Patent Publication Number: US-9837066-B2

Title: System and method for adaptive active noise reduction

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. Ser. No. 14/445,048 filed Jul. 28, 2014, which claims the benefit of U.S. provisional application Ser. No. 61/859,293 filed Jul. 28, 2013, the disclosures of which are hereby incorporated in their entirety by reference herein. 
    
    
     TECHNICAL FIELD 
     This disclosure relates to a system and method for adaptive active noise reduction that may be used in various applications including headphones, headsets, and earphones, for example. 
     BACKGROUND 
     Active noise reduction (ANR) devices have been commercially available for over 20 years. In general, these devices use electronics to generate a signal with the same amplitude but opposite phase of the noise. This is accomplished using a closed loop feedback control system having a sensing microphone to detect the noise with the associated signal passed through a compensating filter and electronics to drive a speaker that produces a pressure wave out of phase with the noise, resulting in a net reduction or attenuation of the noise perceived by a user. 
     Techniques for designing a feedback control system for active noise reduction are well understood by those skilled in the art. In general, the goal may be summarized as selecting components to provide system operating characteristics that satisfy control theory feedback loop stability criteria and provide a net attenuation or reduction of sound pressure at some or all of the frequencies of interest. This is accomplished by determining an appropriate open loop gain G, defined as the output/input ratio when the loop including the driver, sensing microphone, and electronics is driven and measured with the loop open, i.e. without feedback. G is a complex function, such that its magnitude and phase vary with frequency. 
     The corresponding attenuation provided by a system with open loop gain G can be expressed as 1/(1−G). In closed loop ANR circumaural designs having ear cups with a cushion that seals against the head around the circumference of the ear, this is typically limited to frequencies under 1 kHz. Because of a need for more attenuation of the lower frequencies, some boosting or amplification of the sound pressures is tolerated at higher frequencies where passive attenuation is more effective. In closed loop control systems, the amount of attenuation at lower frequencies is dependent on the acceptable phase margin around the upper transition frequency where the magnitude of the open loop gain (|G|) reaches unity. Phase margin is defined as the phase difference between the phase angle of the open loop gain (&lt;G) and zero degrees when |G|=1. If the open loop gain has a magnitude close to unity and a phase of close to zero degrees, the denominator of 1/(1−G) will be much less than unity resulting in the function 1/(1−G) being much greater than unity at those frequencies and thus boosting of the pressure around those frequencies. Any compensation that causes a net decrease in amplitude with increasing frequency, has a resultant negative phase shift with more phase shift associated with steeper attenuation. 
     If 60 degrees or more phase margins can be maintained when the magnitude of the open loop gain (|G|) is close to unity, then no high frequency boosting will exist. Unfortunately, this generally produces inadequate loop gain at lower frequencies where passive attenuation is not as significant. Many designs accept some amount of high frequency boosting (making some frequencies louder when the ANR is on or active) to gain more attenuation at lower frequencies. In the design of such a system, transport or transit delay between the microphone input and driver output uses up valuable phase margin, and without changing the compensation, increases boosting around frequencies where the magnitude of G (|G|) is approximately unity. As a result, the sensing microphone has been placed in close proximity to the speaker (driver) to minimize delay as a result of the travel time of the sound to reach the microphone to provide acceptable phase margin and increase system bandwidth. In addition, the assumption of constant pressure within the front cavity of the ear cup of circumaural headphones at the frequencies the system attenuates also supports this approach as a good design methodology. 
     As such, use of well understood principles of feedback control system design and accepted operating assumptions have resulted in prior art systems that position the sensing microphone close to the speaker (also referred to as the driver) to maximize system bandwidth while providing acceptable phase margin for the system to remain stable and avoid unacceptable boosting of higher frequencies. The system parameters to provide acceptable phase margin are generally determined during product development based on average anatomical data and representative use scenarios. These parameters are generally fixed for the life of the product, or in some cases may be infrequently changed during firmware updates, but do not change during each use. While suitable for many applications, this design methodology does not account for variations among users with respect to ear anatomy as well as ambient environment. 
     Microprocessors and various dedicated purpose digital devices have afforded the opportunity for more complex digital processing of audio signals. However, processing speed remains an important consideration for real-time applications as any significant delay (on the order of 10 milliseconds) may produce an unacceptable lag, echo, distortion, or similar effect leading to an unnatural listening experience that may also affect speech patterns. Delay also imposes an inherent limitation to the bandwidth of broadband cancellation. The desire to avoid these effects may result in limiting the ANR performance over certain frequency bands. 
     SUMMARY 
     A system and method for adaptive active noise reduction according to embodiments of the present disclosure measure the acoustic response for each user to adaptively adjust and customize the ANR operation using adaptive filters to correct for any differences between the measured response and a target response. The system and method of various embodiments incorporate a closed loop control system with a feedforward input. The acoustic measurement and adaptation procedure is performed to adapt or tune at least one of the closed loop and feedforward control loops to provide adaptive ANR customized for each user and current ambient environment. 
     During an initialization or calibration mode, the feedforward control is adapted to the user and ambient environment by measuring the transfer function from the ambient noise to the sense or error microphone positioned within the earcup of the headset. This information is used to implement a corresponding filter having the opposite phase to provide noise reduction or cancelation. To produce an accurate anti-noise signal that matches the acoustic noise in the ear cup using the ambient microphone as the sense microphone, the transfer function of the driver to error microphone must also be known. With the transfer functions of the ambient microphone to error microphone and the driver to error microphone known, it is possible to estimate the required target transfer function to produce perfect cancelation. This target transfer function can then be used to compute a realizable filter. This method differs from the typical approach used with adaptive filters that modifies coefficients to minimize the error energy using any of a number of strategies that may be characterized as gradient descent strategies. In contrast, using a method based on target transfer functions according to embodiments of the present disclosure is fundamentally different in that it is independent of the spectrum of the noise source, i.e. the amount of energy at a given frequency does not affect the target response, or the resulting realizable transfer function. As a further benefit, the problem with convergence of gradient descent methods with wide eigenvalue disparity (i.e. natural frequencies of the transfer function that span a large range of frequency, say 10&#39;s of Hz to several kHz) is avoided. 
     To facilitate substantial contribution from the feedforward input, the sense or error microphone is positioned within at least one earcup to be in very close proximity to the ear canal opening of the user when the headset is worn (as close as practically possible considering variations in anatomy without contacting the user). This minimizes the difference between the error microphone and the sound at the ear canal to provide a more accurate measurement of the sound or noise heard by the user. 
     Positioning the error microphone as described above causes an additional complication in that the differences between each user and even each use/fit are very sensitive to the pinna reflections and ear canal resonance, which would make a traditional fixed filter type of implementation very difficult or cause reduced performance to accommodate different users. Embodiments according to the present disclosure address this problem by adapting or customizing the loop response to each individual. As a result, closed loop performance is improved and, more significantly, feedforward cancelation is substantially improved relative to various prior art ANR devices. A similar method is used in the feedforward cancelation of various disclosed embodiments where the noise transmission transfer function is estimated, and a synthesized transfer function is implemented to provide an anti-noise signal from the driver/speaker. This feature may operate separately, or in combination with the closed loop ANR function. 
     Embodiments according to the present disclosure may continually monitor ANR operation and selectively update or adapt one or more system variables or parameters, such as the driver-to-microphone transfer function T dm  and the noise-to-error-sensing-microphone transfer function T nm  for example. System performance can be continually monitored and filters for closed loop and feedforward noise reduction updated during operation as desired to improve noise cancellation. T dm  can use communication signals as the stimulus to update the estimate of T dm  using a moving average. This method is also useful for correcting variations over time, such as altitude changes for aviation applications and changes in the ear seal caused by perspiration. T nm  is technically not noise dependent, but the amplitude and phase vs frequency weighting used to estimate the feedforward filters may incorporate a factor that focuses the accuracy of the feedforward transfer function T ff  (H ff ). Using a weighting that approximates perceived loudness aids in insuring that future updates to these parameters are perceived by the user as improving performance and not just mathematically better based on a lower weighted calculated energy, where the weighting is an approximation of the psycho-acoustic weighting to perceived loudness. 
     After system characterization, a user can save his personalized response to allow for immediate loading of the personalized response during subsequent use. The saved filters and/or other parameters can be updated during operation to accommodate variations in a particular fit or operating environment. 
     In addition to using communication signals to adapt one or more system parameters, various embodiments provide customized characterization for a user and/or application using an active stimulus signal, which may more quickly provide the characterization parameters by using a known stimulus signal having desired frequency, amplitude, and phase characteristics. Characterization using an active stimulus may not provide optimal ANR performance for each fit, but will typically be sufficient for good performance, and can adapt (or update the T nam  and T dm  estimate) by using passive estimates (i.e. using a communications signal for the stimulus and other data when the comm signal is not present to provide data for T nam  during subsequent operation). 
     In various embodiments, T nam  estimates can also be updated periodically, and used to monitor performance. If T nam  changes significantly, the feedforward filters T ff  can be updated from this data. Filters are only updated if the estimated perceived performance is improved. This is done by weighting the estimated change in noise level at the error sensing microphone by the appropriate weighting filter and the spectrum of the noise at the error sensing microphone. 
     In some embodiments, performance is further improved by the use of two microphones in the ear cavity of the earcups. The second ear cup microphone for error sensing of the closed loop system is optimally positioned to trade off delay from the driver to the closed loop error microphone while providing only enough correlation to the ear to support the closed loop attenuation. This can allow the closed loop attenuation to extend to a higher frequency. The first error sensing microphone is again positioned very close to the ear canal opening, or for applications that will tolerate it, even in the ear cavity opening. In this case, the ear canal error sensing microphone need not be processed as a low latency signal, since it is only used for estimating the pressure at the ear opening. 
     In other embodiments, the error signal is modified to account for the differences between T dm , T de , T nm , and T ne . The goal of the adaptive filter algorithm is then to force the response of the error sensing microphone to a pre-determined function of frequency which reduces or minimizes the noise at the ear drum, as opposed to the adaptive filter attempting to minimize the weighted error. 
     Various embodiments of an adaptive ANR system or method according to the present disclosure provide associated advantages. For example, typically, ANR headsets only perturb the pinna response slightly, and as with any headphone, the response is influenced by the user&#39;s own anatomy, particularly the pinna. The best performing headphones are usually circumaural types that are very leaky, so as to minimize corruption of the users unique pinna response. Embodiments according to the present disclosure significantly reduce or entirely remove any effect of the pinna on sound going into the ear (typically, variations from 2 kHz˜20 kHz). By processing the calibration data done on a flat plate or block head with no pinna, and the user&#39;s calibration based on an active stimulus or a communication signal, the user&#39;s pinna response can be measured and restored. In addition to circumaural headphones, the measured pinna response is valuable for restoring the pinna response to ear bud or in-the-ear type headphones. The restoration of the pinna response as an equalization applied to incoming music signals provides a dramatic improvement over traditional headphone experiences because it is not the result of the pinna and headphone response, but primarily just the pinna response, thus producing an audio response that is very natural, and simultaneously providing very good isolation. 
     Various embodiments according to the present disclosure allow the noise reduction system to come on in a conservative manner that will be stable for all users, and then measure the key variables, such as T nm  and T dm , for example, using one or more measurement strategies. When audio is being played to the user, estimates of T dm  can be calculated. Use of time averaging of the frequency spectra with a weighting that updates the parts of the T dm (f) that have good excitation greatly improves the speed and accuracy. For example, if very low frequency content or very high frequency content was not present, only the part of the response that was adequately excited is used to improve the estimate of T dm (f). T nm  can be estimated ideally without audio. The boom microphone signal provided by headset embodiments can be used to detect if the user is talking, and if this is the case, then the ambient noise is correlated to the communication audio if loop back is present. Also, user speech causes bone conduction that will not be present at the ambient microphone(s), thus it is better to avoid use of measurements when the user is talking. Corrections can be made for communications audio signals if the transfer function is known. 
     As previously described, various embodiments allow user initiated saving of characterization or calibration data within the headset, or the headset can save the adapted filter coefficients before power down. Alternatively, or in combination, calibration data and/or filter coefficients may be saved and restored from a linked device, such as a cell phone. 
     In addition to circumaural headphones, various features of the embodiments according to the present disclosure may be used in supra-aural and intra-aural (or in-the-ear) type of headphones. 
     The above advantages and other advantages and features will be readily apparent to those of ordinary skill in the art based on the following detailed description when read in combination with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A-1C  illustrate a representative circumaural implementation of a system or method for adaptive ANR according to embodiments of the present disclosure; 
         FIG. 2  illustrates a prototype circumaural headset having adaptive ANR according to embodiments of the present disclosure; 
         FIG. 3  is a simplified control system block diagram and supporting equations used to determine various transfer functions associated with an adaptive ANR system or method according to embodiments of the present disclosure; 
         FIG. 4  is a conceptual block diagram illustrating various functional blocks for adaptive ANR including sense microphones, drivers, and external inputs according to embodiments of the present disclosure; 
         FIG. 5  is a block diagram illustrating sample-by-sample (SBS) low latency processing and adaptive filter coefficient calculator for adaptive ANR according to embodiments of the present disclosure; 
         FIG. 6  is a block diagram illustrating system architecture for a representative embodiment of an adaptive ANR headset according to the present disclosure; 
         FIGS. 7A  (Prior Art) and  7 B illustrate improved low latency audio processing for adaptive ANR according to representative embodiments of the present disclosure; 
         FIG. 8  is a block diagram illustrating integration and configurability details provided by a linked device or other user interface for an adaptive ANR system or method according to various embodiments of the present disclosure; 
         FIGS. 9-19  are graphs illustrating improved ANR performance for an adaptive ANR system or method according to embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention. 
     In general, the system and method operate by providing customized or adaptive ANR that adapts to each individual user and environment. The basic concept is that the system and method calibrate or adapt the closed loop system to the user and/or fit that reflects the current position of the headset on the user. Compared to traditional methods, this minimizes the effect of unit-to-unit variations caused by manufacturing, user variables, such as pinna shape and size, leak variations due to more or less hair, etc. Additionally, even for the same users, from fit to fit, and over time, variations occur that are caused by hair and perspiration and slight position variations relative to the sensing microphone and the ear opening. As described in greater detail herein, embodiments according to the present disclosure periodically and/or continuously adapt the system parameters to improve the overall ANR performance over varying user fit and ambient conditions to provide a customized ANR experience. 
       FIGS. 1A-1C  illustrate a representative circumaural implementation of a system or method for adaptive ANR according to embodiments of the present disclosure. While the representative embodiment is depicted as a circumaural headset with a boom microphone, those of ordinary skill in the art will recognize that strategies of various embodiments may also be used to advantage in other types of headphones, earphones, etc, such as in-the-ear (ITE) and on-the ear (or supra-aural) implementations.  FIG. 1A  is a diagram representing a cross section of one embodiment illustrating positioning of various system components. ANR headset  20  includes a pair of similarly equipped ear cups  22 , only one of which is shown, connected by a band ( FIG. 2 ). Ear cup  22  is used to support a cushion  24  that fits over and surrounds the pinna of the ear of a user during use. Cushion  24  is partially compressed to provide a seal around the ear. Ear cup  22  supports a driver or speaker  26  as well as an ANR or error microphone  28 . Acoustically “open” cloth or foam  30  covers driver  26  and error microphone  28 . A second layer  32  of foam or cloth that is more acoustically dense may be provided to cover at least a portion of the driver  26 , but does not cover error microphone  28  in this embodiment. Second layer  32  may also be implemented as a portion of cloth or foam  32 . Ear cup  22  may include one or more vents that may be covered by a cover plate  34  and damping material such as foam  36 . 
       FIGS. 1B and 1C  illustrate an alternative embodiment that is similar to the embodiment of  FIG. 1A , but includes a second ANR or error microphone to detect ambient noise. As illustrated in the inside view of  FIG. 1B  and cross-section of  FIG. 1C , system  40  includes an ear cup  42  having a cushion  44  that partially compresses against the head  70  of a user during operation. The pinna  72  and tragus  74  of the user&#39;s ear extends within the portion of ear cup  42  in front of acoustic fabric  50 . Driver or speaker  46  is positioned within ear cup  42  generally behind sense microphone  48 , which is positioned near the opening of ear canal  76  and tragus  74  of user  70 . An optional second sense or error microphone  60  is used to detect ambient noise and provide a corresponding signal to the ANR processing circuitry to improve performance based on current operating conditions. In this embodiment, ambient noise microphone  60  is positioned behind a corresponding opening in ear cup  42  and covered by a rigid cover plate  54  and layer of foam  56  or similar material. Ear cup  42  may also include a vent  62  sized to provide desired response of driver  46 . 
     As illustrated in  FIGS. 1B-1C , the sense microphone  48  is as close to the tragus  74  of the user  70  as possible. (i.e. over the population, any closer may start to cause comfort issues). The path distance (string length) from the driver  46  to the microphone  48  is greater than the string length from the microphone  48  to the tragus  74  of user  70 . Closeness to the ear opening is believed to be more important than distance from the driver  46  in this embodiment. This would be very problematic in a conventional system that does not adapt to variation associated with fit and anatomy as different shapes and sizes of pinnas can otherwise cause significant variation in the 1 kHz˜3 kHz regions that adversely affect closed loop stability. 
     The close proximity of the sense microphone  48  to the ear opening  76  allows the microphone to match the ear so that cancelation can be up to 20 dB out to 2 kHz and much more at lower frequencies as generally demonstrated by the graphs of  FIGS. 9-19 . 
       FIG. 2  illustrates a prototype circumaural headset having adaptive ANR according to embodiments of the present disclosure. The perspective view of  FIG. 2  illustrates a headset  40  having ear cups  42  connected by a head band  80 . A boom microphone  82  extends from one of the ear cups  42  and is used to capture user speech. Headset  40  may be coupled to a signal source by a corresponding cord or cable  84 , or may be wireless connected in some implementations. 
       FIG. 3  is a simplified control system block diagram and supporting equations used to determine various transfer functions associated with an adaptive ANR system or method and to illustrate operation of a system or method for adaptive ANR according to embodiments of the present disclosure. The control system block diagram of  FIG. 3  may be used to derive a target feed forward response (H B ) that would provide total noise cancellation in an idealized system. The block diagram of  FIG. 3  includes an input for a signal from an ambient noise microphone, although those of ordinary skill in the art will recognize that the same principles may be applied to systems that do not include an ambient noise microphone or associated signal. 
     In contrast to prior art ANR strategies, embodiments of the present disclosure estimate transfer functions to do the noise cancelation. Previous strategies rely on methods that depend on the statistics of the noise. i.e. they cancel the periodic components of the noise. In the method and system according to the present disclosure, an adaptive realizable filter is used, specifically, an IIR filter rather than a FIR filter, with the end result that the performance measured as attenuation vs frequency is totally independent of the statistics of the noise. (i.e. periodic methods don&#39;t work well if the noise is not periodic.) 
     As shown in  FIG. 3 , the sense microphone signal M  302  is multiplied by a linear factor K 1  at  304  and combined at block  306  with the communication (comm) signal  308 . The combined signal is processed by the target response H A  at block  310  and combined at block  312  with the processed signal associated with the noise signal N represented at  320 . Noise signal N is multiplied by a constant K 2  as indicated at  322  and the target feed forward response H B  at block  324  before being combined as described above at block  312 . Noise signal N at  320  is multiplied by T p  at block  326  with the result provided to block  334 . The output of block  312  is multiplied by H C  at  330  and T dm  at  332  before being combined at  334  with the output from block  326  to generate output M at  336 , which represents the error or sense microphone signal used in the feedback loop. Block  332  represents the response or transfer function between the driver D at  340  and the sense microphone M at  336 . 
     Those of ordinary skill in the art will recognize that measuring the driver to error or sense microphone response between the driver/speaker  46  and sense microphone  48  represented by T dm  in use is ideal, and can be done actively or passively. For active measurement of T dm  according to various embodiments of the present disclosure, a test signal is used as the stimulus. This can be any signal that excites the modes of the system. For example, a multitone, chirp, log chirp, or random noise are some examples of a possible test signal or active stimulus. A test signal that is periodic about a value n, where n represents the FFT size eliminates the need for a window function. Of course, an FFT is just one basis and the representative methods illustrated will work independently of the basis chosen for solving the problem. Other adaptive strategies that minimize the error by a gradient search may also be used, such as a least mean squares (LMS) or root mean squares (RMS) optimization, for example. 
     The response or transfer function T dm  of block  332  can also be measured passively, but using normally occurring signals such as the speech or aircraft noise. If only aircraft noise is used, the system closed loop response can be perturbed to allow the simultaneous estimation of both T dm  and T p . Otherwise, there is only one equation and two unknowns. To provide a solution, for the two unknowns requires another equation, i.e. the system is perturbed (the loop gain of the closed loop filters is changed slightly so that two equations are created. During the process the system performance is perturbed for the purpose of determining the two parameters related to the driver to mic response (T dm ) and the noise to mic response (T p ) unknowns. 
     The following control equations may be derived from the block diagram illustrated in  FIG. 3 : 
                     M   N     =           T   p     +       K   2     ⁢     H   B     ⁢     H   C     ⁢     T   DM           1   -       K   1     ⁢     H   A     ⁢     H   C     ⁢     T   DM           +         H       AH   C     ⁢     T   DM           1   -       K   1     ⁢     H   A     ⁢     H   C     ⁢     T   DM           ·     comm   N                 (   1   )               min   ⁢                M   -           H   A     ⁢     H   C     ⁢     T   DM         1   -       K   1     ⁢     H   A     ⁢     H   C     ⁢     T   DM           ·   comm     +     ∈     N          2             (   2   )               ∴     min   ⁢              T   p     +       K   2     ⁢     H   B     ⁢     H   c     ⁢     T   DM              2               (   3   )                   δ   ⁢              T   p     +       K   2     ⁢     H   B     ⁢     H   c     ⁢     T   DM              2         δ   ⁢           ⁢     H   B         =   0           (   4   )                   T   p     +       K   2     ⁢     H   B     ⁢     H   C     ⁢     T   DM         =   0           (   5   )                 H   B     =       -     1     K   2         ⁢       T   p     /     H   C       ⁢     T   DM               (   6   )                   D   ·     T   DM       +     N   ·     T   p         =       M   ⇒     T   p       =       (     M   -     DT   DM       )     N               (   7   )                 H   B     =       -     1     K   2         ⁢         (     M   -     DT   DM       )     /   N     ·     H   C       ⁢     T   DM               (   8   )               =       -     1     K   2         ⁢     (         T   nam         H   C     ⁢     T   DM         -       T   nDm       H   C         )               (   9   )                 T   nam     =       M   ·     N   *         N   ·     N   *                 (   10   )                 T   nDm     =       D   ·     N   *         N   ·     N   *                 (   11   )               
Where the following variable definitions are used in the representative embodiment illustrated in the Figures and mathematically represented above:
 
     M represents the sense/error microphone; 
     N represents the ambient noise measured by the ambient microphone ( 60 ,  FIG. 1C ); 
     T p  represents passive attenuation corresponding to M/N with no active or comm signal present; 
     T nam  represents active attenuation at the sense microphone corresponding to measured M/N with no comm signal present; and 
     T dm  represents the driver to error mic response. 
     The system design allows for the sense/error microphone  48  to be placed much closer to the ear opening than previous implementations. This has the key advantage of being a more accurate estimate of what the user actually hears. i.e. there will be smaller differences in T dm  and T de , and in T nm  and T ne . 
     The system uses a feedforward method that includes a feedback loop. For closed loop feedback operation, the signal from the error microphone M is fed back into the system to reduce noise as generally represented in  FIG. 3  with output  336  and input  302 . In the feedforward mode, the error microphone, which is positioned as close as possible to the ear opening and much closer than in conventional ANR applications, more accurately represents audio heard by the user. This signal is used to monitor performance and continuously update the transfer function of the feedforward filter H B  as shown in the block diagrams illustrated and described in greater detail herein. 
       FIG. 4  is a conceptual block diagram illustrating various functional blocks for adaptive ANR including sense microphones, drivers, and external inputs according to embodiments of the present disclosure. The block diagram of  FIG. 4  provides a more detailed representation of the adaptive ANR strategy generally illustrated in the block diagram of  FIG. 3 . System  400  provides the sense or error microphone signal  402  as feedback, which is multiplied by a constant K 1  at block  404  with the output provided to preamp and anti-aliasing filter  406 . A low latency analog-to-digital (ADC) converter  408  processes the signal to provide error data to adaptive feedback filter H C  at  410 . As used in this application, and as described in greater detail below, a low latency ADC (or DAC) generally refers to a successive approximation converter with successive approximation registers that has virtually no delay and that does not include sigma-delta converters that use linear filters. Oversampling, or sigma-delta type converters, are not necessarily inappropriate for this type of low latency application, but both ADC&#39;s and DAC&#39;s of this type require a filter to average and provide the required resolution, which is typically done with a low pass filter/decimation filter. While these converters are typically linear phase converters that minimize phase distortion, this is accomplished at the expense of latency and provides less than desirable results in an adaptive ANR application such as disclosed herein. 
     Adaptive feedback filter  410  is an IIR (infinite impulse response) filter that is equivalent to a combination of the HA filter or target response  310  and HC filter  330  illustrated in  FIG. 3 . The coefficients of adaptive feedback filter  410  may be provided by an adaptation algorithm as generally represented by block  450 . Alternatively, filter  410  may use predetermined coefficients determined during product development rather than adaptive coefficients determined in response to current operating environment and user fit. The output of filter  410  is then combined at  412  with the processed ambient noise signal and digital and audio noise signals. 
     An ambient noise signal  414  is multiplied by an associated constant K 2  at block  416 . Ambient noise signal  414  may be generated by a corresponding ambient noise microphone, such as microphone  60  ( FIG. 1C ). The result is provided to preamp and anti-aliasing filter  418  with the output of block  418  provided to a low latency ADC  420  to provide ambient noise data to adaptive feed forward filter H FF    422 . Adaptive filter  422  has one or more filter coefficients adaptively determined by an associated adaptation algorithm  450 . Adaptive filter  422  includes aspects of both an IIR and FIR filter as it is a function of filters or target responses H A    310 , H B    324 , H C    330 , and TDM  332  as illustrated and described with reference to  FIG. 3 . The output of adaptive filter  422  is then combined at  412  with the outputs of adaptive feedback filter  410  and adaptive filter  442 . 
     Analog audio input  430 , such as input from a boom microphone or an external analog audio device coupled to the headset is provided to preamp and anti-aliasing filter  432  with the output of filter  432  provided to ADC  434 . As illustrated, while a low latency ADC is suitable, it is not needed to provide desired system performance for processing of the analog audio input  430 . The output of ADC  434  is combined at  436  with external digital audio input  438  after processing by SRC at  440 , which provides stereo cross-feed to more accurately represent stereo signals. The combined signal/data is provided to adaptive filter (CommEQ) at  442 , with filter coefficients determined by adaptation algorithm  450 . Adaptive filter  442  combines features of an IIR and FIR filter. 
     The combined signal from block  412  is provided to digital-to-analog converter (DAC)  444 . The output of DAC  444  is then provided to block  446 , representing the response T DM  from the driver to the error/sense microphone, with the output representing the error signal  402 . 
     As described above, an adaption algorithm  450  provides coefficients to adaptive filters  410 ,  422 , and  442  as generally represented at  460 ,  462 , and  464 , respectively. Adaptation algorithm  450  may be implemented in software and/or hardware. In the representative embodiments illustrated, adaptation algorithm  450  is implemented by software using a programmed microprocessor that receives data error input from ADC  408 , ambient data input from ADC  420  and external audio input data from ADC  434  and SRC  440 . Adaptation algorithm  450  may also receive ambient input from an optional ADC  470  used only during the adaptation process. The input data is used to generate filter coefficients for filter  410  and  422  for enhanced stability and noise attenuation. 
     Various embodiments according to the present disclosure automatically determine the adaptive filter coefficients in response to current operating conditions. According to these embodiments, the adaptation algorithm calculates filter coefficients using only two categories of data corresponding to data representing audio signals without an active stimulus and communication signal from the system panel, and data representing audio signals with either active stimulus or communication from the system panel (or other external source generating audio signals through the driver). In one embodiment, the system uses data generated in response to the active stimulus, and data generated in response to ambient noise with no active stimulus and no external audio signal present for the driver. 
     Because the system estimates both T DM  and either T P  (or alternatively T nam ) across the desired frequency range, there are two unknowns at each frequency. T DM  for example can be estimated very well if no noise is present, or if T nam  is known. Alternatively, T nam  can be estimated if T DM  is known. This is basically solving for two unknowns (at each frequency) with two equations. However, if the data represents two samples at different times, differing only by random measurement errors, but nothing is substantially different, the system cannot solve for two unknowns. As such, the system uses the calibration data (active stimulus) for one equation, and a moving average of subsequent data representing ambient noise without an external audio signal from the panel or a connected device to provide the second equation. A best fit strategy or technique is then used with equal weighting for each data type. Alternatively, the best fit strategy can use unequal weighting, but should be controlled so that it does not minimize the data generated in response to the active stimulus. 
     As recognized by the present inventor, it is possible to estimate the responses using data generated while the user is speaking. However, this data may not provide the desired results because it is affected by bone conduction and the ambient estimate will be biased toward a noise source of the user talking. If the system excludes this operating condition, then it can obtain the necessary equations from data generated with an external communication signal (comm data) present, and no external communication signal present, to estimate the feedforward transfer function, which is based on T DM  and T nam . As such, in one representative embodiment, the system detects a signal from the boom microphone indicative of user generated audio signals and avoids using data generated during these events in the adaptation algorithm to adjust or adapt the coefficients of the feedforward filter. Likewise, the system detects an external audio signal, such as a comm signal from a panel input or another coupled device, and the adaptation algorithm does not use data generated during these events to adjust or adapt the coefficients of the feedforward filter. 
     In contrast to prior art ANR strategies, embodiments of the present disclosure estimate transfer functions to perform noise cancelation. Previous strategies rely on methods that depend on the statistics of the noise, i.e. canceling the periodic components of the noise. In the method and system according to the present disclosure, an adaptive realizable filter is used, which incorporates an IIR filter specifically, rather than relying solely on a FIR filter, with the end result that the performance measured as attenuation over a range of frequencies is independent of the statistics of the noise. (i.e. periodic methods don&#39;t work well if the noise is not periodic.) 
     As described in greater detail herein, data measurement is performed by block  450  as needed to provide data for adapting filters. In addition, stereo cross-feed processing may be performed here to enhance audio performance. Measurement data from the sensors and audio inputs may be used to estimate transfer functions that have the unknowns T DM  and T NM  as generally illustrated and described with reference to  FIG. 3 . These estimates are then used to generate filters having associated coefficients that compensate for the transfer functions. T DM  and the variations caused by individual user&#39;s pinnas can be compensated for to enhance the closed loop performance and/or to estimate the feedforward transfer function T FF  along with the noise attenuation transfer function T NM . The net total attenuation is a function of all system parameters and H B  or H FF  is then solved in terms of the estimated parameters and known parameters such as the digital filters for closed loop functioning. 
       FIG. 5  is a block diagram illustrating sample-by-sample (SBS) low latency processing and an adaptation algorithm strategy for use in adaptive filter coefficient calculations for adaptive ANR according to embodiments of the present disclosure.  FIG. 6  is a block diagram illustrating system architecture for a representative embodiment of an adaptive ANR headset according to the present disclosure.  FIGS. 7A  (prior art) and  7 B illustrate representative low latency audio processing for adaptive ANR according to representative embodiments of the present disclosure. An ANR headset according to embodiments of the present disclosure incorporates successive approximation register (SAR) converters and low latency DAC&#39;s as previously described and illustrated to provide desired system performance. In addition, the system processes the sampled data using a unique low latency strategy in contrast to conventional digital data processing techniques. 
       FIGS. 7A and 7B  provide timing diagrams illustrating processing of sampled signals acquired during particular sample time periods for sequentially sampled channels. A representative prior art digital audio processing strategy is illustrated in  FIG. 7A . Sequential sampling periods are represented at  710  with multiplexed ADC input channels L 1 -L 5  represented at  720 . In the representative embodiment illustrated, five (5) channels are sampled with L 1  having ANR/error microphone data, L 2  having ambient microphone data, L 3  having comm channel data, L 4  having auxiliary input channel data, and L 5  having boom microphone data. The processing task timing of the digital signal processor (DSP) is represented at  730  and the DAC output is represented at  740 . Arrow  750  generally represents the lowest possible latency for a signal on any of the multiplexed inputs to propagate to the DAC (or power amplifier and associated driver/speaker). The sampling rate in this example is 170 ksps in this case. Arrow  750  represents the latency corresponding to two sample periods plus whatever propagation time is required for the DAC to load. In many audio DSP systems, the DAC is actually loaded at the end of the third sample period. 
       FIG. 7B  illustrates an improved low latency processing strategy incorporated into various embodiments of the present disclosure. In  FIG. 7B , the ADC samples represented at  722  are acquired during a first sample period represented at  712  and are used to calculate the filter coefficients for H A , H B , H C  as represented at  732  and output to the DAC as represented at  742  (or  760  for an ideal DAC). The resulting latency of this strategy corresponds to one sample period as represented by arrow  752  for an ideal DAC as represented at  760 , and slightly longer than one sample period accounting for group delays, which include loading delays of a representative DAC as represented at  742 . 
     As such, the representative prior art digital signal processing technique illustrated in  FIG. 7A , samples data during sample period (n), processes previously sampled data from sample period (n−1), and outputs previously processed data from sample period (n−2), requiring approximately 2.2 sample periods or about 12.8 microseconds accounting for loading of the DAC. In contrast, as generally illustrated in  FIGS. 5, 6, and 7B , embodiments according to the present disclosure sample data during sample period (n), and process and output the data (for sample period n) during the same sample period (n) to reduce latency to approximately one sample period in this example, or just over one sample period when accounting for loading delay of the DAC. Stated differently, the data from one or both of the ANR or sense microphones is sampled, filtered, and output to the DAC before the next sample period. As such, for low latency as used herein, the system latency should be such that the DAC output can be influenced by ADC inputs in less than 2 sample periods. 
     As illustrated in the representative embodiment of  FIG. 7B , data processing does not begin at  734  (misc. data handling) and  736  (computations for H A , H B , and H C ) until all five (5) channels are sampled. In another embodiment, latency is further reduced by starting processing of one channel before all the channels have been sampled. For example, processing may start on the channel carrying ANR sense microphone data for calculation of the coefficients of H A  as soon as the data is ready. This introduces aliasing and therefore requires anti-aliasing filters for best performance. However, because the human ear is not sensitive to frequencies beyond about 20 kHz, the anti-aliasing band stop can be set to 20 kHz below the sampling rate. For example, in the case of an 85 kHz sampling rate, the band stop of the anti-aliasing filter can be set to 65 kHz corresponding to (85 kHz-20 kHz). While this results in frequencies above ½ of the sampling rate and below the stop band being aliased, corresponding to 85 kHz/2 (or 42.5 kHz) to 65 kHz, these frequencies will not be audible to the human ear and will not affect perceptible performance. The higher anti-aliasing stop band is advantageous because it allows the associated pass band of the filter to be higher and thus have much lower group delay in the audible range. 
     The audio processing for active noise reduction is performed in real time by a digital signal processor, such as shown in the system architecture block diagram illustrated in  FIG. 6 . However, the filter adaptation described in detail with respect to  FIGS. 4, 5, and 7A-7B , for example, does not need to be performed in real time. Filter adaptation may be performed when the system performance has changed due to a change in operating conditions, such as altitude, fit, or other possible time varying parameters including the ambient noise characteristics. Alternatively, filter adaptation may be continuously performed to detect changes in operating conditions by comparing calculated filter coefficients with current (or preceding) filter coefficients. The new filter coefficients may be used in response to detecting that operating conditions have changed significantly. As previously described, filter coefficients may be temporarily stored in persistent memory for subsequent recall to reduce time associated with adaptation. Of course, previously stored filter coefficients may not be particularly suited for current operating conditions or fit. 
       FIG. 8  is a block diagram illustrating integration and configurability details provided by a linked device or other user interface for an adaptive ANR system or method according to various embodiments of the present disclosure. As described in greater detail below, personal preferences can be set using the enhanced capability of a linked device, such as a smart phone. Bass and treble levels of the intercom and auxiliary inputs can be adjusted independently and separate intercom priority options can be set for Bluetooth and wired input. The voice clarity option boosts frequencies common to human speech without impacting the quality of music from auxiliary devices. 
     As shown in  FIG. 8 , system  800  includes an input selector module  810 , an output selector module  820 , and a DSP block processing module  830  in communication with a controller  840 , which also communicates with Bluetooth (BT) data port  852  and Selector Switch Input port  854 . Input selector  810  communicates with wired input ports including a boom microphone port  842 , a communications (Comm) input port  844 , and an auxiliary (Aux) input port  846 . Output selector module  820  communicates with an auxiliary (Aux) output port  860  and a Bluetooth (BT) audio output port  862 . DSP module  830  communicates with ports  842 ,  844 , and  846  in addition to a first BT audio input port  848  and a second audio input port  850 , which is configured for AD2P stereo input in the representative embodiment illustrated. 
     In the representative embodiment of  FIG. 8 , the routing of either the boom microphone signal/port  842 , or the comm input port  844  is directed to the appropriate output port  860 ,  862  by output selector  820  and may be specified manually by the user or determined automatically by the system via controller  840 . The output selector  820  directs output to the wired auxiliary output port  860  or to the wireless Bluetooth (BT) audio output port  862 . This allows an app running on a connected portable device (such as a smart phone or tablet, for example) to operate as the user interface to the ANR headset to adjust personalization settings and/or headset performance. Voice commands processed by a linked portable device can be communicated to the controller  840  of the headset via the BT data port  852 . Similarly, voice commands captured by the boom microphone applied to port  842  can be sent to a linked device for processing via output ports  860  or  862 . The boom microphone signal on port  842  may be manually or automatically routed to the desired output depending on how the linked device is coupled to the headset (wired, wireless, analog, or digital). For example, the controller may automatically connect (route) the boom microphone input port  842  via input selector  810  and output selector  820  to a coupled cell phone in response to detecting a phone call or dialing command as determined by controller  840 . For a cell phone linked by the Bluetooth modules  848  and  852 , the controller module  840  would connect the boom microphone port  842  to the BT audio output port  862 , whereas for a cell phone linked by the auxiliary input port  846 , the controller module  840  would connect (route) the boom microphone port  842  to the auxiliary output port  860  via controls or commands communicated to input selector  810  and output selector  820 , respectively. A connected device may also communicate personalization commands to controller  840  to control headset features such as personal preference for tone or performance of the noise reduction system (update rate, saved personalization settings, etc.). 
       FIGS. 9-19  are graphs illustrating improved ANR performance for an adaptive ANR system or method according to embodiments of the present disclosure. 
       FIGS. 9 and 10  are graphs illustrating noise attenuation performance of representative embodiments according to the present disclosure for first and second noise inputs, respectively. Lines  910 ,  1010  represent passive attenuation, lines  920 ,  1020  represent closed loop attenuation without feedforward, and lines  930 ,  1030  represent noise attenuation performance with both feedforward and closed loop feedback. 
       FIGS. 11 and 12  illustrate amplitude and phase response, respectively, as a function of frequency for a measured response of the driver to error microphone transfer function on a user  1110 ,  1210  and realized adaptive correction filter H C    1210 ,  1220 . 
       FIGS. 13 and 14  illustrate amplitude and phase response, respectively, of T DM *H C  as a function of the target open loop response for closed loop noise reduction. 
       FIGS. 15 and 16  illustrate amplitude and phase response, respectively, of T DM *H C  as a function of the target closed loop response for closed loop noise reduction. 
       FIGS. 17 and 18  illustrate a representative measured attenuation transfer function  1710 ,  1810  (error mic noise/ambient noise) and calculated/realized T ff    1720 ,  1820  for adaptive feedforward (note that T ff  is plotted as −T ff  since cancelation is the goal). It would not be possible to achieve this level of phase matching without use of low latency components and processing strategies according to embodiments of the present disclosure. 
       FIG. 19  illustrates measured attenuation before and after feedforward and the realized response of the feedforward transfer function Tff. 
     As can be seen from the summary and detailed description and review and analysis of the figures, embodiments of the present disclosure may provide several advantages. For example, the adaptive ANR embodiments according to the disclosure are believed to provide the world&#39;s quietest aviation headset, and the only one that actively conforms to users and the cockpit environment creating custom noise cancellation and a uniquely personal ANR experience based on measurement of transfer functions and determination of adaptive filter coefficients to compensate for them. The personalized experience is provided by acoustically measuring and actively conforming to the user&#39;s ears, environment, and preferences using acoustic response mapping to adaptively adjust various system parameters. This technology uses sound waves and advanced signal processing to measure a user&#39;s unique auditory landscape adapting the audio response to the user&#39;s ears&#39; size and shape for maximum noise attenuation, voice clarity, and music fidelity. 
     Various embodiments include streaming quiet ANR to adapt to the environment with one or more ambient microphones to continuously sample ambient noise before it penetrates the ear cup of the headset. An internal error sensing microphone placed near the ear canal monitors ANR performance. The microphones feed information to the CPU, a powerful digital signal processor that analyzes a stream of both the external ambient noise and internal residual noise at a rate of one million times a second, for example, and seemingly instantaneously creates precise ANR responses customized to a dynamic sound environment. The result is a dramatic extension in the amount, consistency, and frequency range of noise cancellation regardless of the environment, fit, and user, allowing important communication to come through with amazing clarity and producing music with outstanding fidelity. 
     In addition to various personalization features provided by a coupled mobile device such as a smart phone or tablet, embodiments according to the present disclosure leverage the latest technological advances across multiple fields. Rugged cables constructed of silver coated copper alloy wrapped around a Kevlar core deliver extraordinary flexibility, strength, and audio quality. An aviation-friendly CPU provides powerful digital audio processing and convenient access to key controls. Upgradeable firmware provides unlimited potential for new software innovations. 
     While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the invention. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the invention.