with infinite approximation of system characteristics to objective calculation>

It should be further noted that the error signal trend parameter samples include not only the error signal sample variation (i.e., the first derivative of the sound pressure value of the error signal sample), but also the variation of the error signal sample variation (i.e., the second derivative of the sound pressure value of the error signal sample). When the error signal trend parameter sample is the change of the error signal sample, the method for determining the error signal trend parameter sample based on the error signal sample is to calculate 1-order derivative of the sound pressure value of the error signal sample, and similarly, when the error signal trend parameter sample is the change of the error signal sample, the method for determining the error signal trend parameter sample based on the error signal sample is to calculate 2-order derivative of the sound pressure value of the error signal sample.

Fig. 8 is a flowchart illustrating an active noise reduction method according to an embodiment of the application. Fig. 9 is a block diagram of an active noise reduction system corresponding to the active noise reduction method provided in fig. 8. As shown in fig. 8 and 9, the active noise reduction method includes the following steps.

Step 801: and determining a current error signal trend parameter corresponding to the current error signal based on the current error signal acquired by the error microphone.

Specifically, in order to reduce the probability of the overshoot phenomenon in the active noise reduction process, the subsequent operation trend of the current error signal sample needs to be considered, so that the current error signal trend parameter corresponding to the current error signal needs to be obtained, and the first derivative of the sound pressure value of the current error signal is obtained in real time to obtain the current error signal trend parameter.

For example, the current error signal collected by the error microphone is e (n), and the current error signal e (n) is derived to obtain the current error signal trend parameter ec (n).

Step 802: and carrying out fuzzy quantization processing on the current error signal and the current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector.

Specifically, the current error signal and the current error signal trend parameter are both accurate quantities, and the current error signal trend parameter are required to be converted into fuzzy languages to perform fuzzy reasoning, so that fuzzy quantization processing is performed on the current error signal and the current error signal trend parameter respectively based on preset error signal fuzzy partition information and preset error signal trend parameter fuzzy partition information to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector. The specific process of obtaining the preset error signal fuzzy partition information and the preset error signal trend parameter fuzzy partition information is the same as that of obtaining the error signal fuzzy partition information and the error signal trend parameter fuzzy partition information in the noise reduction parameter determination method provided in any of the above embodiments, and is not described herein again.

For example, the current error signal fuzzy vector is obtained by fuzzy quantization processing of e (n)Obtaining a current error signal trend parameter fuzzy vector +.>

Step 803: and determining the current noise reduction signal fuzzy vector corresponding to the current noise reduction signal based on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector and the noise reduction parameter.

Optionally, the noise reduction parameter is determined based on the noise reduction parameter determining method provided in any one of the foregoing embodiments.

Specifically, fuzzy reasoning is carried out on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector and the noise reduction parameter to obtain the current noise reduction signal fuzzy vector.

For example, pair ofNoise reduction parameter +.>Fuzzy reasoning is carried out to obtain fuzzy vector +.>I.e. < ->

Step 804: and performing de-blurring quantization processing on the fuzzy vector of the current noise reduction signal to obtain the current noise reduction signal so that the loudspeaker plays the current noise reduction signal.

Specifically, the current noise reduction signal fuzzy vector obtained through fuzzy reasoning is a fuzzy quantity, and the current noise reduction signal fuzzy vector needs to be subjected to fuzzy quantization processing to obtain an accurate physical signal, namely the current noise reduction signal.

In one embodiment, a current noise reduction signal domain to which the current noise reduction signal fuzzy vector belongs is determined by searching the maximum membership, and a current noise reduction signal corresponding to the current noise reduction signal domain is determined based on a corresponding relation between the noise reduction signal domain and the noise reduction signal.

For example, the current noise reduction signal blur vectorCan be further expressed as:and searching the maximum membership degree of 1, wherein the current noise reduction signal domain is a level of '1', and acquiring the current noise reduction signal corresponding to the level of '1' according to the corresponding relation between the noise reduction signal domain and the noise reduction signal. The corresponding relation between the noise reduction signal domain and the noise reduction signal is determined based on how to divide the domain when the noise reduction signal fuzzy division information is obtained in the noise reduction parameter determination method provided by any embodiment, and the specific process is thatThis will not be described in detail.

In the embodiment of the application, fuzzy quantization processing is carried out on a current error signal and a current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector, fuzzy reasoning is carried out on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector and the noise reduction parameter obtained in any embodiment to obtain a current noise reduction signal fuzzy vector, fuzzy quantization processing is carried out on the current noise reduction signal fuzzy vector to obtain a current noise reduction signal, so that a loudspeaker plays the current noise reduction signal to reduce noise of a target noise reduction area, and when an error microphone acquires an updated noise signal, the process is circulated until steady noise reduction is realized.

Exemplary noise reduction parameter determination apparatus

Fig. 10 is a schematic structural diagram of a noise reduction parameter determining apparatus according to an embodiment of the present application. As shown in fig. 10, the noise reduction parameter determination apparatus 100 includes: an acquisition module 101 configured to acquire a noise reduction signal sample output by the speaker and an error signal sample acquired by the error microphone; a first determining module 102 configured to determine an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample, wherein the error signal trend parameter sample is used to characterize a subsequent running trend of the error signal sample; the second determining module 103 is configured to perform fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample respectively to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector; a third determining module 104 configured to determine a fuzzy control mapping relationship among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, and the noise reduction signal sample fuzzy vector; the fourth determining module 105 is configured to determine the noise reduction parameter based on the error signal sample blur vector, the error signal trend parameter sample blur vector, the noise reduction signal sample blur vector, and the blur control mapping relation.

In one embodiment, the fuzzy control map is used to eliminate the amplitude of the error signal samples while suppressing amplitude overshoot, ultimately placing the amplitude in a minimized convergence state.

Fig. 11 is a schematic structural diagram of a noise reduction parameter determining apparatus according to an embodiment of the present application. As shown in fig. 11, the second determining module 103 further includes: the first fuzzy dividing unit 1031 is configured to determine the error signal fuzzy dividing information and the error signal trend parameter fuzzy dividing information respectively by taking the error signal sample and the error signal trend parameter sample as observed quantities of fuzzy control; a first blurring quantization unit 1032 configured to blur-quantize the error signal samples into error signal sample blur vectors based on the error signal blur division information; a second fuzzy quantization unit 1033 configured to fuzzy quantize the error signal trend parameter samples into error signal trend parameter sample fuzzy vectors based on the error signal trend parameter fuzzy partition information; a second fuzzy dividing unit 1034 configured to determine noise reduction signal fuzzy dividing information with the noise reduction signal sample as a control amount of fuzzy control; the third blurring quantization unit 1035 is configured to quantize the noise reduction signal sample blurring into a noise reduction signal sample blurring vector based on the noise reduction signal blurring division information.

In one embodiment, the first fuzzy dividing unit 1031 is further configured to determine an error signal quantization level and an error signal domain, and an error signal trend parameter quantization level and an error signal trend parameter domain, respectively, using the error signal samples and the error signal trend parameter samples as observables of the fuzzy control; determining an error signal membership degree based on the error signal quantization level and the error signal domain, wherein the error signal membership degree is used for representing the degree that the accurate value of the error signal domain belongs to the error signal quantization level; and determining the membership degree of the error signal trend parameter based on the error signal trend parameter quantization level and the error signal trend parameter domain, wherein the membership degree of the error signal trend parameter is used for representing the degree of the accurate value of the error signal trend parameter domain membership degree of the error signal trend parameter quantization level.

In one embodiment, the second fuzzy dividing unit 1034 is further configured to determine the noise reduction signal quantization level and the noise reduction signal domain with the noise reduction signal sample as the control amount of the fuzzy control; and determining the noise reduction signal membership degree based on the noise reduction signal quantization level and the noise reduction signal domain, wherein the noise reduction signal membership degree is used for representing the degree that the accurate value of the noise reduction signal domain belongs to the noise reduction signal quantization level.

In one embodiment, the third determining module 104 is further configured to, if the error signal quantization level corresponding to the error signal sample blur vector is negative large or negative small and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample blur vector is negative large or negative small, then the noise reduction signal quantization level corresponding to the noise reduction signal sample blur vector is positive large; if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative big or negative little, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive little; if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is zero, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is zero.

In one embodiment, the fourth determining module 105 is further configured to perform cartesian product operation on the mutually mapped error signal sample fuzzy vector and the mutually mapped noise reduction signal sample fuzzy vector corresponding to the fuzzy control mapping relation to obtain a first intermediate value matrix corresponding to the fuzzy control mapping relation, perform cartesian product operation on the mutually mapped error signal trend parameter sample fuzzy vector and the mutually mapped noise reduction signal sample fuzzy vector corresponding to the fuzzy control mapping relation to obtain a second intermediate value matrix corresponding to the fuzzy control mapping relation, and perform minimum operation on elements in the first intermediate value matrix and the second intermediate value matrix to obtain a third intermediate value matrix corresponding to the fuzzy control mapping relation; and performing maximum operation on elements of a third intermediate value matrix corresponding to each of the fuzzy control mapping relations to obtain noise reduction parameters.

The implementation process of the functions and roles of each module in the noise reduction parameter determining device is specifically detailed in the implementation process of the corresponding steps in the noise reduction parameter determining method, and is not repeated here.

Exemplary active noise reduction devices

Fig. 12 is a schematic structural diagram of an active noise reduction device according to an embodiment of the application. As shown in fig. 12, the active noise reduction device 200 includes: a fifth determining module 201 configured to determine a current error signal trend parameter corresponding to the current error signal based on the current error signal collected by the error microphone, wherein the current error signal trend parameter is used to characterize a subsequent running trend of the current error signal; the fuzzy quantization module 202 is configured to perform fuzzy quantization on the current error signal and the current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector; the fuzzy inference module 203 is configured to determine a current noise reduction signal fuzzy vector corresponding to the current noise reduction signal based on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector and the noise reduction parameter, wherein the noise reduction parameter is determined based on the noise reduction parameter determination method provided in any embodiment; the defuzzification quantization module 204 is configured to perform defuzzification quantization on the fuzzy vector of the current noise reduction signal to obtain the current noise reduction signal, so that the current noise reduction signal can be played by the loudspeaker.

In one embodiment, the defuzzification quantization module 204 is further configured to determine a current noise reduction signal argument to which the current noise reduction signal blur vector belongs by searching for a maximum degree of membership; and determining the current noise reduction signal corresponding to the current noise reduction signal domain based on the corresponding relation between the noise reduction signal domain and the noise reduction signal.

The implementation process of the functions and actions of each module in the active noise reduction device is specifically shown in the implementation process of the corresponding steps in the active noise reduction method, and will not be described herein.

Exemplary electronic device

Fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the application. As shown in fig. 13, the electronic device 300 includes one or more processors 310 and memory 320.

The processor 310 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

Memory 320 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that may be executed by the processor 310 to implement the noise reduction parameter determination method or active noise reduction method and/or other desired functions of the various embodiments of the present application described above.

In one example, the electronic device 300 may further include: input device 330 and output device 340, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

Of course, only some of the components of the electronic device 300 that are relevant to the present application are shown in fig. 3 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 300 may include any other suitable components depending on the particular application.

Exemplary computer program product and computer readable storage Medium

In addition to the methods and apparatus described above, embodiments of the application may also be computer program products comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the noise reduction parameter determination method provided according to the various embodiments of the application described in the "exemplary noise reduction parameter determination method" section of the present specification, or the steps of the active noise reduction method provided according to the various embodiments of the application described in the "exemplary active noise reduction method" section.

The computer program product may write program code for performing operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional step programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps of the noise reduction parameter determination method provided according to the embodiments of the present application described in the above-described "exemplary noise reduction parameter determination method" section of the present specification, or the steps of the active noise reduction method provided according to the embodiments of the present application described in the above-described "exemplary active noise reduction method" section.

The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

It should be noted that the above-mentioned embodiments are merely examples of the present application, and it is obvious that the present application is not limited to the above-mentioned embodiments, and many similar variations are possible. All modifications attainable or obvious from the present disclosure set forth herein should be deemed to be within the scope of the present disclosure.

It should be understood that the first, second, etc. qualifiers mentioned in the embodiments of the present application are only used for more clearly describing the technical solutions of the embodiments of the present application, and should not be used to limit the protection scope of the present application.

The foregoing is merely illustrative of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A noise reduction parameter determination method, comprising:

acquiring a noise reduction signal sample output by a loudspeaker and an error signal sample acquired by an error microphone;

determining an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample, wherein the error signal trend parameter sample is used for representing the subsequent operation trend of the error signal sample, and the error signal trend parameter sample comprises error signal sample changes;

Respectively carrying out fuzzy quantization on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector;

determining a fuzzy control mapping relation among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector, wherein the fuzzy control mapping relation comprises a quick response rule for eliminating error signal samples, a damping rule for preventing error signal overshoot and a steady state rule for maintaining error signal sample convergence;

and determining noise reduction parameters based on the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector, the noise reduction signal sample fuzzy vector and the fuzzy control mapping relation.

2. The noise reduction parameter determining method according to claim 1, wherein the performing fuzzy quantization processing on the error signal sample, the error signal trend parameter sample, and the noise reduction signal sample to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector, and a noise reduction signal sample fuzzy vector, respectively, includes:

Respectively determining error signal fuzzy division information and error signal trend parameter fuzzy division information by taking the error signal sample and the error signal trend parameter sample as observed quantity of fuzzy control;

based on the error signal ambiguity partition information, fuzzily quantizing the error signal samples into the error signal sample ambiguity vectors;

based on the error signal trend parameter fuzzy partition information, fuzzily quantizing the error signal trend parameter sample into the error signal trend parameter sample fuzzy vector;

determining fuzzy partition information of the noise reduction signal by taking the noise reduction signal sample as a control quantity of fuzzy control;

and based on the noise reduction signal fuzzy dividing information, the noise reduction signal sample fuzzy is quantized into the noise reduction signal sample fuzzy vector.

3. The noise reduction parameter determination method according to claim 2, wherein the determining the error signal fuzzy partition information and the error signal trend parameter fuzzy partition information with the error signal sample and the error signal trend parameter sample as observed amounts of fuzzy control, respectively, includes:

taking the error signal sample and the error signal trend parameter sample as observed quantity of fuzzy control, respectively determining an error signal quantization level and an error signal domain, and an error signal trend parameter quantization level and an error signal trend parameter domain;

Determining an error signal membership degree based on the error signal quantization level and the error signal domain, wherein the error signal membership degree is used for representing the degree of the accurate value of the error signal domain membership to the error signal quantization level;

determining an error signal trend parameter membership based on the error signal trend parameter quantization level and the error signal trend parameter domain, wherein the error signal trend parameter membership is used for representing the degree that an accurate value of the error signal trend parameter domain belongs to the error signal trend parameter quantization level;

and/or the number of the groups of groups,

the determining the noise reduction signal fuzzy division information by taking the noise reduction signal sample as the control quantity of fuzzy control comprises the following steps:

the noise reduction signal sample is used as a control quantity of fuzzy control, and a noise reduction signal quantization level and a noise reduction signal domain are determined;

and determining a noise reduction signal membership degree based on the noise reduction signal quantization level and the noise reduction signal domain, wherein the noise reduction signal membership degree is used for representing the degree that an accurate value of the noise reduction signal domain is affiliated to the noise reduction signal quantization level.

4. A noise reduction parameter determination method according to any one of claims 1 to 3, wherein the fuzzy control map is configured to cancel the amplitude of the error signal sample while suppressing the amplitude overshoot, and finally to place the amplitude in a minimized convergence state.

5. The noise reduction parameter determination method according to claim 4, wherein the determining of the fuzzy control mapping relationship among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector comprises:

if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative large or negative small, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative large or negative small, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive large;

if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero, and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is negative large or negative small, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is positive small;

and if the error signal quantization level corresponding to the error signal sample fuzzy vector is negative zero or positive zero and the error signal trend parameter quantization level corresponding to the error signal trend parameter sample fuzzy vector is zero, the noise reduction signal quantization level corresponding to the noise reduction signal sample fuzzy vector is zero.

6. A noise reduction parameter determination method according to any one of claims 1 to 3, wherein said determining noise reduction parameters based on said error signal sample blur vector, said error signal trend parameter sample blur vector, said noise reduction signal sample blur vector, and said blur control mapping relation comprises:

for each fuzzy control mapping relation in a plurality of fuzzy control mapping relations, carrying out Cartesian multiplication operation on mutually mapped error signal sample fuzzy vectors and noise reduction signal sample fuzzy vectors corresponding to the fuzzy control mapping relation to obtain a first intermediate value matrix corresponding to the fuzzy control mapping relation, carrying out Cartesian multiplication operation on mutually mapped error signal trend parameter sample fuzzy vectors and noise reduction signal sample fuzzy vectors corresponding to the fuzzy control mapping relation to obtain a second intermediate value matrix corresponding to the fuzzy control mapping relation, and carrying out minimum operation on elements in the first intermediate value matrix and the second intermediate value matrix to obtain a third intermediate value matrix corresponding to the fuzzy control mapping relation;

and performing maximum operation on elements of a third intermediate value matrix corresponding to each of the fuzzy control mapping relations to obtain the noise reduction parameters.

7. An active noise reduction method, comprising:

determining a current error signal trend parameter corresponding to a current error signal based on the current error signal acquired by an error microphone, wherein the current error signal trend parameter is used for representing a subsequent running trend of the current error signal;

performing fuzzy quantization processing on the current error signal and the current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector;

determining a current noise reduction signal blur vector corresponding to a current noise reduction signal based on the current error signal blur vector, the current error signal trend parameter blur vector and a noise reduction parameter, wherein the noise reduction parameter is determined based on the noise reduction parameter determination method according to any one of claims 1 to 6;

and performing de-blurring quantization processing on the current noise reduction signal blurring vector to obtain the current noise reduction signal so that a loudspeaker plays the current noise reduction signal.

8. The method of active noise reduction according to claim 7, wherein the performing a defuzzification quantization process on the current error signal blur vector to obtain the current noise reduction signal comprises:

Determining a current noise reduction signal domain to which the current noise reduction signal fuzzy vector belongs by searching the maximum membership;

and determining the current noise reduction signal corresponding to the current noise reduction signal domain based on the corresponding relation between the noise reduction signal domain and the noise reduction signal.

9. A noise reduction parameter determination apparatus, comprising:

the acquisition module is configured to acquire noise reduction signal samples output by the loudspeaker and error signal samples acquired by the error microphone;

a first determining module configured to determine an error signal trend parameter sample corresponding to the error signal sample based on the error signal sample, wherein the error signal trend parameter sample is used for representing a subsequent operation trend of the error signal sample, and the error signal trend parameter sample comprises an error signal sample change;

the second determining module is configured to perform fuzzy quantization processing on the error signal sample, the error signal trend parameter sample and the noise reduction signal sample respectively to obtain an error signal sample fuzzy vector, an error signal trend parameter sample fuzzy vector and a noise reduction signal sample fuzzy vector;

a third determining module configured to determine a fuzzy control mapping relationship among the error signal sample fuzzy vector, the error signal trend parameter sample fuzzy vector and the noise reduction signal sample fuzzy vector, wherein the fuzzy control mapping relationship comprises a fast response rule for eliminating error signal samples, a damping rule for preventing error signal overshoot, and a steady state rule for maintaining error signal sample convergence;

And a fourth determining module configured to determine a noise reduction parameter based on the error signal sample blur vector, the error signal trend parameter sample blur vector, the noise reduction signal sample blur vector, and the blur control mapping relationship.

10. An active noise reduction device, comprising:

a fifth determining module configured to determine a current error signal trend parameter corresponding to a current error signal based on the current error signal collected by the error microphone, wherein the current error signal trend parameter is used for representing a subsequent operation trend of the current error signal;

the fuzzy quantization module is configured to perform fuzzy quantization processing on the current error signal and the current error signal trend parameter to obtain a current error signal fuzzy vector and a current error signal trend parameter fuzzy vector;

a fuzzy inference module configured to determine a current noise reduction signal fuzzy vector corresponding to a current noise reduction signal based on the current error signal fuzzy vector, the current error signal trend parameter fuzzy vector, and a noise reduction parameter, wherein the noise reduction parameter is determined based on the noise reduction parameter determination method of any one of claims 1 to 6;

And the defuzzification quantization module is configured to perform defuzzification quantization processing on the current noise reduction signal fuzzy vector to obtain the current noise reduction signal so as to facilitate a loudspeaker to play the current noise reduction signal.