CN117041821A - Nonlinear self-adaptive control method and device for loudspeaker - Google Patents
Nonlinear self-adaptive control method and device for loudspeaker Download PDFInfo
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Abstract
The invention discloses a nonlinear self-adaptive control method and a device for a loudspeaker, which relate to the technical field of loudspeaker tone quality control and comprise the following steps: according to the target audio signal vector after target response processing of the loudspeaker, the target audio signal vector is determined, the extended audio vector is determined according to the input audio signal vector after the extended function processing, the extended audio vector is processed by utilizing the self-adaptive weight network and the electroacoustic path transfer function, the nonlinear control output signal vector is determined, the input audio signal vector is processed according to the extended function and the self-adaptive weight network after the target audio signal vector and the nonlinear control output signal vector are subjected to vector difference, and the loudspeaker audio signal after nonlinear distortion compensation is obtained. The method can adapt to different loudspeaker samples, and simultaneously track the states of the loudspeakers in real time, so that the nonlinear control effect is kept optimal.
Description
Technical Field
The invention relates to the technical field of loudspeaker tone quality control, in particular to a nonlinear self-adaptive control method and device for a loudspeaker.
Background
The amplitude of the speaker unit in the low frequency band is large, and nonlinear distortion due to non-ideal suspension, a diaphragm, a magnetic circuit, and the like is increased, reducing the low frequency playback quality of the speaker unit. In the design and production of the loudspeaker unit, the structure is optimized, the low-frequency nonlinear distortion of the loudspeaker unit can be effectively reduced, in addition, a signal processing method can be used for accurately controlling an input signal and compensating the nonlinear response of the loudspeaker unit, so that the distortion of the loudspeaker unit is reduced, the nonlinear control of the loudspeaker generally comprises two key steps, namely, firstly, accurately modeling a nonlinear model of the loudspeaker, predicting the nonlinear response of the loudspeaker unit, then generating a corresponding predistortion signal according to the nonlinear response, superposing the predistortion signal into the input signal, and playing the predistortion signal through the loudspeaker, thereby achieving the effect of compensating the nonlinear distortion.
The existing method generally needs to accurately model a nonlinear system of a loudspeaker, but due to the problem of consistency of the loudspeaker, even for the same type of loudspeaker unit, a fixed nonlinear model is more difficult to match different loudspeaker samples, and moreover, the state of the loudspeaker in operation changes, such as temperature rise, can deviate from the fixed nonlinear model, which can lead to mismatch of the nonlinear model, reduced compensation effect and even failure.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, a first aspect of the present invention proposes a nonlinear adaptive control method for a speaker, including:
determining a target audio signal vector according to the input audio signal vector processed by the target response of the loudspeaker;
determining an extended audio vector according to the input audio signal vector processed by the extended function;
processing the extended audio vector by using the adaptive weight network and the electroacoustic path transfer function to determine a nonlinear control output signal vector;
determining an error signal vector based on a vector difference between the target audio signal vector and the nonlinear control output signal vector;
and processing the number product of the error signal vectors by using a gradient descent algorithm, determining the dynamic parameters of the adaptive weight network and the expansion function corresponding to the expected minimum value of the number product of the error signal vectors, and processing the input audio signal vectors according to the expansion function and the adaptive weight network after the dynamic parameters are determined to obtain the speaker audio signal after nonlinear distortion compensation.
Optionally, the step of processing the extended audio vector with an adaptive weighting network and electroacoustic path transfer function to determine a nonlinear control output signal vector comprises:
determining a first superposition vector factor according to the product of the error signal vector with the weight updating coefficient and the extended audio vector processed by the electroacoustic path transfer function;
updating the weight parameter vector of the next moment according to the sum of the weight parameter vector of the current moment and the first superposition vector factor of the current moment;
processing the input audio vector according to the self-adaptive weight network determined by the product of the transposed vector of the extended audio vector and the weight parameter vector to obtain a loudspeaker input audio vector;
and processing the input audio vector of the loudspeaker by utilizing the electroacoustic path transfer function to obtain a nonlinear control output signal vector.
Optionally, updating the expression of the weight parameter vector at the next time according to the sum of the weight parameter vector at the current time and the first superposition vector factor at the current time, including:
w(n+1)＝w(n)+μ w e(n)g′(n)
wherein mu w e (n) g' (n) denotes a first superposition vector factor,g' (n) represents the first filtered output signal, represents the path filter function, μ w Represents a weight update coefficient, w (N) represents a weight parameter vector at a next time, w (n+1) represents a weight parameter vector at a next time, g (N) represents a tap delay input signal x (N) = [ x (N), x (N-1, x (N-n+1)] T E (n) represents an error signal vector;
g(n)＝{1，x(n)，e -a(n)|x(n)| sin[πx(n)]，e -a(n)|x(n)| cos[πx(n)]，...，e -a(n)|x(n)| sin[Bπx(n)]，e -a(n)|x(n)| cos[Bπx(n)]，x(n-1)，e -a(n)|x(n-1)| sin[πx(n-1)]，e -a(n)|x(n-1)| cos[πx(n-1)]，...，e -a(n)|x(n-1)| sin[Bπx(n-1)]，e -a(n)|x(n-1)| cos[Bπx(n-1)]，...，x(n-N+1)，e -a(n)|x(n-N+1)| sin[πx(n-N+1)]，e -a(n)|x(n-N+1)| cos[πx(n-N+1)]，...，e -a(n)|x(n-N+1)| sin[Bπx(n-N+1)]，e -a(n)|x(n-N+1)| cos[Bπx(n-N+1)]} T
wherein a (n) represents an adaptive exponential factor.
Optionally, determining the expression of the adaptive weight network according to the product of the transpose vector of the extended audio vector and the weight parameter vector includes:
y(n)＝g T (n)w(n)
wherein y (n) represents an adaptive weight network, g T (n) represents a transpose vector of the extension audio vector.
Optionally, the step of determining the extended audio vector includes:
determining a second superposition vector factor according to the product of the error signal vector with the index factor updating coefficient, the signal vector obtained by the adaptive index factor by the extended audio vector after the deviation is calculated by the extended audio vector, the parameter factor signal vector obtained after the path filtering and the weight parameter vector;
updating the adaptive index factor at the next moment according to the sum of the adaptive index factor at the current moment and the second superposition vector factor at the current moment;
the input audio signal vector is processed according to an extension function with an adaptive exponential factor, determining an extension audio vector.
Optionally, updating the expression of the adaptive index factor at the next time according to the sum of the adaptive index factor at the current time and the second superposition vector factor at the current time, including:
a(n+1)＝a(n)+μ a e(n)z′ T (n)w(n)
wherein mu a e(n)z′ T (n) w (n) represents a second superposition vector factor,
the second filtering output signal which represents the self-adaptive exponential factor of the extended audio vector is processed by the path filtering function after being deflected, and the z' (n) calculating method comprises the following steps:
z′(n)＝{0，0，-|x′(n)|e -a(n)|x′(n)| sin[πx′(n)]，-|x′(n)|e -a(n)|x′(n)| cos[πx′(n)]，...，-|x′(n)|e -a(n)|x′(n)| sin[Bπx′(n)]，-|x′(n)|e -a(n)|x′(n)| cos[Bπx′(n)]，...，0，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| sin[πx′(n-N+1)]，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| cos[πx′(n-N+1)]，...，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| sin[Bπx′(n-N+1)]，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| cos[Bπx′(n-N+1)]} T
wherein,
where x' (n) represents the input audio vector after processing by the path filter function.
Another aspect of the present invention also provides a nonlinear adaptive control apparatus for a speaker, including:
the reference audio module is used for determining a target audio signal vector according to the input audio signal vector after target response processing of the loudspeaker;
the intermediate processing module is used for determining an extended audio vector according to the input audio signal vector processed by the extended function;
the nonlinear control audio module is used for processing the extended audio vector by utilizing the adaptive weight network and the electroacoustic path transfer function and determining a nonlinear control output signal vector;
and the feedback adjustment module is used for determining an error signal vector according to the vector difference between the target audio signal vector and the nonlinear control output signal vector, processing the number product of the error signal vector by utilizing a gradient descent algorithm, determining the dynamic parameters of an adaptive weight network and an expansion function corresponding to the expected minimum value of the number product of the error signal vector, and processing the input audio signal vector according to the expansion function and the adaptive weight network determined by the dynamic parameters to obtain the loudspeaker audio signal after compensating nonlinear distortion.
In another aspect, the present invention also provides an electronic device, including a processor and a memory, where at least one instruction, at least one program, a code set, or an instruction set is stored, where at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by the processor to implement the nonlinear adaptive control method of a loudspeaker according to the first aspect.
In another aspect, the present invention also provides a computer readable storage medium, where at least one instruction, at least one program, a code set, or an instruction set is stored, where at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by a processor to implement the nonlinear adaptive control method for a loudspeaker according to the first aspect.
The embodiment of the invention provides a nonlinear self-adaptive control method and device for a loudspeaker, which have the following beneficial effects compared with the prior art:
the invention provides a nonlinear self-adaptive control system for reducing nonlinear distortion of a loudspeaker, which utilizes a feedback signal to adaptively update nonlinear filter coefficients, can adapt to different loudspeaker samples, and simultaneously tracks the state of the loudspeaker in real time so as to ensure that the nonlinear control effect is kept optimal.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the following description will make a brief introduction to the drawings used in the description of the embodiments or the prior art. It should be apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained from these drawings without inventive effort to those of ordinary skill in the art.
Fig. 1 is a flowchart of a nonlinear adaptive control method for a speaker according to an embodiment of the present invention;
fig. 2 is a diagram of speaker frequency response of a method for nonlinear adaptive control of a speaker according to an embodiment of the present invention;
fig. 3 is a comparison chart of nonlinear control effects of a nonlinear adaptive control method for a speaker according to an embodiment of the present invention;
fig. 4 is a block diagram of a nonlinear adaptive control apparatus for a speaker according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention 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 invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The present specification provides method operational steps as described in the examples or flowcharts, but may include more or fewer operational steps based on conventional or non-inventive labor. When implemented in a real system or server product, the methods illustrated in the embodiments or figures may be performed sequentially or in parallel (e.g., in a parallel processor or multithreaded environment).
Fig. 1 is a flowchart of a nonlinear adaptive control method for a speaker according to an embodiment of the present invention;
as shown in fig. 1, the method includes:
step 101, determining a target audio signal vector according to the input audio signal vector processed by the target response of the loudspeaker;
in the method, the entity to be regulated is a loudspeaker, a sensor is arranged at one end of the loudspeaker, and a function expander and an adaptive weight network are arranged at the front end of the loudspeaker.
In step 101, the target response of the speaker may be a linear response of the speaker or a target speaker response after being properly balanced, when the sensor is a microphone, the target response is a linear sound pressure response, when the sensor is a voice coil current voltage sensor, the target response is a current voltage response, the target response of the speaker is used for filtering an input audio signal vector, and then obtaining a linear audio signal vector as a comparison vector and an adjustment target, and when the adjusted input audio vector and the comparison vector are separated by a gap, the input audio vector needs to be adjusted.
102, determining an extended audio vector according to an input audio signal vector processed by an extended function;
specifically, the step 102 of determining the extended audio vector includes:
step 1021, determining a second superposition vector factor according to the product of the error signal vector with the index factor update coefficient, the parameter factor signal vector obtained by the adaptive index factor by the extended audio vector after the path filtering, and the weight parameter vector;
step 1022, updating the adaptive index factor at the next time according to the sum of the adaptive index factor at the current time and the first superposition vector factor at the current time;
step 1023, processing the input audio signal vector according to the expansion function with the adaptive index factor, and determining the expansion audio vector.
There are many methods for processing an input audio signal vector using the spread function determined in steps 1021-1023 to obtain an expanded audio vector, including: the audio cutting is performed by using the spreading function, the time is prolonged, the pitch is changed, the pitch is shifted, the background noise is added, and the like.
In step 1022, updating the expression of the adaptive index factor at the next time according to the sum of the adaptive index factor at the current time and the second superposition vector factor at the current time includes:
a(n+1)＝a(n)+μ a e(n)z′ T (n)w(n)
wherein mu a e(n)z′ T (n) w (n) represents the thTwo superimposed vector factors, mu a Representing the index factor update coefficients, e (n) representing the error signal vector, w (n) representing the weight parameter vector,the second filtering output signal which represents the self-adaptive exponential factor of the extended audio vector is processed by the path filtering function after being deflected, and the z' (n) calculating method comprises the following steps:
z′(n)＝{0，0，-|x′(n)|e -a(n)|x′(n)| sin[πx′(n)]，-|x′(n)|e -a(n)|x′(n)| cos[πx′(n)]，...，-|x′(n)|e -a(n)|x′(n)| sin[Bπx′(n)]，-|x′(n)|e -a(n)|x′(n)| cos[Bπx′(n)]，...，0，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| sin[πx′(n-N+1)]，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| cos[πx′(n-N+1)]，...，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| sin[Bπx′(n-N+1)]，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| cos[Bπx′(n-N+1)]} T
wherein,
where x' (n) represents the input audio vector after processing by the path filter function.
Step 103, processing the extended audio vector by utilizing the self-adaptive weight network and the electroacoustic path transfer function, and determining a nonlinear control output signal vector;
specifically, the step 103 of processing the extended audio vector with the adaptive weighting network and the electroacoustic path transfer function to determine the nonlinear control output signal vector includes:
step 1031, determining a first superposition vector factor according to the product of the error signal vector with the weight update coefficient and the extended audio vector processed by the electroacoustic path transfer function;
step 1032, updating the weight parameter vector of the next moment according to the sum of the weight parameter vector of the current moment and the first superposition vector factor of the current moment;
specifically, in step 1032, updating the expression of the weight parameter vector at the next time according to the sum of the weight parameter vector at the current time and the first superposition vector factor at the current time, including:
w(n+1)＝w(n)+μ w e(n)g(n)
wherein mu w e (n) g' (n) denotes a first superposition vector factor,g' (n) denotes the first filtered output signal, < >>Represents the path filter function, mu w Represents a weight update coefficient, w (N) represents a weight parameter vector at a next time, w (n+1) represents a weight parameter vector at a next time, g (N) represents a tap delay input signal x (N) = [ x (N), x (N-1, x (N-n+1)] T E (n) represents an error signal vector;
g(n)＝{1，x(n)，e -a(n)|x(n)| sin[πx(n)]，e -a(n)|x(n)| cos[πx(n)]，...，e -a(n)|x(n)| sin[Bπx(n)]，e -a(n)|x(n)| cos[Bπx(n)]，x(n-1)，e -a(n)|x(n-1)| sin[πx(n-1)]，e -a(n)|x(n-1)| cos[πx(n-1)]，...，e -a(n)|x(n-1)| sin[Bπx(n-1)]，e -a(n)|x(n-1)| cos[Bπx(n-1)]，...，x(n-N+1)，e -a(n)|x(n-N+1)| sin[πx(n-N+1)]，e -a(n)|x(n-N+1)| cos[πx(n-N+1)]，...，e -a(n)|x(n-N+1)| sin[Bπx(n-N+1)]，e -a(n)|x(n-N+1)| cos[Bπx(n-N+1)]} T
wherein a (n) represents an adaptive exponential factor.
Step 1033, processing the input audio vector by the adaptive weight network determined according to the product of the transposed vector of the extended audio vector and the weight parameter vector to obtain the speaker input audio vector;
specifically, in step 1033, an expression of the adaptive weight network is determined according to a product of the transpose vector of the extended audio vector and the weight parameter vector, including:
y(n)＝g T (n)w(n)
wherein y (n) represents an adaptive weight network, g T (n) represents a transpose vector of the extension audio vector.
Step 1034, processing the speaker input audio vector with the electroacoustic path transfer function to obtain a nonlinear control output signal vector.
Specifically, in step 1034, the speaker input audio vector is processed using the electroacoustic path transfer function to obtain an expression for the nonlinear control output signal vector, including:
y d (n)＝y(n)*s(n)
wherein y is d (n) represents a nonlinear control output signal, i.e., a sensor output signal.
104, determining an error signal vector according to the vector difference between the target audio signal vector and the nonlinear control output signal vector;
specifically, determining an expression of the error signal vector according to a vector difference between the target audio signal vector and the nonlinear control output signal vector includes:
e(n)＝d(n)-y d (n)＝d(n)-y(n)*s(n)
wherein the error signal vector is determined based on the vector difference between the target audio signal vector and the nonlinear control output signal vector
Step 105, processing the number product of the error signal vector by using a gradient descent algorithm, determining the dynamic parameters of the adaptive weight network and the expansion function corresponding to the expected minimum value of the number product of the error signal vector, and processing the input audio signal vector according to the expansion function and the adaptive weight network determined by the dynamic parameters to obtain the speaker audio signal after compensating the nonlinear distortion.
Fig. 2 is a target response of a loudspeaker with saturation effect, the type of target response being the linear sound pressure response of the loudspeaker, the frequency response of the linear sound pressure response being shown.
Fig. 3 is a graph comparing the original total harmonic distortion of the loudspeaker with the total harmonic distortion after processing using the nonlinear control method proposed by the present invention.
Another aspect of the present invention also provides a nonlinear adaptive control apparatus 200 for a speaker, including:
a reference audio module 201, configured to determine a target audio signal vector according to the input audio signal vector after the target response processing of the speaker;
an intermediate processing module 202, configured to determine an extended audio vector according to the input audio signal vector processed by the extended function;
a nonlinear control audio module 203 for processing the extended audio vector using the adaptive weight network and the electroacoustic path transfer function to determine a nonlinear control output signal vector;
the feedback adjustment module 204 is configured to determine an error signal vector according to a vector difference between the target audio signal vector and the nonlinear control output signal vector, process a number product of the error signal vector by using a gradient descent algorithm, determine dynamic parameters of an adaptive weight network and an expansion function corresponding to a desired minimum value of the number product of the error signal vector, process the input audio signal vector according to the expansion function and the adaptive weight network determined by the dynamic parameters, and obtain a speaker audio signal after compensating nonlinear distortion.
In yet another embodiment of the present invention, there is also provided an apparatus including a processor and a memory storing at least one instruction, at least one program, a code set, or an instruction set, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method for nonlinear adaptive control of a speaker described in the embodiments of the present invention.
In yet another embodiment of the present invention, there is further provided a computer readable storage medium having stored therein at least one instruction, at least one program, a code set, or an instruction set, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the method for nonlinear adaptive control of a speaker according to an embodiment of the present invention.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes a plurality of computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of a plurality of available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
It is 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 any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (9)
1. A method for nonlinear adaptive control of a loudspeaker, comprising:
determining a target audio signal vector according to the input audio signal vector processed by the target response of the loudspeaker;
determining an extended audio vector according to the input audio signal vector processed by the extended function;
processing the extended audio vector by using the adaptive weight network and the electroacoustic path transfer function to determine a nonlinear control output signal vector;
according to the vector difference between the target audio signal vector and the nonlinear control output signal vector, determining an error signal vector, processing the number product of the error signal vector by using a gradient descent algorithm, determining the dynamic parameters of an adaptive weight network and an expansion function corresponding to the expected minimum value of the number product of the error signal vector, and processing the input audio signal vector according to the expansion function and the adaptive weight network after the dynamic parameters are determined, so as to obtain the loudspeaker audio signal after nonlinear distortion compensation.
2. The method of claim, wherein the step of processing the extended audio vector using the adaptive weighting network and the electroacoustic path transfer function to determine the nonlinear control output signal vector comprises:
determining a first superposition vector factor according to the product of the error signal vector with the weight updating coefficient and the extended audio vector processed by the electroacoustic path transfer function;
updating the weight parameter vector of the next moment according to the sum of the weight parameter vector of the current moment and the first superposition vector factor of the current moment;
processing the input audio vector according to the self-adaptive weight network determined by the product of the transposed vector of the extended audio vector and the weight parameter vector to obtain a loudspeaker input audio vector;
and processing the input audio vector of the loudspeaker by utilizing the electroacoustic path transfer function to obtain a nonlinear control output signal vector.
3. A method for nonlinear adaptive control of a loudspeaker according to claim 2, wherein updating the expression of the weight parameter vector at the next time based on the sum of the weight parameter vector at the current time and the first superposition vector factor at the current time comprises:
w(n+1)＝w(n)+μ w e(n)g′(n)
wherein mu w e (n) g' (n) denotes a first superposition vector factor,represents the path filter function, g' (n) represents the first filtered output signal, μ w Represents a weight update coefficient, w (N) represents a weight parameter vector at a current time, w (n+1) represents a weight parameter vector at a next time, g (N) represents a tap delay input signal x (N) = [ x (N), x (N-1, x (N-n+1)] T E (n) represents an error signal vector;
g(n)＝{1，x(n)，e -a(n)|x(n)| sin[πx(n)]，e -a(n)|x(n)| cos[πx(n)]，...，e -a(n)|x(n)| sin[Bπx(n)]，e -a(n)|x(n)| cos[Bπx(n)]，x(n-1)，e -a(n)|x(n-1)| sin[πx(n-1)]，e -a(n)|x(n-1)| cos[πx(n-1)]，...，e -a(n)|x(n-1)| sin[Bπx(n-1)]，e -a(n)|x(n-1)| cos[Bπx(n-1)]，...，x(n-N+1)，e -a(n)|x(n-N+1)| sin[πx(n-N+1)]，e -a(n)|x(n-N+1)| cos[πx(n-N+1)]，...，e -a(n)|x(n-N+1)| sin[Bπx(n-N+1)]，e -a(n)|x(n-N+1)| cos[Bπx(n-N+1)]} T
wherein a (n) represents an adaptive exponential factor.
4. The method of claim 2, wherein determining the expression of the adaptive weighting network based on the product of the transposed vector of the extended audio vector and the weighting parameter vector comprises:
y(n)＝g T (n)w(n)
wherein y (n) represents an adaptive weight network, g T (n) represents a transpose vector of the extension audio vector.
5. The method of claim 1, wherein the step of determining the extended audio vector comprises:
determining a second superposition vector factor according to the product of the error signal vector with the index factor updating coefficient, the signal vector obtained by the adaptive index factor by the extended audio vector after the deviation is calculated by the extended audio vector, the parameter factor signal vector obtained after the path filtering and the weight parameter vector;
updating the adaptive index factor at the next moment according to the sum of the adaptive index factor at the current moment and the second superposition vector factor at the current moment;
the input audio signal vector is processed according to an extension function with an adaptive exponential factor, determining an extension audio vector.
6. The method of claim 5, wherein updating the expression of the adaptive index factor at the next time according to the sum of the adaptive index factor at the current time and the second superimposition vector factor at the current time comprises:
a(n+1)＝a(n)+μ a e(n)z′ T (n)w(n)
wherein mu a e(n)z′ T (n) w (n) represents a second superposition vector factor, the second filtering output signal which represents the self-adaptive exponential factor of the extended audio vector is processed by the path filtering function after being deflected, and the z' (n) calculating method comprises the following steps:
z′(n)＝{0，0，-|x′(n)|e -a(n)|x′(n)| sin[πx′(n)]，-|x′(n)|e -a(n)|x′(n)| cos[πx′(n)]，...，-|x′(n)|e -a(n)|x′(n) |sin[Bπx′(n)]，-|x′(n)|e -a(n)|x′(n)| cos[Bπx′(n)]，...，0，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| sin[πx′(n-N+1)]，-|x′(n-N+1)|e -a(n)|x′(n-N+1) |cos[πx′(n-N+1)]，...，-|x′(n-N+1)|e -a(n)|x′(n-N+1)| sin[Bπx′(n-N+1)]，-|x′(n-N+1)|e -a(n)|x′(n-N+1) |cos[Bπx′(n-N+1)]} T
wherein,
where x' (n) represents the input audio vector after processing by the path filter function.
7. A nonlinear adaptive control apparatus for a loudspeaker, comprising:
the reference audio module is used for determining a target audio signal vector according to the input audio signal vector after target response processing of the loudspeaker;
the intermediate processing module is used for determining an extended audio vector according to the input audio signal vector processed by the extended function;
the nonlinear control audio module is used for processing the extended audio vector by utilizing the adaptive weight network and the electroacoustic path transfer function and determining a nonlinear control output signal vector;
and the feedback adjustment module is used for determining an error signal vector according to the vector difference between the target audio signal vector and the nonlinear control output signal vector, processing the number product of the error signal vector by utilizing a gradient descent algorithm, determining the dynamic parameters of an adaptive weight network and an expansion function corresponding to the expected minimum value of the number product of the error signal vector, and processing the input audio signal vector according to the expansion function and the adaptive weight network determined by the dynamic parameters to obtain the loudspeaker audio signal after compensating nonlinear distortion.
8. An electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the method of nonlinear adaptive control of a loudspeaker as recited in any one of claims 1-6.
9. A computer readable storage medium, characterized in that at least one instruction, at least one program, a set of codes or a set of instructions is stored in the storage medium, which is loaded and executed by a processor to implement the method for nonlinear adaptive control of a loudspeaker according to any one of claims 1-6.
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