Patent Publication Number: US-11659326-B2

Title: Apparatus for and method of wind detection

Description:
The present disclosure is a continuation of U.S. patent application Ser. No. 16/993,577, filed Aug. 14, 2020, which is a continuation of U.S. patent application Ser. No. 16/445,538, filed Jun. 19, 2019, each of which is incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the disclosure relate to methods, apparatus and systems for detecting wind, estimating wind parameters and reducing wind noise in microphone signals, and particularly methods apparatus and systems for detecting and estimating wind and reducing wind noise using an accelerometer. 
     BACKGROUND 
     Wind noise in audio systems is generated from turbulence in an airstream flowing past a microphone port or over a microphone membrane. This is in contrast to non-wind noise (e.g. traffic, train, construction, etc.) which is generated due to sound pressure waves incident at a microphone membrane. 
     Wind noise can often have a large enough amplitude to mask more valuable sound in a microphone signal, such as voice. It is therefore desirable to suppress wind noise in microphone signals generated by such turbulence to enable non-wind noise components of the microphone signal to be heard and/or processed. 
     State of the art wind noise reduction algorithms require information concerning wind noise present in a microphone signal, commonly referred to as ‘wind noise parameters’, such as the probability of wind presence, wind velocity, wind direction, short-and long-term spectral amplitude, short- and long-term spectral cut-off frequency to name a few. However, since conventional microphones cannot distinguish between wind noise by itself and wind noise mixed with non-wind noise (e.g. traffic noise), it can be difficult to accurately determine wind noise parameters to be used by wind reduction algorithms. 
     In addition to the above, conventional microphones will often saturate in the presence of high wind resulting in clipping in microphone output signals. Very high winds (e.g. velocities greater than 12 ms −1 ) can lead to total saturation of a microphone signal, meaning that no delineation can be made between the properties of winds having speeds above the velocity at which total saturation occurs. 
     Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims. 
     SUMMARY 
     Embodiments of the present disclosure seek to address or at least alleviate one or more of these problems by using an accelerometer or inertial measurement unit (IMU) to estimate the presence and characteristics of wind noise. The inventors have established that accelerometer signals can be used to delineate between turbulence generated due to a moving airstream and vibrations caused by incident sound pressure waves. This is because the force exerted by the movement of air (e.g. wind) around an accelerometer (or an enclosure in which the accelerometer is located) tends to exceed the threshold of sensitivity of the accelerometer, whereas the force exerted by even a very large sound pressure wave (e.g. greater than 100 dB SPL) is generally insufficient to exceed the threshold of sensitivity of the accelerometer. 
     Additionally, the effective mass of a typical MEMS microphone is orders of magnitude smaller than the proof mass of a MEMS accelerometer. Accordingly, whereas microphones become saturated in the presence of high velocity winds, accelerometers and IMUs are not saturated by high velocity winds. A typical MEMS accelerometer is designed with measurement ranges upwards of +/−16 g which exceeds the force exerted by volume air flows at significant wind speeds (e.g. upwards of 12 ms −1 ). 
     Embodiments of the present disclosure utilise the above phenomena and characteristics of accelerometers and IMUs to detect wind noise and determine wind noise parameters irrespective of non-wind noise levels and in high wind conditions, for example speeds exceeding 12 ms −1 . Further, embodiments of the present disclosure aim to reduce wind noise in microphone signals based on wind noise parameters determined using signals from an accelerometer. 
     According to a first aspect of the disclosure, there is provided a method, comprising: receiving one or more accelerometer signals derived from an accelerometer; and determining one or more parameters of wind at the accelerometer based on the one or more accelerometer signals. 
     The one or more parameters of wind at the accelerometer may comprise a speed of wind at the accelerometer and/or an angle of incidence of wind at the accelerometer. The one or more accelerometer signals may comprise two or more accelerometer signals representing different axes of acceleration. In which case, determining the angle of incidence of wind at the accelerometer may comprise comparing the two or more accelerometer signals. 
     The one or more parameters of wind at the accelerometer may comprise an indication of the presence of wind at the accelerometer and/or a probability of the presence of wind at the accelerometer. 
     The method may further comprise filtering one or more of the one or more accelerometer signals to remove non-wind noise. The one or more parameters of wind may be determined based on the filtered one or more accelerometer signals. Filtering may comprise low pass filtering. Additionally or alternatively, high pass filtering may be applied to remove high frequency components of noise not associated with wind, such as movement. 
     The method may further comprise detecting the presence of non-wind noise in one or more of the one or more accelerometer signals. The determining may be performed only when non-wind noise is not detected. 
     The method may further comprise: receiving a microphone signal from a microphone proximate to the accelerometer; and reducing wind noise in the microphone signal based on the determined one or more parameters of wind at the accelerometer. 
     The determining of the one or more wind parameters at the accelerometer may comprise: determining a subband power in one or more of the accelerometer signals; and estimating a cut-off frequency of noise in the microphone signal based on the determined subband power in the one or more accelerometer signals. Wind noise may then be reduced in the microphone signal using the estimated cut-off frequency. For example, wind noise may be reduced in the microphone signal using a compressor, the knee point of the compressor being dynamically adjusted in dependence on the estimated cut-off frequency. 
     The determining of the one or more wind parameters at the accelerometer may further comprise determining wind speed. The knee point of the compressor may then be determined in dependence on the determined wind speed instead of or in addition to the estimated cut-off frequency. 
     Estimating the cut-off frequency may comprise translating the subband power into the cut-off frequency using a look up table. 
     The method may further comprise detecting the presence of wind at the microphone or determining a probability of wind at the microphone based on the microphone signal. 
     The step of determining one or more parameters of wind at the accelerometer may be performed in response to detecting the presence of wind at the microphone. 
     According to another aspect of the disclosure, there is provided an apparatus, comprising: memory; and a processor coupled to the memory and configured to: receive one or more accelerometer signals derived from an accelerometer; and determine one or more parameters of wind at the accelerometer based on the one or more accelerometer signals. 
     The one or more parameters of wind at the accelerometer may comprise a speed of wind at the accelerometer and/or an angle of incidence of wind at the accelerometer. 
     The one or more accelerometer signals may comprise two or more accelerometer signals representing different axes of acceleration. Determining the angle of incidence of wind at the accelerometer may comprise comparing the two or more accelerometer signals. 
     The one or more parameters of wind at the accelerometer may comprise an indication of the presence of wind at the accelerometer and/or a probability of the presence of wind at the accelerometer. 
     The processor may be further configured to: filter one or more of the one or more accelerometer signals to remove non-wind noise. The one or more parameters of wind may be determined based on the filtered one or more accelerometer signals. 
     The processor may be further configured to: detect the presence of non-wind noise in one or more of the one or more accelerometer signals. The determining may be performed only when non-wind noise is not detected. 
     The processor may be further configured to: receive a microphone signal derived from a microphone proximate to the accelerometer; and reduce wind noise in the microphone signal based on the determined one or more parameters of wind at the accelerometer. 
     Determining one or more wind parameters at the accelerometer may comprise: determining a subband power in one or more of the accelerometer signals; and estimating a cut-off frequency of noise in the microphone signal based on the determined subband power in the one or more accelerometer signals. Wind noise may then be reduced in the microphone signal using the estimated cut-off frequency. For example, the processor may be configured to implement a compressor to reduce wind noise in the microphone signal and the knee point of the compressor may be determined in dependence on the estimated cut-off frequency. In some embodiments, determining one or more wind parameters at the accelerometer may further comprise determining wind speed. In which case, the knee point of the compressor may be determined in dependence on the determined wind speed, in addition or instead of using the estimated cut-off frequency. 
     Estimating the cut-off frequency may comprise translating the subband power into the cut-off frequency using a look up table stored in the memory. 
     The processor may be further configured to: detect the presence of wind at the microphone or determine a probability of wind at the microphone based on the microphone signal. The step of determining one or more parameters of wind at the accelerometer may be performed in response to detecting the presence of wind at the microphone. 
     The apparatus may further comprise the microphone. The apparatus may further comprise the accelerometer. 
     According to another aspect of the disclosure, there is provided an electronic device comprising the apparatus as described above. 
     According to another aspect of the disclosure, there is provided a non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause an electronic apparatus to: receive one or more accelerometer signals derived from an accelerometer; and determine one or more parameters of wind at the accelerometer based on the one or more accelerometer signals. 
     According to another aspect of the disclosure, there is provided a method of reducing wind noise in a microphone signal received from a microphone, the method comprising: receiving one or more accelerometer signals from one or more accelerometers in proximity to the microphone; determining a subband power in one or more of the one or more accelerometer signals; estimating a cut-off frequency of noise in the microphone signal based on the determined subband power; and reducing wind noise in the microphone signal using the estimated cut-off frequency. 
     Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Embodiments of the present disclosure will now be described by way of non-limiting example only with reference to the accompanying drawings, in which: 
         FIGS.  1   a  and  1   b    are schematic diagrams of an apparatus according to an embodiment of the present disclosure; 
         FIG.  1   c    is a plan view showing the apparatus of  FIGS.  1   a  and  1   b    positioned on the ear of a user; 
         FIG.  2    is a graph of frequency vs power for a microphone and an accelerometer of the apparatus shown in  FIGS.  1   a  to  1   c    for various noise conditions; 
         FIGS.  3   a  and  3   b    are power correlation matrices for an output signal of internal and external microphones and outputs of three spatial axis of the accelerometer of the apparatus shown in  FIGS.  1   a    to  1   c;    
         FIGS.  4   a ,  4   b  and  4   c    are scatter plots of angle of incidence of wind versus accelerometer subband signal power below 500 Hz for wind having speeds of 4 m/s, 6 m/s and 8 m/s respectively incident at the accelerometer of the apparatus shown in  FIGS.  1   a    to  1   c;    
         FIGS.  5   a ,  5   b  and  5   c    are density plots representing power density measured by the accelerometer of the apparatus shown in  FIGS.  1   a  to  1   c    in the presence of wind having speeds of 4 m/s, 6 m/s and 8 m/s respectively; 
         FIGS.  6   a ,  6   b  and  6   c    are scatter plots of accelerometer subband power vs microphone subband power for different incident wind angles and wind speeds of wind received at the apparatus shown in  FIGS.  1   a    to  1   c;    
         FIG.  7    is a block diagram of an exemplary parameter estimation module implemented by the apparatus shown in  FIGS.  1   a    to  1   c;    
         FIG.  8    is a flow diagram of a process which may be implemented by the parameter estimation module shown in  FIG.  7   ; and 
         FIGS.  9 ,  10  and  11    are block diagrams of wind noise reduction systems which incorporate the parameter estimation module of  FIG.  7   . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Embodiments of the present disclosure relate to the acquisition and use of accelerometer signals for detecting wind noise, delineating between wind noise and non-wind noise, determining characteristics of wind over a wide range of wind conditions, and reducing wind noise in microphone signals. 
     MEMS microphone and accelerometer devices can both be modelled as simple harmonic oscillators (mass-spring systems). However, the design of these devices is optimised for different problems; one for measuring acceleration, and the other for measuring sound pressure. Accordingly, the effective mass of the MEMS microphone membrane is orders of magnitude smaller than the proof mass of an accelerometer MEMS. The relatively small effective mass of a MEMS microphone membrane makes it a poor transducer of spatial signals. It also makes MEMS microphones more susceptible to wind noise, this problem being exacerbated in MEMS microphones by their construction, in particular their port dimensions. The size of the port is a trade-off between minimising ingress of impurities and limiting turbulent convective pressures. In general, the flow of air around the port of the microphone creates three noise sources; upstream turbulence, trailing edge vortex shedding, and boundary layer turbulence. The level and spectrum of these noise sources depends on incident wind speed, relative microphone orientation and the presence and characteristics of physical barriers, such as wind screening. 
     The inventors have realised that, in contrast to MEMS microphones, MEMS accelerometers can be used to delineate between turbulence generated due to a moving airstream and vibrations caused by incident sound pressure waves. This is because the typical force exerted by the movement of air (e.g. wind) tends to exceed the threshold of sensitivity of a typical MEMS accelerometer, whereas the typical force exerted by even a very large sound pressure wave (e.g. greater than 100 dB SPL) tends to be insufficient to exceed the threshold of sensitivity of the accelerometer. Embodiments of the present disclosure apply the above phenomenon to various aspects of microphone sound processing, such as wind noise reduction and suppression in microphone signals. 
       FIG.  1   a    is a schematic diagram of an apparatus  100  according to an embodiment of the disclosure comprising an external microphone  102 , an internal microphone  103 , and an accelerometer  104 . In some embodiments, the external and internal microphones  102 ,  103  are MEMS microphones. The external and internal microphones  102 ,  103  may be respective reference and error microphones and may be used for noise cancellation using techniques well known in the art. In some embodiments, the accelerometer  104  is a MEMS accelerometer. In some embodiments, the accelerometer  104  may be configured to measure movement in one or more dimensions. For example, the accelerometer  104  may generate output signals representative of acceleration in a plurality of spatial dimensions in three-dimensional space (x and y, or x and y and z, or y and z). Whilst in the following description, the accelerometer  104  will be described as being configured to generate three output signals representative of x, y and z spatial axes, it will be appreciated that embodiments of the present disclosure are not limited to accelerometers having three axes. Whilst the apparatus  100  is shown with both an external microphone  102  and an internal microphone  103 , it will be appreciated that embodiments are not limited to the exemplified provision and placement of microphones in  FIG.  1   a   . For example, in alternative embodiments the apparatus may comprise one microphone or more than two microphones, which may be positioned anywhere on or in the apparatus  100 . 
     In the embodiment shown in  FIG.  1   a   , the apparatus  100  is a headphone configured for placement on an ear  106  of a user  107 . It will be appreciated, however, that techniques described herein may be implemented on any apparatus comprising a microphone and an accelerometer. Such apparatus may include but are not limited to an earphone, headphone, headset, earbud, earphone, ear defender, smartphone, tablet, or other apparatus for delivering sound to and/or cancelling sound at the eardrum (actively or passively). Any such apparatus may be placed over or on the ear or in the ear canal. 
       FIG.  1   b    is a schematic diagram of the apparatus  100  in an exemplary configuration. The apparatus  100  comprises a processor  108 , a memory  110 , and a transceiver  112 . The processor  108  may be provided as a single component or as multiple components. Equally, the memory  110  may be provided as a single component or as multiple components. Data may be transmitted between elements of the apparatus  100  via a bus  114  or the like in any manner known in the art. The processor  108  may be a digital signal processor (DSP) or an applications processor of the apparatus  100 . The processor  108  may be configured to receive and process signals received from the microphone  102  and the accelerometer  104 . The processor  108  may be configured to perform operations on signals received from the microphone  102  and/or accelerometer  104 . The apparatus  100  may further comprise additional processing circuitry  116  for performing interim processing of signals received from the microphone  102  and/or the accelerometer  104 . For example, the processing circuitry may comprise one or more analog-to-digital converters (ADCs), one or more digital-to-analog converters (DACs), and/or one or more FFT modules. For simplicity, only one microphone  102  is shown in  FIG.  1   b   . The internal microphone  103  shown in  FIG.  1   a    may also be in communication with the bus  114  or the like. The memory  110  may be provided for storing data and/or program instructions. The transceiver  112  may be configured to enable communication (wired or wireless) with external devices, such as a smartphone, a computer, or the like. In some embodiments, the transceiver may be configured to establish a Bluetooth connection. It will be appreciated that the apparatus  100  may comprise additional components which are not shown in  FIGS.  1   a  and  1   b   , such as a speaker, additional microphones etc. 
       FIG.  1   c    is an aerial view of the apparatus  100  positioned on the ear  106  of the user  107 , in this case, the ear  106  is the left ear of the user  107 . Throughout the following description of the apparatus  100 , the angle of incidence of wind on the device will be described in degrees.  FIG.  1   c    illustrates the reference frame of these angles, 0° representing wind incident at the front of the user&#39;s face, 90° representing wind incident at the right side of the user&#39;s head and travelling toward the right ear, 180° representing wind incident at the back of the user&#39;s head, and 270° representing wind incident at the left side of the user&#39;s head and travelling toward the left ear  106 . As will be described in more detail below, it will be appreciated that depending on the angle of incidence of wind at the apparatus, the amount of wind noise picked up by the microphones  102 ,  103  and/or the accelerometer  104  may be affected by turbulence around the head, the ear  106  and the apparatus  100  itself, as well as shadowing of the microphones  102 ,  103  and/or the accelerometer  104  by the body of the apparatus  100  and the user  107 . This will become more apparent in the following discussions regarding signal power received at the accelerometer  104  at various angles of incidence of wind. 
       FIG.  2    is a graph of frequency vs power for the microphone  102  and the accelerometer  104  for various noise conditions. Line  202  represents a power spectra of an output signal of the microphone  102  in the presence of wind noise only. Line  204  represents a power spectra of an output signal of the microphone  102  in the presence of wind noise and non-wind noise (train noise). Line  206  represents a power spectra of an output signal of the accelerometer  104  in the presence of wind noise only. Line  208  represents a power spectra of an output signal of the accelerometer  104  in the presence of wind noise and non-wind noise (train noise). 
     It can be seen that the power spectra  202 ,  204  of the output signals from the microphone  102  with and without wind noise are very similar for frequencies below around 2.5 kHz. However, there is a considerable difference in the power spectra of the output signal from the microphone  102  in the presence of non-wind noise, particularly above 2.5 kHz. For some frequencies the dB power difference is over 25 dB. In contrast, the power spectra  206 ,  208  of the output signal from the accelerometer  104  with and without wind noise differs at most by 5 dB and any difference is substantially frequency independent at frequencies above 500 Hz. Below 500 Hz there is no difference between the power spectra  206 ,  208  with or without wind noise. Thus it can be seen that the output signal of the accelerometer  104  is substantially unaffected by non-wind noise. 
       FIGS.  3   a  and  3   b    are power correlation matrices for an output signal of the internal and external microphones  103 ,  102  and output signals for three spatial axes of the accelerometer  104  (“x”, “y” and “z”) in the presence of speech ( FIG.  3   a   ) and wind at 4 ms −1  ( FIG.  3   b   ) respectively. It can be seen that no statistically significant correlation exists between the acoustic domain as recorded by the microphone  102  and the spatial domain as recorded by the accelerometer  104  in any spatial dimension in the presence of either speech or wind. In the presence of wind in particular, there appears to be no correlation whatsoever between signals generated by the microphone  102  and those generated by the accelerometer  104 . In contrast, it can be seen that some correlation exists between each of the x, y and z axis output signals from the accelerometer  104 . 
     In the presence of wind, there is also very little correlation between internal and external microphone signals. It is believed this is due to turbulence existing around ports of the microphones  102 ,  103  due to vortices which form at the port openings associated with each microphone  102 ,  103 . These vortices increase with increasing wind speed and with decreasing port size/diameter. So as wind speed increases, microphone signals begin to saturate due to the turbulent flow around the port opening. This saturation can be mitigated to some extent by increasing the size of the port; for larger ports, less shedding occurs which in turn averages the vortices. There is, however, a limit to the size that ports can be made in practice due to potential contamination and/or ingress at the port entrance. 
     Generally, the saturation in MEMS microphones begins to occur at wind speeds of around 2-3 m/s. MEMS microphones typically have a sound pressure level (SPL) limit of between 120 dB to 130 dBSPL for speech. The crest factor of wind noise is lower than the crest factor of speech, so the SPL limit of MEMS microphones for wind is between 110-120 dBSPL. MEMS microphones also have a high pass response with a 3 dB cut-off of between 35-85 Hz. 
     Conventionally, wind parameters are estimated based on signals received from one or both of the microphones  102 ,  103 . The spectral power of wind noise in a microphone signal is approximately inversely proportional to its frequency below a cut-off frequency at which this relationship breaks down. In other words, wind noise follows a 1/f profile in the spectral domain. Accordingly, an existing approach to estimating wind noise involves determining the subband power spectrum of a microphone signal using, for example, Fourier analysis and subsequently determining the cut-off frequency, i.e. the frequency at which the spectral power of noise in the microphone flattens out. This determined cut-off frequency may then be used, for example, to vary compression bandwidth and knee point of subsequent suppression steps so that such suppression does not excessively remove low frequency components from the noise-affected microphone signal. 
     The problem with this approach is that microphone signals often contains non-linear components of noise in the form of turbulence and noise sources other than wind e.g. car noise, own voice etc. This type of non-linear noise can make it difficult to determine the cut-off frequency of the wind portion of noise present in the microphone signal. 
     Embodiments of the present disclosure utilise the accelerometer  104 ′s insensitivity to non-wind noise sources to determine wind parameters, particularly in the environments in which both wind noise and non-wind noise is present. These determined parameters may in turn be used for wind noise reduction/suppression of audio signals received at one or both of the microphones  102 ,  103 . Several useful parameters can be derived from signals generated by the accelerometer  104 . For example, a linear estimate of wind power, speed and angle of wind incidence may be determined. Such parameters may be used to estimate the cut-off frequency of wind noise at the microphone  102  which may in turn be used for wind noise reduction. Additionally, a reliable estimate of own voice in the form of subband power estimation may be determined from signals from the accelerometer  104 . An estimate of own voice derived from accelerometer signals may be used to determine periods in which the linear estimate of wind speed and angle will be accurate, since the presence of own-voice at the apparatus  100  may affect the relationship between accelerometer signal power and each of wind speed and wind angle of incidence. Similarly, non-wind noise associated with movement of the accelerometer  104  may be estimated from signals output from the accelerometer which may be used to determine periods of accurate wind parameter estimation. 
       FIGS.  4   a ,  4   b  and  4   c    are scatter plots of angle of incidence of wind (x axis) versus accelerometer subband signal power below 500 Hz (y axis) for wind having speeds of 4 m/s, 6 m/s and 8 m/s respectively incident at the accelerometer  104 . It can be seen from  FIGS.  4   a ,  4   b  and  4   c    that accelerometer subband power is dependent on wind speed. At all angles of incidence of wind, the higher the wind speed, the higher the subband power in the accelerometer signal. It can also be seen that accelerometer subband power is dependent on incident wind angle, irrespective of wind speed. It can be seen therefore that accelerometer subband power may be used to estimate the angle of incidence of wind relative to the accelerometer  104 . It can also be seen from these figures, as mentioned above with reference to  FIG.  1   c   , that turbulence and/or shadowing proximate to the accelerometer  104  may lead to a reduction in accelerometer subband power measured by the accelerometer  104  in the presence of wind incident at angles of 0°, 45°, and 225° through 315° relative to the head of the user  107  (as shown in  FIG.  1   c   ). Thus, there is a significant drop in subband power measured at the accelerometer  104  between 180° and 225° degrees which then increases through 275° and 315° incident wind angle. 
       FIGS.  4   a ,  4   b  and  4   c    depict data for a single axis of the accelerometer  104  with the apparatus  100  positioned on the left ear  106  of the user  107  as shown in  FIG.  1   c   . Using a single axis of the accelerometer  104  limits the angular resolution to two classes defined by a grouping of angles. This is illustrated by  FIGS.  5   a ,  5   b  and  5   c    which each show two density curves representing power measured by the accelerometer  104  in the presence of wind incident at 225°, 275° and 315° (left hand curves) and at 0°, 45°, 90°, 135° and 180° (right hand curves).  FIGS.  5   a ,  5   b  and  5   c    show power density plots for wind at speeds of 4 m/s, 6 m/s and 8 m/s respectively. It can be seen again from these Figures that shadowing from the user  107  results in a reduction in measured signal power at the accelerometer  104 . 
     It will be appreciated that by using additional axes of the accelerometer  104 , the measured subband power of each additional axis may be used to increase the angular resolution of the estimate of wind angle by the accelerometer  104 . For example, using a second axis of the accelerometer  104 , any ambiguity as to the incidence angle of wind in one axis of the accelerometer  104  may be resolved using the signal from the second axis. 
       FIGS.  6   a ,  6   b  and  6   c    are scatter plots of accelerometer subband power (horizontal axis) vs microphone subband power (vertical axis) for different incident wind angles and wind speeds in a quiet environment. It will be appreciated that in a quiet environment (with no non-wind noise), power in the microphone signal is due to wind and will result in wind noise in the microphone signal. The same points are plotted on each of the Figures, but are grouped differently by colour/shading in each plot.  FIG.  6   a    delineates by colour/shading between angle of incidence of wind (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°) as depicted in  FIG.  1   c   .  FIG.  6   b    delineates by colour/shading between wind speed (4 m/s, 6 m/s and 8 m/s).  FIG.  6   c    groups the points into two groups based on angle of incidence, the first group including measurements made in the presence of wind at angles 0°, 45° and 225°, and the second group including measurements made in the presence of wind at angles 90°, 135°, 180°, 270° and 315°. 
     These Figures show that microphone subband power (due to wind) increases in variance with increasing wind speed. This is a result of the increasing turbulence around the microphone port as wind speed increases. It can also be seen that there are two angular clusters most clearly illustrated in  FIG.  6   c   , evidenced by a difference in microphone subband power for the same measured accelerometer subband power. These two clusters correspond approximately to wind being incident from the front (forward) and rear (reverse) of the apparatus  100 . Linear regression lines  602 ,  604  are also provided in  FIG.  6   c   . The first regression line  602  represents subband power correlation between accelerometer and microphone signals in response to wind from angles of 0°, 45° and 225°. The second regression line  604  represents subband power correlation (below 500 Hz) between accelerometer and microphone signals in response to wind at angles 90°, 135°, 180°, 270° and 315°. For moderate wind speeds, therefore, the relationship shown in  FIGS.  6   a  to  6   c    between microphone and accelerometer subband power may be used to estimate subband power due to wind noise present in an output signal from the microphone  102  based on subband power in signals output from the accelerometer  104 . For example, a model or look up table or the like may be generated on the basis of the above or similar data to translate one or more subband accelerometer powers of received accelerometer signals into one or more of a wind speed, a wind direction, a microphone noise cut-off frequency. 
       FIG.  7    is a block diagram of parameter estimation module  700  according to an embodiment of the present disclosure which may be implemented by the apparatus  100  shown in  FIG.  1   b   . The parameter estimation module  700  is configured to receive one or more accelerometer signals  702  output from the accelerometer  104 . The accelerometer signals  702  from the accelerometer  104  may first be digitised (quantised and discretised) into frames of a certain duration (number of elements, M) before being provided to the parameter estimation module  700 . The accelerometer  104  may generate a signal for each of one or more axes of the accelerometer  104 . For example, where the accelerometer  104  comprises  3  measurement axes, the one or more accelerometer signals  702  may comprise three signals; one for each axis of measurement. The parameter estimation module  700  is configured to generate a parameter estimation output  704  comprising one or more estimated parameters of wind incident at the accelerometer  104 . Such parameters may include, but are not limited to, the presence of wind at the accelerometer  104 , wind velocity, wind direction, and cut-off frequency of noise in the microphone signal. 
     Optionally, a non-wind noise detector  706  may be provided in addition to the parameter estimation module  700 . In some embodiments, the non-wind noise detector  706  may be incorporated into the parameter estimation module  700 . The non-wind noise detector  706  may be configured to detect the presence of noise at the accelerometer  104  which is not associated with wind. For example, the non-wind noise detector  706  may implement a voice activity detector (VAD) configured to detect user speech at the accelerometer  104 . As mentioned previously, the presence of speech may affect the ability to accurately estimate wind parameters based on accelerometer signals. Accordingly, the non-wind noise detector  706  may output a voice activity signal to the parameter estimation module  700  indicating whether or not speech has been detected. In another example, the non-wind noise detector  706  may determine whether the user  107  is running or walking, which may cause noise at the accelerometer  104  due to violent changes in direction of the accelerometer  104  (i.e. up and down). Such noise due to running, for example, presents as a broadband signal at the accelerometer  105  above around 100 Hz. Accordingly, the non-wind noise detector  706  may output a signal indicating that non-wind noise is present and may be corrupting any wind noise component of the one or more signals output from the accelerometer  104 . In response to one or more signals received from the non-wind noise detector  706 , to avoid inaccurate estimation of parameters, the parameter estimation module  700  may only use accelerometer signals  702  received from the accelerometer  104  during periods where it is indicated that non-wind noise is not present or that such non-wind noise is not substantially effecting signals output from the accelerometer  104 . Additionally, or alternatively, outputs from the non-wind noise detector  706  may be used to toggle one or more filters to remove components of the one or more accelerometer signals related to non-wind noise. 
     In addition to receiving accelerometer signals  702  from the accelerometer  104  of the apparatus  100 , the parameter estimation module  700  may optionally receive additional accelerometer signals  708  from one or more additional accelerometers  710 . For example, the one or more additional accelerometer  710  may be spatially separated from the accelerometer  104  of the apparatus  100 . Where the apparatus  100  comprises an earphone or headphone or a set of earphones or headphones, for example, the one or more additional accelerometer  710  may comprise an accelerometer located in the other earphone or headphone of the pair. The spatial separation of the accelerometer  104  and the one or more additional accelerometers  710  may enable the parameter estimation module  700  to resolve the direction of incidence of wind. This may be achieved, for example, by comparing a common property of accelerometer signals received from each accelerometer, such as subband power. 
     The parameter estimation module  700  may determine one or more of the above parameters by determining various characteristics of the one or more accelerometer signals. In some embodiments, the parameter estimation module may determine the power of a subband of the one or more accelerometer signals  702 . 
       FIG.  8    is a flow diagram of a process which may be implemented by the parameter estimation module  700  shown in  FIG.  7   . At step  802 , the parameter estimation module  700  may receive one or more accelerometer signals  702 ,  708  from the one or more accelerometers  104 ,  710 . The accelerometer signal(s)  702 ,  708  may then be filtered at step  804  to generate one or more subband accelerometer signals. Filtering may comprise low-pass filtering to remove components of the accelerometer signal above a threshold frequency. In some embodiments, the threshold frequency is determined based on the spectral power profile of the accelerometer  104 . The threshold frequency may be chosen so as to remove substantially all non-wind noise present due to speech, cross-talk from the loudspeaker(s) in the apparatus  100 , taps or other physical interactions with the headset and/or any idle channel noise. In some embodiments, the threshold (cut-off) frequency is around 500 Hz. In some embodiments, the threshold may differ between accelerometer signals. Additionally, filtering may also comprise high-pass filtering to remove components of the accelerometer signal(s) associated with motion of the accelerometer, for example, due to movement of the headset (walking/running etc.). The parameter estimation module  700  may then determine a subband power of each of the subband accelerometer signals at step  806 . The determined subband power(s) may then be used at step  808  to estimate one or more parameters or characteristics of wind incident at the microphone. For example, the subband power may be used to determine a wind speed at the accelerometer  104 . Additionally or alternatively, the subband power may be used to determine an angle of incidence of wind at the accelerometer  104 . Additionally or alternatively, the subband power may be used to determine a microphone noise cut-off frequency of one or both of the microphones  102 ,  103  below which noise is affecting signal(s) output from one or both of the microphones  102 . A determination may be made based on one or more models or lookup tables stored in memory. The one or more modules or lookup tables may be generated in advance, as described above. The one or more wind parameters or characteristics may then be output at step  810  in the parameter estimation output  704 . 
     As described above and illustrated in  FIG.  6   c   , the relationship between microphone subband power and accelerometer subband power is dependent on the angle of incidence of the wind at the apparatus  100 . Accordingly, knowledge of incident wind angle may be used to determine which of a plurality of models or lookup tables to use for parameter estimation. For example, where the parameter estimation module  700  receives accelerometer signals  702 ,  708  from the accelerometer  104  in addition to the one or more additional accelerometers  710 , at step  808 , the parameter estimation module  700  may compare the determined subband signal powers of the respective accelerometer signals and make a determination of the angle of incidence of wind relative to the apparatus  100 . The parameter estimation module  700  may then determine, based on this, which of a plurality of models or lookup tables to use for the determination of wind parameters. 
       FIGS.  9 ,  10  and  11    are a block diagram of wind noise reduction system  900 ,  1000 ,  1100  which incorporates the parameter estimation module  700  of  FIG.  7   . For simplicity, the optional non-wind noise detector  706  and additional accelerometer(s)  710  shown in  FIG.  7    are not shown in  FIG.  9 ,  10  or  11   , but may be incorporated into the systems  900 ,  1000 ,  1100 . 
     Referring to  FIG.  9   , the system  900  comprises a wind noise detection (WND) module  902  and a wind noise reduction (WNR) module  904 . The WND  902  comprises an input for receiving a microphone output signal  906  from the microphone  102 . The WND module  902  is configured to detect wind at the microphone  102  based on the received microphone output signal  906  and output a wind detect signal  908  to the WNR module  904 . An exemplary WND module is described in U.S. Pat. No. 9,516,408, the content of which is hereby incorporated by reference in its entirety. 
     The WNR module  904  is configured to receive the wind detect signal  908  from the WND module  902 , the parameter signal  704  from the parameter estimation module  700  and the microphone signal  906  from the microphone  102  and reduce wind noise in the microphone signal  906  when noise is detected by the WND module  902  and based on the parameter signal  704  from the parameter estimation module. For example, the WNR module  904  may determine an intensity of wind in each microphone  102 ,  103  and combine signals such that wind power is reduced in the resultant signal so as to minimise wind. For example, the WNR module  904  may, based on wind intensity in each subband, dynamically attenuate subbands affected by wind. For example, the WNR module  904  may implement suppression or compression using the estimated cut-off frequency to dynamically set up the bandwidth or knee point of the compression algorithm. The amount of compression could therefore be controlled based on the cut-off frequency and/or the intensity of wind. An exemplary method of wind noise reduction is described in U.S. Pat. No. 9,589,573, the content of which is hereby incorporated by reference in its entirety. 
       FIG.  10    is a block diagram of a wind noise reduction system  1000  which is a variation of the system  900  shown in  FIG.  9    with like parts given like numerals. In the wind noise reduction system  1000 , wind noise detection is performed by the parameter estimation module  700 . The parameter estimation module  700  may output one or more wind parameter  1004  in addition to a flag that wind is present or likely to be present to the WNR module  904 . 
       FIG.  11    is a block diagram of a wind noise reduction system  1100  which is a further variation of the system  900  shown in  FIG.  9    with like parts given like numerals. The wind noise reduction system  1100  comprises a WND module  1102 , a parameter estimation module  1104  and a WNR module  1106 . In  FIG.  11    the WND module  1102  may receive one or both of the microphone signal  906  and the accelerometer signal(s)  702  and make a determination of the presence of wind based on one or both of these signals  906 ,  702 . The WND module  1102  may then output a wind detect signal  1108  to the parameter estimation module  1104 . The parameter estimation module  1104  may then determine one or more parameters of wind based on the accelerometer signal(s)  702  only when wind is detected by the WND module  1102 , i.e. only when the wind detect signal  1108  indicates the presence of wind to the parameter estimation module  1104 . The parameter estimation module  1104  may then output one or more parameter signals  704  to the WNR module  1106  when it is determined by the WND module  1102  that wind is present. When it is determined by the WND module  1102  that wind is not present, the parameter estimation module  1104  may output an indication as such to the WNR module  1106  or, alternatively, may output no signal to the WNR module  1106 . Based on the signal(s)  704  received from the parameter estimation module  1104 , the WNR module  1106  may apply wind noise reduction to the microphone signal  906 . The WNR module  1106  may reduce wind noise in the microphone signal  906  in any manner known in the art, such as those described with reference to the WNR module  904  of  FIG.  9    and  FIG.  10   . 
     Embodiments may be implemented in an electronic, portable and/or battery powered host device such as a smartphone, an audio player, a mobile or cellular phone, a handset. Embodiments may be implemented on one or more integrated circuits provided within such a host device. Alternatively, embodiments may be implemented in a personal audio device configurable to provide audio playback to a single person, such as a smartphone, a mobile or cellular phone, headphones, earphones, etc. Again, embodiments may be implemented on one or more integrated circuits provided within such a personal audio device. In yet further alternatives, embodiments may be implemented in a combination of a host device and a personal audio device. For example, embodiments may be implemented in one or more integrated circuits provided within the personal audio device, and one or more integrated circuits provided within the host device. 
     It should be understood—especially by those having ordinary skill in the art with the benefit of this disclosure—that the various operations described herein, particularly in connection with the figures, may be implemented by other circuitry or other hardware components. The order in which each operation of a given method is performed may be changed, and various elements of the systems illustrated herein may be added, reordered, combined, omitted, modified, etc. It is intended that this disclosure embrace all such modifications and changes and, accordingly, the above description should be regarded in an illustrative rather than a restrictive sense. 
     Similarly, although this disclosure makes reference to specific embodiments, certain modifications and changes can be made to those embodiments without departing from the scope and coverage of this disclosure. Moreover, any benefits, advantages, or solutions to problems that are described herein with regard to specific embodiments are not intended to be construed as a critical, required, or essential feature or element. 
     Further embodiments and implementations likewise, with the benefit of this disclosure, will be apparent to those having ordinary skill in the art, and such embodiments should be deemed as being encompassed herein. Further, those having ordinary skill in the art will recognize that various equivalent techniques may be applied in lieu of, or in conjunction with, the discussed embodiments, and all such equivalents should be deemed as being encompassed by the present disclosure. 
     The skilled person will recognise that some aspects of the above-described apparatus and methods, for example the discovery and configuration methods may be embodied as processor control code, for example on a non-volatile carrier medium such as a disk, CD- or DVD-ROM, programmed memory such as read only memory (Firmware), or on a data carrier such as an optical or electrical signal carrier. For many applications embodiments of the disclosure will be implemented on a DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array). Thus the code may comprise conventional program code or microcode or, for example code for setting up or controlling an ASIC or FPGA. The code may also comprise code for dynamically configuring re-configurable apparatus such as re-programmable logic gate arrays. Similarly, the code may comprise code for a hardware description language such as Verilog™ or VHDL (Very high speed integrated circuit Hardware Description Language). As the skilled person will appreciate, the code may be distributed between a plurality of coupled components in communication with one another. Where appropriate, the embodiments may also be implemented using code running on a field-(re)programmable analogue array or similar device in order to configure analogue hardware. 
     Note that as used herein the term module shall be used to refer to a functional unit or block which may be implemented at least partly by dedicated hardware components such as custom defined circuitry and/or at least partly be implemented by one or more software processors or appropriate code running on a suitable general purpose processor or the like. A module may itself comprise other modules or functional units. A module may be provided by multiple components or sub-modules which need not be co-located and could be provided on different integrated circuits and/or running on different processors. 
     It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims or embodiments. The word “comprising” does not exclude the presence of elements or steps other than those listed in a claim or embodiment, “a” or “an” does not exclude a plurality, and a single feature or other unit may fulfil the functions of several units recited in the claims or embodiments. Any reference numerals or labels in the claims or embodiments shall not be construed so as to limit their scope. 
     Although the present disclosure and certain representative advantages have been described in detail, it should be understood that various changes, substitutions, and alterations can be made herein without departing from the spirit and scope of the disclosure as defined by the appended claims or embodiments. Moreover, the scope of the present disclosure is not intended to be limited to the particular embodiments of the process, machine, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments herein may be utilized. Accordingly, the appended claims or embodiments are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps. 
     It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.