CN117037763A - Active noise reduction control method, electronic device and storage medium - Google Patents

Abstract

The application discloses an active noise reduction control method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a first playing signal played by a loudspeaker on the notebook computer based on a microphone sensor, and identifying the first playing signal by using a least mean square algorithm to obtain a secondary path, wherein the microphone sensor is arranged at the ear of a user; acquiring a second playing signal played by a loudspeaker arranged at the ear of the user based on an error sensor, and identifying the second playing signal by using a least mean square algorithm to obtain a virtual error path, wherein the error sensor is arranged on the notebook computer; and performing active noise reduction control according to the secondary path and the virtual error path. According to the embodiment of the application, the secondary path and the virtual error path are subjected to active noise reduction control, so that the residual noise transmitted to the human ear and the human ear can be effectively subjected to active noise control, and the experience of a user is improved.

Description

Active noise reduction control method, electronic device and storage medium

Technical Field

The application belongs to the technical field of noise reduction, and particularly relates to an active noise reduction control method, electronic equipment and a storage medium.

Background

The existing active noise reduction comprises earphone active noise reduction and automobile active noise reduction, the earphone active noise reduction comprises head-wearing type noise reduction and in-ear type noise reduction, a noise processing scene is in pipeline noise, main noise is wind noise and environmental noise, the device belongs to double-channel active noise reduction, noise control frequency is lower than 1500Hz, and noise reduction depth is 20-30dB; the device belongs to multi-channel active noise reduction, has more external devices, comprises a plurality of microphones and loudspeakers, wherein the microphones can be arranged at the positions of a headrest, a side door, a roof and the like, the loudspeakers can share an automobile sound system or use an independent loudspeaker system, the noise control frequency is within 500Hz, and the noise reduction depth is 5-10dB. The main flow of the active noise reduction method is self-adaptive active noise control, and the active noise reduction method comprises the steps of acquiring a reference signal from a noise source, outputting an inverted control signal through an algorithm, playing the reference signal to a target area by using a loudspeaker to counteract noise, and simultaneously acquiring a residual noise signal and feeding the residual noise signal back to the algorithm to calculate so as to perform continuous control.

The defects of the active noise reduction technology are mainly that a certain delay is needed for algorithm calculation and signal transmission in the aspect of noise control precision, the propagation speed of sound in reality is high, and a control signal output by the whole technical process and actual noise need to reach a control area at the same time; the open noise active control area is larger, the single system control area is limited, and more control channels are needed to achieve the ideal effect, which has larger requirements on hardware equipment and calculation pressure.

The open active noise reduction control area is larger, the control area can only be enlarged by adopting a multi-speaker mode in the prior art, meanwhile, the arrangement of the multi-speaker occupies more space, and the loud speaker with good sound reproduction effect cannot use too small speaker, so that contradiction is formed; meanwhile, some application scenes consider that more equipment cannot be arranged in terms of requirements of attractive appearance, space arrangement and the like; the application scene of the open active noise reduction is not fixed, the sound field is possible to be time-varying, and the algorithm needs a certain convergence time, which leads to unstable noise reduction effect when the scene changes or is slightly complex.

The inventors found that: the active noise reduction technology is mostly applied to small vehicles and headphones, and a better scheme does not appear when the active noise reduction technology is applied to a computer, and the difficulty is high.

Disclosure of Invention

Embodiments of the present application aim to solve at least one of the above technical problems.

In a first aspect, an embodiment of the present application provides an active noise reduction control method, including: acquiring a first playing signal played by a loudspeaker on the notebook computer based on a microphone sensor, and identifying the first playing signal by using a least mean square algorithm to obtain a secondary path, wherein the microphone sensor is arranged at the ear of a user; acquiring a second playing signal played by a loudspeaker arranged at the ear of the user based on an error sensor, and identifying the second playing signal by using a least mean square algorithm to obtain a virtual error path, wherein the error sensor is arranged on the notebook computer; and performing active noise reduction control according to the secondary path and the virtual error path.

In a second aspect, an embodiment of the present application provides an electronic device, including: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the active noise reduction control methods of the present application.

In a third aspect, embodiments of the present application provide a storage medium having stored therein one or more programs including execution instructions that are readable and executable by an electronic device (including, but not limited to, a computer, a server, or a network device, etc.) for performing any of the above-described active noise reduction control methods of the present application.

In a fourth aspect, embodiments of the present application also provide a computer program product comprising a computer program stored on a storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform any one of the active noise reduction control methods described above.

According to the embodiment of the application, the secondary path and the virtual error path are actively noise-reduced, so that the residual noise transmitted to the human ear and the human ear can be effectively actively noise-controlled, the influence of the noise around the notebook computer when the user uses the notebook computer can be reduced, and the experience of the user is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of an active noise reduction control method according to an embodiment of the present application;

FIG. 2 is a flow chart of an active noise reduction control method according to another embodiment of the present application;

FIG. 3 is a schematic diagram of a secondary path modeling of the active noise reduction control method of the present application;

FIG. 4 is a schematic diagram of a multi-channel fractional order active noise control algorithm of the active noise reduction control method of the present application;

FIG. 5 is a flow chart of an active noise reduction control process according to an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating a layout of an active noise reduction control method according to the present application;

fig. 7 is a schematic structural diagram of an embodiment of an electronic device of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. 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.

It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In the present application, "module," "device," "system," and the like refer to a related entity, either hardware, a combination of hardware and software, or software in execution, as applied to a computer. In particular, for example, an element may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. Also, the application or script running on the server, the server may be an element. One or more elements may be in processes and/or threads of execution, and elements may be localized on one computer and/or distributed between two or more computers, and may be run by various computer readable media. The elements may also communicate by way of local and/or remote processes in accordance with a signal having one or more data packets, e.g., a signal from one data packet interacting with another element in a local system, distributed system, and/or across a network of the internet with other systems by way of the signal.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," comprising, "or" includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The embodiment of the application provides an active noise reduction control method which can be applied to electronic equipment. The electronic device may be a computer, a server, or other electronic products, etc., which the present application is not limited to.

Referring to fig. 1, an active noise reduction control method according to an embodiment of the application is shown.

In step 101, as shown in fig. 1, a first playing signal played by a speaker on the notebook computer is obtained based on a microphone sensor, and a secondary path is obtained by identifying the first playing signal by using a least mean square algorithm, wherein the microphone sensor is arranged at the ear of a user;

in step 102, a second playing signal played by a loudspeaker arranged at the ear of the user is obtained based on an error sensor, and a virtual error path is obtained by identifying the second playing signal by using a least mean square algorithm, wherein the error sensor is arranged on the notebook computer;

in step 103, active noise reduction control is performed according to the secondary path and the virtual error path.

In this embodiment, for step 101, in the experimental stage, a microphone sensor may be disposed at the ear, then a speaker is disposed on a notebook computer, a first playing signal is played through the speaker disposed on the notebook computer, where the first playing signal is also a random signal, the random signal played by the speaker is obtained by using the microphone sensor disposed at the ear, the playing signal of the speaker and the signal obtained by the microphone sensor are identified by using a least mean square algorithm offline, and a secondary path is obtained by identifying the first playing signal by using the least mean square algorithm, where the secondary path is a propagation path from the speaker to the ear. The secondary paths to be identified are taken into account in the algorithm calculation, so that the signal to be output can be propagated to the human ear.

Then, for step 102, in the experimental stage, a speaker may be disposed at the position of the human ear and a random signal may be played, then an error sensor may be disposed on the notebook computer, the error sensor disposed on the notebook computer may be used to obtain the sound played by the speaker disposed at the human ear, the playing signal of the speaker disposed at the human ear and the obtained signal may be identified by a least mean square algorithm through the error sensor offline, so as to obtain a virtual error path, where the virtual error path is a propagation path from the human ear to the error sensor, and is called a virtual error path.

Finally, for step 103, active noise reduction control is performed according to the obtained secondary path and virtual error path. Calculating the value of the current convergence step length based on the secondary path and the virtual error path, calculating a fractional gradient value and updating a weight vector; and finally, calculating and outputting an inverted signal and transmitting the inverted signal to a secondary sound source loudspeaker.

According to the method provided by the embodiment of the application, the secondary path and the virtual error path are subjected to active noise reduction control, so that the residual noise transmitted to the human ear and the human ear can be effectively subjected to active noise control, the influence of surrounding noise when a user uses the notebook computer can be reduced, and the experience of the user is improved.

Referring to fig. 2, another active noise reduction control method according to an embodiment of the application is shown. The flowchart is mainly a flowchart of the steps further defined before "active noise reduction control based on the secondary path and the virtual error path" in step 103 of the flowchart of fig. 1.

As shown in fig. 2, in step 201, a reference signal and an error signal are obtained, and variables and parameters of the reference signal are set, wherein the reference signal simulates a low-frequency signal which occupies main energy for a fan rotation speed provided by the notebook computer in real time;

in step 202, the reference signal and the error signal are input to a fractional order adaptive controller.

In this embodiment, for step 201, a reference signal and an error signal are obtained, variables and parameters of the reference signal are set, for example, a noise source signal needs to be obtained, a low-frequency signal occupying the main energy is simulated through a fan rotation speed provided by a notebook computer in real time as the reference signal, the reference signal is sent to an algorithm for calculation, and then is output to a human ear by a speaker to be superimposed with noise to generate destructive interference, and an error sensor near the human ear obtains a residual noise signal and feeds the residual noise signal back to the algorithm for calculation of a next control signal.

For step 202, a reference signal and an error signal are input to a fractional order adaptive controller, where x (n) is an input vector, or referred to as a reference signal; w (n) is a weight vector; e (n) is an error signal; d (n) is a desired signal, namely an actual noise signal of the control area; y (n) is the algorithm output; μ (n) is the convergence step size; n is the number of iterations. The reference signal x (n) and the error signal e (n) are input to a fractional order adaptive controller.

According to the method, based on the gradient information in the convergence process of the main algorithm by adopting the fractional order calculation algorithm, mathematical researches show that the fractional order is closer to the description of the physical phenomenon, and the fractional order is used for the algorithm calculation to accelerate the algorithm convergence and reduce the algorithm error.

Secondary path modeling-dual pick-up approach: as shown in fig. 3, the dual-pickup method refers to arranging pickup devices (i.e., error sensors) at the secondary sound source position and the target area, respectively, and performing modeling by sampling the reference input and the desired input of the acoustic wave signal adaptive modeling filter. This approach is also relatively easy to implement in engineering, but ignores the response of the speaker and part of the electronics to the transmission of the acoustic signal.

Wherein e (n) =d (n) -y (n),

taking d (n) and y (n) from the relation to the excitation signal x (n) into the equation e (n) =d (n) -y (n), respectively, yields

The filter weight coefficient is updated according to the same updating criterion as the LMS algorithm, and is W (n+1) =W (n) +2μe (n) X (n)

When the algorithm converges to an ideal state, i.e. e (n) =0, there isThus an impulse response estimate of the secondary path is obtained>Will ∈>The filtered-x signal is calculated instead of h (n).

The secondary path identification experiment adopts an additional white noise method to carry out path identification, a control circuit is used for collecting sound signals sent by a secondary sound source, signal collection is carried out in a target area, the sound source signals and signals collected in the target area are extracted and input into MATLAB to carry out secondary path model identification, white noise signals are input into the secondary sound source to be played, and the target area 20cm and 30cm away from the secondary sound source is selected to form working condition 1 and working condition 2.

Measured data are exported into excel and loaded by MATLAB, and the LMS algorithm is used. The method comprises the steps of using 20000 sampling points, white noise signals in a program as identification output reference signals, measuring and collecting signals as identification expected signals of different transmission paths, and setting the step length in an identification algorithm to be 0.01.

Evaluation of the quality of recognition in the secondary path by means of the calculation of the fitness determining coefficient in the curve fitting

Total Sum of Squares (SST):

residual squaring (SSE):

determining coefficients:

in some alternative embodiments, the value of the current convergence step and the fractional gradient value are calculated based on the secondary path and the virtual error path; and updating the weight vector of the reference signal according to the value of the current convergence step length and the fractional gradient value. After updating the weight vector of the reference signal, receiving an inversion signal output by the fractional order self-adaptive controller, calculating the inversion signal, transmitting the calculated inversion signal to a secondary sound source loudspeaker, increasing the iteration number by 1, and re-inputting the iteration number to the fractional order self-adaptive controller; if the iteration value increases, the reference signal and the error signal are input to the fractional order adaptive controller again.

Referring to fig. 4, the multi-channel fractional order algorithm is designed as follows: in the multichannel control system, the reference signal is a matrix X (n) of kxlx, M sub-filters are in the filter bank W, the length is I, and the error signal is a vector signal e (n) of length L. The noise source is P (n), and the primary path between the noise source and the error sensor is represented by response Hp (z). The secondary path is represented by response H (z), and the secondary path estimates are represented by response H' (z), all of length Lh.

For the whole system, there are

The error signal can be expressed as:

the matrix expression for the error signal can be obtained by rearranging the filtered sequence of the reference signal as:

in the above formula, d _l (n) is the desired signal, which is onPrimary path response H shown in fig. 4 _p (z) filtering the noise source. In addition r _lmk (n) is a filtered reference signal, which can be expressed as:

will beExpressed in vector form:

wherein: w (w) _i ＝(w _11i w _12i …w _1ki w _21i …w _MKi ] ^T

And r ₁ (n)＝[r _l11 (n) r _l12 (n)…r _l1k (n)r _l21 (n)…r _lMK (n)] ^T

The error signal vector and the desired signal vector are respectively:

e(n)＝[e ₁ (n)e ₂ (n)…e _L (n)] ^T

d(n)＝[d ₁ (n)d ₂ (n)…d _L (n)] ^T

the secondary sound source contribution of the overall system can be expressed as:

e(n)＝d(n)+R(n)W(n)

wherein,

in multichannel active control, the controller typically employs minimizing the objective given by the sum of squares of the errorsAnd (5) a standard function. Then

The objective function in (a) can be written as

By utilizing the steepest descent method, the filter coefficient is adjusted according to an iterative formula to minimize the objective function, and a controller weight coefficient formula can be deduced as follows:

where μ is the convergence step, here settable as a variable step, expressed as:

where β is the initial step size, α is the variable gain, and β >0, α >0. The algorithm is named MF-FxLMS algorithm (Multi-channel Fractional-order Filter-xLMS):

and (3) outputting:

error:

and (5) updating weight values:

variable step size:

in some alternative embodiments, a non-zero value is assigned to each of the input vector and the weight vector before the reference signal and the error signal are acquired, wherein the number of iterations is equal to 0. The x and W vectors are assigned a small random non-zero value, let n=0.

In some alternative embodiments, variables and parameters of the first play signal are set, including the input vector X c (n), the instantaneous value x c (n), the weight vector W c (n), the desired signal d c (n); and inputting the variable and the parameter of the first playing signal to a secondary path identification algorithm, and outputting an identification result. The variables and parameters of the second play signal are set, the variables and parameters of the second play signal include an input vector X e (n), an instantaneous value x e (n), a weight vector W e (n) and a desired signal d e (n), the variables and parameters of the second play signal are input to the virtual error path recognition algorithm, and a recognition result is output.

It should be noted that, in the active noise control technology, a speaker and an error sensor need to be disposed in a control area, and a difficulty in the active noise reduction technology of a notebook computer is how to dispose the speaker and the sensor, and a person belongs to an independent individual, so that a device cannot be disposed on the person, so how to transmit a noise control signal to the ear of the person and how to transmit residual noise at the ear of the person to the active noise control system is a problem to be solved by the present application. The active noise control technology comprises the steps of firstly obtaining a noise source signal, simulating a low-frequency signal which occupies main energy through the fan rotating speed provided by a notebook computer in real time to serve as a reference signal, sending the reference signal into an algorithm for calculation, outputting the reference signal to the human ear through a loudspeaker to be overlapped with noise to generate destructive interference, obtaining a residual noise signal through an error sensor near the human ear, and feeding the residual noise signal back to the algorithm for calculation of a next control signal.

Referring to fig. 5, a flowchart of an active noise reduction control method according to the present application is shown.

As shown in fig. 5, step 1, performing secondary path identification to obtain a secondary path;

step 2, identifying a virtual error microphone to obtain a virtual error path;

step 3: the main algorithm sets variables and parameters: x (n) is an input vector, or referred to as a reference signal; w (n) is a weight vector; e (n) is an error signal; d (n) is a desired signal, namely an actual noise signal of the control area; y (n) is the algorithm output; μ (n) is the convergence step size; n is the number of iterations.

Step 4: initializing, assigning a smaller random non-zero value to each of the x and W vectors, and letting n=0.

Step 5: the reference signal x (n) and the error signal e (n) are input to a fractional order adaptive controller.

Step 6: calculating the value mu (n) of the current convergence step length; the fractional gradient values are calculated and the weight vector W (n) is updated. Finally, calculating and outputting an inverted signal y (n) and transmitting the inverted signal y (n) to a secondary sound source loudspeaker; n increases by 1, and the step 5 is continued.

Referring to fig. 6, a schematic diagram of an apparatus layout of the active noise reduction control method of the present application is shown.

As shown in fig. 6, in the first step, how to transmit the sound of the speaker to the ear is a secondary path recognition method. In the experimental stage, a microphone sensor is arranged at the position of the human ear, a loudspeaker is arranged on a notebook computer, a random signal is played through the loudspeaker, the microphone sensor acquires the random signal played by the loudspeaker, the played signal and the acquired signal are identified by a least mean square algorithm in an off-line manner, and a propagation path from the loudspeaker to the position of the human ear, which is called a secondary path, is acquired. The secondary paths to be identified are taken into account in the algorithm calculation, so that the signal to be output can be propagated to the human ear.

In the second step, the virtual error sensor actively reduces noise by acquiring a residual noise signal at the human ear, but cannot be arranged at the human ear, so that the microphone can be arranged only on a notebook computer. In the experimental stage, a loudspeaker is arranged at the position of the human ear and plays a random signal, the error sensor arranged on the notebook computer is used for acquiring sound played by the loudspeaker, the played signal and the acquired signal are identified by a least mean square algorithm in an off-line mode, and a propagation path from the human ear to the error sensor is acquired and is called a virtual error path.

It should be noted that, for simplicity of description, the foregoing method embodiments are all illustrated as a series of acts combined, but it should be understood and appreciated by those skilled in the art that the present application is not limited by the order of acts, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application. In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In some embodiments, embodiments of the present application provide a non-transitory computer readable storage medium having stored therein one or more programs including execution instructions that are readable and executable by an electronic device (including, but not limited to, a computer, a server, or a network device, etc.) for performing any of the above-described active noise reduction control methods of the present application.

In some embodiments, embodiments of the present application also provide a computer program product comprising a computer program stored on a non-volatile computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform any of the active noise reduction control methods described above.

In some embodiments, the present application further provides an electronic device, including: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform an active noise reduction control method.

Fig. 7 is a schematic hardware structure of an electronic device for executing an active noise reduction control method according to another embodiment of the present application, as shown in fig. 7, the device includes:

one or more processors 710, and a memory 720, one processor 710 being illustrated in fig. 7.

The apparatus for performing the active noise reduction control method may further include: an input device 730 and an output device 740.

Processor 710, memory 720, input device 730, and output device 740 may be connected by a bus or other means, for example in fig. 7.

The memory 720 is used as a non-volatile computer readable storage medium for storing non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions/modules corresponding to the active noise reduction control method in the embodiment of the present application. The processor 710 executes various functional applications of the server and data processing by running non-volatile software programs, instructions and modules stored in the memory 720, i.e., implements the active noise reduction control method of the above-described method embodiments.

Memory 720 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the active noise reduction control device, etc. In addition, memory 720 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 720 may optionally include memory remotely located with respect to processor 710, which may be connected to the active noise reduction control device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The input means 730 may receive input digital or character information and generate signals related to user settings and function control of the active noise reduction control device. The output device 740 may include a display device such as a display screen.

The one or more modules are stored in the memory 720 that, when executed by the one or more processors 710, perform the active noise reduction control method of any of the method embodiments described above.

The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present application.

The electronic device of the embodiments of the present application exists in a variety of forms including, but not limited to:

(1) Mobile communication devices, which are characterized by mobile communication functionality and are aimed at providing voice, data communication. Such terminals include smart phones, multimedia phones, functional phones, low-end phones, and the like.

(2) Ultra mobile personal computer equipment, which belongs to the category of personal computers, has the functions of calculation and processing and generally has the characteristic of mobile internet surfing. Such terminals include PDA, MID, and UMPC devices, etc.

(3) Portable entertainment devices such devices can display and play multimedia content. The device comprises an audio player, a video player, a palm game machine, an electronic book, an intelligent toy and a portable vehicle navigation device.

(4) Other on-board electronic devices with data interaction functions, such as on-board devices mounted on vehicles.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. An active noise reduction control method for a notebook computer comprises the following steps:

acquiring a first playing signal played by a loudspeaker on the notebook computer based on a microphone sensor, and identifying the first playing signal by using a least mean square algorithm to obtain a secondary path, wherein the microphone sensor is arranged at the ear of a user;

acquiring a second playing signal played by a loudspeaker arranged at the ear of the user based on an error sensor, and identifying the second playing signal by using a least mean square algorithm to obtain a virtual error path, wherein the error sensor is arranged on the notebook computer;

and performing active noise reduction control according to the secondary path and the virtual error path.

2. The method of claim 1, wherein prior to the active noise reduction control according to the secondary path and the virtual error path, comprising:

acquiring a reference signal and an error signal, and setting variables and parameters of the reference signal, wherein the reference signal simulates a low-frequency signal which occupies main energy for the fan rotating speed provided by the notebook computer in real time;

the reference signal and the error signal are input to a fractional order adaptive controller.

3. The method of claim 2, wherein the actively noise reduction control in accordance with the secondary path and the virtual error path comprises:

calculating the value of the current convergence step length and the fractional gradient value based on the secondary path and the virtual error path;

and updating the weight vector of the reference signal according to the value of the current convergence step length and the fractional order gradient value.

4. A method according to claim 3, wherein after said updating the weight vector of the reference signal according to the value of the current convergence step and the fractional gradient value, comprising:

receiving an inversion signal output by the fractional order self-adaptive controller, calculating the inversion signal, and transmitting the calculated inversion signal to a secondary sound source loudspeaker;

and increasing the iteration times by 1, and re-inputting the iteration times to the fractional order self-adaptive controller.

5. The method of claim 4, re-inputting a reference signal and an error signal to the fractional order adaptive controller if the iteration value increases.

6. The method of claim 2, wherein prior to the acquiring the reference signal and the error signal, comprising:

assigning a non-zero value to each of the input vector and the weight vector, wherein the number of iterations is equal to 0.

7. The method of claim 1, wherein the identifying the first playback signal using a least mean square algorithm to obtain a secondary path comprises:

setting variables and parameters of the first playing signal, wherein the variables and parameters of the first playing signal comprise: input vector, instantaneous value, weight vector, desired signal;

and inputting the variable and parameter of the first playing signal to a secondary path identification algorithm, and outputting an identification result.

8. The method of claim 1, wherein the identifying the second playback signal using a least mean square algorithm to obtain a virtual error path comprises:

setting variables and parameters of the second playing signal, wherein the variables and parameters of the second playing signal comprise: input vector, instantaneous value, weight vector, desired signal;

and inputting the variables and parameters of the second playing signal to a virtual error path identification algorithm, and outputting an identification result.

9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of any one of claims 1 to 8.

10. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1 to 8.