An audio adjustment method, system, electronic device, and computer storage medium

Abstract

This application provides an audio adjustment method, system, electronic device, and computer storage medium. It calls an artificial intelligence analysis model corresponding to at least one sound quality parameter based on at least one timbre mode to compare, analyze, and optimize the digital audio signal to obtain parameter adjustment information and an optimized digital audio signal based on the parameter adjustment information. The sound quality parameters of the pickup device are then adjusted according to the parameter adjustment information, thereby achieving intelligent adjustment of the pickup device's sound quality and reducing human intervention. Simultaneously, this application utilizes a speaker to play back the optimized digital audio signal based on the parameter adjustment information, allowing users to hear the optimized digital audio signal and improving the efficiency of audio adjustment.

Description

Technical Field

[0001] This application relates to the field of audio adjustment technology, and in particular to an audio adjustment method, system, electronic device, and computer storage medium. Background Technology

[0002] Traditional microphones mostly require users to adjust the sound quality themselves. Since most users do not have professional vocal knowledge, after manually adjusting the sound, they need to record to verify the effect. If they are not satisfied with the effect, they need to manually adjust it again and record again for a new round of effect verification. This results in users having to make multiple adjustments and test audio recordings to achieve the effect they expect, leading to a decline in the user experience.

[0003] In existing technologies, audio signal adjustment and processing are based on the input audio signal to produce an optimized audio signal. However, the input audio signal is generated by the recording device, and the gain, EQ, volume, and other attributes of the recording device directly produce the final (irreversible) audio signal. Therefore, the existing audio processing and adjustment methods cannot optimize the input of the recording device, and the adjustment effect is limited. Furthermore, the recording device is adjusted by the user, and the optimization effect is also limited. Therefore, there is an urgent need for an audio device signal attribute parameter adjustment method that can obtain professional adjustment parameters based on the original audio for users to easily adjust or select. Summary of the Invention

[0004] In view of this, embodiments of this application provide an audio adjustment method to at least partially solve the above-mentioned problems.

[0005] According to a first aspect of the embodiments of this application, an audio adjustment method is provided, comprising:

[0006] Digital audio signals are acquired using a microphone.

[0007] The digital audio signal is compared, analyzed, and optimized by an artificial intelligence analysis model that calls at least one timbre mode and at least one sound quality parameter to obtain parameter adjustment information and a digital audio signal optimized based on the parameter adjustment information.

[0008] The sound quality parameters of the pickup device are adjusted according to the parameter adjustment information.

[0009] The digital audio signal optimized according to the parameter adjustment information is played back using a loudspeaker.

[0010] Furthermore, the step of analyzing and optimizing the digital audio signal by calling an artificial intelligence analysis model of at least one sound quality parameter according to at least one timbre mode to obtain parameter adjustment information and optimizing the digital audio signal according to the parameter adjustment information includes:

[0011] The steps are as follows: First, the digital audio signal is analyzed and optimized using an EQ analysis and optimization model based on at least one timbre mode to obtain EQ parameter adjustment information; second, the digital audio signal is optimized based on the EQ parameter adjustment information.

[0012] The steps and / or are as follows: Analyzing and optimizing the digital audio signal by calling a noise reduction analysis and optimization model based on at least one timbre mode to obtain noise reduction parameter adjustment information; and optimizing the digital audio signal based on the noise reduction parameter adjustment information.

[0013] The steps are as follows: analyzing and optimizing the digital audio signal by calling a loudness analysis and optimization model according to at least one timbre mode to obtain loudness parameter adjustment information, and optimizing the digital audio signal according to the loudness parameter adjustment information.

[0014] Furthermore, the step of calling an EQ analysis and optimization model based on at least one timbre mode to analyze and optimize the digital audio signal to obtain EQ parameter adjustment information and optimizing the digital audio signal based on the EQ parameter adjustment information includes:

[0015] Retrieve multiple timbre benchmark models corresponding to different timbre modes from a large database;

[0016] The digital audio signals are then compared and analyzed with the multiple timbre reference models to obtain EQ comparison results.

[0017] Based on the EQ comparison results, the level difference corresponding to each frequency band of the digital audio signal is calculated as EQ parameter adjustment information;

[0018] The digital audio signal is optimized by EQ adjustment in different timbre modes according to the EQ parameter adjustment information to obtain the corresponding optimized digital audio signal according to the EQ parameter adjustment information.

[0019] Furthermore, the step of calling a noise reduction analysis and optimization model according to at least one timbre mode to analyze and optimize the original audio signal to obtain noise reduction parameter adjustment information and the optimized digital audio signal according to the noise reduction parameter adjustment information includes:

[0020] Retrieve multiple noise reduction benchmark models corresponding to the timbre mode from a large database;

[0021] The digital audio signal is then compared and analyzed with the multiple noise reduction benchmark models to obtain noise comparison results.

[0022] Based on the noise comparison results, the required noise reduction level for the timbre mode is calculated as noise reduction parameter adjustment information.

[0023] The digital audio signal optimized according to the EQ parameter adjustment information is subjected to noise reduction optimization in different timbre modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

[0024] Furthermore, the step of analyzing and optimizing the digital audio signal by calling a loudness analysis and optimization model according to at least one timbre mode to obtain loudness parameter adjustment information and optimizing the digital audio signal according to the loudness parameter adjustment information includes:

[0025] Retrieve multiple loudness parameter models corresponding to the noise reduction level and the timbre mode from a large database;

[0026] The digital audio signal optimized according to the noise reduction parameter adjustment information is compared and analyzed with the multiple loudness parameter models to obtain loudness comparison results respectively.

[0027] Based on the loudness comparison results, calculate the loudness parameter adjustment information required for each audio segment;

[0028] The loudness of the audio segments of the digital audio signal optimized according to the noise reduction parameter adjustment information is adjusted accordingly based on the loudness parameter adjustment information to obtain the digital audio signal optimized according to the loudness parameter adjustment information.

[0029] Furthermore, the method also includes the following step: receiving mode instruction information input by the user.

[0030] In one embodiment, the step of adjusting the sound quality parameters of the pickup device according to the parameter adjustment information includes: adjusting the sound quality parameters of the pickup device using the parameter adjustment information corresponding to the mode instruction information; and

[0031] The step of using a loudspeaker to play back the digital audio signal optimized according to the parameter adjustment information includes: using a loudspeaker to play back the digital audio signal optimized according to the parameter adjustment information corresponding to the mode instruction information.

[0032] In one embodiment, the artificial intelligence analysis model is retrieved from a big data model based on the pattern instruction information.

[0033] In one embodiment, the parameter adjustment information and the digital audio signal optimized by the parameter adjustment information are selected from a plurality of parameter adjustment information and the digital audio signal optimized by the parameter adjustment information according to the mode instruction information.

[0034] According to a second aspect of the embodiments of this application, an audio adjustment system is provided, comprising: a server, a user terminal, a microphone, and a speaker. The microphone includes a signal transceiver module, a microphone module, and an attribute adjustment module. The user terminal includes a first original audio library, a first attribute parameter temporary storage library, and a first adjustment audio temporary storage library. The server includes a second attribute parameter temporary storage library, a second adjustment audio temporary storage library, an original audio library, and a comparison and analysis module. The microphone module is used to acquire digital audio signals. The signal transceiver module is used to send the digital audio signals to the first original audio library. The first original audio library is used to forward the digital audio signals to the second original audio library of the server. The comparison and analysis module is used to call an artificial intelligence analysis model of at least one sound quality parameter based on at least one timbre mode. The digital audio signal is compared, analyzed, and optimized to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information. The second adjusted audio temporary storage library is used to temporarily store the digital audio signal optimized according to the parameter adjustment information and forward it to the first adjusted audio temporary storage library. The second attribute parameter temporary storage library is used to temporarily store the parameter adjustment information and forward it to the first attribute parameter temporary storage library. The first adjusted audio temporary storage library is used to send the digital audio signal optimized according to the parameter adjustment information to the speaker device. The first attribute parameter temporary storage library is used to send the parameter adjustment information to the pickup device. The attribute adjustment module is used to adjust the sound quality parameters of the pickup module according to the parameter adjustment information. The speaker device is used to play back the digital audio signal optimized according to the parameter adjustment information.

[0035] Furthermore, the user terminal also includes a mode instruction input module for receiving mode instruction information input by the user.

[0036] In one embodiment, the server further includes an output adjustment module, configured to select at least one parameter adjustment information for adjusting the pickup module from a plurality of parameter adjustment information according to the mode instruction information; and configured to select at least one digital audio signal for playback on the speaker device from a plurality of digital audio signals optimized according to the parameter adjustment information according to the mode instruction information.

[0037] In one embodiment, the server further includes an adjustment input module for retrieving the artificial intelligence analysis model from the big data model based on the pattern instruction information.

[0038] In one embodiment, the first attribute parameter temporary storage library forwards the corresponding parameter adjustment information according to the mode instruction information, and the first adjusted audio temporary storage library forwards the corresponding digital audio signal optimized according to the parameter adjustment information according to the mode instruction information.

[0039] In one embodiment, the attribute adjustment module includes an attribute parameter allocation module, an EQ adjustment module, a gain amplifier, and a noise reduction module. The attribute parameter allocation module is used to allocate parameter adjustment information received by the signal transceiver module to the EQ adjustment module, the gain amplifier, and the noise reduction module. The EQ adjustment module is used to adjust the EQ parameters of the digital audio signal according to the parameter adjustment information. The gain amplifier is used to adjust the loudness parameters of the digital audio signal according to the parameter adjustment information. The noise reduction module is used to adjust the noise reduction level of the digital audio signal according to the parameter adjustment information.

[0040] In one embodiment, the comparison analysis module includes a timbre comparison optimization module, a clarity comparison optimization module, and / or a loudness comparison optimization module.

[0041] Furthermore, the timbre comparison and optimization module includes:

[0042] The timbre comparison module is used to retrieve multiple timbre reference models corresponding to different timbre modes from a large database, and compare and analyze the digital audio signal with the multiple timbre reference models to obtain EQ comparison results.

[0043] The timbre adjustment parameter calculation module is used to calculate the level difference corresponding to each frequency band of the digital audio signal based on the EQ comparison result, as EQ parameter adjustment information; and

[0044] The timbre optimization module is used to perform EQ adjustment optimization on the digital audio signal according to the EQ parameter adjustment information to obtain the corresponding digital audio signal optimized according to the EQ parameter adjustment information.

[0045] Furthermore, the sharpness comparison optimization module includes:

[0046] The clarity comparison module is used to retrieve multiple noise reduction benchmark models corresponding to the timbre mode from a large database, and compare and analyze the digital audio signal with the multiple noise reduction benchmark models to obtain noise comparison results respectively.

[0047] The clarity adjustment parameter calculation module is used to calculate the noise reduction level required for the timbre mode based on the noise comparison results, as noise reduction parameter adjustment information.

[0048] The timbre optimization module is used to perform noise reduction optimization on the digital audio signal optimized according to the EQ parameter adjustment information using different timbre modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

[0049] Furthermore, the loudness comparison and optimization module includes:

[0050] The loudness comparison module is used to retrieve multiple loudness parameter models corresponding to the noise reduction level and the timbre mode from a large database, and to compare and analyze the digital audio signal optimized according to the noise reduction parameter adjustment information with the multiple loudness parameter models to obtain loudness comparison results respectively.

[0051] The loudness adjustment parameter calculation module is used to calculate the loudness parameter adjustment information required for each audio segment based on the loudness comparison results.

[0052] A loudness optimization module is used to adjust the loudness of an audio segment of a digital audio signal optimized according to the noise reduction parameter adjustment information according to the loudness parameter adjustment information to obtain a digital audio signal optimized according to the loudness parameter adjustment information.

[0053] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising: a sound pickup module, an attribute adjustment module, a raw audio library, an adjustable audio temporary storage library, and a comparison and analysis module, wherein the sound pickup module is used to acquire digital audio signals, the raw audio library is used to forward the digital audio signals to the comparison and analysis module, the comparison and analysis module is used to call an artificial intelligence analysis model of at least one sound quality parameter according to at least one timbre mode to perform comparison analysis and optimization on the digital audio signals to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information, the adjustable audio library is used to send the digital audio signal optimized according to the parameter adjustment information to a speaker device, the attribute adjustment module is used to adjust the sound quality parameters of the sound pickup module according to the parameter adjustment information, and the speaker device is used to play back the digital audio signal optimized according to the parameter adjustment information.

[0054] In one embodiment, the attribute adjustment module includes an attribute parameter allocation module, an EQ adjustment module, a gain amplifier, and a noise reduction module. The attribute parameter allocation module is used to allocate parameter adjustment information received by the signal transceiver module to the EQ adjustment module, the gain amplifier, and the noise reduction module. The EQ adjustment module is used to adjust the EQ parameters of the digital audio signal according to the parameter adjustment information. The gain amplifier is used to adjust the loudness parameters of the digital audio signal according to the parameter adjustment information. The noise reduction module is used to adjust the noise reduction level of the digital audio signal according to the parameter adjustment information.

[0055] In one embodiment, the comparison analysis module includes a timbre comparison optimization module, a clarity comparison optimization module, and / or a loudness comparison optimization module.

[0056] Furthermore, the timbre comparison and optimization module includes:

[0057] The timbre comparison module is used to retrieve multiple timbre reference models corresponding to different timbre modes from a large database, and compare and analyze the digital audio signal with the multiple timbre reference models to obtain EQ comparison results.

[0058] The timbre adjustment parameter calculation module is used to calculate the level difference corresponding to each frequency band of the digital audio signal based on the EQ comparison result, as EQ parameter adjustment information; and

[0059] The timbre optimization module is used to perform EQ adjustment optimization on the digital audio signal according to the EQ parameter adjustment information to obtain the corresponding digital audio signal optimized according to the EQ parameter adjustment information.

[0060] Furthermore, the sharpness comparison optimization module includes:

[0061] The clarity comparison module is used to retrieve multiple noise reduction benchmark models corresponding to the timbre mode from a large database, and compare and analyze the digital audio signal with the multiple noise reduction benchmark models to obtain noise comparison results respectively.

[0062] The clarity adjustment parameter calculation module is used to calculate the noise reduction level required for the timbre mode based on the noise comparison results, as noise reduction parameter adjustment information.

[0063] The timbre optimization module is used to perform noise reduction optimization on the digital audio signal optimized according to the EQ parameter adjustment information using different timbre modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

[0064] Furthermore, the loudness comparison and optimization module includes:

[0065] The loudness comparison module is used to retrieve multiple loudness parameter models corresponding to the noise reduction level and the timbre mode from a large database, and to compare and analyze the digital audio signal optimized according to the noise reduction parameter adjustment information with the multiple loudness parameter models to obtain loudness comparison results respectively.

[0066] The loudness adjustment parameter calculation module is used to calculate the loudness parameter adjustment information required for each audio segment based on the loudness comparison results.

[0067] A loudness optimization module is used to adjust the loudness of an audio segment of a digital audio signal optimized according to the noise reduction parameter adjustment information according to the loudness parameter adjustment information to obtain a digital audio signal optimized according to the loudness parameter adjustment information.

[0068] According to a fourth aspect of the present application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the audio adjustment method as described in the first aspect.

[0069] According to the audio adjustment method provided in this application, an artificial intelligence analysis model that calls at least one sound quality parameter based on at least one timbre mode compares, analyzes, and optimizes the digital audio signal to obtain parameter adjustment information and a digital audio signal optimized based on the parameter adjustment information. The sound quality parameters of the pickup device are then adjusted according to the parameter adjustment information, thereby achieving intelligent adjustment of the pickup device's sound quality, reducing human intervention, and improving the efficiency of user adjustment. Simultaneously, this application utilizes a speaker to play back the digital audio signal optimized based on the parameter adjustment information, allowing the user to hear the optimized digital audio signal. The user can then select the parameter adjustment information corresponding to the timbre mode according to their preferences to adjust the sound quality of the pickup device. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0071] Figure 1 This is a schematic diagram of an exemplary system for an audio adjustment method applicable to embodiments of this application;

[0072] Figure 2 This is a flowchart of the steps of the audio adjustment method applicable to the embodiments of this application;

[0073] Figure 3 It is based on Figure 2 Detailed flowchart of step S203;

[0074] Figure 4 It is based on Figure 3 Detailed flowchart of step S301;

[0075] Figure 5 It is based on Figure 3 Detailed flowchart of step S302;

[0076] Figure 6 It is based on Figure 3 Detailed flowchart of step S303;

[0077] Figure 7 This is a structural block diagram of an audio adjustment system according to an embodiment of this application;

[0078] Figure 8 This is a structural block diagram of another audio adjustment system according to an embodiment of this application;

[0079] Figure 9 This is a structural block diagram of another audio adjustment system according to an embodiment of this application;

[0080] Figure 10 This is a structural block diagram of an attribute adjustment module according to an embodiment of this application;

[0081] Figure 11 This is a structural block diagram of a comparison analysis module according to an embodiment of this application;

[0082] Figure 12 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0083] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of this application.

[0084] Furthermore, some of the aforementioned terms, besides indicating orientation or positional relationships, may also have other meanings. For example, the term "above" may, in certain circumstances, indicate a dependency or connection. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances. In addition, the terms "installed," "set," "equipped with," "connected," and "linked" should be interpreted broadly. For example, they may refer to a fixed connection, a detachable connection, or an integral structure; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or constituent parts. Those skilled in the art can understand the specific meaning of the aforementioned terms in this invention based on the specific circumstances.

[0085] Furthermore, the terms "first," "second," etc., are primarily used to distinguish different devices, elements, or components (which may be the same or different in specific type and construction), and are not intended to indicate or imply the relative importance or quantity of the indicated devices, elements, or components. Unless otherwise stated, "a plurality of" means two or more.

[0086] The specific implementation of the embodiments of this application will be further described below with reference to the accompanying drawings.

[0087] Figure 1 An exemplary system for an audio adjustment method applicable to embodiments of this application is shown. For example... Figure 1 As shown, the system 100 may include a server 102, one or more pickup devices 103, a speaker 105, a communication network 104, and / or one or more user terminals 106. Figure 1 The example in the text shows multiple user terminals and multiple microphones.

[0088] Server 102 can be any suitable server for storing information, data, programs, and / or any other suitable type of content. In some embodiments, server 102 can perform any suitable function. For example, in some embodiments, server 102 can be used to store, analyze, and optimize digital audio signals. As an optional example, in some embodiments, server 102 can receive digital audio signals uploaded by user terminal 106 and collected by microphone 103 in response to a request from user terminal 106. As another example, in some embodiments, server 102 can be used to push optimized digital audio signals and parameter adjustment information for adjusting microphone 103 to user terminal 106.

[0089] In some embodiments, communication network 104 may be any suitable combination of one or more wired and / or wireless networks. For example, communication network 104 may include any one or more of the following: the Internet, intranet, wide area network (WAN), local area network (LAN), wireless network, digital subscriber line (DSL) network, frame relay network, asynchronous transfer mode (ATM) network, virtual private network (VPN), and / or any other suitable communication network. User terminal 106 may be connected to communication network 104 via one or more communication links (e.g., communication link 112), and communication network 104 may be linked to server 102 via one or more communication links (e.g., communication link 114). Communication links may be any communication link suitable for transmitting data between user terminal 106 and server 102, such as network links, dial-up links, wireless links, hardwired links, any other suitable communication links, or any suitable combination of such links.

[0090] In some embodiments, user terminal 106 may include any suitable type of device. For example, in some embodiments, user terminal 106 may include mobile devices, tablet computers, laptop computers, desktop computers, wearable computers, game consoles, media players, vehicle entertainment systems, and / or any other suitable type of user terminal. Software may be installed on user terminal 106, for example, to upload digital audio signals collected by pickup device 103 to server 102, and to send parameter adjustment information and optimized digital audio signals generated by server 102 to pickup device 103 and speaker device 105.

[0091] In some embodiments, the microphone 103 may include any suitable type of recording device for capturing digital audio signals and transmitting them to the user terminal 106. For example, in some embodiments, the microphone 103 may include a wireless microphone, a wired microphone, and / or any other suitable type of recording device. The microphone 103 may communicate with the user terminal 106 via a first short-range communication connection 115. The first short-range communication connection 115 may be a wired connection or a wireless connection.

[0092] In some embodiments, the speaker device 105 may include any suitable type of audio equipment for receiving and playing back digital audio signals transmitted by the user terminal 106. For example, in some embodiments, the speaker device 105 may include a wireless speaker, a wired speaker, and / or any other suitable type of audio equipment. The speaker device 105 may communicate with the user terminal 106 via a second short-range communication connection 116. The second short-range communication connection 116 may be a wired connection or a wireless connection.

[0093] Server 102 is illustrated as a single device, but in some embodiments, any suitable number of devices can be used to perform the functions performed by server 102. For example, in some embodiments, multiple devices can be used to implement the functions performed by server 102. Alternatively, cloud services can be used to implement the functions of server 102.

[0094] The audio adjustment method of this embodiment can be executed by any suitable electronic device with audio signal processing capabilities, including but not limited to: servers, mobile terminals (such as mobile phones, PADs, etc.), PCs, and wireless microphones.

[0095] Reference Figure 2 This is a flowchart of an audio adjustment method according to an embodiment of this application. The following is a combination of... Figure 2 The specific flow of an audio adjustment method according to an embodiment of this application is described below. The audio adjustment method includes the following steps:

[0096] S201: Use a pickup device to collect digital audio signals.

[0097] As an example, the digital audio signal can be a digital audio file in its appropriate format. The pickup device can be any type of wired or wireless microphone.

[0098] S203: Based on at least one timbre mode, call an artificial intelligence analysis model of at least one sound quality parameter to compare, analyze and optimize the digital audio signal to obtain parameter adjustment information and a digital audio signal optimized based on the parameter adjustment information.

[0099] In a specific example, the timbre mode could be, for example, "bright," "round," or "full." Sound quality parameters can include one or more of the following: EQ, loudness, and noise reduction parameters.

[0100] In this embodiment, an artificial intelligence analysis model is used to analyze and process digital audio signals to obtain an analysis and comparison result. This result can characterize the predicted category information of the digital audio signal in at least two different dimensions. In this embodiment, the types of dimensions predicted during the analysis and processing of digital audio signals are not limited. For example, in some embodiments, the dimensions predicted during the analysis and processing of digital audio signals may include frequency dimension (pitch), tone dimension (loudness), timbre dimension (waveform), vocal dimension (noise reduction), orchestration dimension, language dimension, genre dimension, tempo dimension, and emotion dimension, etc. The audio data can be categorized into several dimensions. Frequency dimension distinguishes the high and low frequencies of audio data, with specific categories including high, mid, and low frequencies. Pitch dimension distinguishes the high and low pitch of audio data, with specific categories including high, mid, and low frequencies. Timbre dimension distinguishes the timbre of audio data, with specific categories including bright, mellow, soaring, and sharp. Vocal dimension distinguishes the vocal elements in the audio data, with specific categories including male, female, and children's voices. Orchestration dimension distinguishes the instruments used in the audio data. The audio data can be categorized in several ways. The categories include: piano, guitar, violin, guzheng, etc.; language: the language type corresponding to the audio data, such as Chinese, English, French, etc.; genre: the music genre to which the audio data belongs, such as pop, classical, rock, folk, etc.; tempo: the melody speed of the audio data, such as fast, normal, slow, etc.; and emotion: the emotional tendency expressed by the audio data, such as joy, sadness, passion, tranquility, etc.

[0101] Of course, it is understood that the above embodiments are only used to exemplarily introduce the analysis dimensions of the artificial intelligence analysis model in the embodiments of this application, and do not imply any limitation on its actual implementation. Those skilled in the art can flexibly select the dimensions and categories included in the dimensions according to their needs to analyze and process digital audio signals and obtain the corresponding analysis and comparison results. The artificial intelligence analysis model may include multiple different types of analysis and optimization models, which analyze and optimize digital audio signals for the aforementioned different dimensions. The analysis and optimization models may include various benchmark models for audio adjustment, such as timbre benchmark models, noise reduction benchmark models, and loudness benchmark models, which are used to compare and optimize the corresponding parameters of the digital audio signals.

[0102] By comparing and analyzing the digital audio signal using an artificial intelligence analysis model, parameter adjustment information can be obtained. For example, the level difference between the digital audio signal and the corresponding frequency band of the timbre reference model corresponding to the EQ parameters can be used as timbre parameter adjustment information. Alternatively, the noise reduction reference model corresponding to each timbre mode and the digital audio signal can be compared and analyzed to obtain the required noise reduction level for each timbre mode as noise reduction parameter adjustment information. Or, the loudness parameter model corresponding to each timbre mode and the digital audio signal can be compared and analyzed to obtain the loudness difference corresponding to each audio segment as loudness parameter adjustment information. Furthermore, the artificial intelligence analysis model also uses the parameter adjustment information to optimize the digital audio signal, generating a digital audio signal optimized according to the parameter adjustment information. For example, in one embodiment, the level difference between one of multiple timbre reference models and the corresponding frequency band of the digital audio signal (i.e., EQ parameter adjustment information) can be used to optimize the digital audio signal. For example, the level difference corresponding to each frequency band can be directly arithmetically calculated with the corresponding frequency band of the digital audio signal to make the level amplitude of the corresponding frequency band of the digital audio signal approach the level amplitude of the corresponding frequency band learned by the timbre reference model after training.

[0103] S205: Adjust the sound quality parameters of the pickup device according to the parameter adjustment information.

[0104] In this embodiment, the parameter adjustment information may include EQ parameter adjustment information, loudness parameter adjustment information, and / or noise reduction parameter adjustment information, which are used to adjust the EQ parameters, loudness parameters, and / or noise reduction parameters of the pickup module of the pickup device, thereby adjusting the sound quality of the pickup device. Then, during subsequent recording, the pickup module will use the new sound quality parameters to complete the acquisition of digital audio signals, thereby optimizing the original digital audio signals acquired by the pickup device.

[0105] S207: Play back the digital audio signal optimized according to the parameter adjustment information using a loudspeaker.

[0106] In some examples, by playing back the digital audio signal optimized according to the parameter adjustment information using a speaker, users can verify in real time whether the optimized digital audio signal meets the requirements, thereby improving the efficiency of parameter adjustment. Users can also compare the effects of the played-back digital audio signals optimized according to different timbre modes, helping them select their preferred timbre mode. They can then select the parameter adjustment information corresponding to their preferred timbre mode to adjust the pickup device, allowing users to adjust the sound quality of the pickup device and record digital audio signals with their preferred timbre even without professional tuning skills.

[0107] Figure 3 It is based on Figure 2 A detailed flowchart of step S203 is provided. In this embodiment, step S203 specifically includes the following steps:

[0108] S301: The steps of calling an EQ analysis and optimization model according to at least one timbre mode to analyze and optimize the digital audio signal to obtain EQ parameter adjustment information and optimizing the digital audio signal according to the EQ parameter adjustment information;

[0109] Specifically, different timbre modes correspond to different EQ analysis and optimization models. Multiple EQ analysis and optimization models corresponding to multiple timbre modes can be called simultaneously, and these EQ analysis and optimization models can be used to analyze and optimize the digital audio signal to obtain multiple sets of EQ parameter adjustment information and digital audio signals optimized according to the EQ parameter adjustment information.

[0110] S302: The steps of calling a noise reduction analysis and optimization model according to at least one timbre mode to analyze and optimize the digital audio signal to obtain noise reduction parameter adjustment information and optimizing the digital audio signal according to the noise reduction parameter adjustment information; and / or

[0111] Specifically, different timbre modes correspond to different noise reduction analysis and optimization models. Multiple noise reduction analysis and optimization models corresponding to multiple timbre modes can be called simultaneously, and these noise reduction analysis and optimization models can be used to analyze and optimize the digital audio signal to obtain multiple sets of noise reduction parameter adjustment information and digital audio signals optimized according to the noise reduction parameter adjustment information.

[0112] S303: The step of calling a loudness analysis and optimization model according to at least one timbre mode to analyze and optimize the digital audio signal to obtain loudness parameter adjustment information and the step of optimizing the digital audio signal according to the loudness parameter adjustment information.

[0113] Specifically, different timbre modes correspond to different analysis and optimization models in the artificial intelligence analysis model. Multiple analysis and optimization models corresponding to different timbre modes can be invoked simultaneously, and these models can be used to analyze and optimize the digital audio signal to obtain multiple sets of parameter adjustment information and a digital audio signal optimized based on that information. In some embodiments, all analysis and optimization models corresponding to each timbre mode can be selected to compare and optimize the digital audio signal sequentially. For example, EQ analysis and optimization models, noise reduction analysis and optimization models, and loudness analysis and optimization models corresponding to each timbre mode can be used to compare and optimize the digital audio signal sequentially, obtaining an optimized digital audio signal for each timbre mode. This allows users to select their preferred timbre mode from the optimized digital audio signal playback.

[0114] Figure 4 It is based on Figure 3 A detailed flowchart of step S301 is provided. In this embodiment, step S301 specifically includes the following steps:

[0115] S401: Retrieves multiple timbre reference models corresponding to different timbre modes from a large database;

[0116] S402: The digital audio signal is compared and analyzed with the multiple timbre reference models to obtain EQ comparison results;

[0117] S403: Calculate the level difference corresponding to each frequency band of the digital audio signal based on the EQ comparison results, and use it as EQ parameter adjustment information;

[0118] S404: Optimize the digital audio signal with different timbre modes according to the EQ parameter adjustment information to obtain a corresponding optimized digital audio signal according to the EQ parameter adjustment information.

[0119] It is easy to understand that artificial intelligence analysis models include multiple different types of analysis and optimization models. These models can include various benchmark models for audio adjustment, such as timbre benchmark models, noise reduction benchmark models, and loudness benchmark models, which are used to compare and optimize the corresponding parameters of digital audio signals.

[0120] The digital audio signal is compared with multiple timbre reference models corresponding to the timbre mode. The level difference between the digital audio signal and each frequency band of each timbre reference model can be calculated based on the comparison results. The level difference between the digital audio signal and each frequency band of each timbre reference model is used as EQ parameter adjustment information to optimize the digital audio signal. Then, the pickup device is adjusted using the EQ parameter adjustment information.

[0121] Bright timbre refers to a high level of audio frequency in the high-frequency range, mellow timbre to a high level of audio frequency in the mid-frequency range, and rich timbre to a high level of audio frequency in the low-frequency range. Different timbre patterns correspond to different timbre benchmark models. These models can be generated by processing digital audio signals using deep learning networks (such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Transformers) to analyze the range of audio signal frequencies. The model can be trained by inputting a large amount of labeled audio data. For example, audio data labeled "bright timbre" clearly shows a higher level of audio frequency in the high-frequency range. The trained timbre benchmark model then uses the processing logic of the deep learning network to capture the dependencies between the sequential data and the time series data in the audio signal, thus completing the model construction. Once the model is built, when the digital audio signal to be compared is input into the benchmark model, the timbre benchmark model optimizes the digital audio signal based on the analysis of the frequency band level values ​​of the digital audio signal according to the user's optimization requirements. Specifically, if the user requires a bright timbre, the timbre benchmark model corresponding to the bright timbre can calculate the difference between the peak level of the high-frequency band of the digital audio signal and the benchmark level of the corresponding bright timbre label. Based on the difference, an optimized digital audio signal is generated, and the difference or the label corresponding to the difference is output as parameter adjustment information for adjusting the pickup device. The construction and working principle of the noise reduction benchmark model and the loudness benchmark model are similar. When the analysis and optimization model optimizes a segment of digital audio signal, the high-frequency, mid-frequency, and low-frequency parts can be marked accordingly. The benchmark level provided by the timbre benchmark model can be in the form of a line segment, used to compare different frequency bands separately; the comparison result can be the average difference between the level amplitude of the corresponding frequency band of the digital audio signal and the benchmark amplitude of the corresponding frequency band of the timbre benchmark model. When optimizing the digital audio signal, the analysis and optimization model can uniformly adjust the level amplitude of the corresponding frequency band. That is, the average difference between the level amplitude of the corresponding frequency band of the digital audio signal and the reference amplitude of the corresponding frequency band of the timbre reference model is used to adjust the amplitude of each peak in the frequency band.

[0122] Figure 5 It is based on Figure 3 A detailed flowchart of step S302 is provided. In this embodiment, step S302 specifically includes the following steps:

[0123] S501: Retrieve multiple noise reduction benchmark models corresponding to the timbre mode from the large database;

[0124] S502: The digital audio signal is compared and analyzed with the multiple noise reduction benchmark models to obtain noise comparison results respectively;

[0125] S503: Calculate the noise reduction level required for the timbre mode based on the noise comparison results, and use it as noise reduction parameter adjustment information;

[0126] S504: Perform noise reduction optimization on the digital audio signal optimized according to the EQ parameter adjustment information using different timbre modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

[0127] It is easy to understand that by comparing the digital audio signal with multiple noise reduction benchmark models corresponding to the timbre mode, the noise reduction level required for each timbre mode can be calculated based on the comparison results. The noise reduction level mode is used as the noise reduction parameter adjustment information, and then the pickup device is adjusted using the EQ parameter adjustment information, thereby reducing noise and improving the sound quality of the digital audio signal acquired by the pickup device.

[0128] Figure 6 It is based on Figure 3 A detailed flowchart of step S303 is provided. In this embodiment, step S303 specifically includes the following steps:

[0129] S601: Retrieve multiple loudness parameter models corresponding to the noise reduction level and the timbre mode from the large database;

[0130] S602: The digital audio signal optimized according to the noise reduction parameter adjustment information is compared and analyzed with the multiple loudness parameter models to obtain loudness comparison results respectively;

[0131] S603: Calculate the loudness parameter adjustment information corresponding to each audio segment based on the loudness comparison results;

[0132] S604: Adjust the loudness of the audio segment of the digital audio signal optimized according to the loudness parameter adjustment information according to the loudness parameter adjustment information to obtain the digital audio signal optimized according to the loudness parameter adjustment information.

[0133] It is easy to understand that by comparing the digital audio signal with multiple loudness parameter models corresponding to the timbre mode, the noise reduction level mode corresponding to each audio segment can be calculated according to the comparison results. The noise reduction level mode is used as the noise reduction parameter adjustment information, and then the loudness parameter adjustment information is used to adjust the pickup device, thereby improving the sound quality of the digital audio signal collected by the pickup device.

[0134] In some embodiments, the method further includes the step of receiving mode instruction information input by the user. Specifically, the mode instruction information may include information about the tone mode selected by the user, such as the ID, name, or identifier of the tone mode selected by the user, so as to determine the corresponding parameter adjustment information according to the tone mode selected by the user in order to complete the tone adjustment of the pickup device.

[0135] In one embodiment, the step of adjusting the sound quality parameters of the pickup device according to the parameter adjustment information includes: adjusting the sound quality parameters of the pickup device using the parameter adjustment information corresponding to the mode instruction information; and the step of playing back the digital audio signal optimized according to the parameter adjustment information using a speaker includes: playing back the digital audio signal optimized according to the parameter adjustment information corresponding to the mode instruction information using a speaker. It is readily understood that the user's mode instruction information includes information about the user-selected timbre mode. Adjusting the sound quality parameters of the pickup device according to the parameter adjustment information corresponding to the user-selected timbre mode can improve the efficiency of adjusting the sound quality parameters of the pickup device.

[0136] In one embodiment, the artificial intelligence analysis model is retrieved from a big data model based on the pattern instruction information. It is readily understood that selecting the artificial intelligence analysis model corresponding to the user's preferred timbre mode before comparing and analyzing digital audio signals can reduce unnecessary analysis and comparison, improve the efficiency of digital audio signal analysis and comparison, reduce energy consumption costs, and accelerate processing speed.

[0137] Reference Figure 7This diagram illustrates a structural block diagram of an audio adjustment system according to an embodiment of this application. In this example, the audio adjustment system includes: a server, a user terminal, a pickup device, and a speaker. The pickup device includes a signal transceiver module, a pickup module, and an attribute adjustment module. The user terminal includes a first original audio library, a first attribute parameter temporary storage library, and a first adjustment audio temporary storage library. The server includes a second attribute parameter temporary storage library, a second adjustment audio temporary storage library, a second original audio library, and a comparison and analysis module. The pickup module is used to acquire digital audio signals. The signal transceiver module is used to send the digital audio signals to the first original audio library. The first original audio library is used to forward the digital audio signals to the second original audio library of the server. The comparison and analysis module is used to call an artificial intelligence analysis model of at least one sound quality parameter based on at least one timbre mode to analyze the digital audio signals. The system performs comparative analysis and optimization to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information. The second adjustment audio temporary storage library is used to temporarily store the digital audio signal optimized according to the parameter adjustment information and forward it to the first adjustment audio temporary storage library. The second attribute parameter temporary storage library is used to temporarily store the parameter adjustment information and forward it to the first attribute parameter temporary storage library. The first adjustment audio temporary storage library is used to send the digital audio signal optimized according to the parameter adjustment information to the speaker device. The first attribute parameter temporary storage library is used to send the parameter adjustment information to the pickup device. The attribute adjustment module is used to adjust the sound quality parameters of the pickup module according to the parameter adjustment information. The speaker device is used to play back the digital audio signal optimized according to the parameter adjustment information.

[0138] The audio adjustment device of this embodiment is used to implement the corresponding audio adjustment methods in the foregoing method embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here. Furthermore, the functional implementation of each module in the audio adjustment device of this embodiment can be referred to the description of the corresponding part in the foregoing method embodiments, which will also not be repeated here.

[0139] Furthermore, the user terminal also includes a mode instruction input module for receiving mode instruction information input by the user.

[0140] Figure 8 This is a structural block diagram of another audio adjustment system according to an embodiment of this application. Figure 8As shown, another audio adjustment system in this application embodiment is basically the same as the aforementioned audio adjustment system. The differences are only described in detail below. In another audio adjustment system in this application embodiment, the server further includes an adjustment output module, which is used to select at least one parameter adjustment information for adjusting the pickup module from a plurality of parameter adjustment information according to the mode instruction information; and is used to select at least one digital audio signal for playback on the speaker device from a plurality of digital audio signals optimized according to the parameter adjustment information according to the mode instruction information.

[0141] Figure 9 This is a structural block diagram of another audio adjustment system according to an embodiment of this application. Figure 9 As shown, another audio adjustment system according to an embodiment of this application is basically the same as the aforementioned audio adjustment system; only the differences are described in detail below. In another audio adjustment system according to an embodiment of this application, the server further includes an adjustment input module, used to retrieve the artificial intelligence analysis model from the big data model according to the mode instruction information.

[0142] In this embodiment, the first attribute parameter temporary storage library forwards the corresponding parameter adjustment information according to the mode instruction information, and the first adjusted audio temporary storage library forwards the corresponding digital audio signal optimized according to the parameter adjustment information according to the mode instruction information.

[0143] like Figure 10 As shown, in one embodiment, the attribute adjustment module includes an attribute parameter allocation module, an EQ adjustment module, a gain amplifier, and a noise reduction module. The attribute parameter allocation module is used to allocate parameter adjustment information received by the signal transceiver module to the EQ adjustment module, the gain amplifier, and the noise reduction module. The EQ adjustment module is used to adjust the EQ parameters of the digital audio signal according to the parameter adjustment information. The gain amplifier is used to adjust the loudness parameters of the digital audio signal according to the parameter adjustment information. The noise reduction module is used to adjust the noise reduction level of the digital audio signal according to the parameter adjustment information.

[0144] like Figure 11 As shown, in one embodiment, the comparison analysis module includes a timbre comparison optimization module, a clarity comparison optimization module, and / or a loudness comparison optimization module.

[0145] Specifically, the timbre comparison and optimization module includes:

[0146] The timbre comparison module is used to retrieve multiple timbre reference models corresponding to different timbre modes from a large database, and compare and analyze the digital audio signal with the multiple timbre reference models to obtain EQ comparison results.

[0147] The timbre adjustment parameter calculation module is used to calculate the level difference corresponding to each frequency band of the digital audio signal based on the EQ comparison result, as EQ parameter adjustment information; and

[0148] The timbre optimization module is used to perform EQ adjustment optimization on the digital audio signal according to the EQ parameter adjustment information to obtain the corresponding digital audio signal optimized according to the EQ parameter adjustment information.

[0149] Specifically, the sharpness comparison optimization module includes:

[0150] The clarity comparison module is used to retrieve multiple noise reduction benchmark models corresponding to the timbre mode from a large database, and compare and analyze the digital audio signal with the multiple noise reduction benchmark models to obtain noise comparison results respectively.

[0151] The clarity adjustment parameter calculation module is used to calculate the noise reduction level required for the timbre mode based on the noise comparison results, as noise reduction parameter adjustment information.

[0152] The timbre optimization module is used to perform noise reduction optimization on the digital audio signal optimized according to the EQ parameter adjustment information using different timbre modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

[0153] Specifically, the loudness comparison optimization module includes:

[0154] The loudness comparison module is used to retrieve multiple loudness parameter models corresponding to the noise reduction level and the timbre mode from a large database, and to compare and analyze the digital audio signal optimized according to the noise reduction parameter adjustment information with the multiple loudness parameter models to obtain loudness comparison results respectively.

[0155] The loudness adjustment parameter calculation module is used to calculate the loudness parameter adjustment information required for each audio segment based on the loudness comparison results.

[0156] A loudness optimization module is used to adjust the loudness of an audio segment of a digital audio signal optimized according to the noise reduction parameter adjustment information according to the loudness parameter adjustment information to obtain a digital audio signal optimized according to the loudness parameter adjustment information.

[0157] Figure 12 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 12As shown, the electronic device provided according to an embodiment of this application includes: a sound pickup module, an attribute adjustment module, a raw audio library, an adjusted audio temporary storage library, and a comparison and analysis module. The sound pickup module is used to acquire digital audio signals. The raw audio library is used to forward the digital audio signals to the comparison and analysis module. The comparison and analysis module is used to call an artificial intelligence analysis model of at least one sound quality parameter based on at least one timbre mode to perform comparison analysis and optimization on the digital audio signals to obtain parameter adjustment information and a digital audio signal optimized based on the parameter adjustment information. The adjusted audio library is used to send the digital audio signal optimized based on the parameter adjustment information to a speaker device. The attribute adjustment module is used to adjust the sound quality parameters of the sound pickup module according to the parameter adjustment information. The speaker device is used to play back the digital audio signal optimized based on the parameter adjustment information. The electronic device can be various types of wireless or wired microphones and can communicate with a speaker device via wired or wireless connection.

[0158] It is easy to understand that integrating the comparison and analysis module with the sound pickup module and attribute adjustment module locally on the electronic device can significantly reduce the system size, thereby saving hardware and software costs. At the same time, it can reduce the latency of adjusting sound quality parameters and improve the real-time performance of sound quality parameter adjustment.

[0159] In one embodiment, the attribute adjustment module includes an attribute parameter allocation module, an EQ adjustment module, a gain amplifier, and a noise reduction module. The attribute parameter allocation module is used to allocate parameter adjustment information received by the signal transceiver module to the EQ adjustment module, the gain amplifier, and the noise reduction module. The EQ adjustment module is used to adjust the EQ parameters of the digital audio signal according to the parameter adjustment information. The gain amplifier is used to adjust the loudness parameters of the digital audio signal according to the parameter adjustment information. The noise reduction module is used to adjust the noise reduction level of the digital audio signal according to the parameter adjustment information.

[0160] In one embodiment, the comparison analysis module includes a timbre comparison optimization module, a clarity comparison optimization module, and / or a loudness comparison optimization module.

[0161] Furthermore, the timbre comparison and optimization module includes:

[0162] The timbre comparison module is used to retrieve multiple timbre reference models corresponding to different timbre modes from a large database, and compare and analyze the digital audio signal with the multiple timbre reference models to obtain EQ comparison results.

[0163] The timbre adjustment parameter calculation module is used to calculate the level difference corresponding to each frequency band of the digital audio signal based on the EQ comparison result, as EQ parameter adjustment information; and

[0164] The timbre optimization module is used to perform EQ adjustment optimization on the digital audio signal according to the EQ parameter adjustment information to obtain the corresponding digital audio signal optimized according to the EQ parameter adjustment information.

[0165] Furthermore, the sharpness comparison optimization module includes:

[0166] The clarity comparison module is used to retrieve multiple noise reduction benchmark models corresponding to the timbre mode from a large database, and compare and analyze the digital audio signal with the multiple noise reduction benchmark models to obtain noise comparison results respectively.

[0167] The clarity adjustment parameter calculation module is used to calculate the noise reduction level required for the timbre mode based on the noise comparison results, as noise reduction parameter adjustment information.

[0168] The timbre optimization module is used to perform noise reduction optimization on the digital audio signal optimized according to the EQ parameter adjustment information using different timbre modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

[0169] Furthermore, the loudness comparison and optimization module includes:

[0170] The loudness comparison module is used to retrieve multiple loudness parameter models corresponding to the noise reduction level and the timbre mode from a large database, and to compare and analyze the digital audio signal optimized according to the noise reduction parameter adjustment information with the multiple loudness parameter models to obtain loudness comparison results respectively.

[0171] The loudness adjustment parameter calculation module is used to calculate the loudness parameter adjustment information required for each audio segment based on the loudness comparison results.

[0172] A loudness optimization module is used to adjust the loudness of an audio segment of a digital audio signal optimized according to the noise reduction parameter adjustment information according to the loudness parameter adjustment information to obtain a digital audio signal optimized according to the loudness parameter adjustment information.

[0173] According to a fourth aspect of the present application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the audio adjustment method as described in the first aspect.

[0174] The audio adjustment device of this embodiment is used to implement the corresponding audio adjustment methods in the foregoing method embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here. Furthermore, the functional implementation of each module in the audio adjustment device of this embodiment can be referred to the description of the corresponding part in the foregoing method embodiments, which will also not be repeated here.

[0175] This application also provides a computer program product, including computer instructions that instruct a computing device to perform an operation corresponding to any of the audio adjustment methods in the above-described multiple method embodiments.

[0176] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0177] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code downloaded over a network that is originally stored in a remote recording medium or a non-transitory machine-readable medium and will be stored in a local recording medium. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the audio adjustment methods described herein. Furthermore, when a general-purpose computer accesses the code used to implement the audio adjustment methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the audio adjustment methods shown herein.

[0178] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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 embodiments of this application.

[0179] The above embodiments are only used to illustrate the embodiments of this application, and are not intended to limit the embodiments of this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of this application. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of this application, and the patent protection scope of the embodiments of this application should be defined by the claims.

Claims

1. An audio conditioning method, characterized by, The application relates to a method for adjusting and optimizing digital audio signals, comprising the following steps: A user terminal collects a digital audio signal by using a sound pickup device and sends the digital audio signal to a server; wherein the function of the server is realized by using a cloud service; The server calls an artificial intelligence analysis model of at least one sound quality parameter according to at least one timbre mode to perform comparison analysis and optimization on the digital audio signal to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information; wherein the timbre mode comprises "bright", "round" and "mellow", and the sound quality parameter comprises one or more of an EQ, a loudness and a noise reduction parameter; The server sends the parameter adjustment information to the user terminal, the user terminal sends the parameter adjustment information to the sound pickup device, and the sound pickup device adjusts the sound quality parameter of the sound pickup device according to the parameter adjustment information; The server sends the digital audio signal optimized according to the parameter adjustment information to the user terminal, and the user terminal sends the optimized digital audio signal to a loudspeaker device to make the loudspeaker device play back the digital audio signal optimized according to the parameter adjustment information.

2. The audio conditioning method of claim 1, wherein, The step of calling an artificial intelligence analysis model of at least one sound quality parameter according to at least one timbre mode to perform analysis and optimization on the digital audio signal to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information comprises the following steps: The step of calling an EQ analysis optimization model according to at least one timbre mode to perform analysis and optimization on the digital audio signal to obtain EQ parameter adjustment information and a digital audio signal optimized according to the EQ parameter adjustment information; The step of calling a noise reduction analysis optimization model according to at least one timbre mode to perform analysis and optimization on the digital audio signal to obtain noise reduction parameter adjustment information and a digital audio signal optimized according to the noise reduction parameter adjustment information; and / or The step of calling a loudness analysis optimization model according to at least one timbre mode to perform analysis and optimization on the digital audio signal to obtain loudness parameter adjustment information and a digital audio signal optimized according to the loudness parameter adjustment information.

3. The audio conditioning method of claim 2, wherein, The step of calling an EQ analysis optimization model according to at least one timbre mode to perform analysis and optimization on the digital audio signal to obtain EQ parameter adjustment information and a digital audio signal optimized according to the EQ parameter adjustment information comprises the following steps: The server calls a plurality of timbre reference models corresponding to different timbre modes from a large database; The server respectively performs comparison analysis on the digital audio signal and the plurality of timbre reference models to obtain an EQ comparison result; The server respectively calculates a level difference value corresponding to each frequency band of the digital audio signal as EQ parameter adjustment information according to the EQ comparison result; The server performs EQ adjustment and optimization of different timbre modes on the digital audio signal according to the EQ parameter adjustment information to obtain a corresponding digital audio signal optimized according to the EQ parameter adjustment information.

4. The audio conditioning method of claim 2, wherein, The step of the server analyzing and optimizing the digital audio signal according to at least one tone mode to obtain noise reduction parameter adjustment information and a digital audio signal optimized according to the noise reduction parameter adjustment information comprises: The server retrieves a plurality of noise reduction benchmark models corresponding to the tone mode from a large database; The server compares and analyzes the digital audio signal with the plurality of noise reduction benchmark models respectively to obtain noise comparison results respectively; The server calculates the noise reduction level required by the tone mode as noise reduction parameter adjustment information according to the noise comparison results respectively; The server performs noise reduction optimization of the digital audio signal in different tone modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

5. The audio conditioning method of claim 2, wherein, The step of the server analyzing and optimizing the digital audio signal according to at least one tone mode to obtain noise reduction parameter adjustment information and a digital audio signal optimized according to the noise reduction parameter adjustment information comprises: The server retrieves a plurality of noise reduction benchmark models corresponding to the tone mode from a large database; The server compares and analyzes the digital audio signal with the plurality of noise reduction benchmark models respectively to obtain noise comparison results respectively; The server calculates the noise reduction level required by the tone mode as noise reduction parameter adjustment information according to the noise comparison results respectively; The server performs noise reduction optimization of the digital audio signal in different tone modes to obtain a digital audio signal optimized according to the noise reduction parameter adjustment information.

6. The audio conditioning method of claim 1, wherein, The method further comprises the following steps: receiving mode instruction information input by a user.

7. The audio conditioning method of claim 6, wherein, The step of the pickup device adjusting the sound quality parameters of the pickup device according to the parameter adjustment information comprises: the pickup device adjusts the sound quality parameters of the pickup device using the parameter adjustment information corresponding to the mode instruction information; and The step of the loudspeaker device playing back the digital audio signal optimized according to the parameter adjustment information comprises: the loudspeaker device plays back the digital audio signal optimized according to the parameter adjustment information corresponding to the mode instruction information.

8. The audio conditioning method of claim 6, wherein, The artificial intelligence analysis model is retrieved from a large data model according to the mode instruction information.

9. The audio conditioning method of claim 6, wherein, The parameter adjustment information and the digital audio signal optimized according to the parameter adjustment information are selected from a plurality of parameter adjustment information and digital audio signals optimized according to the parameter adjustment information according to the mode instruction information.

10. An audio conditioning system characterized by, Comprise: A server, a user terminal, a sound pickup device and a loudspeaker device, wherein the sound pickup device comprises a signal transceiver module, a sound pickup module and an attribute adjustment module, the user terminal comprises a first original audio library, a first attribute parameter temporary storage library and a first adjusted audio temporary storage library, the server comprises a second attribute parameter temporary storage library, a second adjusted audio temporary storage library, an original audio library and a comparison analysis module, wherein the sound pickup module is configured to collect a digital audio signal, the signal transceiver module is configured to send the digital audio signal to the first original audio library, the first original audio library is configured to forward the digital audio signal to the second original audio library of the server, the comparison analysis module is configured to perform comparison analysis and optimization on the digital audio signal according to at least one tone mode to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information by using an artificial intelligence analysis model calling at least one sound quality parameter, the second adjusted audio temporary storage library is configured to temporarily store the digital audio signal optimized according to the parameter adjustment information and forward it to the first adjusted audio temporary storage library, the second attribute parameter temporary storage library is configured to temporarily store the parameter adjustment information and forward it to the first attribute parameter temporary storage library, the first adjusted audio temporary storage library is configured to send the digital audio signal optimized according to the parameter adjustment information to a loudspeaker device, the first attribute parameter temporary storage library is configured to send the parameter adjustment information to the sound pickup device, the attribute adjustment module is configured to adjust the sound quality parameter of the sound pickup module according to the parameter adjustment information, and the loudspeaker device is configured to play back the digital audio signal optimized according to the parameter adjustment information; wherein the functions of the server are implemented using cloud services, the tone mode comprises "bright", "round" and "thick", and the sound quality parameter comprises one or more of an EQ, a loudness and a noise reduction parameter.

11. An electronic device, comprising: Comprise: a sound pickup module, an attribute adjustment module, an original audio library, an adjusted audio temporary storage library and a comparison analysis module, wherein the sound pickup module is configured to collect a digital audio signal, the original audio library is configured to forward the digital audio signal to the comparison analysis module, the comparison analysis module is configured to perform comparison analysis and optimization on the digital audio signal according to at least one tone mode to obtain parameter adjustment information and a digital audio signal optimized according to the parameter adjustment information by using an artificial intelligence analysis model calling at least one sound quality parameter, the adjusted audio temporary storage library is configured to send the digital audio signal optimized according to the parameter adjustment information to a loudspeaker device, the attribute adjustment module is configured to adjust the sound quality parameter of the sound pickup module according to the parameter adjustment information, and the loudspeaker device is configured to play back the digital audio signal optimized according to the parameter adjustment information; wherein the tone mode comprises "bright", "round" and "thick", and the sound quality parameter comprises one or more of an EQ, a loudness and a noise reduction parameter.

12. A computer storage medium having stored thereon a computer program, which, when executed by a processor, implements the audio adjustment method according to any one of claims 1-9.

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