Wind noise suppression methods, devices, equipment and storage media

Abstract

This application discloses a wind noise suppression method, apparatus, device, and storage medium, relating to the field of smart device technology. The method includes: detecting the degree of temporal waveform abrupt changes in the pickup data of each pickup component integrated in the smart device; determining the peak factor of the temporal waveform of each channel in each frame based on the detection results; selecting target channels with peak factors greater than a preset peak factor threshold and removing abrupt waveform changes in the target channels; and performing wind noise suppression based on the removed target channels and a target beamforming algorithm. Through this method, due to the influence of wind noise, the pickup components integrated in the smart device exhibit differences, and waveform abrupt changes occur in different channels. In this case, removing the abrupt waveform changes in the target channels from the temporal dimension, and then combining this with a target beamforming algorithm for wind noise suppression, effectively improves the accuracy of wind noise suppression and ensures voice fidelity compared to existing technologies that use pure beamforming algorithms for wind noise suppression.

Description

Technical Field

[0001] This application relates to the field of smart device technology, and in particular to wind noise suppression methods, apparatus, devices and storage media. Background Technology

[0002] As the functions of smart devices continue to improve, they are becoming increasingly popular, such as true wireless Bluetooth earbuds, smart speakers, and smart glasses. However, users are inevitably affected by the surrounding environment when wearing these smart devices, such as noisy human voices and the "whooshing" wind noise generated by running. Currently, the common method for wind noise suppression is based on pure beamforming algorithms. However, under the influence of wind noise, even after using pure beamforming algorithms to suppress wind noise, there will still be residual wind noise. Therefore, the accuracy of the above methods for wind noise suppression is relatively low.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a wind noise suppression method, apparatus, device, and storage medium, which aims to solve the technical problem of low accuracy in wind noise suppression in the prior art.

[0005] To achieve the above objectives, this application proposes a wind noise suppression method, the method comprising:

[0006] Acquire the pickup data of each pickup component integrated in the smart device, and detect the degree of temporal waveform change in the pickup data of each pickup component;

[0007] The peak factor of the time-domain waveform of each channel in each frame is determined based on the detection results;

[0008] Target channels with peak factors greater than a preset peak factor threshold are selected, and abrupt waveform changes in the target channels are removed.

[0009] Wind noise suppression is performed based on the erased target channel and the target beamforming algorithm.

[0010] In one embodiment, the step of determining the peak factor of the temporal waveform of each channel for each frame based on the detection results includes:

[0011] Based on the detection results, determine the temporal waveform data of each sampling point within the frame of each channel;

[0012] Obtain the number of sampling points in each frame;

[0013] Based on the target channel frame algorithm, the root mean square value of the channel frame is calculated according to the time-domain waveform data and the number of sampling points;

[0014] The peak factor of the time-domain waveform of each channel in each frame is calculated based on the root mean square value of the channel frame and the peak value of the target channel frame.

[0015] In one embodiment, before the step of filtering target channels where the peak factor is greater than a preset peak factor threshold, the method further includes:

[0016] Acquire peak factor distribution data under wind noise scenario, and determine the current peak factor based on the peak factor distribution data under wind noise scenario;

[0017] Acquire peak factor distribution data under wind-free scenarios, and determine the target peak factor based on the peak factor distribution data under wind-free scenarios;

[0018] The threshold setting range is determined based on the current peak factor and the target peak factor, and the preset peak factor threshold is set based on the threshold setting range.

[0019] In one embodiment, the step of erasing the abrupt waveform of the target channel includes:

[0020] The location of the time-domain waveform abrupt change in the target channel is determined based on the detection results;

[0021] The abrupt waveform of the target channel is erased based on the abrupt change position of the time-domain waveform.

[0022] In one embodiment, after the step of erasing the abrupt waveform of the target channel based on the abrupt change position of the time-domain waveform, the method further includes:

[0023] Obtain the set of sampling points for the target channel;

[0024] Select the target number of initial reference sampling points from the set of sampling points;

[0025] The target time-domain normal waveform is generated based on the initial reference sampling points using a target spline interpolation algorithm.

[0026] Based on the target time-domain normal waveform and the abrupt change position of the time-domain waveform, the waveform of the erased target channel is completed.

[0027] In one embodiment, the step of suppressing wind noise based on the erased target channel and the target beamforming algorithm includes:

[0028] Acquire the time-domain waveform data of the target channel after erasure and the time-domain waveform data of other normal channels;

[0029] A microphone array is generated based on the location information of each microphone component integrated into the smart device;

[0030] According to the target beamforming algorithm of the cascaded pickup component array;

[0031] Wind noise suppression is performed based on the time-domain waveform data of the target channel after erasure, the time-domain waveform data of other normal channels, and the target beamforming algorithm.

[0032] In one embodiment, after the step of suppressing wind noise based on the erased target channel and the target beamforming algorithm, the method further includes:

[0033] Acquire speech information after wind noise suppression;

[0034] Wind noise detection is performed on the voice information;

[0035] When the detection result shows that there is no residual wind noise, the fidelity of the voice information is detected to obtain the current fidelity feature value.

[0036] When the current fidelity feature value is greater than a preset value, the voice information is played through the playback component integrated into the smart device.

[0037] Furthermore, to achieve the above objectives, this application also proposes a wind noise suppression device, which includes:

[0038] The acquisition module is used to acquire the pickup data of each pickup component integrated in the smart device, and to detect the degree of temporal waveform change in the pickup data of each pickup component.

[0039] The determination module is used to determine the peak factor of the time-domain waveform of each channel in each frame based on the detection results;

[0040] The erasure module is used to filter target channels whose peak factor is greater than a preset peak factor threshold, and to erase the abrupt waveforms of the target channels.

[0041] The suppression module is used to suppress wind noise based on the erased target channel and the target beamforming algorithm.

[0042] In addition, to achieve the above objectives, this application also proposes a wind noise suppression device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the wind noise suppression method as described above.

[0043] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the wind noise suppression method described above.

[0044] One or more technical solutions proposed in this application have at least the following technical effects: acquiring the pickup data of each pickup component integrated in a smart device, and detecting the degree of temporal waveform abrupt change in the pickup data of each pickup component; determining the peak factor of the temporal waveform of each channel in each frame based on the detection results; screening target channels whose peak factors are greater than a preset peak factor threshold, and removing the abrupt waveforms of the target channels; and performing wind noise suppression based on the removed target channels and a target beamforming algorithm. Through the above method, due to the influence of wind noise, the pickup components integrated in the smart device are different and waveform abrupt changes occur in different channels. In this case, removing the abrupt waveforms of target channels whose peak factors are greater than a preset peak factor threshold from the temporal dimension, and then combining this with a target beamforming algorithm for wind noise suppression, can effectively improve the accuracy of wind noise suppression and ensure voice fidelity compared to existing technologies that use pure beamforming algorithms for wind noise suppression. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating an embodiment of the wind noise suppression method of this application.

[0048] Figure 2 This is a flowchart illustrating Embodiment 2 of the wind noise suppression method of this application;

[0049] Figure 3 This is a schematic diagram of the module structure of the wind noise suppression device according to an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the wind noise suppression method in the embodiments of this application.

[0051] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or wind noise suppression device capable of performing the above functions. The following description uses a wind noise suppression device as an example to illustrate this embodiment and the subsequent embodiments.

[0053] Based on this, the embodiments of this application provide a wind noise suppression method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the wind noise suppression method of this application.

[0054] In this embodiment, the wind noise suppression method includes steps S10 to S40:

[0055] Step S10: Obtain the pickup data of each pickup component integrated in the smart device, and detect the degree of temporal waveform change in the pickup data of each pickup component.

[0056] It should be noted that the smart device can be a true wireless Bluetooth headset, smart speaker, smart glasses, etc. with an embedded real-time platform. The wind noise suppression method mentioned above can be quickly deployed to the embedded real-time platform. The sound pickup component can be a microphone. There can be multiple sound pickup components integrated into the smart device, such as MIC1, MIC2, MIC3, ..., MICN. After each sound pickup component integrated into the smart device is started, it will pick up sound in real time and detect the degree of temporal waveform change in the sound pickup data of each sound pickup component.

[0057] Step S20: Determine the peak factor of the time-domain waveform of each channel for each frame based on the detection results.

[0058] It is understandable that the peak factor refers to a key indicator for measuring the degree of abrupt changes in the time-domain waveform. After detecting the degree of abrupt changes in the time-domain waveform of the pickup data of each pickup component, the peak factor of the time-domain waveform of each channel for each frame can be determined based on the detection results.

[0059] Further, step S20 includes: determining the temporal waveform data of each sampling point within the frame of each channel based on the detection results; obtaining the number of sampling points in each frame; calculating the root mean square value of the channel frame based on the target channel frame algorithm, according to the temporal waveform data and the number of sampling points; and calculating the peak factor of the temporal waveform of each frame of each channel based on the root mean square value of the channel frame and the peak value of the target channel frame.

[0060] It should be understood that the target channel frame algorithm refers to the algorithm used to calculate the root mean square value of the channel frame, which can be expressed as:

[0061] .

[0062] in, Represents the root mean square value of the channel frame. Indicates the number of sampling points. This represents the time-domain waveform data of each sampling point within the frame of each channel.

[0063] It should be noted that the aforementioned root mean square (RMS) value of the channel frames can also be called the RMS value of the channel frames, which is a quantitative indicator used to measure the average energy of the time-domain waveform. The target channel frame peak value refers to the maximum absolute value of all sampling points in a frame of the time-domain waveform of a channel. Then, based on the channel frame RMS value and the target channel frame peak value, the peak factor of each frame of the time-domain waveform of each channel is calculated, specifically as follows:

[0064] .

[0065] in, This represents the peak value factor of the time-domain waveform of each channel in each frame. Indicates the peak value of the target channel frame. This represents the root mean square value of the channel frame.

[0066] Step S30: Filter target channels whose peak factor is greater than a preset peak factor threshold, and remove abrupt waveforms of the target channels.

[0067] It should be understood that after obtaining the peak factor of the time-domain waveform of each channel for each frame, the peak factor of the time-domain waveform of each channel for each frame is compared sequentially with a preset peak factor threshold to filter out target channels whose peak factors are greater than the preset peak factor threshold. Specifically, this can be expressed as:

[0068] .

[0069] in, This indicates the preset peak factor threshold. This represents the peak value factor of the time-domain waveform of each channel in each frame. This indicates the target channel whose peak factor is greater than the preset peak factor threshold.

[0070] It should be noted that the target channel represents the channel with a large degree of abrupt change. In the spectrum, the abrupt change waveform of the target channel is displayed as vertical bars, which can be called vertical bar noise. These vertical bar noises do not appear simultaneously on the channels of each pickup component. In order to effectively improve the accuracy of wind noise suppression, it is necessary to eliminate the above-mentioned vertical bar noise. In this embodiment, a target spline interpolation algorithm can be used to remove the abrupt change waveform of the target channel.

[0071] Furthermore, before the step of filtering target channels where the peak factor is greater than a preset peak factor threshold, the method further includes: acquiring peak factor distribution data under wind noise scenarios and determining the current peak factor based on the peak factor distribution data under wind noise scenarios; acquiring peak factor distribution data under wind-free scenarios and determining the target peak factor based on the peak factor distribution data under wind-free scenarios; determining a threshold setting range based on the current peak factor and the target peak factor, and setting a preset peak factor threshold based on the threshold setting range.

[0072] It is understood that, in this embodiment, a preset peak factor threshold can be set based on the difference in peak factor distribution data between wind noise scenarios and windless scenarios. Specifically, the current peak factor is determined based on the peak factor distribution data in wind noise scenarios, and the target peak factor is determined based on the peak factor distribution data in windless scenarios. The current peak factor can be the minimum peak factor in the peak factor distribution data in wind noise scenarios, and similarly, the target peak factor can be the maximum peak factor in the peak factor distribution data in windless scenarios.

[0073] It should be understood that, in order to ensure that the mutation waveform is accurately identified, this embodiment determines the threshold setting range based on the current peak factor and the target peak factor, and sets the preset peak factor threshold based on the threshold setting range. For example, if the current peak factor is 14dB and the target peak factor is 10dB, then the threshold setting range is [10,14], and the preset peak factor threshold set at this time can be 12dB.

[0074] Furthermore, the step of erasing the abrupt waveform of the target channel includes: determining the time-domain waveform abrupt position of the target channel based on the detection result; and erasing the abrupt waveform of the target channel based on the time-domain waveform abrupt position.

[0075] It should be understood that the time-domain waveform change position refers to the position where the time-domain waveform of the target channel changes significantly. The cause of the change could be that wind noise causes the diaphragm of the pickup component to vibrate violently, generating high-amplitude instantaneous noise. In this case, the change waveform of the target channel is erased according to the time-domain waveform change position.

[0076] Furthermore, after the step of erasing the abrupt waveform of the target channel based on the abrupt waveform position in the time domain, the method further includes: obtaining a set of sampling points for the target channel; selecting a target number of initial reference sampling points from the set of sampling points; generating a target normal time domain waveform based on the initial reference sampling points using a target spline interpolation algorithm; and completing the waveform of the erased target channel based on the target normal time domain waveform and the abrupt waveform position in the time domain.

[0077] Understandably, the target spline interpolation algorithm is a piecewise polynomial interpolation algorithm, specifically a cubic spline interpolation algorithm. After obtaining the set of sampling points for the target channel, an initial number of reference sampling points is selected from it. Taking one target channel as an example, the target number of initial reference sampling points can be init_N (init_N<=8), achieving the effect of reducing the computational load. At this point, the target time-domain normal waveform can be generated using the target spline interpolation algorithm, which can be specifically represented as:

[0078] .

[0079] in, This represents the target's normal time-domain waveform. Indicates the initial reference sampling point. This represents the target spline interpolation algorithm.

[0080] Step S40: Suppress wind noise based on the erased target channel and the target beamforming algorithm.

[0081] Understandably, after removing abrupt waveforms in the target channel and completing the normal waveform in the target time domain, a cascaded target beamforming algorithm is used to suppress wind noise. The target beamforming algorithm can be a common beamforming algorithm, such as Minimum Variance Distortionless Response (MVDR) or Generalized Sidelobe Canceller (GSC), to suppress sudden and directional wind noise and significantly improve speech clarity.

[0082] Further, step S40 includes: acquiring time-domain waveform data of the erased target channel and time-domain waveform data of other normal channels; generating a pickup component array based on the position information of each pickup component integrated on the smart device; cascading a target beamforming algorithm based on the pickup component array; and performing wind noise suppression based on the time-domain waveform data of the erased target channel, the time-domain waveform data of other normal channels, and the target beamforming algorithm.

[0083] It should be understood that the microphone array refers to an array composed of various microphone components integrated on a smart device. The generated microphone array is different for microphone components in different positions. After generating the microphone array and obtaining the time-domain waveform data of the erased target channel and other normal channels, the cascaded target beamforming algorithm further suppresses low-frequency wind noise.

[0084] This embodiment acquires the pickup data of each pickup component integrated on a smart device and performs temporal waveform abrupt change detection on the pickup data of each pickup component. Based on the detection results, the peak factor of the temporal waveform of each channel in each frame is determined. Target channels with peak factors greater than a preset peak factor threshold are selected, and the abrupt waveforms of the target channels are removed. Wind noise suppression is performed based on the removed target channels and a target beamforming algorithm. Through the above method, due to the influence of wind noise, the pickup components integrated on the smart device are different, and waveform abrupt changes occur in different channels. At this time, the abrupt waveforms of the target channels with peak factors greater than the preset peak factor threshold are removed from the temporal dimension, and then wind noise suppression is performed in combination with the target beamforming algorithm. Compared with the existing technology that uses a pure beamforming algorithm for wind noise suppression, this method can effectively improve the accuracy of wind noise suppression and ensure voice fidelity.

[0085] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 After step S40, steps S501 to S504 are also included:

[0086] Step S501: Obtain the voice information after wind noise suppression.

[0087] Step S502: Perform wind noise detection on the voice information.

[0088] Understandably, in order to verify the wind noise suppression effect, after obtaining the voice information after wind noise suppression, it is also necessary to perform wind noise detection on the voice information to determine whether there is any residual wind noise.

[0089] Step S503: When the detection result shows that there is no residual wind noise, the fidelity of the voice information is detected to obtain the current fidelity feature value.

[0090] It should be understood that when the detection result is determined to be that there is no residual wind noise, it indicates that the wind noise suppression method of this embodiment can avoid the phenomenon of residual wind noise. The current fidelity feature value refers to the feature value used to measure the fidelity of speech. The larger the current fidelity feature value, the more fidelity the speech information after wind noise suppression is.

[0091] Step S504: When the current fidelity feature value is greater than a preset value, the voice information is played through the playback component integrated on the smart device.

[0092] It is understandable that when the current fidelity feature value is greater than the preset value, it indicates that the voice information after wind noise suppression has not been distorted, and thus will not affect the listening experience after wind noise suppression. At this time, the voice information can be played through the playback component integrated into the smart device, which can be a speaker.

[0093] This embodiment acquires voice information after wind noise suppression; performs wind noise detection on the voice information; when the detection result shows no residual wind noise, performs fidelity detection on the voice information to obtain a current fidelity feature value; when the current fidelity feature value is greater than a preset value, plays the voice information through a playback component integrated into the smart device. By acquiring the wind noise-suppressed voice information and performing wind noise and fidelity detection on it respectively, and playing the voice information through the playback component when both detection results meet the requirements, the fidelity of the voice information can be ensured while avoiding residual wind noise, thereby improving the listening experience after wind noise suppression.

[0094] This application also provides a wind noise suppression device, please refer to... Figure 3 The wind noise suppression device includes:

[0095] The acquisition module 10 is used to acquire the pickup data of each pickup component integrated on the smart device, and to detect the degree of temporal waveform change in the pickup data of each pickup component.

[0096] The determination module 20 is used to determine the peak factor of the time-domain waveform of each channel for each frame based on the detection results.

[0097] The erasure module 30 is used to filter target channels whose peak factor is greater than a preset peak factor threshold and to erase the abrupt waveforms of the target channels.

[0098] The suppression module 40 is used to suppress wind noise based on the erased target channel and the target beamforming algorithm.

[0099] This embodiment acquires the pickup data of each pickup component integrated on a smart device and performs temporal waveform abrupt change detection on the pickup data of each pickup component. Based on the detection results, the peak factor of the temporal waveform of each channel in each frame is determined. Target channels with peak factors greater than a preset peak factor threshold are selected, and the abrupt waveforms of the target channels are removed. Wind noise suppression is performed based on the removed target channels and a target beamforming algorithm. Through the above method, due to the influence of wind noise, the pickup components integrated on the smart device are different, and waveform abrupt changes occur in different channels. At this time, the abrupt waveforms of the target channels with peak factors greater than the preset peak factor threshold are removed from the temporal dimension, and then wind noise suppression is performed in combination with the target beamforming algorithm. Compared with the existing technology that uses a pure beamforming algorithm for wind noise suppression, this method can effectively improve the accuracy of wind noise suppression and ensure voice fidelity.

[0100] The wind noise suppression device provided in this application, employing the wind noise suppression method in the above embodiments, can solve the technical problem of low accuracy in wind noise suppression in the prior art. Compared with the prior art, the beneficial effects of the wind noise suppression device provided in this application are the same as those of the wind noise suppression method provided in the above embodiments, and other technical features in the wind noise suppression device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0101] In one embodiment, the determining module 20 is further configured to determine the temporal waveform data of each sampling point within the frame of each channel based on the detection results; obtain the number of sampling points in each frame; calculate the root mean square value of the channel frame based on the target channel frame algorithm, according to the temporal waveform data and the number of sampling points; and calculate the peak factor of the temporal waveform of each frame of each channel based on the root mean square value of the channel frame and the peak value of the target channel frame.

[0102] In one embodiment, the erasure module 30 is further configured to acquire peak factor distribution data under wind noise scenario, and determine the current peak factor based on the peak factor distribution data under wind noise scenario; acquire peak factor distribution data under no wind noise scenario, and determine the target peak factor based on the peak factor distribution data under no wind noise scenario; determine a threshold setting range based on the current peak factor and the target peak factor, and set a preset peak factor threshold based on the threshold setting range.

[0103] In one embodiment, the erasure module 30 is further configured to determine the time-domain waveform abrupt change position of the target channel based on the detection result; and to erase the abrupt waveform of the target channel based on the time-domain waveform abrupt change position.

[0104] In one embodiment, the erasure module 30 is further configured to: acquire a set of sampling points for the target channel; select a target number of initial reference sampling points from the set of sampling points; generate a target time-domain normal waveform based on the initial reference sampling points using a target spline interpolation algorithm; and perform waveform completion on the erased target channel based on the target time-domain normal waveform and the abrupt change position of the time-domain waveform.

[0105] In one embodiment, the suppression module 40 is further configured to acquire time-domain waveform data of the erased target channel and time-domain waveform data of other normal channels; generate a pickup component array based on the position information of each pickup component integrated on the smart device; cascade a target beamforming algorithm based on the pickup component array; and perform wind noise suppression based on the time-domain waveform data of the erased target channel, the time-domain waveform data of other normal channels, and the target beamforming algorithm.

[0106] In one embodiment, the suppression module 40 is further configured to acquire voice information after wind noise suppression; perform wind noise detection on the voice information; when the detection result indicates that there is no residual wind noise, perform fidelity detection on the voice information to obtain a current fidelity feature value; and when the current fidelity feature value is greater than a preset value, play the voice information through a playback component integrated on the smart device.

[0107] This application provides a wind noise suppression device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the wind noise suppression method in the first embodiment described above.

[0108] The following is for reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the wind noise suppression device in the embodiments of this application. The wind noise suppression device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The wind noise suppression device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application.

[0109] like Figure 4As shown, the wind noise suppression device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the wind noise suppression device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the wind noise suppression device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows wind noise suppression devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0110] Specifically, according to the embodiments disclosed in this application, the process described above with reference to the flowcharts can be implemented as a computer software program. This computer program includes program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0111] The wind noise suppression device provided in this application, employing the wind noise suppression method described in the above embodiments, can solve the technical problem of low accuracy in wind noise suppression in the prior art. Compared with the prior art, the beneficial effects of the wind noise suppression device provided in this application are the same as those of the wind noise suppression method provided in the above embodiments, and other technical features of the wind noise suppression device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0112] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0113] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0114] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the wind noise suppression method in the above embodiments.

[0115] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having 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 thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0116] The aforementioned computer-readable storage medium may be included in the wind noise suppression device; or it may exist independently and not assembled into the wind noise suppression device.

[0117] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0120] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described wind noise suppression method, thereby solving the technical problem of low accuracy in wind noise suppression in the prior art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the wind noise suppression method provided in the above embodiments, and will not be repeated here.

[0121] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method of wind noise suppression, characterized by, The method comprises: acquiring pickup data of each pickup component integrated on the intelligent device, and performing time-domain waveform mutation degree detection on the pickup data of each pickup component; determining a peak factor of each channel time-domain waveform according to the detection result; screening a target channel with a peak factor greater than a preset peak factor threshold, and erasing a mutation waveform of the target channel; the mutation waveform of the target channel is a vertical bar-shaped noise, and the vertical bar-shaped noise does not appear on each channel of the pickup component at the same time; performing wind noise suppression on the target channel after erasing and a target beamforming algorithm; the step of erasing the mutation waveform of the target channel comprises: determining a time-domain waveform mutation position of the target channel according to the detection result; erasing the mutation waveform of the target channel according to the time-domain waveform mutation position; acquiring a sample point set of the target channel; selecting a target number of initial reference sample points from the sample point set; generating a target time-domain normal waveform according to the initial reference sample points through a target spline interpolation algorithm; completing the waveform of the target channel after erasing according to the target time-domain normal waveform and the time-domain waveform mutation position.

2. The method of claim 1, wherein, the step of determining the peak factor of each channel time-domain waveform according to the detection result comprises: determining time-domain waveform data of each sample point in each channel according to the detection result; acquiring a sample point number of each frame; calculating a channel frame root mean square value according to the time-domain waveform data and the sample point number based on a target channel frame algorithm; calculating a peak factor of each channel time-domain waveform according to the channel frame root mean square value and a target channel frame peak value.

3. The method of claim 1, wherein, before the step of screening the target channel with the peak factor greater than the preset peak factor threshold, the method further comprises: acquiring peak factor distribution data in a wind noise scene, and determining a current peak factor according to the peak factor distribution data in the wind noise scene; acquiring peak factor distribution data in a wind noise-free scene, and determining a target peak factor according to the peak factor distribution data in the wind noise-free scene; determining a threshold setting interval according to the current peak factor and the target peak factor, and setting a preset peak factor threshold according to the threshold setting interval.

4. The method of claim 1, wherein, the step of performing wind noise suppression on the target channel after erasing and a target beamforming algorithm comprises: acquiring time-domain waveform data of the target channel after erasing and time-domain waveform data of other normal channels; generating a pickup component array according to position information of each pickup component integrated on the intelligent device; concatenating a target beamforming algorithm according to the pickup component array; performing wind noise suppression according to the time-domain waveform data of the target channel after erasing, the time-domain waveform data of other normal channels, and the target beamforming algorithm.

5. The method of any one of claims 1 to 4, wherein, after the step of performing wind noise suppression on the target channel after erasing and a target beamforming algorithm, the method further comprises: acquiring voice information after wind noise suppression; performing wind noise detection on the voice information; when the detection result is that there is no remaining wind noise residue, performing fidelity detection on the voice information to obtain a current fidelity characteristic value; When the current fidelity characteristic value is greater than a preset value, the voice information is played by a playing component integrated on the smart device.

6. A wind noise suppression apparatus, characterized by comprising: The device comprises: An acquisition module is configured to acquire pickup data of each pickup component integrated on a smart device and perform time-domain waveform mutation degree detection on the pickup data of each pickup component. A determination module is configured to determine a peak factor of each frame of time-domain waveform of each channel according to a detection result. An erasing module is configured to screen a target channel with a peak factor greater than a preset peak factor threshold and erase a mutation waveform of the target channel; the mutation waveform of the target channel is a vertical bar-shaped noise, and the vertical bar-shaped noise does not simultaneously appear on channels of each pickup component. An inhibiting module is configured to perform wind noise inhibition according to the target channel after erasing and a target beamforming algorithm. The erasing module is further configured to determine a time-domain waveform mutation position of the target channel according to a detection result, erase the mutation waveform of the target channel according to the time-domain waveform mutation position, acquire a sample point set of the target channel, select a target number of initial reference sample points from the sample point set, generate a target time-domain normal waveform according to the initial reference sample points through a target spline interpolation algorithm, and complete waveform of the target channel after erasing according to the target time-domain normal waveform and the time-domain waveform mutation position.

7. A wind noise suppression apparatus, characterized by, The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement steps of the wind noise inhibition method according to any one of claims 1 to 5.

8. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement steps of the wind noise inhibition method according to any one of claims 1 to 5.

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