Equalizer adjusting method applied to microphone and related equipment

Abstract

The embodiment of the invention belongs to the technical field of microphones, and relates to an equalizer adjusting method applied to a microphone and related equipment, and the method comprises the steps: receiving an equalizer adjusting request which is transmitted by a user terminal and carries a user identifier and a microphone identifier; acquiring an equalizer adjustment parameter corresponding to the user identifier in a system database; the equalizer adjustment parameter is loaded to a target microphone corresponding to the microphone identifier; and adjusting audio data collected by a recording module of the target microphone according to the equalizer adjustment parameter. According to the application, the problem that the traditional equipment such as a KTV microphone and an offline karaoke cabin has obvious limitation in the aspect of meeting diversified audio requirements can be effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of microphones, and in particular to an equalizer adjustment method and related equipment applied to microphones. Background Art

[0002] Traditional KTV microphones and offline karaoke booth equipment typically use fixed settings for the microphone's equalizer (EQ), which cannot be adjusted to meet user needs. This means that if users want a more personalized sound experience, they often need to rely on algorithms in the host computer for processing.

[0003] However, the host computer's algorithm cannot fully compensate for the shortcomings of microphone EQ adjustment, especially when processing different user audio needs, which can lead to significant deviations. For example, when a user wants to enhance the low frequencies of the sound for a richer listening experience, the fixed microphone EQ setting may not meet their needs. Even after the algorithm processes the raw audio signal input to the host computer, it may still not achieve the desired effect.

[0004] Existing fixed microphone EQ parameter settings make it difficult to fully adapt to the needs of various users, especially those with specific requirements for reverberation levels. Therefore, traditional KTV microphones and offline karaoke booths have significant limitations in meeting diverse audio needs. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to propose an equalizer adjustment method and related equipment for a microphone, so as to solve the problem that traditional KTV microphones, offline KTV booths and other equipment have significant limitations in meeting diverse audio needs.

[0006] In order to solve the above technical problems, the present invention provides an equalizer adjustment method for a microphone, which adopts the following technical solution:

[0007] receiving an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier;

[0008] Obtaining an equalizer adjustment parameter corresponding to the user identifier in a system database;

[0009] loading the equalizer adjustment parameter to the target microphone corresponding to the microphone identifier;

[0010] An adjustment operation is performed on the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameter.

[0011] Furthermore, before the step of receiving the equalizer adjustment request carrying the user identifier sent by the user terminal, the method further includes the following steps:

[0012] receiving a user registration request sent by the user terminal, wherein the user registration request includes the user identifier;

[0013] creating user account data corresponding to the user identifier in the system database;

[0014] Obtaining initial equalizer adjustment parameters sent by the user terminal;

[0015] The initial equalizer adjustment parameters are stored in the user account data.

[0016] Furthermore, before the step of receiving the equalizer adjustment request carrying the user identifier sent by the user terminal, the method further includes the following steps:

[0017] receiving a user registration request sent by the user terminal, wherein the user registration request includes registration voice data corresponding to the user;

[0018] Performing a voiceprint feature extraction operation on the registered voice data to obtain voiceprint feature data;

[0019] Using the voiceprint feature data as the user identification, and creating user account data corresponding to the voiceprint feature data in the system database;

[0020] Obtaining initial equalizer adjustment parameters sent by the user terminal;

[0021] Storing the initial equalizer adjustment parameters in the user account data;

[0022] When the user uses the microphone, the current voice data of the current user is collected;

[0023] And perform feature extraction operation on the current voice data to obtain current voiceprint feature data:

[0024] The step of acquiring the equalizer adjustment parameter corresponding to the user identifier in the system database specifically includes the following steps:

[0025] Determine whether there is user account data consistent with the current voiceprint feature data in the system database;

[0026] If there is user account data that is consistent with the current voiceprint feature data, obtaining the equalizer adjustment parameters corresponding to the user account data from the database;

[0027] If there is no user account data that is consistent with the current voiceprint feature data, the parameter call operation is not performed.

[0028] Furthermore, the step of performing a feature extraction operation on the current voice data to obtain current voiceprint feature data specifically includes the following steps:

[0029] Performing a preprocessing operation on the current voice data to obtain preprocessed voice data;

[0030] Calling the constructed overcomplete dictionary and converting the preprocessed speech data into a sparse linear combination in the overcomplete dictionary;

[0031] The sparse coefficients in the sparse linear combination are used as the current voiceprint feature data.

[0032] Furthermore, the step of using the sparse coefficients in the sparse linear combination as the current voiceprint feature data specifically includes the following steps:

[0033] Normalizing the sparse coefficients in the sparse linear combination to obtain normalized sparse coefficients;

[0034] The normalized sparse coefficient is used as the current voiceprint feature data.

[0035] Furthermore, the step of performing a preprocessing operation on the current voice data to obtain preprocessed voice data specifically includes the following steps:

[0036] A pre-emphasis processing operation is performed on the current voice data to obtain the pre-processed voice data.

[0037] In order to solve the above technical problems, the present application also provides an equalizer adjustment device for a microphone, which adopts the following technical solution:

[0038] An adjustment request receiving module, configured to receive an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier;

[0039] A parameter acquisition module, configured to acquire, from a system database, an equalizer adjustment parameter corresponding to the user identifier;

[0040] a parameter loading module, configured to load the equalizer adjustment parameter to a target microphone corresponding to the microphone identifier;

[0041] The audio adjustment module is used to adjust the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameters.

[0042] Furthermore, the device further comprises:

[0043] a first registration request receiving module, configured to receive a user registration request sent by the user terminal, wherein the user registration request includes the user identifier;

[0044] a first account creation module, configured to create user account data corresponding to the user identifier in the system database;

[0045] A first initial parameter receiving module, configured to obtain the initial equalizer adjustment parameters sent by the user terminal;

[0046] The first initial parameter storage module is used to store the initial equalizer adjustment parameters in the user account data.

[0047] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0048] The invention comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the equalizer adjustment method applied to a microphone as described above when executing the computer-readable instructions.

[0049] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0050] The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the equalizer adjustment method applied to a microphone as described above.

[0051] The present application provides an equalizer adjustment method for a microphone, comprising: receiving an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier; obtaining an equalizer adjustment parameter corresponding to the user identifier in a system database; loading the equalizer adjustment parameter to a target microphone corresponding to the microphone identifier; and adjusting the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameter. Compared with the prior art, the present application uses cloud terminal storage interaction to act as a microphone EQ adjustment memory function. Users can directly apply their own tuning solutions in the cloud to the microphone through identity recognition, thereby effectively improving the user experience of different users when using the same microphone in microphone application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0054] Figure 2 is a flowchart of an implementation of an equalizer adjustment method applied to a microphone provided in an embodiment of the present application;

[0055] Figure 3 1 is a schematic structural diagram of an equalizer adjustment device applied to a microphone provided in an embodiment of the present application;

[0056] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0058] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0060] like Figure 1As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0061] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0062] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0063] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0064] It should be noted that the equalizer adjustment method applied to the microphone provided in the embodiment of the present application is generally executed by a server / terminal device. Accordingly, the equalizer adjustment device applied to the microphone is generally provided in the server / terminal device.

[0065] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0066] Continue to refer Figure 2 , shows a flow chart of an embodiment of an equalizer adjustment method for a microphone according to the present application. The equalizer adjustment method for a microphone includes: step S201, step S202, step S203 and step S204.

[0067] In step S201, an equalizer adjustment request carrying a user identifier and a microphone identifier is received from a user terminal;

[0068] In step S202, the equalizer adjustment parameter corresponding to the user identifier is obtained from the system database;

[0069] In step S203, the equalizer adjustment parameters are loaded into the target microphone corresponding to the microphone identifier;

[0070] In step S204, an adjustment operation is performed on the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameters.

[0071] In the embodiment of the present application, the microphone to which the present application applies refers to an energy conversion device that converts sound signals into electrical signals. The microphone may be a microphone, a handset, etc. The microphone includes a recording module, a DSP processing module, and an MCU main control module, wherein:

[0072] Recording module: mainly includes microphone element, microphone element bracket, sponge, pop filter, etc.;

[0073] DSP processing module: an electronic control component used to control the recording module. After receiving the audio signal input from the recording module, the DSP processing module performs preset adjustments based on the currently set EQ parameters and outputs the adjusted audio signal to the preset terminal (mobile phone, speaker, computer, etc.);

[0074] MCU main control module: includes parameter transceiver module, parameter calling module, parameter temporary storage library, and signal transceiver module. The signal transceiver module requests the parameter information of the corresponding account from the cloud library through the user terminal according to the user account information. The cloud library sends all adapted parameter information to the signal receiving module according to the account information. The signal receiving module sends the parameter information to the parameter temporary storage library. The parameter calling module retrieves the parameter information required by the current user from the parameter temporary storage library to the parameter transceiver module. The parameter transceiver module sends the parameter information to the DSP processing module. The DSP processing module uses the new EQ tuning parameters to process the original audio signal output by the recording module.

[0075] In an embodiment of the present application, a user inputs a question or inquiry through their terminal device (such as a mobile phone, computer, etc.), and this input is received by the system. This inquiry text data is the specific content that the user wants the system to answer. Specifically, the inquiry text data can be "transaction data or payment data or business data or purchase data" related to a financial institution (such as a bank, etc.). The inquiry text data can also be medical data related to medical scenarios, such as personal health records, prescriptions, examination reports, etc. It should be understood that the examples of inquiry text data here are only for convenience of understanding and are not used to limit this application.

[0076] In an embodiment of the present application, when a user needs to apply EQ parameters suitable for him / herself to the current microphone, the user logs in to the cloud platform through the terminal connected to the current microphone. The cloud platform sends the EQ tuning scheme corresponding to the account to the microphone based on the account information. The microphone adapts the EQ tuning scheme to the DSP processor, and the sound recorded by the user through the microphone is processed by the EQ adjustment suitable for him / herself before being output.

[0077] In an embodiment of the present application, the parameter information sent by the cloud library is first transmitted to the terminal via a wireless network, and the terminal transmits the parameter information to the microphone MCU via a wireless protocol transmission method; in other schemes, a wireless network card can be built into the microphone, and the terminal only serves the function of requesting information transmission, and the microphone interacts directly with the cloud library through the wireless network card.

[0078] In an embodiment of the present application, after use, for example, the microphone is disconnected from the terminal, the user logs out of the account on the terminal application, etc., the parameter temporary library will delete all parameter information, and the EQ adjustment template of the DSP processing module will be retained; in other microphone application scenarios, the DSP processing module can be synchronously reset when the above situation occurs, and operate with the basic EQ parameters set by the factory.

[0079] In an embodiment of the present application, a method for adjusting an equalizer for a microphone is provided, comprising: receiving an equalizer adjustment request carrying a user identifier and a microphone identifier sent by a user terminal; calling a system database to obtain an equalizer adjustment parameter corresponding to the user identifier in the system database; sending the equalizer adjustment parameter to a signal transceiver module of a target microphone corresponding to the microphone identifier; receiving the equalizer adjustment parameter according to the signal transceiver module and storing the equalizer adjustment parameter in a parameter temporary storage library of the target microphone; calling the equalizer adjustment parameter in the parameter temporary storage library according to the parameter calling module of the target microphone; sending the equalizer adjustment parameter to a DSP processing module of the target microphone according to the parameter transceiver module of the target microphone to adjust the audio data collected by the recording module of the target microphone. Compared with the prior art, the present application uses cloud terminal storage interaction to act as a microphone EQ adjustment memory function. Users can directly apply their own tuning solutions in the cloud to the microphone through identity recognition, thereby effectively improving the user experience of different users when using the microphone in microphone application scenarios.

[0080] In some optional implementations of the embodiments of the present application, before the step of receiving the equalizer adjustment request carrying the user identifier sent by the user terminal, the following steps are further included:

[0081] receiving a user registration request sent by a user terminal, wherein the user registration request includes a user identifier;

[0082] Create user account data corresponding to the user ID in the system database;

[0083] Obtaining initial equalizer adjustment parameters sent by the user terminal;

[0084] The initial equalizer adjustment parameters are stored in the user account data.

[0085] In an embodiment of the present application, when the user uses the tuning content for the first time, after the user adjusts the required EQ parameters through the host computer, the microphone will memorize and archive the tuning parameters. At the same time, the user registers an account on the cloud platform through the terminal host computer, and the adjusted EQ parameters are uploaded to the cloud through the corresponding account.

[0086] In some optional implementations of the embodiments of the present application, the above-mentioned host computer can introduce AI to perform intelligent EQ adjustment.

[0087] In some optional implementations of the embodiments of the present application, one account may correspond to multiple memory archives to meet multiple tuning needs of a single user.

[0088] In some optional implementation methods of the embodiments of the present application, when the user needs to change the tuning content, the user can use the above-mentioned host computer to adjust the required EQ parameters, and the microphone will overwrite the original archive with the tuning parameters. At the same time, the user registers an account on the cloud platform through the terminal host computer, and the adjusted EQ parameters are uploaded to the cloud through the corresponding account.

[0089] In some optional implementations of the embodiments of the present application, before the step of receiving the equalizer adjustment request carrying the user identifier sent by the user terminal, the following step is also included:

[0090] receiving a user registration request sent by the user terminal, wherein the user registration request includes registration voice data corresponding to the user;

[0091] Performing a voiceprint feature extraction operation on the registered voice data to obtain voiceprint feature data;

[0092] Using the voiceprint feature data as the user identification, and creating user account data corresponding to the voiceprint feature data in the system database;

[0093] Obtaining initial equalizer adjustment parameters sent by the user terminal;

[0094] Storing the initial equalizer adjustment parameters in the user account data;

[0095] When the user uses the microphone, the current voice data of the current user is collected;

[0096] and performing a feature extraction operation on the current voice data to obtain current voiceprint feature data;

[0097] The step of acquiring the equalizer adjustment parameter corresponding to the user identifier in the system database specifically includes the following steps:

[0098] Determine whether there is user account data consistent with the current voiceprint feature data in the system database;

[0099] If there is user account data that is consistent with the current voiceprint feature data, obtaining the equalizer adjustment parameters corresponding to the user account data from the database;

[0100] If there is no user account data that is consistent with the current voiceprint feature data, the parameter call operation is not performed.

[0101] In an embodiment of the present application, when a user uses the device for the first time, the user records a sound through the recording module, and the signal sending module in the MCU sends the audio signal to the cloud or the terminal (the terminal forwards it to the cloud), and the cloud or the terminal extracts the voiceprint features that are exclusive to a single user in the audio signal. At the same time, the user establishes an account profile in the cloud for adapting to the voiceprint features.

[0102] In an embodiment of the present application, when the user uses the microphone for the second time, he first inputs a voice into the microphone recording module, and the audio signal is sent to the cloud through the signal sending module. The cloud extracts the voiceprint features of the audio signal and compares the extracted voiceprint features with the voiceprint features in the server inventory, thereby obtaining the EQ parameters in the corresponding account information, and sending all EQ parameters corresponding to the account information to the parameter temporary storage library of the microphone. After the user selects the required EQ parameters through the terminal, the parameter calling module sends the parameter information to the DSP processing module.

[0103] In the embodiments of the present application, the technical features of voiceprint extraction can be technical means related to voice passwords and WeChat logins. The present application transfers the voiceprint feature extraction technology to the field of microphones. On the one hand, when the parameter information is unique and the wireless network card is integrated into the microphone, the user only needs to speak, and the microphone can automatically download the EQ adjustment plan corresponding to the user, completely independent of the terminal; on the other hand, voiceprint extraction has defects such as insufficient hardware level in other industries, and microphones have unique industry advantages in picking up sounds. Therefore, integrated voiceprint extraction as account identification can achieve unexpected improvement in recognition accuracy.

[0104] In some optional implementations of the embodiments of the present application, the step of performing a feature extraction operation on the current voice data to obtain the current voiceprint feature data specifically includes the following steps:

[0105] Performing a preprocessing operation on the current voice data to obtain preprocessed voice data;

[0106] Call the constructed overcomplete dictionary and convert the preprocessed speech data into a sparse linear combination in the overcomplete dictionary;

[0107] The sparse coefficients in the sparse linear combination are used as the current voiceprint feature data.

[0108] In the embodiments of this application, the core idea of sparse representation is to represent the signal as a linear combination of a few basis functions (or atoms), that is, the signal is sparse under a certain basis or dictionary. In voiceprint feature extraction, methods based on sparse representation can effectively capture the essential characteristics of the sound signal and improve the performance of voiceprint recognition.

[0109] In an embodiment of the present application, the present application pre-constructs a dictionary containing a large number of basis functions (atoms), so that the signal can be sparsely represented by a small number of atoms in the dictionary.

[0110] In the embodiment of the present application, the overcomplete dictionary can be constructed using basis functions such as discrete Fourier transform (DFT), discrete cosine transform (DCT), and wavelet transform. The overcomplete dictionary can be learned using training data and a dictionary learning algorithm (such as the K-SVD algorithm) to obtain an overcomplete dictionary that is adapted to a specific signal.

[0111] In the embodiment of the present application, since the number of atoms in the overcomplete dictionary is much larger than the signal dimension, the flexibility of the representation is guaranteed.

[0112] In the embodiment of the present application, the sound signal is represented as a sparse linear combination of atoms in the dictionary, specifically:

[0113] Sparse coding: solving sparse representation coefficients so that the signal can be represented by a small number of atoms in the dictionary.

[0114] Mathematical model: Minimize the objective function min||x||0subject to y=D x , where y is the signal, D is the dictionary, x is the sparse representation coefficient, and ||·||0 represents the l0 norm (the number of non-zero elements).

[0115] Optimization algorithm: Since l0 norm optimization is an NP-hard problem, greedy algorithms (such as orthogonal matching pursuit OMP) or convex relaxation methods (such as basis pursuit BP, replacing l0 norm with l1 norm) are usually used.

[0116] In the embodiment of the present application, the sparse representation coefficients can be used to extract discriminative features for subsequent voiceprint recognition.

[0117] In some optional implementations of the embodiments of the present application, the above step of using the sparse coefficients in the sparse linear combination as the current voiceprint feature data specifically includes the following steps:

[0118] Normalizing the sparse coefficients in the sparse linear combination to obtain normalized sparse coefficients;

[0119] The normalized sparse coefficient is used as the current voiceprint feature data.

[0120] In the embodiment of the present application, the sparse coefficients may be further processed, such as dimensionality reduction, normalization, etc., to extract more robust features.

[0121] In the embodiments of this application, sparse representation-based feature extraction constructs an overcomplete dictionary, decomposing the sound signal into a linear combination of a small number of atoms, from which discriminative features are extracted. This method performs well in voiceprint feature extraction, but also faces challenges such as computational complexity and dictionary selection. By properly selecting algorithms and optimizing parameters, the advantages of sparse representation in voiceprint feature extraction can be fully utilized.

[0122] In some optional implementations of the embodiments of the present application, the step of performing a preprocessing operation on the current voice data to obtain preprocessed voice data specifically includes the following steps:

[0123] Perform a pre-emphasis processing operation on the current voice data to obtain pre-processed voice data.

[0124] In the embodiment of the present application, the pre-emphasis processing operation is mainly used to enhance the high-frequency part, compensate for the attenuation of the high-frequency components of the sound signal during transmission, and enhance the clarity of the voice. Specifically, a first-order high-pass filter is used to process the signal, and the transfer function is H(z)=1-az -1 , where a is the pre-emphasis coefficient, which is usually between 0.9 and 1.0.

[0125] In the embodiment of the present application, by performing pre-emphasis processing on the current voice data, the high-frequency components can be enhanced, making the spectrum flatter, which is beneficial for subsequent feature extraction.

[0126] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0127] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0128] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0129] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0130] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in FIG, the present application provides an embodiment of an equalizer adjustment device for a microphone. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0131] like Figure 3 As shown, the equalizer adjustment device 200 applied to a microphone according to an embodiment of the present application includes:

[0132] An adjustment request receiving module 210 is configured to receive an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier;

[0133] A parameter acquisition module 220 is configured to acquire, from a system database, an equalizer adjustment parameter corresponding to the user identifier;

[0134] a parameter loading module 230, configured to load the equalizer adjustment parameter to a target microphone corresponding to the microphone identifier;

[0135] The audio adjustment module 240 is configured to adjust the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameters.

[0136] In an embodiment of the present application, an equalizer adjustment device 200 for a microphone is provided, comprising: an adjustment request receiving module 210 for receiving an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier; a parameter acquisition module 220 for acquiring an equalizer adjustment parameter corresponding to the user identifier in a system database; a parameter loading module 230 for loading the equalizer adjustment parameter to a target microphone corresponding to the microphone identifier; and an audio adjustment module 240 for adjusting the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameter. Compared with the prior art, the present application uses cloud terminal storage interaction to act as a microphone EQ adjustment memory function. Users can directly apply their own tuning solutions in the cloud to the microphone through identity recognition, thereby effectively improving the user experience of different users when using the microphone in microphone application scenarios.

[0137] In some optional implementations of the embodiments of the present application, the equalizer adjustment device 200 applied to a microphone further includes: a first registration request receiving module, a first account creation module, a first initial parameter receiving module, and a first initial parameter storage module, wherein:

[0138] A first registration request receiving module, configured to receive a user registration request sent by a user terminal, wherein the user registration request includes a user identifier;

[0139] A first account creation module, configured to create user account data corresponding to the user identifier in a system database;

[0140] A first initial parameter receiving module, configured to obtain initial equalizer adjustment parameters sent by a user terminal;

[0141] The first initial parameter storage module is used to store the initial equalizer adjustment parameters in the user account data.

[0142] In some optional implementations of the embodiments of the present application, the equalizer adjustment device 200 applied to the microphone further includes: a second registration request receiving module, a voiceprint feature extraction module, a second account creation module, a second initial parameter receiving module, a second initial parameter storage module, a current voice collection module, and a feature extraction module. The parameter acquisition module 220 includes: an account judgment submodule, a parameter calling submodule, and a stop calling submodule, wherein:

[0143] a second registration request receiving module, configured to receive a user registration request sent by a user terminal, wherein the user registration request includes registration voice data corresponding to the user;

[0144] A voiceprint feature extraction module is used to perform voiceprint feature extraction on the registered voice data to obtain voiceprint feature data;

[0145] A second account creation module is configured to use the voiceprint feature data as a user identifier and create user account data corresponding to the voiceprint feature data in a system database;

[0146] A second initial parameter receiving module, configured to obtain initial equalizer adjustment parameters sent by a user terminal;

[0147] A second initial parameter storage module, configured to store the initial equalizer adjustment parameters in the user account data;

[0148] The current voice collection module is used to collect the current voice data of the current user when the user uses the microphone;

[0149] The feature extraction module is used to perform feature extraction operations on the current voice data to obtain the current voiceprint feature data;

[0150] The account judgment submodule is used to judge whether there is user account data consistent with the current voiceprint feature data in the system database;

[0151] The parameter calling submodule is further used to obtain the equalizer adjustment parameters corresponding to the user account data in the database if there is user account data consistent with the current voiceprint feature data;

[0152] The stop calling submodule is used to not execute the parameter calling operation if there is no user account data consistent with the current voiceprint feature data.

[0153] In some optional implementations of the embodiments of the present application, the feature extraction module includes: a preprocessing submodule, a conversion submodule, and a voiceprint feature determination submodule, wherein:

[0154] The preprocessing submodule is used to perform preprocessing operations on the current voice data to obtain preprocessed voice data;

[0155] The conversion submodule is used to call the constructed overcomplete dictionary and convert the preprocessed speech data into a sparse linear combination in the overcomplete dictionary;

[0156] The voiceprint feature determination submodule is used to use the sparse coefficients in the sparse linear combination as the current voiceprint feature data.

[0157] In some optional implementations of the embodiments of the present application, the voiceprint feature determination submodule includes: a normalization unit and a voiceprint feature determination unit, wherein:

[0158] A normalization unit, used to normalize the sparse coefficients in the sparse linear combination to obtain normalized sparse coefficients;

[0159] The voiceprint feature determination unit is used to use the normalized sparse coefficient as the current voiceprint feature data.

[0160] In some optional implementations of the embodiments of the present application, the pre-processing submodule includes: a pre-emphasis processing unit, wherein:

[0161] The pre-emphasis processing unit is used to perform a pre-emphasis processing operation on the current voice data to obtain pre-processed voice data.

[0162] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device according to an embodiment of the present application.

[0163] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0164] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0165] The memory 310 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disk, optical disk, etc. In some embodiments, the memory 310 may be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 may also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 310 may also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiment of the present application, the memory 310 is generally used to store an operating system and various application software installed on the computer device 300, such as computer-readable instructions for a method for adjusting an equalizer of a microphone. In addition, the memory 310 can also be used to temporarily store various data that has been output or is about to be output.

[0166] In some embodiments, the processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiment of the present application, the processor 320 is used to execute computer-readable instructions or process data stored in the memory 310, such as executing computer-readable instructions for the equalizer adjustment method applied to the microphone.

[0167] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0168] The computer device provided in this application acts as a microphone EQ adjustment memory function through cloud terminal storage interaction. Users can use identity recognition to directly apply their own tuning solutions in the cloud to the microphone, thereby effectively improving the user experience of different users when using the microphone in microphone application scenarios.

[0169] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the equalizer adjustment method applied to the microphone as described above.

[0170] The computer-readable storage medium provided in this application acts as a microphone EQ adjustment memory function through cloud terminal storage interaction. Users can directly apply their own tuning solutions in the cloud to the microphone through identity recognition, thereby effectively improving the user experience of different users when using the microphone in microphone application scenarios.

[0171] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0172] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for adjusting an equalizer applied to a microphone, characterized in that: The steps include: receiving an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier; Obtaining an equalizer adjustment parameter corresponding to the user identifier in a system database; loading the equalizer adjustment parameter to the target microphone corresponding to the microphone identifier; An adjustment operation is performed on the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameter.

2. The equalizer adjustment method for a microphone according to claim 1, characterized in that: Before the step of receiving the equalizer adjustment request carrying the user identifier sent by the user terminal, the method further includes the following steps: receiving a user registration request sent by the user terminal, wherein the user registration request includes the user identifier; creating user account data corresponding to the user identifier in the system database; Obtaining initial equalizer adjustment parameters sent by the user terminal; The initial equalizer adjustment parameters are stored in the user account data.

3. The equalizer adjustment method for a microphone according to claim 1, wherein: Before the step of receiving the equalizer adjustment request carrying the user identifier sent by the user terminal, the method further includes the following steps: receiving a user registration request sent by the user terminal, wherein the user registration request includes registration voice data corresponding to the user; Performing a voiceprint feature extraction operation on the registered voice data to obtain voiceprint feature data; Using the voiceprint feature data as the user identification, and creating user account data corresponding to the voiceprint feature data in the system database; Obtaining initial equalizer adjustment parameters sent by the user terminal; Storing the initial equalizer adjustment parameters in the user account data; When the user uses the microphone, the current voice data of the current user is collected; Performing a feature extraction operation on the current voice data to obtain current voiceprint feature data; The step of acquiring the equalizer adjustment parameter corresponding to the user identifier in the system database specifically includes the following steps: Determine whether there is user account data consistent with the current voiceprint feature data in the system database; If there is user account data that is consistent with the current voiceprint feature data, obtaining the equalizer adjustment parameters corresponding to the user account data from the database; If there is no user account data that is consistent with the current voiceprint feature data, the parameter call operation is not performed.

4. The equalizer adjustment method for a microphone according to claim 3, wherein: The step of performing a feature extraction operation on the current voice data to obtain current voiceprint feature data specifically includes the following steps: Performing a preprocessing operation on the current voice data to obtain preprocessed voice data; Calling the constructed overcomplete dictionary and converting the preprocessed speech data into a sparse linear combination in the overcomplete dictionary; The sparse coefficients in the sparse linear combination are used as the current voiceprint feature data.

5. The equalizer adjustment method for a microphone according to claim 4, characterized in that: The step of using the sparse coefficients in the sparse linear combination as the current voiceprint feature data specifically includes the following steps: Normalizing the sparse coefficients in the sparse linear combination to obtain normalized sparse coefficients; The normalized sparse coefficient is used as the current voiceprint feature data.

6. The equalizer adjustment method for a microphone according to claim 4, characterized in that: The step of performing a preprocessing operation on the current voice data to obtain preprocessed voice data specifically includes the following steps: A pre-emphasis processing operation is performed on the current voice data to obtain the pre-processed voice data.

7. An equalizer adjustment device for a microphone, characterized in that: include: An adjustment request receiving module, configured to receive an equalizer adjustment request sent by a user terminal and carrying a user identifier and a microphone identifier; A parameter acquisition module, configured to acquire, from a system database, an equalizer adjustment parameter corresponding to the user identifier; a parameter loading module, configured to load the equalizer adjustment parameter to a target microphone corresponding to the microphone identifier; The audio adjustment module is used to adjust the audio data collected by the recording module of the target microphone according to the equalizer adjustment parameters.

8. The equalizer adjustment device for a microphone according to claim 7, characterized in that: The device further comprises: a first registration request receiving module, configured to receive a user registration request sent by the user terminal, wherein the user registration request includes the user identifier; a first account creation module, configured to create user account data corresponding to the user identifier in the system database; A first initial parameter receiving module, configured to obtain the initial equalizer adjustment parameters sent by the user terminal; The first initial parameter storage module is used to store the initial equalizer adjustment parameters in the user account data.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the equalizer adjustment method applied to a microphone according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the equalizer adjustment method applied to a microphone according to any one of claims 1 to 6.

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