Loudspeaker noise recognition method and device, electronic equipment and storage medium

Abstract

The invention provides a loudspeaker noise recognition method and device, electronic equipment and a storage medium. The loudspeaker noise recognition method comprises the steps of obtaining a power model of a loudspeaker; determining a first comparison signal and a second comparison signal of the loudspeaker according to the power model and the input signal of the voice coil of the loudspeaker; according to the first comparison signal and the second comparison signal, determining whether there is a distinguishable noise signal in the input signal of the loudspeaker voice coil; wherein the input signal of the loudspeaker voice coil comprises at least one of an input voltage signal and an input current signal of the loudspeaker voice coil, and the first comparison signal and the second comparison signal are both current signals or voltage signals. According to the application, the first comparison signal and the second comparison signal of the loudspeaker are determined through the power model and the input signal, and then whether the input signal has the distinguishable noise signal is judged by comparing the first comparison signal with the second comparison signal, so that effective identification of the noise signal is realized.

Description

Technical Field

[0001] The present application relates to the field of audio technology, and in particular to a speaker noise recognition method, device, electronic device and storage medium. Background Art

[0002] At present, due to the nonlinear characteristics of the speaker, noise that exceeds the original audio signal will be generated when the speaker is playing. This sound will affect the listener's hearing and is often called piano noise. In order to eliminate this noise phenomenon, it is necessary to attenuate the baseband signal in the audio or compensate for the nonlinear characteristics of the speaker to ensure the user experience.

[0003] In the prior art, the existing solutions mainly identify the energy proportion of the baseband signal that may generate noise. If the energy proportion is too high, the baseband signal is compressed to suppress the piano noise. However, this method mainly relies on experience summary, and the means of identifying the baseband signal of noise is not accurate enough. The noise signal suppression is usually triggered incorrectly, which causes the audio signal to be over-suppressed and affects the listener's hearing. Summary of the invention

[0004] The present application provides a speaker noise recognition method, device, electronic device and storage medium, aiming to solve the technical problem that the current recognition of the baseband signal of the noise relies on experience summary and is not accurate enough.

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

[0006] Get the power model of the speaker;

[0007] Determine a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal of the speaker voice coil;

[0008] Determining whether there is a discernible noise signal in the input signal of the loudspeaker voice coil according to the first comparison signal and the second comparison signal;

[0009] The input signal of the loudspeaker voice coil includes at least one of an input voltage signal and an input current signal of the loudspeaker voice coil, and the first comparison signal and the second comparison signal are both current signals or both voltage signals.

[0010] In some embodiments, the first comparison signal includes at least one of an input voltage signal of the speaker voice coil, an input current signal, a nonlinear current signal determined according to the input voltage signal and a nonlinear model of the speaker, and a nonlinear voltage signal determined according to the input current signal and a nonlinear model of the speaker;

[0011] The second comparison signal includes a predicted current signal determined according to the input voltage signal and an admittance model of the speaker, or a predicted voltage signal determined according to the input current signal and an impedance model of the speaker.

[0012] In some embodiments, the step of determining whether an input signal of the speaker voice coil has a discernible noise signal according to the first comparison signal and the second comparison signal comprises:

[0013] According to the first comparison signal and the second comparison signal, identifying a noise signal in the first comparison signal that is redundant with respect to the second comparison signal;

[0014] Calculating a signal-to-noise ratio of the speaker signal according to the first comparison signal and the noise signal;

[0015] When the signal-to-noise ratio of the loudspeaker signal is less than a preset value, it is determined that a discernible noise signal exists in the input signal of the loudspeaker voice coil.

[0016] In some embodiments, the method further comprises:

[0017] When there is a discernible noise signal in the input signal of the speaker voice coil, the noise is suppressed on the audio signal input to the speaker.

[0018] In some embodiments, the input signal includes an input voltage signal and an input current signal of a speaker voice coil, the power model includes an admittance model, and the step of determining a first contrast signal and a second contrast signal of the speaker according to the power model and the input signal includes:

[0019] Determining the input current signal as a first comparison signal;

[0020] A predicted current signal of the speaker is determined according to the input voltage signal and the admittance model, and the second comparison signal includes the predicted current signal.

[0021] In some embodiments, the input signal includes an input voltage signal and an input current signal of a speaker voice coil, the power model includes an impedance model, and the step of determining the first comparison signal and the second comparison signal of the speaker according to the power model and the input signal includes:

[0022] Determining that the input voltage signal is a first comparison signal;

[0023] A predicted voltage signal of the speaker is determined according to the input current signal and the impedance model, and the second comparison signal includes the predicted voltage signal.

[0024] In some embodiments, the input signal includes an input voltage signal, the power model includes an admittance model and a nonlinear model, and the step of determining the first contrast signal and the second contrast signal of the speaker according to the power model and the input signal includes:

[0025] Determine a nonlinear current signal of the speaker according to the input voltage signal and the nonlinear model, wherein the first comparison signal includes the nonlinear current signal;

[0026] A predicted current signal of the speaker is determined according to the input voltage signal and the admittance model, and the second comparison signal includes the predicted current signal.

[0027] In some embodiments, the input signal includes an input current signal, the power model includes an impedance model and a nonlinear model, and the step of determining the first contrast signal and the second contrast signal of the speaker according to the power model and the input signal includes:

[0028] Determine a nonlinear voltage signal of the speaker according to the input current signal and the nonlinear model, wherein the first comparison signal includes the nonlinear voltage signal;

[0029] A predicted voltage signal of the speaker is determined according to the input current signal and the impedance model, and the second comparison signal includes the predicted voltage signal.

[0030] In some embodiments, the step of obtaining the power model of the speaker includes:

[0031] Get the voltage data and current data of the speaker;

[0032] Determine power model calculation parameters and a power model including the power model calculation parameters according to the voltage data and the current data;

[0033] The voltage data and the current data include voltages and currents corresponding to N different frequencies, where N is an integer greater than or equal to 3.

[0034] In some embodiments, the steps of determining the power model calculation parameters and the power model including the power model calculation parameters according to the voltage data and the current data include:

[0035] Determine the actual DC resistance of the speaker based on the speaker voltage and current corresponding to at least one frequency;

[0036] According to the loudspeaker voltages and currents corresponding to the N different frequencies, the actual resonance angular frequency, the actual quality factor and the actual mechanical quality factor of the loudspeaker are determined.

[0037] In a second aspect, the present application provides a speaker noise recognition device, comprising:

[0038] An acquisition module, which is used to acquire a power model of the speaker;

[0039] A prediction module, the prediction module is used to determine a first comparison signal and a second comparison signal of the speaker according to the power model and an input signal of the speaker voice coil;

[0040] A noise determination module, the noise determination module is used to determine whether there is a discernible noise signal in the input signal of the loudspeaker voice coil according to the first comparison signal and the second comparison signal;

[0041] The input signal of the loudspeaker voice coil includes at least one of an input voltage signal and an input current signal of the loudspeaker voice coil, and the first comparison signal and the second comparison signal are both current signals or both voltage signals.

[0042] In a third aspect, the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the speaker noise recognition method as described in the first aspect.

[0043] In a fourth aspect, the present application provides a storage medium storing a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute the steps in the speaker noise recognition method as described in the first aspect.

[0044] The present application determines the first comparison signal and the second comparison signal of the speaker through the power model and the input signal, and then determines whether there is a discernible noise signal in the input signal by comparing the first comparison signal and the second comparison signal, thereby achieving effective identification of the noise signal. After suppressing the noise of the audio signal input to the speaker, the phenomenon of the speaker playing noise can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 It is a flow chart of a method for identifying loudspeaker noise provided in an embodiment of the present application;

[0047] Figure 2 It is a flow chart of a method for determining noise provided in an embodiment of the present application;

[0048] Figure 3 It is a flow chart of a noise suppression method provided in an embodiment of the present application;

[0049] Figure 4is another flow chart of the method for determining noise provided in the embodiment of the present application;

[0050] Figure 5 is another flow chart of the method for determining noise provided in the embodiment of the present application;

[0051] Figure 6 It is a flow chart of a method for determining a power model provided in an embodiment of the present application;

[0052] Figure 7 It is a schematic diagram of a process for determining power model calculation parameters provided in an embodiment of the present application;

[0053] Figure 8 It is a module schematic diagram of a speaker noise recognition device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0055] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0056] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0057] The embodiments of the present application provide a speaker noise recognition method, device, electronic device, and storage medium, which are described in detail below.

[0058] First, see Figure 1 , Figure 1 A schematic diagram of the structure of a method for identifying loudspeaker noise in an embodiment of the present application is shown, wherein the method for identifying loudspeaker noise includes:

[0059] Step S101, obtaining a power model of a speaker;

[0060] In some embodiments of the present application, the power model of the speaker includes a linear model. In some embodiments of the present application, the power model of the speaker has power model parameters such as DC resistance, resonance angular frequency, quality factor, and mechanical quality factor. In some embodiments of the present application, the power model of the speaker includes a linear impedance model, so that the predicted voltage signal can be predicted according to the input current signal of the speaker voice coil through the impedance model. For example, the impedance model of the speaker can be constructed by the speaker characteristic curve, and the impedance model of the speaker can be expressed by the following function:

[0061]

[0062] Where Hz is the impedance amplitude, Re is the DC resistance of the speaker, w0 is the resonant angular frequency of the speaker, Q ts is the quality factor of the speaker, Q ms is the mechanical quality factor of the loudspeaker, and Le is the inductance of the loudspeaker voice coil.

[0063] In some embodiments of the present application, the power model of the speaker includes a linear admittance model, so that the predicted current signal can be predicted according to the input voltage signal of the speaker voice coil through the admittance model. For example, the admittance model of the speaker can be expressed by the following function:

[0064]

[0065] Among them, I(s) is the current signal function, U(s) is the voltage signal function, w0 is the resonant angular frequency of the speaker, Q ts is the quality factor of the speaker, Q ms is the mechanical quality factor of the loudspeaker, R e is the DC resistance of the speaker, and Le is the inductance of the speaker voice coil.

[0066] In some embodiments of the present application, the power model of the speaker includes a nonlinear model, so that a nonlinear current signal or a nonlinear voltage signal is obtained according to an input voltage signal / current signal of the speaker voice coil through the nonlinear model. Exemplarily, the nonlinear model of the speaker can be expressed by the following function:

[0067]

[0068]

[0069] Where u is the voltage of the speaker voice coil, R e is the DC resistance of the speaker, Le is the inductance of the speaker voice coil, Bl is the electromechanical coupling factor of the speaker, m is the effective vibration mass of the speaker vibration system, R m is the equivalent effective resistance of the speaker suspension system, k is the equivalent effective force of the speaker suspension system, x1 is the current of the speaker voice coil, x2 is the displacement of the speaker diaphragm, and x3 is the speed of the speaker diaphragm.

[0070] Specifically, we can use nonlinear models and some parameters (such as L e (x2), k(x2), Bl(x2)) to construct a discrete state space model, and then substitute the input voltage signal / current signal of the loudspeaker voice coil into the discrete state space model, and select the current data or voltage data in the output state quantity as a nonlinear current signal or a nonlinear voltage signal. For example, after the input voltage signal of the loudspeaker voice coil is input into the discrete state space model, the state quantity output by the discrete state space model includes the current, the displacement and the velocity of the diaphragm, and the nonlinear current signal can be obtained by selecting the current data; for another example, after the input current signal of the loudspeaker voice coil is input into the discrete state space model, the state quantity output by the discrete state space model includes the voltage, the displacement and the velocity of the diaphragm, and the nonlinear voltage signal can be obtained by selecting the voltage data.

[0071] It can be understood that the power model of the speaker can also include an impedance model and an admittance model at the same time, so as to predict the predicted voltage signal according to the input current signal of the speaker voice coil through the impedance model, and predict the predicted current signal according to the input voltage signal of the speaker voice coil through the admittance model; or, the power model of the speaker can also include an impedance model and a nonlinear model at the same time; or, the power model of the speaker can also include an admittance model and a nonlinear model at the same time.

[0072] Step S102, determining a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal of the speaker voice coil;

[0073] After the power model is obtained, the first comparison signal and the second comparison signal of the speaker can be determined according to the input signal through the power model. Specifically, the input signal includes at least one of an input current signal of the speaker voice coil and an input voltage signal of the speaker voice coil, and the first comparison signal and the second comparison signal are both current signals or both voltage signals.

[0074] In some embodiments of the present application, the input signal of the speaker voice coil can be obtained by monitoring the voltage and current of the speaker voice coil, for example, the voltage signal at both ends of the speaker voice coil is collected by a voltage collector, and the current signal of the speaker voice coil is collected by a current collector. In some embodiments of the present application, the input signal of the speaker voice coil can also be obtained by converting the input audio digital signal of the speaker, for example, the input audio digital signal of the speaker is combined with the gain frequency response of the audio link to obtain the input voltage and current signal of the speaker voice coil.

[0075] In some embodiments of the present application, the first comparison signal includes at least one of an input voltage signal of the speaker voice coil, an input current signal, a nonlinear current signal determined based on the input voltage signal and a nonlinear model of the speaker, and a nonlinear voltage signal determined based on the input current signal and a nonlinear model of the speaker; the second comparison signal includes a predicted current signal determined based on the input voltage signal and an admittance model of the speaker, or a predicted voltage signal determined based on the input current signal and an impedance model of the speaker.

[0076] For example, the first comparison signal may be an input voltage signal of the speaker voice coil, and the second comparison signal may be a predicted voltage signal determined according to the input current signal of the speaker voice coil and an impedance model of the speaker.

[0077] For another example, the first comparison signal may be an input current signal of the speaker voice coil, and the second comparison signal may be a predicted current signal determined according to the input voltage signal of the speaker voice coil and an impedance model of the speaker.

[0078] For another example, the first comparison signal may be a nonlinear current signal determined according to the input voltage signal of the speaker voice coil and the nonlinear model of the speaker, and the second comparison signal may be a predicted current signal determined according to the input voltage signal of the speaker voice coil and the impedance model of the speaker.

[0079] For another example, the first comparison signal may be a nonlinear voltage signal determined according to the input current signal of the speaker voice coil and the nonlinear model of the speaker, and the second comparison signal may be a predicted voltage signal determined according to the input current signal of the speaker voice coil and the impedance model of the speaker.

[0080] In some embodiments of the present application, for example, for an embodiment in which the power model includes an impedance model, since the impedance model can measure the impedance value of the corresponding frequency, the impedance model can predict the predicted voltage signal according to the input current signal of the speaker voice coil. In some other embodiments of the present application, for example, for an embodiment in which the power model includes an admittance model, since the admittance model can measure the inverse of the impedance value of the corresponding frequency, the admittance model can predict the predicted current signal according to the input voltage signal of the speaker voice coil.

[0081] In some other embodiments of the present application, for example, for an embodiment in which the power model includes both an impedance model and an admittance model, the impedance model can predict a predicted voltage signal based on an input current signal of the speaker voice coil, and the admittance model can predict a predicted current signal based on the input voltage signal of the speaker voice coil.

[0082] In some embodiments of the present application, for example, for an embodiment in which the input signal includes an input voltage signal and an input current signal of a speaker voice coil, and the power model includes an admittance model, the step of determining a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal may include: determining the input current signal as the first comparison signal; determining a predicted current signal of the speaker according to the input voltage signal and the admittance model, and the second comparison signal includes the predicted current signal. In other words, by comparing the input current signal and the predicted current signal, it can be determined whether there is a discernible noise signal in the input current signal of the speaker voice coil.

[0083] In some embodiments of the present application, for example, for an embodiment in which the input signal includes an input voltage signal and an input current signal of a speaker voice coil, and the power model includes an impedance model, the step of determining the first comparison signal and the second comparison signal of the speaker according to the power model and the input signal includes: determining the input voltage signal of the speaker voice coil as the first comparison signal; determining the predicted voltage signal of the speaker according to the input current signal of the speaker voice coil and the impedance model, and the second comparison signal includes the predicted voltage signal. In other words, by comparing the input voltage signal and the predicted voltage signal, it can be determined whether there is a discernible noise signal in the input voltage signal of the speaker voice coil.

[0084] In some embodiments of the present application, for example, for an embodiment in which the input signal includes an input voltage signal of a speaker voice coil, and the power model includes an admittance model and a nonlinear model, the step of determining a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal includes: determining a nonlinear current signal of the speaker according to the input voltage signal of the speaker voice coil and the nonlinear model, the first comparison signal including the nonlinear current signal; determining a predicted current signal of the speaker according to the input voltage signal of the speaker voice coil and the admittance model, the second comparison signal including the predicted current signal. In other words, the nonlinear current signal and the linear predicted current signal can also be compared to determine whether there is a discernible noise signal in the input voltage signal of the speaker voice coil.

[0085] In some embodiments of the present application, for example, for an embodiment in which the input signal includes an input current signal of a speaker voice coil, and the power model includes an impedance model and a nonlinear model, the step of determining a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal includes: determining a nonlinear voltage signal of the speaker according to the input current signal of the speaker voice coil and the nonlinear model, the first comparison signal including the nonlinear voltage signal; determining a predicted voltage signal of the speaker according to the input current signal of the speaker voice coil and the impedance model, the second comparison signal including the predicted voltage signal. In other words, the nonlinear voltage signal and the linear predicted voltage signal can also be compared to determine whether there is a discernible noise signal in the input voltage signal of the speaker voice coil.

[0086] Step S103, judging whether the input signal of the speaker voice coil contains a discernible noise signal according to the first comparison signal and the second comparison signal;

[0087] After obtaining the first comparison signal and the second comparison signal, the first comparison signal and the second comparison signal can be compared to determine whether there is a discernible noise signal in the input signal. Specifically, the first comparison signal and the second comparison signal can be compared to identify the redundant signal contained in the first comparison signal relative to the second comparison signal, and the redundant signal can be used to determine whether there is a discernible noise signal in the input signal.

[0088] It should be noted that noise is usually caused by nonlinear distortion of the loudspeaker. The input current signal, input voltage signal, nonlinear current signal determined by the nonlinear model, and nonlinear voltage signal determined by the nonlinear model of the loudspeaker will contain noise signals due to the nonlinear distortion of the loudspeaker. The admittance model and the impedance model are linear models. The predicted current signal and predicted voltage signal output by the two are linear signals without noise. Therefore, by comparing the first comparison signal (the input current signal, input voltage signal of the loudspeaker, nonlinear current signal determined by the nonlinear model, and nonlinear voltage signal determined by the nonlinear model) with the second comparison signal (predicted current signal, predicted voltage signal), it can be determined whether the input signal of the loudspeaker voice coil contains a discernible noise signal.

[0089] As an exemplary embodiment, for the first comparison signal including the input current signal of the speaker voice coil and the second comparison signal including the predicted current signal, the input current signal of the speaker voice coil includes a frequency band of 4000Hz to 6000Hz, while the predicted current signal includes a frequency band of 4000Hz to 5000Hz. If the input current signal of the speaker voice coil and the predicted current signal have consistent energy in the frequency band of 5000Hz to 6000Hz, then the redundant signal is 4000Hz to 5000Hz. Therefore, it is possible to determine whether there is a discernible noise signal in the input current signal based on the redundant signal (4000Hz to 5000Hz).

[0090] As another exemplary embodiment, for the first comparison signal including the input current signal of the speaker voice coil and the second comparison signal including the predicted current signal, the input current signal of the speaker voice coil includes a frequency band of 4000Hz to 6000Hz, while the predicted current signal includes a frequency band of 4000Hz to 6000Hz. In this case, the input current signal of the speaker voice coil and the predicted current signal can be subjected to spectral analysis, and the spectral difference obtained is the noise spectrum. Finally, the noise spectrum can be used to determine whether the input voltage signal has a discernible noise signal.

[0091] As another exemplary embodiment, for the first comparison signal including the input voltage signal of the speaker voice coil and the second comparison signal including the predicted voltage signal, the input voltage signal of the speaker voice coil includes a frequency band of 300Hz to 1500Hz, while the predicted voltage signal includes a frequency band of 500Hz to 1500Hz. If the input current signal of the speaker voice coil and the predicted current signal have consistent energy in the frequency band of 500Hz to 1500Hz, then the redundant signal is 300Hz to 500Hz. Therefore, based on the redundant signal (300Hz to 500Hz), it can be judged that there is a discernible noise signal in the input voltage signal.

[0092] In an embodiment of the present application, the present application determines a first comparison signal and a second comparison signal of a speaker through an electric power model and an input signal, and then determines whether there is a discernible noise signal in the input signal by comparing the first comparison signal with the second comparison signal, thereby achieving effective identification of the noise signal. After suppressing the noise of the audio signal input to the speaker, the phenomenon of the speaker playing noise can be avoided.

[0093] In some embodiments of the present application, see Figure 2 , Figure 2 A schematic diagram of a process for determining a noise signal in an embodiment of the present application is shown, wherein the step of determining the noise signal includes:

[0094] Step S201, identifying a noise signal in the first comparison signal that is redundant with respect to the second comparison signal according to the first comparison signal and the second comparison signal;

[0095] Step S202, calculating the signal-to-noise ratio of the speaker signal according to the first comparison signal and the noise signal;

[0096] Step S203: when the signal-to-noise ratio of the speaker signal is less than a preset value, it is determined that a discernible noise signal exists in the input signal of the speaker voice coil.

[0097] In some embodiments of the present application, the first comparison signal and the second comparison signal may be subtracted (for example, in the spectrum or in the time domain) to obtain a noise signal. In some embodiments of the present application, the first comparison signal may contain a signal with redundant frequency bands relative to the second comparison signal as a noise signal.

[0098] After the noise signal is obtained, the signal-to-noise ratio of the speaker signal can be calculated. As an example, the signal-to-noise ratio dB of the speaker signal can be calculated according to the following formula:

[0099] dB=10log10(s / n)

[0100] Wherein, s is the effective power of the first comparison signal, and n is the effective power of the noise signal.

[0101] When the signal-to-noise ratio of the speaker signal is determined, it can be determined whether the signal-to-noise ratio of the speaker signal is less than a preset value, thereby determining whether there is a discernible noise signal in the input signal. For example, when the signal-to-noise ratio of the speaker signal is less than a preset value (e.g., 20 dB), it can be determined that there is a discernible noise signal in the input signal, and it is necessary to perform noise suppression processing on the audio signal input to the speaker; conversely, when the signal-to-noise ratio of the speaker signal is greater than a preset value (e.g., 20 dB), it can be determined that there is no discernible noise signal in the input signal, and therefore it is not necessary to perform noise suppression processing on the audio signal input to the speaker.

[0102] It can be understood that the preset value of the signal-to-noise ratio can be adjusted by those skilled in the art according to actual needs, and no specific limitation is made here.

[0103] In some embodiments of the present application, for example, an embodiment of performing noise suppression processing on an input audio signal, please refer to Figure 3 , Figure 3 A schematic diagram of a process of a noise suppression method in an embodiment of the present application is shown, wherein the noise suppression method includes:

[0104] Step S301, obtaining a power model of a speaker;

[0105] Step S302, determining a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal of the speaker voice coil;

[0106] Step S303, judging whether the input signal of the loudspeaker voice coil contains a discernible noise signal according to the first comparison signal and the second comparison signal;

[0107] Step S304: when there is a discernible noise signal in the input signal, noise suppression is performed on the audio signal input to the speaker.

[0108] As an example, when there is a discernible noise signal in the input signal, the audio data can be compressed by dynamically adjusting the parameters of the equalizer EQ, thereby suppressing the noise signal input to the speaker. As another example, when there is a discernible noise signal in the input signal, the gain of a part of the frequency band in the speaker input audio signal can be suppressed, thereby compressing the audio data and finally suppressing the noise signal input to the speaker.

[0109] It can be understood that other methods may be used to suppress noise on the audio signal input to the speaker, for example, attenuating the signal level in the attenuation frequency band of the audio signal input to the speaker.

[0110] In some embodiments of the present application, for example, for an embodiment in which the power model includes an admittance model, continue to refer to Figure 4 , Figure 4 Another schematic flow chart of the method for determining noise in an embodiment of the present application is shown, wherein the step of determining whether there is noise includes:

[0111] Step S401, determining a predicted current signal of the speaker according to an input voltage signal of the speaker voice coil and an admittance model;

[0112] Step S402, identifying a noise current signal in the input current signal of the speaker voice coil relative to a frequency band of the predicted current signal according to the predicted current signal and the input current signal of the speaker voice coil;

[0113] Step S403, calculating the current signal-to-noise ratio of the speaker according to the input current signal of the speaker voice coil and the noise current signal;

[0114] Step S404: when the current signal-to-noise ratio is less than the first preset value, it is determined that a discernible noise current signal exists in the input current signal of the speaker voice coil.

[0115] Specifically, the admittance model of the loudspeaker can be expressed by the following function:

[0116]

[0117] It can be seen that in the admittance model, the current and voltage of the corresponding frequency satisfy the above function, so the predicted current signal can be obtained by directly multiplying the input voltage signal of the speaker voice coil by the admittance model. After obtaining the predicted current signal, the predicted current signal (i.e., the second comparison signal) can be compared with the input current signal of the speaker voice coil (i.e., the first comparison signal) to identify the noise current signal in the input current signal of the speaker voice coil that is redundant with the predicted current signal. In some embodiments of the application, the input current signal of the speaker voice coil can be subtracted from the predicted current signal to obtain the noise current signal. In some embodiments of the present application, the signal in the input current signal of the speaker voice coil with redundant frequency bands relative to the predicted current signal can be identified as the noise current signal.

[0118] Then, the current signal-to-noise ratio of the speaker can be calculated based on the input current signal and the noise current signal of the speaker voice coil. If the current signal-to-noise ratio is less than a first preset value (e.g., 30 dB), it can be determined that there is a discernible noise current signal in the input current signal of the speaker voice coil, that is, there is piano noise, so as to facilitate the subsequent suppression of the noise signal in the input audio signal. For example, if the calculated current signal-to-noise ratio is less than 30 dB, it can be determined that there is a discernible noise current signal in the input current signal of the speaker voice coil.

[0119] In some other embodiments of the present application, for example, for an embodiment in which the power model includes an impedance model, please continue to refer to Figure 5 , Figure 5 Another schematic flow chart of the method for determining noise in an embodiment of the present application is shown, wherein the step of determining whether there is noise includes:

[0120] Step S501, determining a predicted voltage signal of the speaker according to an input current signal of the speaker voice coil and an impedance model;

[0121] Step S502, identifying a noise voltage signal redundant with respect to the predicted voltage signal in the input voltage signal of the speaker voice coil according to the predicted voltage signal and the input voltage signal of the speaker voice coil;

[0122] Step S503, calculating the voltage signal-to-noise ratio of the speaker according to the input voltage signal of the speaker voice coil and the noise voltage signal;

[0123] Step S504: when the voltage signal-to-noise ratio is less than the second preset value, it is determined that a discernible noise voltage signal exists in the input voltage signal of the speaker voice coil.

[0124] Specifically, the impedance model of the speaker can be expressed by the following function:

[0125]

[0126] It can be seen that in the impedance model, the impedance of the corresponding frequency can be calculated according to the above function, so according to the impedance and the input current signal of the speaker voice coil, the predicted voltage signal can be calculated. After obtaining the predicted voltage signal, the predicted voltage signal (i.e., the second comparison signal) can be compared with the input voltage signal of the speaker voice coil (i.e., the first comparison signal) to identify the noise voltage signal in the input voltage signal of the speaker voice coil that is redundant with the predicted voltage signal. In some embodiments of the present application, the predicted voltage signal can be subtracted from the input voltage signal of the speaker voice coil to obtain the noise voltage signal. In some embodiments of the present application, the signal in the input voltage signal of the speaker voice coil with redundant frequency bands relative to the predicted voltage signal can be used as the noise voltage signal.

[0127] Then, the voltage signal-to-noise ratio of the speaker can be calculated based on the input voltage signal and the noise voltage signal of the speaker voice coil. If the voltage signal-to-noise ratio is less than a second preset value (e.g., 40 dB), it can be determined that there is a discernible noise voltage signal in the input voltage signal of the speaker voice coil, that is, there is piano noise in the speaker, so as to facilitate the subsequent suppression of the noise signal in the input audio signal. For example, if the calculated voltage signal-to-noise ratio is less than 40 dB, it can be determined that there is a discernible noise voltage signal in the input voltage signal of the speaker voice coil.

[0128] In some embodiments of the present application, see Figure 6 , Figure 6 A schematic diagram of a process for determining a power model in an embodiment of the present application is shown, wherein the step of determining a current model includes:

[0129] Step S601, obtaining voltage data and current data of the speaker;

[0130] Specifically, the voltage data and current data of the speaker refer to the voltage and current at both ends of the speaker voice coil. The voltage data and current data are obtained by monitoring the voltage and current at both ends of the speaker voice coil. For example, the voltage at both ends of the speaker voice coil is collected by a voltage collector, and the current at both ends of the speaker voice coil is collected by a current collector; alternatively, the voltage data and current data can also be obtained through analog signals, by intercepting the voltage values ​​and current values ​​corresponding to multiple frequencies of the analog signal, and converting the voltage values ​​and current values ​​into digital signals, so as to calculate the power model measurement parameters of the speaker.

[0131] In some embodiments of the present application, the voltage data and the current data include speaker voice coil voltages and currents corresponding to N different frequencies, where N is an integer greater than or equal to 3. Preferably, the voltage data and the current data include speaker voice coil voltages and currents corresponding to 3 different frequencies. It can be understood that the voltage data and the current data can also be selected from more frequency points.

[0132] Step S602, determining power model calculation parameters and a power model including the power model calculation parameters according to the voltage data and the current data;

[0133] The power model measurement parameters may include DC resistance, resonance angular frequency, quality factor and mechanical quality factor. It should be noted that the power model measurement parameters include all parameters of the power model, such as parameters of the impedance model and / or the admittance model, so as to construct the impedance model and / or the admittance model according to the power model measurement parameters.

[0134] As an example, the impedance model of the speaker can be expressed by the following function:

[0135]

[0136] Since the impedance amplitude of the speaker at the corresponding frequency can be directly calculated through the voltage data and current data, if the solution of the above function is known, the parameters of the function can be reversely deduced through the above function to solve the actual impedance model parameters of the speaker, such as the resonance angular frequency w0 of the speaker in the above function, the mechanical quality factor Q of the speaker ms , the quality factor Q of the speaker tsEtc. For example, the impedance model parameters of the loudspeaker can be obtained by least square method or solving multiple sets of equations.

[0137] It is understandable that the parameters corresponding to the admittance model of the loudspeaker can also be calculated in the above manner, which will not be described in detail here. Alternatively, the parameters corresponding to the impedance model can be calculated and then applied to the admittance model.

[0138] In some embodiments of the present application, see Figure 7 , Figure 7 A schematic diagram of a process for determining power model calculation parameters in an embodiment of the present application is shown, wherein the steps of determining the power model calculation parameters and the power model including the power model calculation parameters according to the voltage data and the current data include:

[0139] Step S701, determining the actual DC resistance of the speaker according to the speaker voltage and current corresponding to at least one frequency;

[0140] It should be noted that the actual DC resistance of the speaker can be calculated based on the speaker voltage and current corresponding to the minimum frequency among N different frequencies. Since the speaker voltage and current corresponding to the minimum frequency can be considered as DC voltage and DC current, the actual DC resistance of the impedance model can be calculated by the speaker voice coil voltage and current at the minimum frequency. In some embodiments of the present application, for example, for the above-mentioned embodiment of determining the actual DC resistance of the speaker based on the speaker voltage and current corresponding to multiple frequencies, the calculated impedance value may be multiple values, so the impedance values ​​of multiple groups of data can also be processed to obtain the actual DC resistance. Exemplarily, the data processing can be probability statistics or filtering of the voltage and current data, and then the actual DC resistance is calculated.

[0141] Step S702: determining the actual resonance angular frequency, the actual quality factor and the actual mechanical quality factor of the speaker according to the speaker voltages and currents corresponding to N different frequencies.

[0142] It should be noted that, since the impedance amplitude of the speaker at the corresponding frequency can be directly calculated through the voltage data and the current data, in the case of the solution of the impedance model function, the parameter value of the function can be reversely deduced through the impedance model function, so as to solve the actual resonance angular frequency, actual quality factor and actual mechanical quality factor of the speaker. Exemplarily, the actual impedance model parameters of the speaker can be obtained by solving the least square method or multiple sets of equations.

[0143] For example, the impedance corresponding to each frequency can be calculated based on the speaker voice coil voltage and current corresponding to N different frequencies; then, the impedance function corresponding to each frequency is constructed based on the impedance corresponding to each frequency, N different frequencies and the impedance model; finally, the actual resonant angular frequency, actual quality factor and actual mechanical quality factor of the speaker voice coil are determined based on the impedance function corresponding to each frequency.

[0144] Specifically, the impedance can be calculated using the following formula:

[0145] Z(f)=V(f) / I(f)

[0146] Where Z(f) is the impedance, V(f) is the speaker voice coil voltage, and I(f) is the speaker voice coil current.

[0147] Therefore, using the above formula, the impedance corresponding to each frequency of the speaker can be calculated based on the speaker voice coil voltage and current corresponding to N different frequencies. Therefore, when the N solutions corresponding to different frequencies of the impedance model function are obtained, the frequency and impedance are substituted into the impedance model function to obtain multiple impedance functions, and by jointly solving the multiple impedance functions, the actual resonant angular frequency, actual quality factor and actual mechanical quality factor of the speaker voice coil can be obtained.

[0148] In some embodiments of the present application, the frequency, impedance, actual resonant angular frequency, actual quality factor, and actual mechanical quality factor satisfy the following formula:

[0149]

[0150] Where w0 is the actual resonant angular frequency, Q ts is the actual quality factor, Q ms is the actual mechanical quality factor.

[0151] That is to say, in the above formula, there are three unknown parameters, namely the actual resonance angular frequency w0, the actual quality factor Q ts , the actual mechanical quality factor Q ms , since N is an integer greater than or equal to 3, at least three of the above formulas can be constructed through three sets of frequency and impedance values, and finally the actual resonant angular frequency, the actual quality factor and the actual mechanical quality factor can be obtained by combining them.

[0152] In some embodiments of the present application, when N is 3, that is, three unknown parameters can be uniquely solved by three equations. In some other embodiments of the present application, when N is greater than 3, multiple sets of three-variable equations can be constructed and multiple solutions can be used to find the optimal solution; or, multiple three-variable equations can be constructed and the least squares solution can be finally found.

[0153] It is understandable that the power model of the speaker can also be calculated using other algorithms based on the voltage and current data of the speaker. For example, the parameters of the impedance model or admittance model of the speaker can be calculated based on the voltage and current data of the speaker using an adaptive algorithm.

[0154] Furthermore, in order to better implement the speaker noise recognition method in the embodiment of the present application, on the basis of the speaker noise recognition method, the present application also provides a speaker noise recognition device, see Figure 8 , Figure 8 A schematic diagram of a module of a speaker noise recognition device in an embodiment of the present application is shown, wherein the speaker noise recognition device 800 includes:

[0155] An acquisition module 801 is used to acquire a power model of a speaker;

[0156] A prediction module 802, the prediction module 802 is used to determine a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal of the speaker voice coil;

[0157] The noise determination module 803 is used to determine whether the input signal of the speaker voice coil contains a discernible noise signal according to the first comparison signal and the second comparison signal.

[0158] It should be understood that Figure 8 The device and its modules shown can be implemented in various ways. For example, in some embodiments, the device and its modules can be implemented by hardware, software or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0159] It should be noted that the above description of the device and its modules is only for convenience of description and does not limit the present application to the scope of the embodiments. It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. For example, Figure 8 The acquisition module 801, prediction module 802, and noise determination module 803 disclosed in the system can be different modules in a system, or a module that implements the functions of two or more of the above modules. For example, the acquisition module 801 and the prediction module 802 can be two modules with acquisition and parameter determination functions respectively, or a module with both acquisition and parameter determination functions.

[0160] Furthermore, an embodiment of the present application also provides an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the speaker noise recognition method described in any of the above embodiments. The electronic device can be, but is not limited to, a weight scale, a body fat scale, a nutrition scale, an infrared electronic thermometer, a pulse oximeter, a human body composition analyzer, a mobile power supply, a wireless charger, a fast charger, a car charger, an adapter, a display, a Universal Serial Bus (USB) docking station, a stylus, a True Wireless Stereo (TWS) headset, a car central control screen, a car, a smart wearable device, a mobile terminal, a smart home device, etc.

[0161] Among them, smart wearable devices include but are not limited to smart watches, smart bracelets, and cervical massagers. Mobile terminals include but are not limited to smart phones, laptops, tablets, and point of sales terminals (POS). Smart home devices include but are not limited to smart sockets, smart rice cookers, smart sweepers, and smart lights.

[0162] Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any one of the speaker noise recognition methods provided in the embodiments of the present invention. For example, the computer program may be loaded by a processor to execute the following steps:

[0163] Get the power model of the speaker;

[0164] Determine a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal of the speaker voice coil;

[0165] Determining whether there is a discernible noise signal in the input signal of the loudspeaker voice coil according to the first comparison signal and the second comparison signal;

[0166] The input signal of the loudspeaker voice coil includes at least one of an input voltage signal and an input current signal of the loudspeaker voice coil, and the first comparison signal and the second comparison signal are both current signals or both voltage signals.

[0167] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the detailed description of other embodiments above, and will not be repeated here.

[0168] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0169] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0170] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0171] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0172] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety, except for application history documents that are inconsistent with or conflicting with the content of this application, and documents that limit the broadest scope of the claims of this application (currently or later attached to this application). It should be noted that if the descriptions, definitions, and / or use of terms in the accompanying materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0173] The above is a detailed introduction to a speaker noise recognition method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for identifying loudspeaker noise, characterized in that: include: Get the power model of the speaker; Determining a first comparison signal and a second comparison signal of the speaker according to the power model and an input signal of the speaker voice coil; Determining whether there is a discernible noise signal in the input signal of the speaker voice coil according to the first comparison signal and the second comparison signal; The input signal of the speaker voice coil includes at least one of an input voltage signal and an input current signal of the speaker voice coil, and the first comparison signal and the second comparison signal are both current signals or both voltage signals.

2. The speaker noise recognition method according to claim 1, characterized in that: The first comparison signal includes at least one of an input voltage signal of the voice coil of the speaker, an input current signal, a nonlinear current signal determined according to the input voltage signal and a nonlinear model of the speaker, and a nonlinear voltage signal determined according to the input current signal and a nonlinear model of the speaker; The second comparison signal includes a predicted current signal determined according to the input voltage signal and an admittance model of the speaker, or a predicted voltage signal determined according to the input current signal and an impedance model of the speaker.

3. The speaker noise recognition method according to claim 1, characterized in that: The step of judging whether there is a discernible noise signal in the input signal of the speaker voice coil according to the first comparison signal and the second comparison signal comprises: According to the first comparison signal and the second comparison signal, identifying a noise signal in the first comparison signal that is redundant with respect to the second comparison signal; Calculating a signal-to-noise ratio of the speaker signal according to the first comparison signal and the noise signal; When the signal-to-noise ratio of the speaker signal is less than a preset value, it is determined that a discernible noise signal exists in the input signal of the speaker voice coil.

4. The speaker noise recognition method according to claim 3, characterized in that: The method further comprises: When a discernible noise signal exists in the input signal of the speaker voice coil, the audio signal input to the speaker is subjected to noise suppression.

5. The speaker noise recognition method according to claim 1, characterized in that: The input signal includes an input voltage signal and an input current signal of the speaker voice coil, the power model includes an admittance model, and the step of determining a first contrast signal and a second contrast signal of the speaker according to the power model and the input signal includes: Determining that the input current signal is a first comparison signal; A predicted current signal of the speaker is determined according to the input voltage signal and the admittance model, and the second comparison signal includes the predicted current signal.

6. The speaker noise recognition method according to claim 1, characterized in that: The input signal includes an input voltage signal and an input current signal of the speaker voice coil, the power model includes an impedance model, and the step of determining a first comparison signal and a second comparison signal of the speaker according to the power model and the input signal includes: Determining that the input voltage signal is a first comparison signal; A predicted voltage signal of the speaker is determined according to the input current signal and the impedance model, and the second comparison signal includes the predicted voltage signal.

7. The speaker noise recognition method according to claim 1, characterized in that: The input signal includes an input voltage signal, the power model includes an admittance model and a nonlinear model, and the step of determining the first contrast signal and the second contrast signal of the speaker according to the power model and the input signal includes: Determine a nonlinear current signal of the speaker according to the measured voltage signal and the nonlinear model, wherein the first comparison signal includes the nonlinear current signal; A predicted current signal of the speaker is determined according to the input voltage signal and the admittance model, and the second comparison signal includes the predicted current signal.

8. The speaker noise recognition method according to claim 1, characterized in that: The input signal includes an input current signal, the power model includes an impedance model and a nonlinear model, and the step of determining the first contrast signal and the second contrast signal of the speaker according to the power model and the input signal includes: Determine a nonlinear voltage signal of the speaker according to the input current signal and the nonlinear model, wherein the first comparison signal includes the nonlinear voltage signal; A predicted voltage signal of the speaker is determined according to the input current signal and the impedance model, and the second comparison signal includes the predicted voltage signal.

9. The speaker noise recognition method according to claim 1, characterized in that: The step of obtaining the power model of the speaker comprises: Get the voltage data and current data of the speaker; Determining, according to the voltage data and the current data, power model calculation parameters and a power model including the power model calculation parameters; The voltage data and the current data include voltages and currents corresponding to N different frequencies, where N is an integer greater than or equal to 3.

10. The speaker noise recognition method according to claim 9, characterized in that: The step of determining the power model calculation parameters and the power model including the power model calculation parameters according to the voltage data and the current data comprises: Determine the actual DC resistance of the speaker according to the speaker voltage and current corresponding to at least one frequency; According to the loudspeaker voltages and currents corresponding to the N different frequencies, the actual resonance angular frequency, the actual quality factor and the actual mechanical quality factor of the loudspeaker are determined.

11. A speaker noise recognition device, characterized in that: include: An acquisition module, the acquisition module is used to acquire a power model of the speaker; A prediction module, the prediction module is used to determine a first comparison signal and a second comparison signal of the speaker according to the power model and an input signal of the speaker voice coil; a noise determination module, the noise determination module being used to determine whether there is a discernible noise signal in the input signal of the speaker voice coil according to the first comparison signal and the second comparison signal; The input signal of the speaker voice coil includes at least one of an input voltage signal and an input current signal of the speaker voice coil, and the first comparison signal and the second comparison signal are both current signals or both voltage signals.

12. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the speaker noise recognition method according to any one of claims 1 to 10.

13. A storage medium, characterized in that: The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the speaker noise recognition method according to any one of claims 1 to 10.

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