Filter parameter determination method, audio signal processing method and device

Abstract

The application provides a filter parameter determination method and an audio signal processing method and device. The method comprises the following steps: obtaining a first sweep signal and a second sweep signal, wherein the first sweep signal is a signal collected by an audio collection module at a different position in a smart device when a plurality of audio channels in the smart device play the sweep signal one by one, and the second sweep signal is a signal collected by a target audio collection module in the smart device when all the audio channels play the sweep signal simultaneously; determining a first impulse response signal based on the first sweep signal, and determining a second impulse response signal based on the second sweep signal; and determining filter parameters corresponding to each audio channel based on the first impulse response signal and the second impulse response signal. The method can determine relatively accurate filter parameters, and can improve the adjustment accuracy of the phase of the audio signal when applied to the smart device, which is helpful to improve the sound quality, improve the listening experience, and improve the overall performance of the audio system of the smart device.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of audio signal processing, and particularly relates to a filter parameter determination method, an audio signal processing method and device. BACKGROUND

[0002] With the rapid development of intelligent electric vehicles and the demand of consumers for high-quality music experience in the vehicle cabin, the number of loudspeakers equipped in the vehicle cabin of electric vehicles has significantly increased. However, due to the limitations of the seating position and the layout position of the loudspeakers, the sound signals emitted by different loudspeakers not only are difficult to reach the listener synchronously, but also the reflection of the sound signals in the small space is more serious, so there are a series of listening problems caused by phase, such as blurred sound image, inaccurate positioning, and short sound field distance. In order to solve the sound phase problem in the vehicle cabin, the related technology adopts delay adjustment for phase correction, but the method of delay adjustment is not fine enough for phase adjustment, and cannot solve the phase distortion caused by sound reflection in space. SUMMARY

[0003] In view of the above problems, the embodiments of the present application provide a filter parameter determination method, an audio signal processing method and device to solve the problem that the phase adjustment in the related technology is not fine enough.

[0004] In a first aspect, the embodiments of the present application provide a filter parameter determination method, which comprises:

[0005] obtaining a first sweep signal and a second sweep signal, wherein the first sweep signal is a signal collected by an audio collection module at different positions in the smart device under the condition that the sweep signal is played by the multiple audio channels in the smart device one by one, and the second sweep signal is a signal collected by a target audio collection module in the smart device under the condition that the sweep signal is played by all the audio channels at the same time;

[0006] determining a first impulse response signal based on the first sweep signal, and determining a second impulse response signal based on the second sweep signal;

[0007] determining a target filter parameter corresponding to each audio channel based on the first impulse response signal and the second impulse response signal.

[0008] In some embodiments, determining the target filter parameter corresponding to each audio channel based on the first impulse response signal and the second impulse response signal comprises:

[0009] inputting the first impulse response signal and the current filter parameter corresponding to each audio channel into a neural network model to obtain a first filter parameter corresponding to each audio channel, wherein a first optimization target of the neural network model is that the first filter parameter corresponding to any audio channel can minimize phase distortion of an audio signal played by the any audio channel and maximize phase consistency;

[0010] inputting the first filter parameter corresponding to each audio channel and the second impulse response signal into the neural network model to obtain a second filter parameter corresponding to each audio channel, wherein a second optimization target of the neural network model is that the second filter parameter corresponding to each audio channel can minimize phase distortion of the audio signal played by each audio channel collected by the target audio acquisition module;

[0011] determining a target filter parameter corresponding to any audio channel according to the second filter parameter corresponding to the any audio channel.

[0012] In some embodiments, determining the target filter parameter corresponding to any audio channel according to the second filter parameter corresponding to the any audio channel comprises:

[0013] in a case where the second filter parameter corresponding to any audio channel meets a preset condition, determining the second filter parameter corresponding to the any audio channel as the target filter parameter corresponding to the any audio channel, the preset condition comprising: minimizing phase distortion of the audio signal played by the any audio channel and maximizing phase consistency, and minimizing phase distortion of the audio signal played by each audio channel collected by the target audio acquisition module;

[0014] in a case where the second filter parameter corresponding to any audio channel cannot meet the preset condition, determining the target filter parameter corresponding to the any audio channel again by taking the second filter parameter corresponding to the any audio channel as a current filter parameter of the any audio channel.

[0015] In some embodiments, in a case where the second filter parameter corresponding to any audio channel cannot meet the preset condition, determining the target filter parameter corresponding to the any audio channel again by taking the second filter parameter corresponding to the any audio channel as a current filter parameter of the any audio channel comprises:

[0016] in a case where the second filter parameter corresponding to any audio channel does not meet the preset condition, updating the current filter parameter corresponding to each audio channel to the second filter parameter corresponding to each audio channel;

[0017] performing time delay compensation on each audio channel based on the first impulse response signal and the second impulse response signal;

[0018] acquire a third scan signal and a fourth scan signal from each of the audio channels after the time delay compensation, wherein the third scan signal is the time-delay-compensated first sweep signal, and the fourth scan signal is the time-delay-compensated second sweep signal.

[0019] perform iterative optimization based on the third scan signal and the fourth scan signal to re-determine the target filter parameter corresponding to any of the audio channels.

[0020] In some embodiments, the current filter parameter includes:

[0021] the initial filter parameter, and the method further includes:

[0022] determining the initial filter parameter of each of the audio channels based on the second impulse response signal and a preset restriction condition, wherein the preset restriction condition includes that the order of the initial filter parameter is less than or equal to a preset order, the initial filter parameter can minimize the change of the amplitude-frequency response of the second impulse response signal, and the phase distortion is less than or equal to a preset threshold.

[0023] In some embodiments, the constraint condition of the neural network model includes a main frequency band constraint, wherein when the main frequency band corresponding to the main frequency band constraint is the first main frequency band, the target filter parameter corresponding to each of the audio channels is the third filter parameter, and when the main frequency band corresponding to the main frequency band constraint is the second main frequency band, the target filter parameter corresponding to each of the audio channels is the fourth filter parameter.

[0024] In some embodiments, the method further includes: controlling the multiple audio channels to play the sweep signal one by one, so that each of the audio acquisition modules acquires the first sweep signal;

[0025] controlling the multiple audio channels to play the sweep signal simultaneously, so that the target acquisition module acquires the second scan signal.

[0026] In some embodiments, the method further includes: sending the filter parameter corresponding to each of the audio channels to the smart device.

[0027] In a second aspect, the embodiments of the present application provide a filter parameter determination device, including:

[0028] an acquisition module, configured to acquire a first sweep signal and a second sweep signal, wherein the first sweep signal is acquired by an audio acquisition module at a different position in the smart device when the multiple audio channels in the smart device play the sweep signal one by one, and the second sweep signal is acquired by a target audio acquisition module in the smart device when all the audio channels play the sweep signal simultaneously;

[0029] a first determination module, configured to determine a first impulse response signal based on the first sweep signal, and determine a second impulse response signal based on the second sweep signal;

[0030] The second determining module is configured to determine filter parameters corresponding to each audio channel based on the first impulse response signal and the second impulse response signal.

[0031] In a third aspect, an embodiment of the present application provides an audio signal processing method, which further includes:

[0032] obtaining audio information to be played;

[0033] determining target audio information based on the audio information and filter parameters corresponding to each audio channel in the smart device, wherein the filter parameters corresponding to each audio channel are determined based on the method provided in the first aspect;

[0034] playing the target audio information.

[0035] In some embodiments, determining the target audio information based on the audio information and the filter parameters corresponding to each audio channel in the smart device includes:

[0036] inputting the audio information into a main frequency band determination model to determine a main frequency band in the audio information;

[0037] determining application filter parameters corresponding to each audio channel based on the main frequency band and the target filter parameters corresponding to each audio channel;

[0038] processing the audio information based on the application filter parameters corresponding to each audio channel to obtain the target audio information.

[0039] In a fourth aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method provided in the first aspect when executing the computer program.

[0040] In a fifth aspect, an embodiment of the present application provides a smart device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method provided in the third aspect when executing the computer program.

[0041] In a sixth aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method provided in the third aspect when executing the computer program.

[0042] In a seventh aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executable on a processor to implement the method provided in the first aspect.

[0043] In an eighth aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method provided in the third aspect.

[0044] In a ninth aspect, the embodiments of the present application provide a computer program product, which comprises a computer program. The computer program is executed by a processor to implement at least the method in any one of the first aspect or the third aspect.

[0045] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0046] The method for determining filter parameters provided by the embodiments of the present application can obtain a first sweep signal and a second sweep signal, wherein the first sweep signal is a signal collected by an audio collection module at a different position in the smart device in a case where the sweep signal is played by the multiple audio channels in the smart device one by one, and the second sweep signal is a signal collected by a target audio collection module in the smart device in a case where the sweep signal is played by all the audio channels at the same time; a first impulse response signal is determined based on the first sweep signal, and a second impulse response signal is determined based on the second sweep signal; and a target filter parameter corresponding to each audio channel is determined based on the first impulse response signal and the second impulse response signal. The method can determine relatively accurate filter parameters, and when applied to the smart device, can improve the adjustment accuracy of the phase of the audio signal, help to improve the sound quality, improve the listening experience, and improve the overall performance of the audio system of the smart device.

[0047] It can be understood that the beneficial effects of the second aspect to the ninth aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 The flowchart of the method for determining filter parameters provided by the embodiments of the present application is shown in the figure;

[0050] Figure 2 The flowchart of the method for determining filter parameters provided by the embodiments of the present application is shown in the figure;

[0051] Figure 3 The implementation flowchart of the method for processing audio signals provided by the embodiments of the present application is shown in the figure;

[0052] Figure 4 A schematic diagram of a filter parameter determination device provided in an embodiment of this application;

[0053] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0054] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0055] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0056] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0057] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected," or "in response to detection."

[0058] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0060] Based on the technical problems of the related art, the embodiment of the present application provides a filter parameter determination method which can be applied to an electronic device. For example, the filter parameter determination method can be applied to a vehicle-mounted device in a smart device (such as an electric vehicle). The vehicle-mounted device can include a controller in the electric vehicle. Of course, the filter parameter determination method provided by the embodiment of the present application can also be executed by an electronic device in communication with the electric vehicle. For example, the electronic device can include a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and the embodiment of the present application does not limit the specific type of the electronic device. Of course, the electronic device can also be a smart device.

[0061] The embodiment of the present application provides a filter parameter determination method, Figure 1 The flowchart of the filter parameter determination method provided by the embodiment of the present application is shown in Figure 1 The method comprises the following steps:

[0062] In step S101, a first sweep signal and a second sweep signal are obtained. The first sweep signal is a signal collected by an audio acquisition module at different positions in the smart device when the sweep signal is played by each audio channel in the smart device. The second sweep signal is a signal collected by a target audio acquisition module in the smart device when the sweep signal is played by all audio channels at the same time.

[0063] In the embodiment of the present application, the sweep signal is a signal whose frequency changes linearly or nonlinearly with time. In audio system debugging, the sweep signal is often used to test the response of the audio system of the smart device to obtain the impulse response signal of the audio system in the smart device. The audio channel refers to an independent signal path in the audio system of the smart device. In the smart device, each audio output device usually corresponds to an audio channel. The audio output device can be a loudspeaker, and the audio acquisition module is a device responsible for capturing and recording audio signals. In the smart device, these modules can be installed at different positions to capture sound from different loudspeakers or audio channels.

[0064] In the embodiment of the present application, the target audio acquisition module is a designated audio acquisition device, and the target audio acquisition module is usually set at an important listening position in the vehicle, which can be the center of the smart device. Taking the smart device as an example, the target audio acquisition module can be set at the center of the vehicle.

[0065] In the embodiments of the present application, the audio acquisition module can be a full-directional microphone. Taking a vehicle as an example, different positions can correspond to different seating positions, and the microphone at each of the different seating positions can be a full-directional acoustic measurement microphone arranged at the positions of the main driver, the co-driver, the rear left, the rear center and the rear right. In addition, a microphone is arranged at the central position of the vehicle. The microphone arranged at the central position is the target audio acquisition module.

[0066] In the embodiments of the present application, the audio channels can be controlled by a digital signal processing (DSP, Digital Signal Processing) power amplifier of the intelligent device to play the sweep frequency signals one by one, at which time all the audio acquisition modules collect the sweep frequency signals to obtain the first sweep frequency signal. The DSP power amplifier can be controlled to make all the audio channels play the sweep frequency signals at the same time, and the target audio acquisition module collects the second sweep frequency signal.

[0067] For example, if the number of audio channels of the vehicle is M, and six microphones are arranged in the vehicle, one of the six microphones is a central microphone. First, the vehicle plays the sweep frequency signal through the audio channel 1, at which time the six microphones collect the sweep frequency signal played by the channel 1. Then, the channel 2 plays the sweep frequency signal, at which time the six microphones collect the sweep frequency signal played by the channel 2. In this way, the six microphones can collect the sweep frequency signals of all the audio channels, i.e., the first sweep frequency signal. For the second sweep frequency signal, all the audio channels play the sweep frequency signal at the same time, at which time the central microphone can collect the sweep frequency signal, which is the second sweep frequency signal.

[0068] In step S102, a first impulse response signal is determined based on the first sweep frequency signal, and a second impulse response signal is determined based on the second sweep frequency signal.

[0069] In the embodiments of the present application, the impulse response signal is the response of the audio system of the intelligent device to the sweep frequency signal. In the audio system, the impulse response signal reflects the frequency response, phase response and distortion characteristics of the audio system. The second impulse response signal reflects the impulse response characteristics of the audio system when all the audio channels work at the same time.

[0070] In the embodiments of the present application, the first impulse response signal and the second impulse response signal can be determined by signal processing and spectrum analysis technologies. In the above example, if the number of channels of the vehicle loudspeaker is M, and six microphones are arranged in the vehicle, then the number of the first impulse response signals is 6M, and the number of the second impulse response signals is 1.

[0071] In step S103, target filter parameters corresponding to each audio channel are determined based on the first impulse response signal and the second impulse response signal.

[0072] In the embodiments of the present application, the neural network model can be used to determine the target filter parameters of each audio channel based on the first impulse response signal and the second impulse response signal. The filter parameters can be filter coefficients. The filter coefficients are a set of key numbers in a digital filter, which are used to describe the characteristics of the digital filter. The neural network model can be a machine learning model, which can output the target filter parameters corresponding to each audio channel based on the first impulse response signal and the second impulse response signal.

[0073] The method provided in the embodiments of the present application can obtain a first sweep signal and a second sweep signal. The first sweep signal is a signal collected by an audio collection module at different positions in the smart device in the case of playing the sweep signal by each audio channel of the smart device. The second sweep signal is a signal collected by a target audio collection module in the smart device in the case of playing the sweep signal by all audio channels at the same time. The first impulse response signal is determined based on the first sweep signal, and the second impulse response signal is determined based on the second sweep signal. The filter parameters corresponding to each audio channel are determined based on the first impulse response signal and the second impulse response signal. The sweep signal is collected by different audio collection modules, the impulse response signal is determined based on the sweep signal, and the target filter parameters corresponding to each audio channel are determined based on the impulse response signal. The filter parameters can be determined more accurately, and the adjustment accuracy of the phase of the audio signal can be improved when the filter parameters are applied to the smart device. This can help to improve the sound quality, improve the listening experience, and improve the overall performance of the audio system of the smart device.

[0074] In some embodiments, step S103 can be implemented by the following steps:

[0075] In step S1031, the first impulse response signal and the current filter parameters corresponding to each audio channel are input into the neural network model to obtain the first filter parameters corresponding to each audio channel. The first optimization target of the neural network model is that the first filter parameters corresponding to any audio channel can minimize the phase distortion of the audio signal played by any audio channel and maximize the phase consistency.

[0076] In the embodiments of the present application, the current filter parameters are the filter parameters currently used by each audio channel. The current filter parameters can be initial filter parameters or filter parameters updated in the iteration process. The initial filter parameters can be filter parameters determined based on the second impulse response signal and a preset restriction condition, or the initial filter parameters can be the default filter parameters of a certain audio channel, or the initial filter parameters can be the filter parameters of a certain audio channel set in the last time. Alternatively, the initial filter parameters can be the filter parameters of a certain audio channel after the smart device is started this time. The embodiments of the present application do not limit this.

[0077] In the embodiments of the present application, the phase distortion refers to the deviation of the phase of the audio signal in the transmission or processing process. The phase distortion affects the clarity and positioning of the audio. The phase consistency refers to the synchronization degree of the phase of the audio signals played by different audio channels. High phase consistency can ensure the correct positioning of the audio signal in space.

[0078] In the embodiments of the present application, the first impulse response signal and the current filter parameters of each audio channel can be input into the neural network model. The neural network model processes these data and outputs the first filter parameters that are preliminarily optimized. The first optimization target of the neural network model is to minimize the phase distortion of the audio signals played by each audio channel and maximize the phase consistency. By adjusting the filter parameters, the obtained filter parameters can minimize the phase distortion of the audio signals played by each audio channel and maximize the phase consistency.

[0079] In step S1032, the first filter parameters corresponding to each audio channel and the second impulse response signal are input into the neural network model to obtain the second filter parameters corresponding to each audio channel, wherein the second optimization target of the neural network model is that the second filter parameters corresponding to each audio channel can minimize the phase distortion of the audio signals played by each audio channel and collected by the target audio acquisition module.

[0080] In the embodiments of the present application, the preliminarily optimized first filter parameters and the second impulse response signal are input into the neural network model again. The neural network model further processes these data and outputs the second filter parameters. The second optimization target of the neural network model is to minimize the phase distortion of the audio signals played by each audio channel and collected by the target audio acquisition module. By adjusting the filter parameters, the second filter parameters can minimize the phase distortion of the audio signals played by each audio channel and collected by the target audio acquisition module.

[0081] In step S1033, the target filter parameters corresponding to any audio channel are determined according to the second filter parameters corresponding to any audio channel.

[0082] In the embodiments of the present application, in the case that the second filter parameters corresponding to any audio channel meet the preset conditions, the second filter parameters corresponding to any audio channel are determined as the target filter parameters corresponding to any audio channel, and the preset conditions include minimizing the phase distortion of the audio signals played by any audio channel and maximizing the phase consistency, and minimizing the phase distortion of the audio signals played by each audio channel and collected by the target audio acquisition module. In the case that the second filter parameters corresponding to any audio channel do not meet the preset conditions, the second filter parameters corresponding to any audio channel are used as the current filter parameters of any audio channel to determine the target filter parameters corresponding to any audio channel again.

[0083] The method provided in the embodiments of the present application achieves the goal of minimizing phase distortion and maximizing phase coherence by utilizing the optimization capability of the neural network model, through multiple iterations and adjustment of filter parameters.

[0084] In some embodiments, step S1033 comprises:

[0085] Step S1, in the case that the second filter parameter corresponding to any audio channel fails to meet the preset condition, updating the current filter parameter corresponding to each audio channel to the second filter parameter corresponding to each audio channel.

[0086] In the embodiments of the present application, if the audio system fails to meet the following preset conditions after applying the second filter parameter, it is considered that the second filter parameter does not meet the requirements.

[0087] In the embodiments of the present application, after determining that the second filter parameter does not meet the requirements, the current filter parameter corresponding to each audio channel is updated to the second filter parameter corresponding to each audio channel.

[0088] Step S2, performing time delay compensation on each audio channel based on the first impulse response signal and the second impulse response signal.

[0089] In the embodiments of the present application, time delay compensation is the adjustment of signals in time to eliminate or reduce the signal time delay caused by factors such as transmission path difference and equipment processing time. In the optimization of the audio system, time delay compensation is often used to improve the phase coherence between different audio channels.

[0090] In the embodiments of the present application, by analyzing the first impulse response signal and the second impulse response signal, the transmission delay of sound waves from the sound source to each microphone position can be determined. After determining the transmission delay of each microphone position, the delay difference between them and the reference position (i.e. the position of the first impulse response signal) needs to be calculated. These differences will be used for subsequent time delay compensation. According to the size of the delay difference and the requirements of the audio system, a suitable time delay compensation method is selected. Common methods include fixed time delay compensation and adaptive time delay compensation. Fixed time delay compensation is to adjust the time delay of the signal of each audio channel according to the delay difference measured in advance. Adaptive time delay compensation is to dynamically adjust the signal delay of each audio channel according to the real-time measured delay difference.

[0091] Step S3, obtaining a third scan signal and a fourth scan signal based on each audio channel after time delay compensation, wherein the third scan signal is the first frequency sweeping signal after time delay compensation, and the fourth scan signal is the second frequency sweeping signal after time delay compensation.

[0092] In the embodiments of the present application, after the time delay compensation, the third scan signal and the fourth scan signal can be obtained again according to the method of step S101.

[0093] In step S4, iterative optimization is performed based on the third scan signal and the fourth scan signal to redetermine the target filter parameters corresponding to any audio channel.

[0094] In the embodiments of the present application, steps S102 and S103 can be performed in this way. In the case where the filter parameters corresponding to each audio channel meet the preset condition, the filter parameters corresponding to each audio channel that meet the preset condition obtained through optimization are determined as the target filter parameters corresponding to each audio channel.

[0095] In some embodiments, the current filter parameters include initial filter parameters, and the method further includes:

[0096] The initial filter parameters of each audio channel are determined based on the second impulse response signal and a preset restriction condition, wherein the preset restriction condition includes that the order of the initial filter parameters is less than a preset order, the initial filter parameters can minimize the amplitude-frequency response change of the second impulse response signal, and the phase distortion is less than or equal to a preset threshold.

[0097] In the embodiments of the present application, the order refers to the number of independent parameters in the filter. The higher the order, the better the performance of the filter, but the calculation complexity will also increase. The amplitude-frequency response refers to the gain or attenuation characteristics of the filter for signals of different frequencies. The preset threshold is a standard used to judge whether the filter parameters meet the requirements in the optimization process. Exemplarily, the preset order can be 256 orders. The preset threshold can be 0.

[0098] In the embodiments of the present application, the initial filter parameters of each audio channel can be determined using the least square method, genetic algorithm, etc. according to the second impulse response signal and the preset restriction condition. These parameters should meet the preset restriction condition and be as close to the optimal solution as possible, so as to obtain the initial filter parameters.

[0099] In some embodiments, the constraint condition of the neural network model includes a main frequency band constraint, wherein when the main frequency band corresponding to the main frequency band constraint is the first main frequency band, the filter parameters corresponding to each audio channel are the third filter parameters, and when the main frequency band corresponding to the main frequency band constraint is the second main frequency band, the filter parameters corresponding to each audio channel are the fourth filter parameters.

[0100] In the embodiments of the present application, the neural network model can adjust the filter parameters of each audio channel according to the main frequency range (i.e. the main frequency band) of the audio signal. The constraint condition aims to ensure that the filter parameters can be optimized for specific audio content or application scenarios, thereby improving the sound quality and performance.

[0101] In the embodiments of the present application, when training the neural network model, sample data containing the main frequency band constraint is input into the neural network model. The neural network model will learn how to adjust the filter parameters according to the input data and the constraint condition. The response impulse and the filter parameters can be input into the trained neural network model, and the corresponding filter parameters are output according to the main frequency band constraint.

[0102] For example, the first main frequency band can be the frequency band of human voice, and the second main frequency band can be the frequency band of the sound of a musical instrument. The filter parameters corresponding to the human voice are the third filter parameters, and the filter parameters corresponding to the sound of the musical instrument are the fourth filter parameters.

[0103] In some embodiments, the method further comprises: sending the filter parameters corresponding to each audio channel to the smart device.

[0104] In the embodiments of the present application, a communication connection with the smart device can be established. The filter parameters corresponding to each audio channel can be sent to the smart device through wired or wireless means. For example, OBD-II interface, Bluetooth, Wi-Fi, etc. can be used to establish the connection.

[0105] In the embodiments of the present application, after the smart device receives the filter parameters, the audio system needs to apply these parameters to each audio channel.

[0106] The method provided by the embodiments of the present application effectively sends the filter parameters corresponding to each audio channel to the smart device, which can improve the sound quality and performance of the smart device.

[0107] Based on the foregoing embodiments, the embodiments of the present application provide a method for determining the parameters of a filter Figure 2 As shown in the flowchart of the method for determining the parameters of a filter provided by the embodiments of the present application, Figure 2 the method comprises:

[0108] The swept frequency signals measured at different positions are obtained, and the impulse response is solved based on the swept frequency signals. Then, through all-pass FIR filter design, the initial filter parameters corresponding to each audio channel are obtained. The initial filter parameters of each audio channel are output to the neural network model for optimization, and the filter parameters of each channel are obtained. After optimization, the channel delay needs to be compensated based on the impulse response of the microphone of the whole vehicle. This is repeated until the optimization target meets the requirements, and then the all-pass FIR filter is obtained.

[0109] In the embodiments of the present application, if the number of channels of the full-car loudspeaker is M, and 6 microphones are arranged in the car, then the number of impulse responses required for a single iteration is N=M*6+1. First, the impulse response of the central microphone is taken as the benchmark to design a full-path FIR filter. The design principle is to minimize the phase distortion to zero while keeping the amplitude-frequency response unchanged, and to obtain the initial filter parameters. Subsequently, the M FIR filter parameters are taken as the input of the neural network model to further optimize the coefficients of the FIR filter, so as to fully approach the performance upper limit of the filter to meet the optimization target of all N groups of impulse responses. The target of the preliminary optimization of the model is to minimize the phase distortion of the M*6 audio channels of all seating positions, while maximizing the phase consistency of all channels. The target of the second optimization of the model is to ensure that the phase distortion of the impulse response signal measured by the central microphone when all the loudspeakers sound together is minimized.

[0110] The method provided by the embodiments of the present application can automatically measure the phase responses at different positions, and realize optimization of the filter coefficients through the channel-by-channel phase distortion constraint and the phase consistency constraint of all loudspeakers. By setting a preset order, high-precision phase correction can be realized with a low filter order.

[0111] Based on the foregoing various embodiments, the embodiments of the present application provide an audio signal processing method which can be applied to an electronic device. The audio signal processing method can be applied to a vehicle-mounted device in an electric vehicle. The vehicle-mounted device can include a controller in the electric vehicle. Of course, the audio signal processing method provided by the embodiments of the present application can also be executed by an electronic device in communication with the electric vehicle. For example, the electronic device can include a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and the embodiments of the present application do not limit the specific type of the electronic device.

[0112] Figure 3 The implementation flowchart of the audio signal processing method provided by the embodiments of the present application is shown in FIG. 3 as follows. Figure 3 As shown in FIG. 3, the method includes the following steps.

[0113] In step S301, audio information to be played is obtained.

[0114] In the embodiments of the present application, the audio information to be played can be obtained from an audio source (such as a file, a network stream, a microphone input, etc.).

[0115] In step S302, the target audio information is determined based on the audio information and the target filter parameters corresponding to the audio channels in the smart device.

[0116] In the embodiments of the present application, the audio information can be processed using the filter parameters to obtain the processed target audio information.

[0117] In step S302, the target audio information is played.

[0118] In the embodiments of the present application, the processed target audio can be sent to the audio playing device for playing.

[0119] The method provided by the embodiments of the present application can achieve various audio effects and improve user experience through the processing of the filter.

[0120] In some embodiments, step S302 can include:

[0121] In step S3021, the main frequency band in the audio information is determined by inputting the audio information into a main frequency band determination model.

[0122] In the embodiments of the present application, the main frequency band determination model can be an algorithm based on machine learning or deep learning, which determines the main frequency component, i.e., the main frequency band, in the audio information by analyzing the spectral characteristics of the audio signal. Before inputting the main frequency band determination model, the audio data can be processed, which can be denoising processing and normalization processing, and then the processed audio data is input into the model to obtain the main frequency band.

[0123] In step S3022, the application filter parameters corresponding to the audio channels are determined from the target filter parameters corresponding to the audio channels based on the main frequency band.

[0124] In the embodiments of the present application, the smart device can store a plurality of target filter parameters corresponding to the main frequency bands. For example, when the main frequency band corresponding to the main frequency band constraint is the first main frequency band, the target filter parameters corresponding to the audio channels are the third filter parameters, and when the main frequency band corresponding to the main frequency band constraint is the second main frequency band, the target filter parameters corresponding to the audio channels are the fourth filter parameters.

[0125] In the embodiments of the present application, the electronic device can match the main frequency band in the storage through the main frequency band, and determine the target filter parameters corresponding to the matched main frequency band as the application filter parameters corresponding to the audio channels.

[0126] For example, the first main frequency band can be a frequency band of human voice, the second main frequency band can be a frequency band of sound of a musical instrument, the filter parameter corresponding to the human voice is the third filter parameter, and the filter parameter corresponding to the musical instrument is the fourth filter parameter. At this time, if it is determined that the main frequency band is the frequency band of the human voice, it is determined that the filter parameter to be applied is the third filter parameter, and if it is determined that the main frequency band is the frequency band of the sound of the musical instrument, it is determined that the filter parameter to be applied is the fourth filter parameter.

[0127] In step S3023, the audio information is processed based on the application filter parameter corresponding to each audio channel to obtain target audio information.

[0128] In the embodiments of the present application, the application filter parameter corresponding to each audio channel can be loaded, so as to realize the processing of the audio information. When processing, real-time processing or offline processing can be performed.

[0129] The method provided by the embodiments of the present application can determine the main frequency band in the audio information by inputting the audio information into the main frequency band determination model, determine the application filter parameter corresponding to each audio channel from the target filter parameter corresponding to each audio channel based on the main frequency band, and process the audio information based on the application filter parameter corresponding to each audio channel to obtain target audio information, so as to improve the playing effect of music.

[0130] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0131] According to the foregoing embodiments, the embodiments of the present application provide a filter parameter determination device. Each module included in the device and each unit included in the module can be realized by a processor in a computer device. Of course, it can also be realized by a specific logic circuit. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA).

[0132] The embodiments of the present application provide a filter parameter determination device, Figure 4 The structure diagram of the filter parameter determination device provided by the embodiments of the present application is shown in FIG. 4. As shown in FIG. 4, the filter parameter determination 400 includes: Figure 4

[0133] ​The acquisition module 401 is configured to acquire a first sweep frequency signal and a second sweep frequency signal. The first sweep frequency signal is a signal collected by an audio collection module at a different position in the smart device in a case where the plurality of audio channels in the smart device play the sweep frequency signal one by one. The second sweep frequency signal is a signal collected by a target audio collection module in the smart device in a case where all the audio channels play the sweep frequency signal at the same time.

[0134] The first determination module 402 is configured to determine a first impulse response signal based on the first sweep frequency signal, and determine a second impulse response signal based on the second sweep frequency signal.

[0135] The second determination module 403 is configured to determine a target filter parameter corresponding to each audio channel based on the first impulse response signal and the second impulse response signal.

[0136] In some embodiments, the second determination module includes:

[0137] The first optimization unit is configured to input the first impulse response signal and the current filter parameter corresponding to each audio channel into a neural network model to obtain a first filter parameter corresponding to each audio channel, where a first optimization target of the neural network model is that the first filter parameter corresponding to any audio channel can minimize the phase distortion of the audio signal played by each audio channel and maximize the phase consistency.

[0138] The second optimization unit is configured to input the first filter parameter corresponding to each audio channel and the second impulse response signal into the neural network model to obtain a second filter parameter corresponding to each audio channel, where a second optimization target of the neural network model is that the second filter parameter corresponding to each audio channel can minimize the phase distortion of the audio signal played by each audio channel and collected by the target audio collection module.

[0139] The determination unit is configured to determine the target filter parameter corresponding to any audio channel according to the second filter parameter corresponding to any audio channel.

[0140] In some embodiments, the determination unit includes:

[0141] The first determination sub-unit is configured to determine the second filter parameter corresponding to any audio channel as the target filter parameter corresponding to any audio channel in a case where the second filter parameter corresponding to any audio channel meets a preset condition, where the preset condition includes minimizing the phase distortion of the audio signal played by any audio channel and maximizing the phase consistency, and minimizing the phase distortion of the audio signal played by each audio channel and collected by the target audio collection module.

[0142] The second determining sub-unit is configured to, in a case where the second filter parameter corresponding to any audio channel fails to meet the preset condition, determine the target filter parameter corresponding to any audio channel again by taking the second filter parameter corresponding to any audio channel as the current filter parameter of any audio channel.

[0143] In some embodiments, the second determining sub-unit is configured to, in a case where the second filter parameter corresponding to any audio channel fails to meet the preset condition, update the current filter parameter corresponding to each audio channel to the second filter parameter corresponding to each audio channel; perform time delay compensation on each audio channel based on the first impulse response signal and the second impulse response signal; obtain a third scan signal and a fourth scan signal based on each audio channel after time delay compensation, wherein the third scan signal is the first sweep signal after time delay compensation, and the fourth scan signal is the second sweep signal after time delay compensation; and perform iterative optimization based on the third scan signal and the fourth scan signal to determine the target filter parameter corresponding to any audio channel again.

[0144] In some embodiments, the current filter parameter includes an initial filter parameter, and the first optimization unit is further configured to determine the initial filter parameter of each audio channel based on the second impulse response signal and a preset restriction condition, wherein the preset restriction condition includes that an order of the initial filter parameter is less than or equal to a preset order, the initial filter parameter can minimize a change in an amplitude-frequency response of the second impulse response signal, and a phase distortion is less than or equal to a preset threshold.

[0145] In some embodiments, the constraint condition of the neural network model includes a main frequency band constraint, wherein when a main frequency band corresponding to the main frequency band constraint is a first main frequency band, the filter parameter corresponding to each audio channel is a third filter parameter, and when the main frequency band corresponding to the main frequency band constraint is a second main frequency band, the filter parameter corresponding to each audio channel is a fourth filter parameter.

[0146] In some embodiments, the filter parameter determination apparatus further includes:

[0147] The control module is configured to control the plurality of audio channels to play the sweep signal one by one, so that the first sweep signal is collected by each audio acquisition module; and control all the audio channels to play the sweep signal at the same time, so that the second scan signal is collected by the target acquisition module.

[0148] In some embodiments, the filter parameter determination apparatus further includes:

[0149] The sending module is configured to send the filter parameter corresponding to each audio channel to the intelligent device.

[0150] In addition, Figure 4The determination of the filter parameters shown can be a software unit, a hardware unit or a combination of software and hardware built into an existing electronic device, can be integrated into an electronic device as a stand-alone plug-in, or can exist as a stand-alone terminal device.

[0151] The embodiments of the present application provide a processing device of an audio signal, each module included in the device and each unit included in each module can be implemented by a processor in a computer device; of course, the device can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA).

[0152] The embodiments of the present application further provide a processing device of an audio signal, comprising:

[0153] An audio acquisition module is configured to acquire audio information to be played;

[0154] A filter module is configured to determine target audio information based on the audio information and target filter parameters corresponding to each audio channel in the intelligent device.

[0155] A playing module is configured to play the target audio information.

[0156] In some embodiments, the filter module comprises:

[0157] A main frequency band determination unit is configured to input the audio information into a main frequency band determination model to determine a main frequency band in the audio information.

[0158] A filter parameter determination unit is configured to determine application filter parameters corresponding to each audio channel based on the main frequency band from the target filter parameters corresponding to each audio channel.

[0159] A processing unit is configured to process the audio information based on the application filter parameters corresponding to each audio channel to obtain the target audio information.

[0160] It should be noted that the information interaction, execution process and the like between the above-mentioned devices / units are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by the same can be referred to the method embodiments part, and will not be described here.

[0161] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0162] The embodiment of the present application provides an electric vehicle, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the audio signal processing method in the above embodiment when executing the computer program.

[0163] Figure 5 The structure schematic diagram of the electronic device provided by the embodiment of the present application is shown in the figure. Figure 5 As shown in the figure, the electronic device 3 of the embodiment can include at least one processor 30 (only one processor 30 is shown in the figure), a memory 31 and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, and the processor 30 implements the steps in any of the above method embodiments when executing the computer program 32, or the processor 30 implements the functions of each module / unit in the above device embodiments when executing the computer program 32. Figure 5

[0164] For example, the computer program 32 can be divided into one or more modules / units, one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present application. One or more modules / units can be a series of computer program 32 instruction segments that can complete a specific function, which is used to describe the execution process of the computer program 32 in the electronic device 3.

[0165] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program 32, and the computer program 32 is executed by the processor 30 to implement the steps in the above method embodiments.

[0166] ​The embodiment of the present application provides a computer program product, when the computer program product runs on the electronic device, causes the electronic device to execute the steps in the above-mentioned various method embodiments.

[0167] The integrated unit, if in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. According to such an understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods, which can be completed by instructing related hardware through a computer program 32. The computer program 32 can be stored in a computer-readable storage medium, and the computer program 32 can implement the steps in the above-mentioned various method embodiments when executed by a processor 30. The computer program 32 includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form. The computer-readable medium at least includes any entity or device capable of carrying the computer program code to the terminal, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunications signal.

[0168] In the above-mentioned embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0169] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0170] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other manners. For example, the embodiments of the apparatus / network device described above are merely illustrative. For example, the division of the modules or units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0171] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0172] The above-described embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of determining filter parameters, characterized by, The method comprises: obtaining a first sweep signal and a second sweep signal, wherein the first sweep signal is a signal collected by an audio collection module at a different position in the smart device in a case where the sweep signal is played by each of a plurality of audio channels in the smart device, and the second sweep signal is a signal collected by a target audio collection module in the smart device in a case where the sweep signal is played by all the audio channels at the same time; determining a first impulse response signal based on the first sweep signal, and determining a second impulse response signal based on the second sweep signal; determining a target filter parameter corresponding to each of the audio channels based on the first impulse response signal and the second impulse response signal.

2. The method of claim 1, wherein, The method of determining the target filter parameter corresponding to each of the audio channels based on the first impulse response signal and the second impulse response signal comprises: inputting the first impulse response signal and a current filter parameter corresponding to each of the audio channels into a neural network model to obtain a first filter parameter corresponding to each of the audio channels, wherein a first optimization target of the neural network model is that the first filter parameter corresponding to any of the audio channels minimizes phase distortion of an audio signal played by the audio channel and maximizes phase consistency; inputting the first filter parameter corresponding to each of the audio channels and the second impulse response signal into the neural network model to obtain a second filter parameter corresponding to each of the audio channels, wherein a second optimization target of the neural network model is that the second filter parameter corresponding to each of the audio channels minimizes phase distortion of an audio signal played by each of the audio channels and collected by the target audio collection module; determining the target filter parameter corresponding to any of the audio channels according to the second filter parameter corresponding to the audio channel.

3. The method of claim 2, wherein, The method of determining the target filter parameter corresponding to any of the audio channels according to the second filter parameter corresponding to the audio channel comprises: in a case where the second filter parameter corresponding to any of the audio channels meets a preset condition, determining the second filter parameter corresponding to the audio channel as the target filter parameter corresponding to the audio channel, the preset condition comprising: minimizing phase distortion of an audio signal played by the audio channel and maximizing phase consistency, and minimizing phase distortion of an audio signal played by each of the audio channels and collected by the target audio collection module; in a case where the second filter parameter corresponding to any of the audio channels fails to meet the preset condition, determining the target filter parameter corresponding to the audio channel again by taking the second filter parameter corresponding to the audio channel as a current filter parameter of the audio channel.

4. The method of claim 3, wherein, The method of determining the target filter parameter corresponding to any of the audio channels again by taking the second filter parameter corresponding to the audio channel as a current filter parameter of the audio channel in a case where the second filter parameter corresponding to the audio channel fails to meet the preset condition comprises: In a case where the second filter parameter corresponding to any of the audio channels fails to meet the preset condition, the current filter parameter corresponding to each of the audio channels is updated to the second filter parameter corresponding to each of the audio channels; delay compensation is performed on each of the audio channels based on the first impulse response signal and the second impulse response signal; third scan signals and fourth scan signals are obtained based on the audio channels after the delay compensation, wherein the third scan signals are the first sweep signals after the delay compensation, and the fourth scan signals are the second sweep signals after the delay compensation; iterative optimization is performed based on the third scan signals and the fourth scan signals to redetermine the target filter parameter corresponding to any of the audio channels.

5. The method of claim 2, wherein, The current filter parameter comprises an initial filter parameter, and the method further comprises: determining the initial filter parameter of each of the audio channels based on the second impulse response signal and a preset limit condition, wherein the preset limit condition comprises that the order of the initial filter parameter is less than or equal to a preset order, the initial filter parameter can minimize the change of the amplitude-frequency response of the second impulse response signal, and the phase distortion is less than or equal to a preset threshold.

6. The method of claim 2, wherein, The constraint condition of the neural network model comprises a main frequency band constraint, wherein when the main frequency band corresponding to the main frequency band constraint is a first main frequency band, the target filter parameter corresponding to each of the audio channels is a third filter parameter, and when the main frequency band corresponding to the main frequency band constraint is a second main frequency band, the target filter parameter corresponding to each of the audio channels is a fourth filter parameter.

7. The method of claim 1, wherein, The method further comprises: controlling the plurality of audio channels to play the sweep signal one by one, so that each audio acquisition module acquires the first sweep signal; controlling the plurality of audio channels to play the sweep signal simultaneously, so that the target acquisition module acquires the second sweep signal.

8. The method according to any one of claims 1 to 7, characterized in that, The method further comprises: sending the target filter parameter corresponding to each of the audio channels to the smart device.

9. An apparatus for determining filter parameters, characterized by comprises: an acquisition module configured to acquire first sweep signals and second sweep signals, wherein the first sweep signals are acquired by audio acquisition modules at different positions in a smart device when a plurality of audio channels in the smart device play sweep signals one by one, and the second sweep signals are acquired by a target audio acquisition module in the smart device when all the audio channels play the sweep signals simultaneously; a first determination module configured to determine a first impulse response signal based on the first sweep signals, and determine a second impulse response signal based on the second sweep signals; a second determination module configured to determine filter parameters corresponding to each of the audio channels based on the first impulse response signal and the second impulse response signal.

10. A method of processing an audio signal, characterized by, comprises: acquiring audio information to be played; determining target audio information based on the audio information and target filter parameters corresponding to each of the audio channels in a smart device, wherein the target filter parameters corresponding to each of the audio channels are determined based on the method of any one of claims 1 to 6; playing the target audio information.

11. The method of claim 10, wherein, The target audio information is determined based on the audio information and filter parameters corresponding to each audio channel in the smart device, and the method comprises: inputting the audio information into a main frequency band determination model to determine a main frequency band in the audio information; determining application filter parameters corresponding to each audio channel based on the target filter parameters corresponding to each audio channel; processing the audio information based on the application filter parameters corresponding to each audio channel to obtain target audio information.

12. An electronic device, comprising: The method comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

13. A smart device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 10 to 11 when executing the computer program.

14. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the method according to any one of claims 1 to 8 or claims 10 to 11.

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