Wind noise detection system and method

Abstract

The system and method include: a wind detector for receiving an audio input signal and outputting a wind detection flag, the wind detection flag including a single-channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating the presence or absence of wind noise; and a fusion smoothing module for receiving a plurality of wind detection flags and generating an output wind detection flag. A microphone generates a plurality of audio input signals. The wind detector and the fusion smoothing module may include program instructions stored in a memory for execution by a digital signal processor. The wind detector is: a single-channel detector for receiving a single audio channel of the audio input signal and generating a single-channel wind noise flag; and a cross-channel detector for calculating autocorrelation and cross-correlation between two or more audio channels.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit and priority of U.S. Patent Application No. 16 / 399,961, filed on Apr. 30, 2019, and entitled “WIND NOISE DETECTION SYSTEMS AND METHODS”, which is incorporated herein by reference in its entirety. Technical Field

[0003] This application generally relates to noise cancellation systems and methods, and more particularly, for example, to the cancellation and / or suppression of wind noise in audio processing devices such as headphones (e.g., over - ear, on - ear, and in - ear), earbuds, and hearing aids, as well as other personal listening devices. Background Art

[0004] Audio processing devices typically include one or more microphones to sense sound from the environment and generate corresponding audio signals. For example, active noise cancellation (ANC) headphones include a reference microphone to generate an anti - noise signal that is approximately equal in magnitude but opposite in phase to the sensed ambient noise. The ambient noise and the anti - noise signal cancel each other acoustically, allowing the user to hear the desired audio signal.

[0005] However, traditional ANC systems (and other noise reduction or noise cancellation systems) do not completely eliminate all noise, leaving residual noise and / or generating audible artefacts that may distract the user. For example, unlike the ambient sounds cancelled in an ANC system, wind noise may occur at the microphone in response to local air turbulence at the microphone component. Wind noise may be unrelated to the ambient noise reaching the user's ear canal, and the corresponding anti - noise signal may be audible to the user. Noise suppression systems that attempt to remove background noise from an audio signal face similar challenges in removing wind noise.

[0006] In view of the foregoing, there is a continuing need for improved noise reduction and noise cancellation systems and methods for audio signals that may include sensed wind noise. There is also a continuing need for improved active noise cancellation systems and methods for headphones, earbuds, and other personal listening devices that may operate in windy environments. Summary of the Invention

[0007] Improved systems and methods are disclosed herein for active noise cancellation and / or noise suppression in audio devices that can be used in windy environments. In one or more embodiments, a system includes: a wind detector operable to receive a plurality of audio input signals and output a plurality of wind detection flags, the plurality of wind detection flags including a single-channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating the presence or absence of wind noise; and a fusion smoothing module operable to receive the plurality of wind detection flags and generate an output wind detection flag.

[0008] The system may further include a plurality of microphones operable to sense sound and generate a plurality of audio input signals, a memory storing program instructions, and a digital signal processor operable to execute the program instructions. In various embodiments, the system may include: a noise suppression module operable to receive the audio input signals and the output wind detection flag and reduce wind noise detected in the audio input signals; and / or an active noise cancellation system operable to generate an anti-noise signal based on the output wind detection flag to cancel a portion of the audio input signals.

[0009] In various embodiments, the wind detector includes a single-channel detector operable to receive a single audio channel of the plurality of audio input signals and generate a single-channel wind detection flag. The single-channel detector is capable of operably comparing the single audio channel with a wind spectrum model that includes a mean and a standard deviation of a power ratio of portions of frequency components and a spectral slope. The wind detector is operable to: clear the flag if the mean of the power ratio is less than a threshold mean and the standard deviation is greater than a threshold standard deviation (e.g., when it is determined that there is no wind noise); and set the flag if the spectral slope is greater than a predetermined threshold spectral slope (e.g., when it is determined that there is wind noise). The wind detector may further include a cross-channel detector operable to calculate the autocorrelation and cross-correlation between two or more audio channels and set the flag if the autocorrelation is less than the cross-correlation.

[0010] If the cross-channel wind detection flag is on and at least one single-channel wind detection flag is on, the fusion smoothing module is capable of operably setting the output wind detection flag to "present", and if a predetermined number of previously generated fusion wind flags are on, the fusion smoothing module is capable of operably setting the fusion wind flag.

[0011] In one or more embodiments, a method includes receiving a plurality of audio input signals, generating a plurality of preliminary wind detection flags, the plurality of preliminary wind detection flags including single-channel wind detection flags and cross-channel wind detection flags, each wind detection flag indicating the presence or absence of wind noise in a portion of the audio input signal, and outputting the wind detection flags. The method may further include reducing wind noise in the audio input signal if the wind detection flags are active, and / or generating an anti-noise signal based on the wind detection flags to cancel a portion of the audio input signal.

[0012] In various embodiments, the method includes receiving a single audio channel of an audio input signal and generating a single-channel wind detection flag, generating a wind spectrum model by calculating a mean and a standard deviation of a power ratio of specific frequency components and a spectral slope, and comparing the single audio channel with the wind spectrum model. If the mean of the power ratio is less than a threshold mean and the standard deviation is greater than a threshold standard deviation, the method may set the single-channel wind detection flag to indicate the absence of wind noise. If the spectral slope is greater than a predetermined threshold spectral slope, the method may set the single-channel wind noise flag to indicate the presence of wind noise.

[0013] The method may further include calculating an autocorrelation and a cross-correlation between two or more audio channels, and determining the presence of wind noise if the autocorrelation is less than the cross-correlation. If the cross-channel detector wind noise flag is on and at least one of the single-channel audio flags is on, the final wind detection flag may be set to "present". The method may further smooth the fused wind detection flag based on the number of previously determined fused wind detection flag values.

[0014] The scope of the present invention is defined by the claims, which are incorporated herein by reference. By considering the following detailed description of one or more embodiments, a more complete understanding of the embodiments of the present disclosure and the realization of their additional advantages will be provided to those skilled in the art. Reference will be made to the appended drawings, which will be briefly described first. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Aspects of the present disclosure and their advantages may be better understood with reference to the following drawings and subsequent detailed description. It should be understood that like reference numerals are used to identify like elements illustrated in one or more of the drawings, where the illustration in the drawings is for the purpose of illustrating embodiments of the present disclosure and not for the purpose of limiting the embodiments of the present disclosure. The components in the drawings are not necessarily to scale, but rather the emphasis is placed on clearly illustrating the principles of the present disclosure.

[0016] Figure 1 A wind detection system according to one or more embodiments of the present disclosure is illustrated.

[0017] Figure 2Illustrates a flowchart of a single-channel wind detector according to one or more embodiments of the present disclosure.

[0018] Figure 3 Illustrates a flowchart of a cross-channel wind detector according to one or more embodiments of the present disclosure.

[0019] Figure 4 Illustrates a flowchart of a fusion stage in a fusion smoothing module according to one or more embodiments of the present disclosure.

[0020] Figure 5 Illustrates a flowchart of a smoothing stage in a fusion smoothing module.

[0021] Figure 6 Illustrates an embodiment of an audio device according to one or more embodiments of the present disclosure. Detailed Description

[0022] Improved wind noise detection systems and methods are disclosed, which can be implemented in various audio processing systems, including active noise cancellation (ANC) systems, mobile phones, smart speakers, voice command and processing systems, automotive systems (e.g., hands-free voice control), and other audio processing systems that may operate in windy environments.

[0023] In one embodiment, a wind noise detection system includes two or more spatially separated microphones. Each microphone senses the sound in the environment, which may include wind noise sensed due to air turbulence local to each microphone. Thus, different microphones can independently sense different wind noise events. The wind noise detection system analyzes single-channel wind characteristics associated with each microphone and cross-channel wind characteristics of two or more audio channels. In one embodiment, the single-channel wind characteristics characterize the spectrum of the wind noise, while the cross-channel wind characteristics evaluate the cross-correlation between microphone signal pairs. The fusion smoothing stage works to fuse the resulting characteristics, filter the detection results, and improve system stability.

[0024] The systems and methods disclosed herein provide many advantages over traditional solutions. For example, the wind detection systems and methods of the present disclosure explore both single-channel wind features and cross-channel wind features and employ a fusion smoothing stage to filter the detection results. For example, the single-channel feature detector can include a unique decision tree structure as disclosed herein, and the features can include the mean and standard deviation of the low-frequency component power ratio and the spectral slope between 500 Hz and 1000 Hz. The calculated ratio mean and standard deviation provide good metrics for separating wind noise from speech for use in various speech applications. Additionally, the spectral slope allows for distinguishing wind noise from ambient background noise (such as office and street noise). The cross-channel features provide the cross-correlation of two-channel signals. Different from methods that work in the time domain, the proposed wind detection system can calculate the cross-correlation in the frequency domain. In various embodiments, for example, the phase information can be discarded, and / or the cross-correlation can be performed over the entire frequency band or only using the low-frequency components.

[0025] Reference Figure 1 , a wind detection system 100 according to one or more embodiments will now be described. The wind detection system 100 can implement an audio processing system having two or more microphones to monitor the presence of wind in the environment. In some embodiments, the wind detection system 100 is implemented in an audio input processing component of an audio device, which can include a digital signal processor (DSP) configured to suppress noise, detect speech, separate target signals, and / or perform other multi-channel audio input processing. The wind detection system 100 can generate wind noise information, which can be used to optimize the noise suppression performance of the audio device. For example, a two-channel wind detection system can be used on a headset equipped with two external microphones (e.g., on the left and right sides) to monitor ambient sound.

[0026] The wind detection system 100 includes a plurality of microphones or other audio sensors, such as a left microphone 102 and a right microphone 104, a wind detector module 110, and a fusion smoothing module 140. Each microphone (102 and 104) senses sound in the external environment, which can include sound from a desired target source 106, sound from noise sources, and sound locally generated by the wind. Each microphone generates an input audio signal, which is digitally sampled and transformed into the frequency domain as the left channel and the right channel , where f is the frequency.

[0027] The wind detector module 110 receives and As an input. The wind detector module 110 includes a plurality of detector sub - modules configured to analyze the characteristics of the input signal. In the illustrated embodiment, the wind detector module 110 includes a single left - channel detector 112, a single right - channel detector 116, and a cross - channel detector 114. The wind detector system 100 may include additional microphones and the wind detector module 110 may include additional single - channel detectors corresponding to each microphone and additional cross - channel detectors corresponding to groupings of two of the plurality of microphones.

[0028] The single left - channel detector 112 will be compared with the wind spectrum model. The characteristics to be considered in the comparison may include: (1) the mean and standard deviation of the power ratio of the low - frequency component and ; and (2) the spectral slope . Now the calculation of the mean and standard deviation of the power ratio of the low - frequency component and will be described. It has been observed that wind noise is typically concentrated in the low - frequency band (e.g., < 1000 Hz), while human speech (e.g., the target audio desired in a voice - controlled device) has higher high - frequency power and its power distribution is time - dependent. Thus, compared with the speech signal, the power ratio of the low - frequency component in wind noise is less time - correlated and more stable. In one embodiment, the power ratio is calculated by the following formula

[0029]

[0030] where f th is the low - frequency threshold. The single left - channel detector 112 will compare the mean and standard deviation , and with its thresholds and . If or , it indicates the absence of wind noise and that speech dominates in the signal.

[0031] Now the calculation of the spectral slope Figure 2 will be described with reference to . It has been observed that wind noise typically has a linear spectral slope between 500 Hz and 1000 Hz. The single left - channel detector 112 will be compared with the expected slope threshold . If , it indicates the absence of wind and that background noise dominates in the signal. Figure 2An embodiment of a process 200 for operating a single left-channel detector 112 is illustrated. The process 200 can be implemented in various combinations of hardware and software, including, for example, program instructions stored in a memory for execution by a digital processor.

[0032] In step 202, the single left-channel detector calculates the total signal power . In step 204, the single left-channel detector calculates the power of the low-frequency component as . Next, in step 206, the power ratio is calculated , and in step 208, the mean and standard deviation are updated , and . In step 210, if or , then the current signal is speech and there is no wind. Then the detector clears the wind flag and provides the wind flag as an output in step 218. If or is false, then the single left-channel detector calculates the spectral slope between 500 Hz and 1000 Hz in step 212 . In step 214, if , then the current signal is background noise and there is no wind. The detector clears and outputs the wind flag in step 218. If is false, then the current signal is neither speech nor background noise. Wind is determined to be present in step 216 and the wind flag is set and output.

[0033] The process 200 can also be used to detect the presence or absence of noise by a single right-channel detector 116. The single right-channel detector 116 can store program instructions for causing a processor to execute the process 200, which is applied to to set the wind flag for the right input audio channel.

[0034] In various embodiments, a two-stage decision checking process is used to distinguish wind noise from speech and background noise. The process includes processing and both by a cross-channel detector 114. In some embodiments, the cross-channel detector 114 is implemented as program instructions stored in a memory for instructing a digital signal processor to execute the processes disclosed herein. In one embodiment, the cross-channel detector 114 is configured to calculate the autocorrelation and cross-correlation of the left and right channels as follows:

[0035]

[0036] Note that the correlation parameters are calculated in the above example without phase information , and . Wind noise may be created by local air turbulence at each microphone, which results in a difference between the wind signals observed at the left and right microphones. The cross-channel detector will with compared, where is the threshold coefficient. If , it is determined that wind exists and the wind flag is set.

[0037] Figure 3 FIG. illustrates an embodiment of a process 300 for operating a cross-channel detector 114. In step 302, the cross-channel detector calculates the autocorrelation and . In step 304, the cross-correlation is calculated. In step 306, if , then in step 308 it is determined that wind exists and the cross-channel detector 114 sets the wind flag. Otherwise, in step 310, the cross-channel detector 114 clears the wind flag.

[0038] Returning to reference Figure 1 , the wind detector module 110 outputs the results of the single left-channel detector 112, the single right-channel detector 116, and the cross-channel detector 114 to the fusion smoothing module 140. The results of each of the three detectors are fused by rules for determining wind detection. For example, when the wind flag of the cross-channel detector 114 and at least one of the single-channel detectors 112 and 116 is set, the outputs of each of the three detectors can be fused by rules for determining the presence of wind.

[0039] Referring to Figure 4 , an embodiment of the operation of the fusion module 142 will now be described. Process 400 can be implemented in various hardware and / or software configurations, including, for example, program instructions stored in the memory of an audio processor for execution by a digital signal processor. In step 402, if the wind flag of the cross-channel detector 114 is off, then there is no wind, and in step 410 the fused wind flag is cleared. In step 404, if the wind flag of the cross-channel detector 114 is on and the single left-channel detector 112 flag is on, then in step 408 it is determined that wind exists and the fused wind flag is set. In step 406, if the single left-channel detector flag is not on and the wind flag of the single right-channel detector 116 is on, then in step 408 it is determined that wind exists and the fused wind flag is set. Otherwise, there is no wind, and in step 410 the fused wind flag is cleared.

[0040] The fused wind flag is further smoothed to address missed detection and false alarm events. For example, in one embodiment, the smoothing method examines the last N fused wind flags to determine whether to change the wind detection status. If all of the last N wind flags are on, the smoothed wind flag is on. If all of the last N wind flags are off, the smoothed wind flag is off. Otherwise, the smoothed wind flag can remain in its current state. Depending on the goals of the system, other settings and algorithms can also be used to increase or decrease the sensitivity to wind detection events.

[0041] Reference Figure 5 , an embodiment of the smoothing operation performed by the Figure 1 smoothing module 144 will now be described. The smoothing operation 500 can be implemented in a variety of hardware and software configurations, including, for example, program instructions stored in memory for execution by a digital signal processor. In step 502, if the smoothed wind flag is "on" and the fused stage wind flag is "on" (step 504), then in step 506 the fused flag counter is reset. If the fused stage wind flag is "off", then in step 508 the fused flag counter is incremented by one. Returning to reference step 502, if the smoothed wind flag is "off" and the fused stage wind flag is "off" (step 510), then in step 512 the fused flag counter is reset. If the fused stage wind flag is "on" (step 510), then the fused flag counter is incremented by one in step 508. In step 514, if the fused flag counter is greater than or equal to N (e.g., where N is equal to the number of consecutive wind flags), then the smoothed wind flag is set equal to the fused wind flag (step 516).

[0042] In various embodiments, wind detection can be implemented in various devices having two or more microphones, such as mobile phones, PDAs, smart speakers, smart watches, headphones, and hearing aids. There are many frequency domain transformation algorithms for microphone signals, such as Fourier transform and wavelet transform. The present disclosure is not limited to a specific algorithm. The proposed wind detector can be extended to the multi-microphone case. The wind detector module can output , , and eigenvalues instead of the detector wind flag. The wind detector module can smooth the features , , and To obtain long-term feature estimates before threshold comparison. The features can be smoothed by FIR filters and IIR filters. The fusion smoothing module can employ other common machine learning algorithms to fuse the results of the wind detector module, such as logistic regression, naive Bayes, and neural networks. The fusion smoothing module can employ other common filtering algorithms to perform result smoothing, such as median filtering, FIR filtering, and IIR filtering.

[0043] Reference Figure 6 , an example system incorporating the wind detection process of the present disclosure will now be described. The audio device 600 includes an audio input, such as an audio sensor array 605, an audio signal processor 620, and host system components 650. The audio sensor array 605 includes one or more sensors, each of which can convert sound waves into audio signals. In the illustrated environment, the audio sensor array 605 includes a plurality of microphones 605a - 605n, each microphone generating one audio channel of a multi-channel audio signal.

[0044] The audio signal processor 620 includes an audio input circuit 622, a digital signal processor 624, and an optional audio output circuit 626. In various embodiments, the audio signal processor 620 can be implemented as an integrated circuit including analog circuits, digital circuits, and the digital signal processor 624, which is operable to execute program instructions stored in a memory. For example, the audio input circuit 622 can include an interface to the audio sensor array 605, an anti-aliasing filter, an analog-to-digital converter circuit, an echo cancellation circuit, and other audio processing circuits and components.

[0045] The digital signal processor 624 can include one or more of a processor, a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)), a digital signal processing (DSP) device, or other logic devices that can be configured to perform the various operations discussed herein for embodiments of the present disclosure by hardwiring, executing software instructions, or a combination of both.

[0046] The digital signal processor 624 is operable to process a multi-channel digital audio input signal to generate an enhanced audio signal, which is output to one or more host system components 650. The digital signal processor 624 is operable to interface and communicate with the host system components 650, such as via a bus or other electronic communication interface. In various embodiments, the multi-channel audio signal includes a mixture of a noise signal and at least one desired target audio signal (e.g., human voice), and the digital signal processor 624 is operable to isolate or enhance the desired target signal while reducing or eliminating the undesired noise signal. The digital signal processor 624 is capable of being operable to perform wind noise detection, voice / keyword detection and processing, echo cancellation, noise cancellation, target signal tracking and enhancement, post-filtering, and other audio signal processing.

[0047] In the illustrated embodiment, the digital signal processor 624 includes a wind detector 628 (e.g., Figure 1 wind detector module 110 of Figure 1 ), and a fusion smoothing component 630 (e.g.,

[0048] fusion smoothing module 140 of

[0049] ), which are operable to determine whether wind noise is present in the current audio sample. The audio signal processor 620 may be configured to produce an enhanced target signal for further processing by the host system components (e.g., voice input for voice communication, voice command processing, etc.). The digital signal processor 624 may use an indication of the presence or absence of wind noise during a noise suppression process to assist in removing the detected wind noise. In another embodiment, the audio signal processor 620 may be configured for active noise cancellation, and the digital signal processor 624 may use an indication of the presence or absence of wind noise to assist in the generation of an anti-noise signal. The digital signal processor 624 may also include other modules that utilize the final wind detection flag, such as a noise suppression / cancellation module 632. In various embodiments, the noise suppression / cancellation module 632 may provide noise suppression of wind noise in the input audio signal and / or generate an anti-noise for active noise cancellation under windy conditions.

[0048] The audio output circuit 626 processes the audio signal received from the digital signal processor 624 for output to at least one speaker, such as speakers 610a and 610b. The audio output circuit 626 may include a digital-to-analog converter that converts one or more digital audio signals into corresponding analog signals and one or more amplifiers for driving speakers 610a and 610b.

[0049] The audio device 600 can be implemented as any device operable to receive and detect target audio data, such as, for example, a mobile phone, a smart speaker, a tablet computer, a laptop computer, a desktop computer, a voice-controlled appliance, or an automobile. The host system components 650 can include various hardware and software components for operating the audio device 600. In the illustrated embodiment, the host system components 650 include a processor 652, a user interface component 654, a communication interface 656 for communicating with external devices and networks (such as network 680 (e.g., the Internet, cloud, local area network, or cellular network) and mobile device 684), and a memory 658.

[0050] The processor 652 can include one or more of a processor, a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a programmable logic device (PLD) (e.g., a field-programmable gate array (FPGA)), a digital signal processing (DSP) device, or other logic devices that can be configured to perform the various operations discussed herein for embodiments of the present disclosure by hardwiring, executing software instructions, or a combination of both. The host system components 650 are operable to engage and communicate with the audio signal processor 620 and other host system components 650, such as via a bus or other electronic communication interface.

[0051] It will be understood that although the audio signal processor 620 and the host system components 650 are shown as incorporating a combination of hardware components, circuits, and software, in some embodiments, at least some or all of the functionality that the hardware components and circuits are operable to perform can be implemented as software modules executed by the processor 652 and / or the digital signal processor 624 in response to software instructions and / or configuration data stored in the firmware of the memory 658 or the digital signal processor 624.

[0052] The memory 658 can be implemented as one or more memory devices operable to store data and information, including audio data and program instructions. The memory 658 can include one or more different types of memory devices, including volatile and non-volatile memory devices, such as RAM (random access memory), ROM (read-only memory), EEPROM (electrically erasable read-only memory), flash memory, hard disk drives, and / or other types of memory.

[0053] The processor 652 is operable to execute software instructions stored in the memory 658. In various embodiments, the voice recognition engine 660 is operable to process the enhanced audio signals received from the audio signal processor 620, including recognizing and executing voice commands. The voice communication component 662 is operable to facilitate voice communication with one or more external devices, such as the mobile device 684 or the user device 686, such as through a voice call over a mobile or cellular telephone network or a VoIP call over an IP (Internet Protocol) network. In various embodiments, the voice communication includes transmitting the enhanced audio signals to an external communication device.

[0054] The user interface component 654 may include a display, a touchpad display, a keypad, one or more buttons, and / or other input / output components operable to enable a user to directly interact with the audio device 600. The communication interface 656 facilitates communication between the audio device 600 and an external device. For example, the communication interface 656 may enable a Wi-Fi (e.g., 802.11) or Bluetooth connection between the audio device 600 and one or more local devices, such as the mobile device 684, or a wireless router providing network access to a remote server 682, such as over a network 680. In various embodiments, the communication interface 656 may include other wired and wireless communication components that facilitate direct or indirect communication between the audio device 600 and one or more other devices.

[0055] The foregoing disclosure is not intended to limit the present disclosure to the precise forms or particular fields of use disclosed. Accordingly, it is contemplated that various alternative embodiments and / or modifications to the present disclosure, whether explicitly described or implied herein, are possible in accordance with the present disclosure. Having thus described examples of the present disclosure, those of ordinary skill in the art will recognize that changes may be made in form and detail without departing from the scope of the present disclosure. Accordingly, the present disclosure is limited only by the claims.

Claims

1. A wind noise detection system, comprising: A wind detector capable of operating to receive a plurality of audio input signals and output a plurality of wind detection flags, the plurality of wind detection flags including a single-channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating the presence or absence of wind noise; And A fusion smoothing module capable of operating to receive the plurality of wind detection flags and generate a fused wind flag, and the fusion smoothing module is configured to: Set the fused wind flag to on if the cross-channel wind detection flag is on and at least one single-channel wind detection flag is on; and Set the smoothed wind flag to on if a predetermined number of previously generated fused wind flags are on.

2. The system according to claim 1, further comprising a plurality of microphones capable of operating to sense sound and generate the plurality of audio input signals.

3. The system according to claim 1, further comprising a memory storing program instructions and a digital signal processor capable of operating to execute the program instructions; and wherein the wind detector and the fusion smoothing module include program instructions stored in the memory.

4. The system according to claim 1, further comprising a noise suppression module capable of operating to receive the audio input signals and the fused wind flag and reduce the wind noise detected in the audio input signals.

5. The system according to claim 1, further comprising an active noise cancellation system capable of operating to generate an anti-noise signal according to the fused wind flag to cancel a part of the audio input signals.

6. The system according to claim 1, wherein the wind detector includes a single-channel detector capable of operating to receive a single audio channel of the plurality of audio input signals and generate the single-channel wind detection flag.

7. The system according to claim 6, wherein the single-channel detector is capable of operating to compare the single audio channel with a wind spectrum model.

8. The system according to claim 7, wherein the wind spectrum model includes a mean and a standard deviation of a power ratio of a part of frequency components and a spectral slope, and wherein if the mean of the power ratio is less than a threshold mean or the standard deviation of the power ratio is greater than a threshold standard deviation, it is determined that there is no wind noise; and wherein if the spectral slope is greater than a predetermined threshold spectral slope, it is determined that there is wind.

9. The system according to claim 6, wherein the wind detector includes a cross-channel detector capable of operating to calculate the autocorrelation and cross-correlation between two or more audio channels, and wherein if the autocorrelation is less than the cross-correlation, it is determined that there is wind.

10. A wind noise detection method, comprising: Receiving a plurality of audio input signals; Generating a plurality of preliminary wind detection flags, the plurality of preliminary wind detection flags including a single-channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating the presence or absence of wind noise in a part of the audio input signals; If the cross-channel wind detection flag is on and at least one single-channel wind detection flag is on, set the fused wind flag to on; And If a predetermined number of previously generated fused wind flags are on, set the smoothed wind flag to on.

11. The method according to claim 10, further comprising reducing wind noise in the audio input signal if the fused wind flag is on.

12. The method according to claim 10, further comprising generating an anti-noise signal based on the fused wind flag to cancel a portion of the audio input signal.

13. The method according to claim 10, further comprising receiving a single audio channel of the audio input signal and generating the single-channel wind detection flag.

14. The method according to claim 13, further comprising comparing the single audio channel with a wind spectrum model.

15. The method according to claim 14, further comprising generating the wind spectrum model by calculating a mean and a standard deviation of a power ratio of specific frequency components and a spectral slope; If the mean of the power ratio is less than a threshold mean or the standard deviation is greater than a threshold standard deviation, set the single-channel wind detection flag to indicate the absence of wind noise; and If the spectral slope is greater than a predetermined threshold spectral slope, set the single-channel wind detection flag to indicate the presence of wind noise.

16. The method according to claim 14, further comprising calculating an autocorrelation and a cross-correlation between two or more audio channels; and determining the presence of wind noise if the autocorrelation is less than the cross-correlation.

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