Abstract:
A device includes a windscreen in a first surface, a gradient microphone housed in a capsule having first and second outlets coupled to openings in a second surface displaced from the first surface, a pressure microphone mounted between the first and second surfaces, and circuitry coupled to the gradient microphone and the pressure microphone and operable to combine the signals of the microphones and provide a combined microphone signal.

Description:
BACKGROUND 
     This disclosure relates to using paired microphones to reject noise. 
     A headset for communicating through a telecommunication system, whether wired or wireless, will generally include a microphone for detecting the voice of the wearer. Such microphones are exposed to several types of noise, including ambient noise from the environment, such as other people talking, and wind noise caused by air moving past the microphone. 
       FIG. 1  shows an in-ear headset  10  commercially available from Bose Corporation in Framingham, Mass. The headset  10  includes an electronics module  12 , an acoustic driver module  14 , and an ear interface  16  that fits into the wearer&#39;s ear to retain the headset and couple the acoustic output of the driver module  14  to the user&#39;s ear canal. In the example headset of  FIG. 1 , the ear interface  16  includes an extension  18  that fits into the upper part of the wearer&#39;s ear to help retain the headset. The headset may be wireless, that is, there may be no wire or cable that mechanically or electronically couples the earpiece to any other device. This headset is shown only for reference. The ideas disclosed below are applicable to any device having a microphone to be used in a potentially noisy environment. 
     SUMMARY 
     In general, in one aspect, a device includes a windscreen in a first surface, a gradient microphone housed in a capsule having first and second outlets coupled to openings in a second surface displaced from the first surface, a pressure microphone mounted between the first and second surfaces, and circuitry coupled to the gradient microphone and the pressure microphone and operable to combine the signals of the microphones and provide a combined microphone signal. 
     Implementations may include one or more of the following. The first surface and the second surface may be displaced a non-zero distance from each other. The first surface, the second surface, and at least one wall between the first surface and the second surface enclose a volume, and the openings in the second surface and a sensing element of the pressure microphone may both be coupled to the volume. The pressure microphone may be mounted in the wall between the first surface and the second surface. 
     In general, in one aspect, a system for combining signals includes a first microphone generating a first input signal having a first voice component and a first noise component, a second microphone generating a second input signal having a second voice component and a second noise component, a mixing circuit, and an adaptive filter. The mixing circuit applies a first gain having a value a to the first input signal to produce a first scaled signal, applies a second gain having a value 1−α to the second input signal to produce a second scaled signal, and sums the first scaled signal and the second scaled signal to produce a summed signal. The adaptive filter computes an updated value of α to minimize the energy of the summed signal based on the summed signal, the first input signal and the second input signal, and provides the updated value of α to the mixing circuit. 
     Implementations may include one or more of the following. The first noise component may have a greater contribution from ambient noise than from wind noise. The first microphone may include a pressure microphone. The second noise component may have a greater contribution from wind noise than from ambient noise. The first microphone may be more sensitive to ambient noise than to wind noise. The second microphone may be more sensitive to wind noise than to ambient noise. The second microphone may include a gradient microphone. The first microphone may include a pressure microphone, the second microphone may include a gradient microphone, and the first and second microphones may be located at a common location within the system. 
     The adaptive filter may be configured to apply a least-mean-squared algorithm to compute the updated value of α. The adaptive filter may be implemented in a digital signal processor programmed to compute a difference between the first and second signals, multiply the summed signal by the difference and by a pre-determined step size value, and subtract the product from the current value of α to produce the updated value of α. The adaptive filter may be implemented in a digital signal processor programmed to decompose the summed signal and the first and second input signals into frequency bands and to minimize the energy of the summed signal in a first energy band. The mixing circuit may apply the first and second gains by applying different values of α and 1−α, respectively, in different frequency bands. 
     An equalizer may receive at least one of the first input signal or second input signal and equalize the received signal according to a pre-defined equalization curve to match the first voice component to the second voice component. The equalizer may include a first equalizer to apply a first equalization curve to the first input signal to produce a first equalized signal, and a second equalizer to apply a second equalization curve to the second input signal to produce a second equalized signal, the first and second equalized signals having matching voice components. The equalizer may include a single equalizer configured to apply an equalization curve to the first input signal to produce a first equalized signal, the first equalized signal having an equalized voice component matching the second voice component from the second input signal. A low-pass filter may filter the second input signal before the second input signal is provided to the adaptive filter. A second equalizer may be coupled to the output of the mixing circuit to optimize a voice response of the summed signal for use in a communications system. 
     The mixing circuit may be further configured to apply a gain to at least one of the first input signal or the second input signal before providing the first and second input signals to the adaptive filter. Either or both of the mixing circuit and the adaptive filter may be implemented in a digital signal processor. The mixing circuit may include a first voltage-controlled amplifier configured to apply the first gain, and a second voltage-controlled amplifier configured to apply the second gain, the outputs of the first and second voltage-controlled amplifiers being coupled to produce the summed signal. 
     Advantages include rejecting noise in various environments, seamlessly combining signals from different microphones each best-suited for the noise found in different environments. 
     Other features and advantages will be apparent from the description and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a wireless headset. 
         FIG. 2  shows a block diagram of a microphone signal mixing circuit. 
         FIG. 3  shows a cutaway view of a microphone housing in a wireless headset. 
     
    
    
     DESCRIPTION 
     A commercial embodiment of the Bluetooth headset shown in  FIG. 1  uses a single microphone encapsulated in a two-port physical structure behind a screen to reduce noise in far-end voice communications, as described in co-pending application Ser. No. 13/075,732, which is incorporated here by reference. The physical structure decreases the amount of noise detected by the microphone, reducing noise in the sounds heard by the far end communication partner. Adding a second microphone and mixing the electrical signals from the two microphones as shown in  FIG. 2  offers further improvements in noise rejection. In particular, the encapsulated microphone  102  offers good rejection of ambient noise (e.g., other people talking nearby, traffic, machinery), but it tends to pick up noise from wind, i.e., the noise of air moving past the headset. The second microphone  104  is selected to provide good rejection of wind noise, even if that means it is more likely to pick up ambient noises. The mixing circuit  106  combines the signals  108 ,  110  from the two microphones to produce an output signal  112  that has a strong voice component and little noise. 
     We represent the microphone signal  108  from the first microphone  102  as having a value W=V w +N w , where V w  is the voice component and N w  is the noise component, which is influenced more by wind noise than it is by ambient noise. Similarly, we represent the microphone signal  110  from the second microphone  104  as having a value D=V d +N d , where V d  is the voice component and N d  is the noise component, which for this microphone is influenced more by ambient noise than it is by wind noise. In this particular example, the noise component N w  is influenced more by wind noise than by ambient noise, and the noise component N d  is influenced more by ambient noise than by wind noise, but the mixing circuit  106  is generally applicable to any system for combining two inputs with different responses to noise. The mixing circuit  106  first equalizes one or both of the microphone signals. Equalizers  114  and  116  apply an equalization curve to the respective microphone signals  108  and  110  to produce equalized signals  118 ,  120 , which we represent as W e =V we +N we  and D e =V de +N de . The equalization curves applied by the equalizers  114  and  116  are designed to match the microphones&#39; voice responses, independently of what their noise response might be, so that V we =V de . In some examples, only one equalizer is used, matching the corresponding microphone signal to the unequalized voice response of the other microphone signal, e.g., V we =V d  or V de =V w . The equalization can be carried out in a digital signal processor (DSP), a microprocessor, or by analog components, such as an R-L-C network. 
     The equalized signals are then scaled, one by a scaling factor α and the other by 1−α, in scaling blocks  124  and  126 , to produce scaled signals  128  and  130  with values (1−α)(V we +N we ) and α(V de +N de ). The scaled signals  128  and  130  are then summed by a summer  132 . The summed signal  134 , with value Y=(1−α)(V we +N we )+α(V de +N de ), is passed on to a voice equalizer  136  that equalizes the summed signal to produce the appropriate voice response for use by subsequent communications circuitry  138 . We refer to the scaling and summing of the signals as “mixing.” As with the equalization, the mixing can be carried out in a DSP or a microprocessor programmed to multiply the signals by the scaling factors and add the results. Alternatively, the mixing may be done in analog components, such as a pair of voltage-controlled amplifiers with their outputs coupled to produce the summed signal. 
     The microphone signals and the summed signal are also provided to an adaptive filter  122 , which outputs the scaling factor α. The filter  122  may use either the unequalized signals  108  and  110  or the equalized signals  118  and  120 . In some examples, it is advantageous to use the equalized signals so that the voice components are already matched. The scaling factor α is computed to provide that whichever of the microphone signals has less noise will provide a greater contribution to the summed signal  134 . In some examples, a varies between zero and one. Other values may also be used, including a narrower range (e.g., to assure at least some signal is used from each microhpone), a wider range (e.g., to allow one signal to over-drive the summed signal), or a discrete set of values rather than a continuously variable value. 
     The summed signal  134  will have a voice component of αV de −αV we +V we , and a noise component of αN de −αN we +N we . Because the equalization earlier provided that V we =V de , the total voice component is equal to V we , which is independent of the value of α. Because only the noise component is affected by the scaling factor α, the value of α can be selected to minimize the noise, whatever its source, without affecting the voice signal. In a DSP implementation, the adaptive filter output α is provided as data to control the gains of the scaling stages; in an analog implementation, the filter output may be a voltage to control voltage controlled amplifiers. Other implementations are also possible. 
     In some examples, the adaptive filter  122  applies an algorithm that selects α by treating the summed signal  134  as an error input and setting the output α to minimize the total energy of the summed “error” signal. As the summed signal has a constant voice component, minimizing the total energy will result in the filter decreasing the contribution of whichever microphone signal is contributing more noise to the total. When there is little ambient noise or wind noise at the same time, the adaptive algorithm may cause α to vary continuous because neither microphone contributes significant noise to the total. This may be undesirable. To address that, the filter may be biased in favor of whichever microphone has a better overall quality in situations having high signal to noise ratios. Additional noise removing algorithms may be applied in the subsequent circuitry  138 . 
     The adaptive filter  122  used to determine the mixing coefficient α may be implemented in many different ways. In one example, a least-mean-squared adaptive filter is used to minimize the total energy in the mixed signal. This has the advantage of being relatively simple and cost-effective to implement. Building on the signal representations noted above, the total mixed signal Y at a given time t is:
 
 Y   t   =αD   t +(1−α) W   t =α( D   t   −W   t )+ W   t   (1)
 
where W t  and D t  are the total equalized microphone signals  118  and  120  at time t. The LMS filter works to minimize the energy of the total mixed “error” signal Y,
 
min α   E{|Y|   2 }=min α   E {(α( D   t   −W   t )+ W   t ) 2 }.  (2)
 
     The cost function in (2) is a quadratic in α and has a single optimal solution that varies with changing noise environments. A steepest-descent algorithm using a small step size parameter μ can be used in the adaptive filter, with the updated α found as: 
     
       
         
           
             
               
                 
                   
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     For a short-time adaptive solution, the instantaneous estimate of the derivative is used in place of the expectation to provide the LMS filter output:
 
α t+1 =α t   μY   t ( D   t   −W   t ),  (4)
 
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     In another example, a multi-tap adaptive filter may be used to provide for frequency-dependent blending of the signals. Similarly, a frequency-domain analysis may be performed, again with different values of α produced for different frequency bands. Using frequency-dependent blending may allow optimization of the voice component with improved filtering of noise that is outside the voice band, or more generally, allow optimal blending of inputs with different response characteristics. As with the other components, the filter may be implemented using analog circuitry or a DSP, or other suitable circuitry, such as a programmed microprocessor. In some examples, it is possible to power a system implemented with low-power analog electronics entirely by the microphone bias power supply. The order of steps may also be varied, for example, the overall voice response equalization may be performed as part of the microphone-matching equalization, optimizing the microphones for the later voice processing independently of each other. 
     In some examples, an additional low-pass filter is applied to the wind-sensitive microphone signal  118  when it is input to the adaptive filter  122  to band-limit the signal to frequencies where the wind noise is dominant. This has the effect of biasing the filter in favor of the wind-sensitive microphone when the wind is not present, which is preferred in cases where the wind-sensitive microphone has a better overall signal to noise ratio with regard to voice. 
     In some examples, scaling factors may be added to bias one or the other microphone signal by a few dB to compensate for expected drift in the microphone responses. In addition, one or both microphone signals may have a gain applied to adjust a given unit for the specific sensitivities of its microphones, which tend to have significant part-to-part variability. This is advantageous as it helps to assure that the two microphones&#39; voice responses are matched. 
     The two microphones  102  and  104  are represented in  FIG. 2  as a gradient microphone and a pressure microphone to differentiate them, but the mixing carried out by the circuit  106  is generally applicable to combining signals from any two systems that provide different responses to noise. For the microphone  102  with less sensitivity to ambient noise, examples may include a velocity microphone or a higher-order differential microphone array. For the microphone  104  with less sensitivity to wind noise, other examples may include a delay and sum beamformer, which may have more ambient noise suppression than a pressure microphone alone while still being less sensitive to wind than a gradient microphone. One particular embodiment for use in the headset shown in  FIG. 1  is described below. 
     In one example, the first microphone  102  is a gradient microphone located inside a two-port capsule. By gradient microphone, we mean an electroacoustic transducer that is responsive to the pressure gradient between two points. Gradient microphones tend to have bidirectional microphone patterns, which is useful in providing a good voice response in a wireless headset, where the microphone can be pointed in the general direction of the user&#39;s mouth. Such a microphone provides a good response in ambient noise, but is susceptible to wind noise. The second microphone  104  is a pressure microphone, which tends to have an omnidirectional microphone pattern. By pressure microphone, we mean an electroacoustic transducer that is responsive to the pressure in the air to which it is exposed, and which produces an electrical signal representative of that pressure. A single pressure microphone may provide a good response in wind noise, especially if a proper wind screen is used, but will provide little rejection of ambient noise. In some examples, a pair of pressure microphones is used together as a gradient microphone for the first microphone signal (the difference between the signals from the pressure microphones representing the gradient between them), and in that case, one of the same pressure microphones may be used on its own as a pressure microphone for the second microphone signal, or a third microphone may be used. 
     One embodiment using a gradient microphone and a pressure microphone is shown in  FIG. 3 . In this example, a wireless headset  200  has a recessed shelf  202  at the front to accommodate both microphones. The shelf  202  is covered by a screen  204  in the outer shell of the headset, shown partially cut away to reveal the shelf. The screen may extend beyond the limits of the shelf for cosmetic reasons. A gradient microphone  206  is located in a capsule  208  under the surface  210  of the recessed shelf. Two ports  212  and  214  connect the two sides of the gradient microphone  206  to the volume of air within the shelf. The pressure microphone  216  is located on a side wall  218  of the recessed shelf  202 . Both microphones are connected to circuitry elsewhere in the headset (not shown). 
     Placing the microphones under a windscreen advantageously eliminates some wind noise from both microphones. In one example, a windscreen reduced the signal due to wind noise at the pressure microphone by about 8 dB and at the gradient microphone by about 16 dB, relative to having no windscreen at all, allowing the signal mixing circuit to have less noise to remove in the first place. The position of the shelf below the windscreen also provides an air volume and linear distance between the windscreen and the microphones, which further decrease the amount of wind noise at the microphones. In particular, to be most effective, the windscreen should have a greater total surface area than the faces of the microphones (in the area of the screen that is actually exposed to the microphones—the cosmetic portions don&#39;t have any effect). Without the shelf, only the part of the screen directly over the microphones would matter, and would be effectively the same area as the microphones, decreasing its effectiveness. The resistance of the windscreen can also be selected to control the frequency at which the response of the gradient microphone rolls off. In one example, a resistance of 15 Rayls causes the gradient microphone to roll off below about 100 Hz. Higher or lower values may be used in a given embodiment based on the inherent wind sensitivity and roll-off frequency of the microphones used. 
     The microphone layout described here is not limited to headsets, but may also be useful in other communications devices that may be used in noisy environments, such as a portable speaker phone or conferencing system, for example. One or more gradient microphones may be used to pick up the voices of the people around the phone, while an omni-directional microphone with better wind noise rejection is used to capture the same voices when wind compromises the performance of one or more of the gradient microphones. 
     Other implementations are within the scope of the following claims and other claims to which the applicant may be entitled.