A microphone filtering method and terminal based on environment perception

Abstract

The application provides a microphone filtering method and terminal based on environment perception, wherein the method comprises: acquiring real-time motion state data of the terminal; acquiring real-time geographic position information of the terminal and current environment data corresponding to the real-time geographic position information; determining relative wind speed and relative wind direction of the terminal relative to the current environment according to the real-time motion state data, real-time environment wind direction included in the environment data and the real-time environment wind direction; determining target filtering configuration parameters of a microphone in the terminal according to the relative wind speed, the relative wind direction and a preset filtering strategy mapping database; and performing real-time front-end filtering processing on original audio signals collected by the microphone according to the target filtering configuration parameters to obtain target audio signals. The scheme provided by the application can perform real-time and dynamic adjustment on audio signals collected by the terminal, thereby ensuring the accuracy and clarity of the collected audio signals and improving the user experience.

Description

Technical Field

[0001] This invention relates to the field of microphone filtering and noise reduction technology, and in particular to a microphone filtering method and terminal based on environmental perception. Background Technology

[0002] With the development of smart technology and the popularization of outdoor sports, smart wearable devices, especially smart sports wearable devices, have emerged. Existing devices mainly integrate communication and music playback functions, greatly improving the safety and entertainment of cycling. However, some existing smart wearable devices have a significant problem: during exercise, environmental wind noise severely interferes with the clarity and accuracy of the user's speech. Voice communication functions (such as Bluetooth calls, team intercoms, and voice assistants) are one of the core values ​​of smart wearable devices. During cycling, environmental wind noise is the primary factor leading to the degradation of voice input quality. To address the above problem, existing solutions and their existing problems are as follows:

[0003] 1. Fixed filtering scheme: A preset high-pass filter is used to suppress low-frequency wind noise. However, because the characteristics of wind noise change drastically with speed, the fixed filter cannot adaptively adjust, resulting in excessive reduction of low-frequency components of speech at low speeds (making the sound muffled), or insufficient suppression of wind noise at high speeds.

[0004] 2. Adaptive filtering based on posterior analysis (such as spectral subtraction and Wiener filtering): This method estimates the noise spectrum by analyzing only the mixed signal (speech + wind noise) captured by the microphone. This method has an inherent lag. When strong winds suddenly arrive, the algorithm needs time to learn new noise features, during which time call quality deteriorates sharply. More importantly, when wind noise intensity is much greater than human voice, noise estimation becomes severely inaccurate, leading to "musical noise" artifacts or speech distortion. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a microphone filtering method and terminal based on environmental perception, so as to reduce the impact of environmental wind noise on the audio signal collected by the microphone in the terminal, and improve the accuracy and timeliness of the terminal in collecting audio signals in complex environments.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide an environment-aware microphone filtering method applied to a terminal, comprising:

[0007] Acquire real-time motion state data of the terminal, the real-time motion state data including the terminal's real-time angular velocity information and real-time acceleration information;

[0008] The terminal's real-time geographic location information and the corresponding current meteorological data are obtained, including real-time ambient wind speed and real-time ambient wind direction.

[0009] Based on the real-time motion status data, the real-time environmental wind direction, and the real-time environmental wind direction, the relative wind speed and relative wind direction of the terminal relative to the current environment are determined;

[0010] Based on the relative wind speed, the relative wind direction, and the preset filtering strategy mapping database, the target filtering configuration parameters of the microphone in the terminal are determined.

[0011] The raw audio signal acquired by the microphone is pre-filtered in real time according to the target filtering configuration parameters to obtain the target audio signal.

[0012] In one embodiment, determining the relative wind speed and relative wind direction of the terminal relative to the current environment based on the real-time motion state data, the real-time environmental wind direction, and the real-time environmental wind direction includes:

[0013] Based on the real-time motion status data, the real-time motion direction and real-time motion speed of the terminal are determined.

[0014] Based on the real-time movement direction, the real-time movement speed, the real-time ambient wind direction, and the real-time ambient wind direction, the relative wind speed and relative wind direction of the terminal relative to the current environment are determined.

[0015] In one embodiment, determining the real-time motion direction and real-time motion speed of the terminal based on the real-time motion state data includes:

[0016] Based on the real-time angular velocity information, the real-time direction of motion of the terminal is determined;

[0017] The real-time speed of the terminal is determined based on the real-time acceleration information.

[0018] In one embodiment, determining the real-time motion direction of the terminal based on the real-time angular velocity information includes:

[0019] The real-time attitude angle of the terminal is determined based on the real-time angular velocity information.

[0020] The real-time attitude angle is calculated based on a preset quaternion representation to obtain the real-time motion direction of the terminal.

[0021] In one embodiment, determining the relative wind speed and relative wind direction of the terminal relative to the current environment based on the real-time movement direction, the real-time movement speed, the real-time ambient wind direction, and the real-time ambient wind direction includes:

[0022] The first motion vector is determined based on the real-time motion direction and the real-time motion speed;

[0023] The second motion vector is determined based on the real-time environmental wind direction.

[0024] The relative wind speed and the relative wind direction are determined based on the first motion vector and the second motion vector.

[0025] In one embodiment, determining the relative wind speed and the relative wind direction based on the first motion vector and the second motion vector includes:

[0026] The relative wind speed vector is determined by the formula: Vrelative = Vwind + Vmotion; where Vrelative represents the relative wind speed vector; Vwind represents the second motion vector; and Vmotion represents the first motion vector.

[0027] The relative wind speed vector is decomposed and synthesized to obtain the relative wind speed and the relative wind direction.

[0028] In one embodiment, determining the target filtering configuration parameters of the microphone in the terminal based on the relative wind speed, the relative wind direction, and a filtering strategy mapping database includes:

[0029] Based on the relative wind speed and the relative wind direction, determine the wind noise spectrum characteristic parameters of the environment;

[0030] The target filter configuration parameters are determined based on the wind noise spectrum characteristic parameters and the filter strategy mapping database.

[0031] In one embodiment, determining the environmental wind noise spectrum characteristic parameters based on the relative wind speed and the relative wind direction includes:

[0032] Based on the relative wind speed and the relative wind direction, a matching process is performed in a preset wind noise spectrum feature database to determine the wind noise spectrum feature parameters, or

[0033] The relative wind speed and relative wind direction are input into a preset wind noise spectrum feature fitting model for prediction processing to determine the wind noise spectrum feature parameters.

[0034] In one embodiment, the above-described environment-aware microphone filtering method further includes:

[0035] Based on the target filtering configuration parameters, an audio signal filtering control command is generated and fed back to the audio codec unit in the terminal for real-time pre-filtering processing of the microphone in the terminal.

[0036] Embodiments of the present invention also provide a terminal, including an inertial measurement unit, a navigation and positioning unit, and a data processing unit, wherein the inertial measurement unit and the navigation and positioning unit are respectively communicatively connected to the data processing unit; wherein,

[0037] The inertial measurement unit is used to acquire real-time motion state data of the terminal, including real-time angular velocity information and real-time acceleration information of the terminal.

[0038] The navigation and positioning unit is used to obtain the terminal's real-time geographical location information and the current environmental data corresponding to the geographical location information. The environmental data includes real-time environmental wind speed and real-time environmental wind direction.

[0039] The data processing unit is used to determine the relative wind speed and relative wind direction of the terminal relative to the current environment based on the real-time motion state data, the real-time environmental wind direction, and the real-time environmental wind direction; to determine the target filtering configuration parameters of the microphone in the terminal based on the relative wind speed, the relative wind direction, and a preset filtering strategy mapping database; and to perform real-time pre-filtering processing on the raw audio signal collected by the microphone based on the target filtering configuration parameters to obtain the target audio signal relative to the wind speed and relative wind direction.

[0040] The above-described solution of the present invention has at least the following beneficial effects:

[0041] 1. The filter configuration parameters of the predictive filter based on the device's motion status and external environment data improve the accuracy of microphone filter adjustment, avoid discomfort caused by sudden volume changes due to gusts of wind or acceleration, effectively improve the clarity of the acquired audio signal, and thus enhance the user experience.

[0042] 2. By integrating multi-source data (GNSS navigation and positioning unit, IMU inertial measurement unit, cloud network data) for comprehensive adjustment, automated and intelligent adjustment can be achieved, thereby improving the safety of equipment use.

[0043] 3. Volume adjustment is based on a determined ambient wind noise intensity value, avoiding overcompensation and ineffective volume boosting in low wind noise scenarios such as tailwinds, thereby improving adjustment efficiency and reducing adjustment energy consumption and cost; at the same time, real-time perception and prediction based on the external environment, rather than post-event analysis, solves the inherent lag problem of traditional solutions and achieves real-time suppression of wind noise in real-time acquired audio signals.

[0044] It should be understood that the implementation of any embodiment of the present invention does not mean that it will simultaneously possess or achieve multiple or all of the above-mentioned beneficial effects. Attached Figure Description

[0045] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0046] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0047] Figure 1 This is a schematic flowchart of the microphone filtering method based on environment perception provided in an embodiment of the present invention;

[0048] Figure 2 This is a terminal architecture diagram provided in an optional embodiment of the present invention;

[0049] Figure 3 This is a flowchart of a terminal filtering the original audio signal during use, provided in an optional embodiment of the present invention;

[0050] Figure 4 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0051] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0052] It should be understood that the terms "comprising / including," "consisting of," or any other variations are intended to cover non-exclusive inclusion, such that a product, apparatus, process, or method that comprises a list of elements includes not only those elements but may also include, where necessary, other elements not expressly listed, or elements inherent to such a product, apparatus, process, or method. Without further limitation, an element defined by the phrases "comprising / including," "consisting of," does not exclude the presence of additional identical elements in the product, apparatus, process, or method that includes said element.

[0053] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0054] like Figure 1 As shown, an embodiment of the present invention proposes a microphone filtering method based on environment perception, comprising:

[0055] Step 11: Obtain the real-time motion status data of the terminal, which includes the real-time angular velocity information and real-time acceleration information of the terminal.

[0056] Step 12: Obtain the terminal's real-time geographical location information and the corresponding current environmental data, including real-time environmental wind speed and real-time environmental wind direction.

[0057] Step 13: Based on real-time motion status data, real-time environmental wind direction, and real-time environmental wind direction, determine the relative wind speed and relative wind direction of the terminal relative to the current environment;

[0058] Step 14: Determine the target filtering configuration parameters of the microphone in the terminal based on the relative wind speed, relative wind direction, and preset filtering strategy mapping database;

[0059] Step 15: Perform real-time pre-filtering on the raw audio signal acquired by the microphone according to the target filtering configuration parameters to obtain the target audio signal.

[0060] In this embodiment, the terminal can be a carrier for user interaction, including but not limited to vehicle terminals, mobile terminals, wearable devices, such as mobile phones, headphones, smartwatches, smart glasses, smart helmets, etc.

[0061] Here, environmental data can be meteorological data corresponding to real-time geographic location information, which can be obtained through the cloud. It should be understood that meteorological data is consistent within a certain area; that is, in this embodiment, the environmental data corresponding to real-time geographic location information obtained within a certain area should be consistent. Here, real-time motion state data can be obtained through real-time measurement using accelerometers and gyroscopes.

[0062] During the movement of the terminal while the user is using it (cycling, driving, or gliding), the motion state data generated by the terminal during the movement is fused and calculated based on environmental data such as wind speed and wind direction. This allows for the real-time acquisition of the terminal's relative wind speed and direction relative to the current environment. Furthermore, based on the relative wind direction and relative wind speed, the wind noise spectrum characteristic parameters generated by the current environment on the moving terminal can be determined first. Then, based on the wind noise spectrum characteristic parameters, the target filtering configuration parameters of the microphone in the terminal can be determined. This allows the microphone to perform pre-filtering adjustment on the acquired audio signal to remove noise from the acquired audio signal, thereby improving the accuracy of the audio signal acquisition and ensuring the clarity of the final output audio.

[0063] Noise filtering is applied to the real-time audio signal based on real-time motion and environmental data, avoiding the use of fixed noise reduction modes or manual adjustment for volume control. This ensures timely audio filtering and thus guarantees the clarity of the audio signal, while also improving the user experience.

[0064] In an optional embodiment of the present invention, step 13 above may include:

[0065] Step 131: Based on the real-time motion state data, determine the real-time motion direction and speed of the terminal. By integrating and calculating the real-time angular velocity and acceleration information in the real-time motion state data, the terminal's motion direction and speed can be accurately obtained, thereby ensuring the accuracy of subsequent prediction of wind noise intensity values ​​and further ensuring the accuracy of volume adjustment.

[0066] In an optional embodiment of the present invention, step 131 above may include:

[0067] Step 1311: Determine the real-time motion direction of the terminal based on the real-time angular velocity information;

[0068] Step 1312: Determine the real-time motion speed of the terminal based on the real-time acceleration information.

[0069] Here, real-time angular velocity information can include the angular velocities of the terminal on three orthogonal axes (x-axis, y-axis, and z-axis). Through integration calculation and attitude determination, the real-time motion direction of the terminal can be determined, thereby ensuring the accuracy of subsequent wind noise intensity prediction; here, real-time motion velocity should be a scalar of motion velocity.

[0070] Specifically, step 1311 above may include:

[0071] Step 13111: Determine the real-time attitude angle of the terminal based on the real-time angular velocity information;

[0072] Step 13112: Calculate the real-time attitude angle based on the preset quaternion representation to obtain the real-time motion direction of the terminal.

[0073] Here, the real-time angular velocity information is first integrated to convert the angular velocity into a real-time attitude angle; preferably, it can be calculated using the following formula:

[0074]

[0075] in, This represents the attitude angle at time t; Indicates the initial attitude angle; This represents the real-time angular velocity at time t. Here, the real-time attitude angles can include the heading angle, pitch angle, and roll angle. The heading angle can represent the rotation angle of the terminal on the horizontal plane, usually with the north direction as the reference point. Therefore, the real-time motion direction of the terminal on the horizontal plane can be determined based on the heading angle, thereby ensuring the accuracy of subsequent prediction of environmental wind noise intensity values.

[0076] Furthermore, the real-time attitude angles can be calculated using a preset quaternion representation. Specifically:

[0077] Step 131121, initialize quaternion q0 = [q w0 q x0 q y0 q z0 ], where q w0 It is the real part, q x0 q y0 q z0 It is the imaginary part;

[0078] Step 131122, using the formula: q1(t)=q0(t-Δt) Δq updates the initial quaternion; where Δq is a small rotational quaternion generated by the measured angular velocity; q(t) represents the updated quaternion;

[0079] Step 131123, using the formula: q2(t)=q1(t)+Δqcorr The corrected quaternion; where q2(t) represents the corrected quaternion, Δq corr The correction parameter can be calculated based on the acquired real-time acceleration information using optimization algorithms (such as gradient descent).

[0080] Step 131124, according to the formula: Yaw=arctan2(2(q w2 q z2 +q x2 q y2 ), 1-2 ( + The corrected quaternion is solved to obtain the heading angle; where Yaw represents the heading angle.

[0081] In this embodiment, the real-time motion direction of the terminal can be accurately determined through integral calculation and attitude calculation; at the same time, the quaternion representation has good scalability and can be applied to various complex motion scenarios and motion environments, further improving the accuracy of wind noise intensity prediction in extremely high environments.

[0082] In an optional embodiment of the present invention, step 131 above may include:

[0083] Step 1312: Determine the real-time motion speed of the terminal based on the real-time acceleration information.

[0084] Here, the real-time angular velocity information can be integrated to obtain the terminal's real-time velocity information; specifically, it can be expressed by the following formula:

[0085] v(t)=v(t0)+ dτ;

[0086] Where v(t) represents the real-time velocity information at time t; v(t0) represents the initial velocity information; a(t) represents the real-time acceleration information at time t; and Δt represents the sampling time interval.

[0087] In an optional embodiment of the present invention, step 13 above may include:

[0088] Step 132: Determine the relative wind speed and relative wind direction of the terminal relative to the current environment based on the real-time movement direction, real-time movement speed, real-time ambient wind direction, and real-time ambient wind direction.

[0089] Here, the real-time motion direction, real-time motion speed, real-time ambient wind direction, and real-time ambient wind speed are fused and calculated, which may specifically include:

[0090] Step 1321: Determine the first motion vector based on the real-time motion direction and real-time motion speed;

[0091] Step 1322: Determine the second motion vector based on the real-time ambient wind direction;

[0092] Step 1323: Determine the relative wind speed and relative wind direction based on the first motion vector and the second motion vector.

[0093] In this embodiment, the scalar values ​​of real-time motion direction and real-time motion speed are combined to construct a motion vector, which is the first motion vector; the scalar values ​​of real-time environmental wind direction and real-time environmental wind speed are combined to construct an environmental vector, which is the second motion vector.

[0094] Furthermore, the first motion vector and the second motion vector are vector synthesized to obtain the relative wind speed vector, which specifically includes the relative wind speed and the relative wind direction; here, the relative wind speed is the motion speed scalar.

[0095] The specific process of vector synthesis can be represented as follows:

[0096] Vrelative = Vwind + Vmotion; where Vrelative represents the relative wind speed vector; Vwind represents the second motion vector; and Vmotion represents the first motion vector.

[0097] θ rel =arctan2(V rel,y V rel,x );

[0098] θ rel Indicates relative wind direction; V rel,y V represents the component of the relative wind speed vector on the y-axis; rel,x This represents the component of the relative wind speed vector on the x-axis; here, the relative wind speed (scalar value) can be obtained by synthesizing the relative wind speed vectors.

[0099] In an optional embodiment of the present invention, step 14 above may include:

[0100] Step 141: Determine the wind noise spectrum characteristic parameters of the environment based on the relative wind speed and the relative wind direction.

[0101] Specifically, it includes:

[0102] Step 1411a: Based on the relative wind speed and relative wind direction, match the data in a preset wind noise spectrum feature database to determine the wind noise spectrum feature parameters; or

[0103] Step 1411b involves inputting the relative wind speed and relative wind direction into a preset wind noise spectrum feature fitting model for prediction processing to determine the wind noise spectrum feature parameters.

[0104] Here, the preset wind noise spectrum feature database can be obtained based on wind tunnel experiments and pre-stored in the terminal. This preset wind noise spectrum feature database contains multiple sets of relative wind speeds, relative wind directions, and corresponding combinations of wind noise spectrum feature parameters. By pre-storing the preset wind noise spectrum feature database in the terminal, it is possible to retrieve, search, and match in a timely manner, thus achieving timely filtering and noise reduction. Here, during the actual search and matching, the final wind noise spectrum feature parameters can be determined by interpolation.

[0105] Here, the data obtained from the wind tunnel experiment can also be fitted and trained to obtain a preset wind noise spectrum feature fitting model (that is, a mathematical fitting model of relative wind speed, relative wind direction and wind noise spectrum feature parameters); preferably, the relative wind speed, relative wind direction and wind noise intensity values ​​obtained from the experiment can be fitted according to a preset multi-fit model and a preset algorithm to obtain a preset wind noise spectrum feature fitting model.

[0106] Here, the experimental data obtained from the experiment can first be preprocessed to reduce noise and error; preferably, the training data can be filtered and preprocessed using Kalman or moving average methods.

[0107] Preferably, the preset multinomial fitting model can be expressed as:

[0108] dB0 = a0 + a1 × V x +a2×θ x +a3× +a4× +a5×V x ×θ x ;

[0109] Where dB0 represents the wind noise spectrum characteristic parameter obtained from the experiment; V x θ represents the relative wind speed obtained from the experiment. x The relative wind direction obtained from the experiment is represented by a1, a2, a3, a4, and a5, which represent the parameters of a pre-defined multinomial fitting model. These model parameters can be solved using matrix operations or numerical optimization methods, such as the gradient descent algorithm.

[0110] Here, the acquired experimental data can be grouped to facilitate fitting calculations; each group of experimental data includes a relative wind speed, a relative wind direction, and corresponding wind noise spectrum characteristic parameters; preferably, the least squares method can be used for fitting to minimize the sum of squared errors; specifically, it can be expressed as:

[0111] min=

[0112] Where N represents the number of training data sets; Represents the relative velocity in the experimental data of the i-th group; Let i represent the relative wind direction in the i-th set of experimental data; i = 1, 2, 3, ..., N.

[0113] By fitting experimental data, a preset wind noise spectrum feature fitting model is obtained to accurately predict wind noise spectrum feature parameters. The model obtained after fitting can be applied to different terminal devices, and the prediction calculation speed is fast, making it suitable for real-time systems.

[0114] Here, after obtaining the trained preset wind noise spectrum feature fitting model, the relative wind speed and relative wind direction calculated in the above embodiment are used as variables and input into the preset wind noise spectrum feature fitting model for prediction processing to obtain the predicted wind noise spectrum feature parameters.

[0115] In an optional embodiment of the present invention, step 141 above may include:

[0116] Step 142: Determine the target filter configuration parameters based on the wind noise spectrum characteristic parameters and the filter strategy mapping database. Here, the filter strategy mapping database is obtained by combining the historical filter configuration parameters of the microphone's filters with the corresponding historical wind noise spectrum characteristic parameters. This database is pre-stored in the terminal and contains multiple sets of historical filter configuration parameters and corresponding historical wind noise spectrum characteristic parameters, facilitating the matching and search for the target filter configuration parameters based on the determined wind noise spectrum characteristic parameters. In an optional embodiment of the present invention, based on steps 11 to 15 above, it may further include:

[0117] Step 16: Generate audio signal filtering control instructions based on the target filtering configuration parameters, and feed the audio signal filtering control instructions back to the audio codec unit in the terminal to perform real-time pre-filtering processing on the microphone in the terminal, thereby reducing noise in the acquired original audio signal and ensuring the clarity of the audio signal.

[0118] like Figure 2As shown, embodiments of the present invention also provide a terminal, which may include an inertial measurement unit, a navigation and positioning unit, and a data processing unit. The inertial measurement unit and the navigation and positioning unit are communicatively connected to the data processing unit. The inertial measurement unit is used to acquire real-time motion state data of the terminal, including real-time angular velocity information and real-time acceleration information of the device. The navigation and positioning unit is used to acquire real-time geographical location information of the terminal and corresponding current environmental data, including real-time environmental wind speed and real-time environmental wind direction. The data processing unit is used to determine the relative wind speed and relative wind direction of the terminal relative to the current environment based on the real-time motion state data, real-time environmental wind direction, and real-time environmental wind direction; determine the target filtering configuration parameters of the microphone in the terminal based on the relative wind speed, relative wind direction, and a preset filtering strategy mapping database; and perform real-time pre-filtering processing on the raw audio signal collected by the microphone according to the target filtering configuration parameters to obtain the target audio signal relative wind speed and relative wind direction.

[0119] Furthermore, the data processing unit can also be used to generate filter adjustment control commands based on the target filter configuration parameters, and feed the filter adjustment control commands back to the audio codec unit in the terminal for real-time pre-filtering processing of the microphone in the terminal.

[0120] Here, the terminal may also include a memory unit, a storage unit, and an audio codec unit. The storage unit and the audio codec unit can be communicatively connected to the data processing unit, and the audio codec unit is also communicatively connected to the microphone for pre-filtering.

[0121] Preferably, the terminal has a user interface to receive user settings or commands, and to enable or disable the terminal's audio signal filtering function; the terminal integrates a data processing unit, a navigation and positioning unit, an inertial measurement unit, an audio codec unit, and a storage unit; the terminal is also equipped with a microphone and a user switch, which can be a physical button or a touch area on the interface; before using the terminal, the user switch is turned on to facilitate audio signal filtering during use.

[0122] The data processing unit can be a main control chip (MCU), such as the ESP32 series, which integrates a processor, Wi-Fi or Bluetooth functions to transmit, process and convert data, and generate and transmit control commands. The data processing unit can also communicate with a cloud server and, after receiving the real-time geographical location information of the terminal sent by the navigation and positioning unit, request the cloud server to send environmental data within the area corresponding to the real-time geographical location information.

[0123] The navigation and positioning unit can be a GNSS (Global Navigation Satellite System) module, such as U-blox NEO-6M, to provide the terminal with real-time geographic location information and then feed it back to the data processing unit after obtaining the real-time geographic location information.

[0124] The inertial measurement unit can be an IMU (Inertial Measurement Unit) module, such as the MPU-605, which has a built-in three-axis gyroscope and a three-axis accelerometer to monitor the motion status data of the terminal in real time and feed it back to the data processing unit.

[0125] The audio codec unit can be a codec with programmable and filtering functions.

[0126] The storage unit can be a memory to store relative wind speed, relative wind direction, wind noise spectrum characteristic parameters, and wind noise filtering configuration parameters obtained from multiple wind tunnel experiments.

[0127] like Figure 3 As shown, the process of filtering the acquired raw audio signal during the use of this terminal is as follows:

[0128] Step 301, User sets automatic audio signal filtering function to start: The user turns on the user switch in the interactive interface to set the automatic filtering of the acquired audio signal to start, and generates an start command to be fed back to the data processing unit.

[0129] Step 302, Data processing unit starts: After receiving the start command, the data processing unit sends the command to the navigation and positioning unit to obtain the real-time geographical location information of the terminal;

[0130] Step 303, the data processing unit obtains data from the navigation and positioning unit: the navigation and positioning unit sends real-time geographic location information to the data processing unit;

[0131] Step 304: The data processing unit obtains environmental data from the local cloud server based on real-time geographic location information;

[0132] Step 305, the data processing unit acquires data from the inertial measurement unit: the data processing unit sends an information acquisition command to the inertial measurement unit, and the inertial measurement unit transmits the real-time motion state data of the terminal to the data processing unit;

[0133] Step 306, the data processing unit performs fusion calculation: the data processing unit performs fusion calculation with the acquired environmental data (real-time environmental wind speed and real-time environmental wind direction) and real-time motion state data to calculate the real-time relative wind speed and real-time relative wind direction of the terminal, and performs wind noise spectrum characteristic parameter search or prediction processing; further, the target filter configuration parameters are determined based on the wind noise spectrum characteristic parameters.

[0134] Step 307: The data processing unit generates and sends audio signal filtering control instructions: The data processing unit generates audio signal filtering adjustment control instructions according to the filtering configuration parameters, and sends the audio signal filtering control instructions to the audio encoding and decoding unit;

[0135] Step 308, the audio codec unit executes the audio signal filtering control instruction: the audio codec unit performs audio signal filtering processing of the microphone according to the audio signal filtering control instruction.

[0136] Step 309: The system is set to execute the above process from steps 301 to 308 once in a preset time period. At the same time, it is set to trigger calculation immediately when the inertial measurement unit detects a significant change in speed / direction, thereby realizing dynamic and real-time adjustment.

[0137] Figure 4 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown, the computing device being used to perform... Figure 1 or Figure 3 At least one operation in. For example... Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for device operation. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0138] Multiple components in the electronic device are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, optical disk, etc.; and a communication unit 409, such as a network interface card, modem, wireless transceiver, etc. The communication unit 409 allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0139] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the methods of the various embodiments of this disclosure. For example, in some embodiments, the methods of the various embodiments of this disclosure may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods of the various embodiments of this disclosure described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the methods of the various embodiments of this disclosure by any other suitable means (e.g., by means of firmware).

[0140] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0142] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0143] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0144] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0145] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0146] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0147] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

[0148] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A microphone filtering method based on environmental perception, characterized in that, Applied to terminals, including: Acquire real-time motion state data of the terminal, the real-time motion state data including the terminal's real-time angular velocity information and real-time acceleration information; The terminal's real-time geographic location information and the corresponding current meteorological data are obtained, including real-time ambient wind speed and real-time ambient wind direction. Based on the real-time motion status data, the real-time motion direction and real-time motion speed of the terminal are determined. Based on the real-time movement direction, the real-time movement speed, the real-time ambient wind speed, and the real-time ambient wind direction, the relative wind speed and relative wind direction of the terminal relative to the current environment are determined; Based on the relative wind speed, the relative wind direction, and the preset filtering strategy mapping database, the target filtering configuration parameters of the microphone in the terminal are determined. The original audio signal acquired by the microphone is pre-filtered in real time according to the target filtering configuration parameters to obtain the target audio signal, so as to achieve real-time suppression of wind noise in the real-time acquired audio signal. The target filtering configuration parameters of the microphone in the terminal are determined based on the relative wind speed, the relative wind direction, and the preset filtering strategy mapping database, including: Based on the relative wind speed and relative wind direction, matching is performed in a preset wind noise spectrum feature database to determine wind noise spectrum feature parameters, or the relative wind speed and relative wind direction are input into a preset wind noise spectrum feature fitting model for prediction processing to determine wind noise spectrum feature parameters; the preset wind noise spectrum feature database is obtained based on wind tunnel experimental tests, and the preset wind noise spectrum feature fitting model is obtained by fitting the relative wind speed, relative wind direction, and wind noise intensity values ​​obtained from the wind tunnel experiment according to a preset multi-variable fitting model and a preset algorithm. The target filter configuration parameters are determined based on the wind noise spectrum characteristic parameters and the preset filter strategy mapping database.

2. The microphone filtering method based on environmental perception according to claim 1, characterized in that, Based on the real-time motion state data, the real-time motion direction and real-time motion speed of the terminal are determined, including: Based on the real-time angular velocity information, the real-time direction of motion of the terminal is determined; The real-time speed of the terminal is determined based on the real-time acceleration information.

3. The microphone filtering method based on environmental perception according to claim 2, characterized in that, Determining the real-time motion direction of the terminal based on the real-time angular velocity information includes: The real-time attitude angle of the terminal is determined based on the real-time angular velocity information. The real-time attitude angle is calculated based on a preset quaternion representation to obtain the real-time motion direction of the terminal.

4. The microphone filtering method based on environmental perception according to claim 1, characterized in that, Based on the real-time movement direction, the real-time movement speed, the real-time ambient wind speed, and the real-time ambient wind direction, the relative wind speed and relative wind direction of the terminal relative to the current environment are determined, including: The first motion vector is determined based on the real-time motion direction and the real-time motion speed; The second motion vector is determined based on the real-time ambient wind speed and the real-time ambient wind direction; The relative wind speed and the relative wind direction are determined based on the first motion vector and the second motion vector.

5. The microphone filtering method based on environmental perception according to claim 4, characterized in that, Determining the relative wind speed and the relative wind direction based on the first motion vector and the second motion vector includes: The relative wind speed vector is determined by the formula: Vrelative = Vwind + Vmotion; where Vrelative represents the relative wind speed vector; Vwind represents the second motion vector; and Vmotion represents the first motion vector. The relative wind speed vector is decomposed and synthesized to obtain the relative wind speed and the relative wind direction.

6. The microphone filtering method based on environment perception according to claim 1, characterized in that, Also includes: Based on the target filtering configuration parameters, an audio signal filtering control command is generated and fed back to the audio codec unit in the terminal for real-time pre-filtering processing of the microphone in the terminal.

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