Patent Publication Number: US-11039247-B2

Title: Extended bandwidth adaptive noise cancelling system and methods

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62/782,312 filed Dec. 19, 2018 and entitled “EXTENDED BANDWIDTH ADAPTIVE NOISE CANCELLING SYSTEM AND METHODS”, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present application relates generally to noise cancelling systems and methods, and more specifically, for example, to adaptive noise cancelling systems and methods for use in headphones (e.g., circum-aural, supra-aural and in-ear types), earbuds, hearing aids, and other personal listening devices. 
     BACKGROUND 
     Adaptive noise cancellation (ANC) systems commonly operate by sensing noise through a reference microphone and generating a corresponding anti-noise signal that is approximately equal in magnitude, but opposite in phase, to the sensed noise. The noise and anti-noise signal cancel each other acoustically, allowing the user to hear only a desired audio signal. To achieve this effect, a low-latency, programmable filter path from the reference microphone to a loud-speaker that outputs the anti-noise signal may be implemented. In operation, conventional anti-noise filtering systems do not completely cancel all noise, leaving residual noise and/or generating audible artefacts that may be distracting to the user. There is therefore a continued need for improved adaptive noise cancellation systems and methods for headphones, earbuds and other personal listening devices. 
     SUMMARY 
     Systems and methods are disclosed for providing noise amplification control for adaptive noise cancellation in audio listening devices. In various embodiments, adaptive noise cancellation systems and methods provide improved bandwidth at the listener&#39;s ear-drum in an adaptive approach. 
     In one or more embodiments, an adaptive noise cancellation system includes a reference sensor operable to sense environmental noise and generate a corresponding reference signal, an error sensor operable to sense noise in a noise cancellation zone and generate a corresponding error signal, a noise cancellation path comprising a noise cancellation filter and a variable gain component, the noise cancellation path operable to receive the reference signal and generate an anti-noise signal to cancel the environmental noise at an eardrum reference point, and an adaptation module operable to receive the reference signal and the error signal and adaptively adjust weights of the noise cancellation filter and/or the variable gain component. 
     In some embodiments, the adaptation module comprises an adaptive gain control block operable to update the variable gain component. Inputs to the adaptive gain control block may be conditioned using programmable filters operable to protect against low frequency transients and/or high frequency distractors in the environmental noise. The programmable filters may include a low pass filter that filters out high frequencies determined to be in a range that creates constructive interference between the cancellation zone and the eardrum reference point, and/or a high pass filter that filters out low frequencies determined to be in a range that cannot be heard by a user of the noise cancellation system. The adaptation module may be tuned to cancel noise at the eardrum reference point, using the error signal sensed in the noise cancellation zone. 
     In one or more embodiments, a method includes receiving a reference signal from a first sensor, the reference signal representing external noise, processing the reference signal through a noise cancellation path comprising a noise cancellation filter and a variable gain component, to generate an anti-noise signal, receiving an error signal from a second sensor, the error signal representing noise in a noise cancellation zone and adaptively adjusting the noise cancellation filter in response to the reference signal, the error signal and an adaptive gain control process to cancel the external noise at an eardrum reference point. 
     The method may further include conditioning inputs to the adaptive gain control process using programmable filters to protect against low frequency transients and/or high frequency distractors in the external noise. The conditioning may further include low pass filtering out high frequencies determined to be in a range that (i) creates constructive interference between the cancellation zone and the eardrum reference point and (ii) differs in noise cancellation performance between the cancellation zone and the eardrum reference point, and/or high pass filtering out low frequencies determined to be in a range that cannot be heard by a user. The method may further include tuning the noise cancellation path to cancel noise at the eardrum reference point, using the error signal sensed in the noise cancellation zone. 
     The scope of the invention is defined by the claims, which are incorporated into this section by reference. A more complete understanding of embodiments of the invention will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. Reference will be made to the appended sheets of drawings that will first be described briefly. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Aspects of the disclosure and their advantages can be better understood with reference to the following drawings and the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, wherein showings therein are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the same. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. 
         FIG. 1  illustrates an adaptive noise cancellation headset in accordance with one or more embodiments of the present disclosure. 
         FIG. 2  illustrates an adaptive noise cancellation system in accordance with one or more embodiments of the present disclosure. 
         FIG. 3  illustrates an adaptive noise cancellation system, including a noise amplification control subsystem, in accordance with one or more embodiments of the present disclosure. 
         FIGS. 4A-B  illustrate an adaptive noise cancellation system, including an adaptive gain control subsystem, in accordance with one or more embodiments of the present disclosure. 
         FIG. 5  illustrates a transient activity detector for an adaptive noise cancellation system in accordance with one or more embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In accordance with various embodiments, improved adaptive noise cancellation (ANC) systems and methods are disclosed. An ANC system for a headset or other personal listening device may include a noise sensing reference microphone for sensing environmental noise, an error microphone for sensing an acoustic mixture of the noise and anti-noise generated by the ANC device, and a signal processing sub-system that generates the anti-noise to cancel the environmental noise. The signal processing sub-system may be configured to continually adjust the anti-noise signal to achieve consistent cancellation performance across users, environmental noise conditions, and device units. In various embodiments, the adaptation systems and methods disclosed herein improve cancellation of environmental noise and reduce perceptible adaptation artefacts. 
     The present disclosure addresses numerous challenges associated with general purpose adaptive noise cancellation systems, including unwanted noise amplification (e.g., due to constructive interference between the environmental noise and the anti-noise signal), noise cancellation performance during transient noise events, and reduction of audible artefacts produced during adaptation. The systems and methods disclosed herein provide robust, practical ANC solutions that generalize well to various listening devices and form-factors. 
     In various embodiments, systems and methods are disclosed to reduce noise amplification that occurs when there is constructive interference between noise and anti-noise within a frequency range. Adaptive methods are disclosed which include defining a composite error signal that incorporates a noise-shaping filter and deriving a new weight update rule for controlling the adaptation. The solutions disclosed herein are adaptive, computationally inexpensive, and may be implemented as an improvement to conventional adaptive frameworks. 
     In various embodiments, systems and methods disclosed herein reduce adaptation artefacts that may be perceived by a listener. For example, low sound pressure level (SPL) artefacts may be present due to the proximity of the anti-noise source to the listener&#39;s ear drum. It is further recognized that some artefacts are caused by wideband fluctuations in the magnitude and phase response of the anti-noise path. Improved adaptive systems and methods disclosed herein include an adaptive gain element in the anti-noise signal path to generate a robust error correcting signal. 
     In various embodiments, systems and methods disclosed herein provide improved robustness to transient noise events. Many intermittent and unexpected noise events (e.g., head/jaw movement that moves the microphones relative to the noise, closing a door, turbulence during air travel, etc.) produce low frequency transients that can potentially disrupt the adaptation loop, leaving unwanted residual noise or producing noise artefacts. In various embodiments, a transient activity detector (TAD) tracks transient behavior and controls adaptation during transient activity. 
     Example embodiments of adaptive noise cancelling systems of the present disclosure will now be described with reference to the figures. Referring to  FIG. 1 , an adaptive noise cancelling system  100  includes an audio device, such as headphone  110 , and audio processing circuitry, such as digital signal processor (DSP)  120 , a digital to analog converter (DAC)  130 , an amplifier  132 , a reference microphone  140 , a loudspeaker  150 , an error microphone  162 , and other components. 
     In operation, a listener may hear external noise d (n) through the housing and components of the headphone  110 . To cancel the noise d(n), the reference microphone  140  senses the external noise, producing a reference signal x(n) which is fed through an analog-to-digital converter (ADC)  142  to the DSP  120 . The DSP  120  generates an anti-noise signal y(n), which is fed through the DAC  130  and the amplifier  132  to the loudspeaker  150  to generate anti-noise in a noise cancellation zone  160 . The noise d (n) will be cancelled in the noise cancellation zone  160  when the anti-noise is equal in magnitude and opposite in phase to the noise d (n) in the noise cancellation zone  160 . The resulting mixture of noise and anti-noise is captured by the error microphone  162  which generates an error signal e (n) to measure the effectiveness of the noise cancellation. The error signal e(n) is fed through ADC  164  to the DSP  120 , which adjusts the magnitude and phase of the anti-noise signal y(n) to minimize the error signal e(n) within the cancellation zone  162  (e.g., drive the error signal e(n) to zero). In some embodiments, the loudspeaker  150  may also generate desired audio (e.g., music) which is received by the error microphone  162  and removed from the error signal e (n) during processing. It will be appreciated that the embodiment of  FIG. 1  is one example of an adaptive noise cancellation system and that the systems and methods disclosed herein may be implemented with other adaptive noise cancelling implementations that include a reference microphone and an error microphone. 
       FIG. 2  illustrates a robust, configurable adaptive noise cancelling system  200  that achieves improved noise cancellation performance, substantially free of audio artefacts. The system  200  senses environmental noise at an external microphone (e.g., microphone  140  of  FIG. 1 ) which produces an external noise signal, x(n). The environmental noise also passes through a noise path P(z), including the housing and components of the listening device, where it is received as d(n) at an error microphone (e.g., error microphone  162 ). An adaptive filter  202  receives the external noise signal x(n) and estimates the noise path P(z) to produce an anti-noise signal y(n) for cancelling the noise signal d(n). The anti-noise signal y(n) is gain adjusted by adaptive gain control  204  and further modified by system  206  to account for the secondary path S(z) between the adaptive filter  202  and the error microphone. 
     The system  200  further includes an adaptation block  220 , which includes a noise amplification control (NAC) block  222  and an adaptive gain control block (ADG)  224 . In various embodiments, the NAC  222  is operable to minimize frequency dependent constructive interference, and the ADG  224  is operable to minimize wide-band fluctuations in the anti-noise path. The system  200  further includes a transient activity detector (TAD)  226 , which is operable to control the system  200  in response to sudden noise fluctuations and impulsive environmental events. The filters  208 ,  210 ,  212 , 228 ,  230 ,  232  provide additional filtering as described further herein with reference to  FIGS. 3-5 . 
     Referring to  FIG. 3 , embodiments of a noise amplification control (NAC) sub-system  300  will now be described. A goal of many adaptive noise cancellation systems is to estimate the noise at the ear drum of the listener. This is often accomplished by using the noise measurements from the reference and error microphones, which are located a small distance from the ear drum. The estimated noise is then inverted into an anti-noise signal that destructively interferes with the actual noise leading to cancellation of the noise. The anti-noise signal is produced using a filter that adapts to estimate the amplitude and phase shift for each frequency to align the anti-noise with the noise. Depending on the latency and the physical transfer functions at issue, the destructive interference may be maintained in certain bandwidths, while constructive interference may be experienced beyond these bandwidths. This constructive interference may be perceived by the listener as a narrowband amplification of the ambient noise (e.g., a “hiss” sound). Reducing or eliminating the “hiss” sound without sacrificing the depth and bandwidth of cancellation is a challenge in many ANC product designs. In conventional, low power embedded systems (e.g., consumer headphones) reduction of hiss may be computationally prohibitive and hard to control and tune. 
     The NAC sub-system  300  of  FIG. 3  provides an approach for controlling hiss and related sound artefacts that adaptively controls the noise amplification in hiss regions, while efficiently achieving cancellation in non-hiss regions. An NAC block  320  is configured to define a composite error signal that incorporates a noise-shaping filter C(z) (e.g., noise shaping block  308  and noise shaping block  310 ) and derive new weight update rules for the adaptive filter  302 . In some embodiments, a least mean squares (LMS) framework may be used, including a composite error signal that incorporates the noise-shaping filter that is used to derive a new weight update rule. 
     In operation, the NAC block  320  updates the adaptive filter  302 , W(z), based on the error signal e(n) and a filtered version of the reference signal, x(n). In the illustrated embodiment, the NAC block  320  receives a signal x 1  (n) from filter  312 , Ŝ(z), and signal x 2 (n) from filter  308 , C(z). The cost function minimizes the mean square error: Minimize E {e 2  (n)+γ E {e 1   2 (n)}}. In various embodiments, the anti-noise signal is filtered using a noise-shaping filter C(z) (such as noise-shaping filter  308  and noise-shaping filter  310 ) which may be configured to enhance signals in the hiss region. In some embodiments, the hiss region for a particular headset may be detected, and the noise-shaping filter C(z) may be tuned, in a test environment prior to distribution. In some embodiments, the hiss level may be detected during operation and the noise-shaping filter C(z) may be adaptively tuned during operation. The hiss level may be determined, for example, by comparing the error signal, e(n), to the noise signal to determine regions of constructive interference. 
     The cost function is adapted to minimize E {e 2  (n)+γ E {e 1   2 (n)}} where E{.} is the expectation operator, γ is a constant that controls the aggressiveness, and e 1 (n) is noise-shaped anti-noise signal, y′ (n). In some embodiments, a weight update rule is derived by the NAC  320  based on gradient methods. Embodiments of the method can be applied to filtered least mean squared approaches, adaptive feedback, adaptive hybrid approaches and other noise cancellation approaches. In various embodiments, the adaptation is controlled in a way that minimizes noise amplification by defining a cost function optimization and deriving an adaptive algorithm that can achieve it. 
     Referring to  FIGS. 4A and 4B , embodiments of an adaptive gain (ADG) subsystem  400  are disclosed. In various embodiments, an adaptive gain control block  420  continuously updates a gain element  404  to adjust for variations in the various coupling paths. The inputs to the ADG are conditioned using a programmable filter B G (z) (e.g., programmable filter  408  and programmable filter  410 ), which is designed to protect against low frequency transients and high frequencies distractors in the environment. In some embodiments, the filter B G (z) may comprise a low pass filter and/or a band pass filter that further filters out very low frequencies (e.g., &lt;20 Hz that cannot be heard out of a loudspeaker). 
     It will be appreciated that the physical geometries and person-to-person fit variations of the headphone can affect noise cancellation performance. For example, the shape of the outer ear and length of the ear canal can alter the acoustic transfer functions of interest in an ANC application. In some embodiments, an ANC system in a headphone or other personal listening device (e.g., the system of  FIG. 1 ) uses a noise sensing reference microphone, an error microphone, and a DSP sub-system that generates the appropriate anti-noise to cancel the noise field as measured by the error microphone. This results in a cancellation zone where the degree of cancellation is maximized at the error microphone location and degrades inversely proportional to the wavelength. As a result, the cancellation performance at the eardrum (which is roughly 25 mm away from the error microphone) drops significantly for higher frequencies (lower wavelengths) leading to loss of cancellation bandwidth as perceived by the user of the noise cancelling system. The embodiments of  FIGS. 4A-B  address these and other issues by maximizing the cancellation bandwidth at the eardrum during the tuning stage and formulating an adaptive approach that uses the error microphone to adapt to user specific characteristics during operation. 
     For the purposes of this disclosure, let the error microphone location be termed as ERP (Error Reference Point) and the ear-drum location be termed as DRP (Drum Reference Point). For ANC systems tuned at the DRP, the error microphone is a good indicator of low frequency cancellation at DRP and hence a robust error correcting signal can be derived from a low-passed version of the error microphone signal. This correcting signal may then be used to adapt a gain in the anti-noise signal path. 
     To maximize cancellation, an ideal placement of an error microphone would be at the eardrum, but that location is not practical for many consumer devices. Thus, the ERP is used to provide a practical signal that is roughly indicative of the cancellation performance at the DRP. The adaptive algorithm attempts to minimize the ERP signal which results in (i) diminished cancellation at high frequency signals at the DRP, and (ii) higher possibility of hiss sounding artefacts due to constructive interference of high frequencies at the DRP. In conventional approaches, adaptive algorithms are employed that use the transfer function from ERP to DRP. These approaches have many drawbacks including that the transfer function estimation is inaccurate at high frequencies, low estimation accuracy can affect the broad band cancellation performance and cause transitory hiss levels, high computational costs, and difficulty to tune and calibrate for all use conditions making deployment impractical for many devices. The embodiments of  FIGS. 4A-B  provide a computationally inexpensive approach that overcomes many of the drawbacks of conventional systems, is easy to tune, for example by measuring certain transfer functions during system design, and is self-calibrating. 
       FIG. 4A  illustrates a calibration and tuning arrangement for the adaptive gain subsystem. In this arrangement, the ANC filter  402  is optimized to cancel noise at the DRP during an initial tuning stage. In one embodiment, the device is placed on a head and torso simulator which has a second error microphone at the DRP. P E2D  (z), S E2D  (z) model the ERP to DRP transfer functions in the denoted acoustic paths. The system can then be optimized using least mean squares block  422  to perform ANC tuning to derive an optimum W DRP  (Z), based on the error signal, e′ (n). Tuning in this manner helps achieve extended cancellation bandwidth and better performance in high frequency bands. Second, as illustrated in  FIG. 4B , the adaptive algorithm is set-up to continuously update a gain element  404 , G, that empowers the proposed approach to adjust for variations in the various coupling paths. In some embodiments, the signal is low pass filtered and gain adjusted for good low frequency cancellation. Third, the inputs to the adaptive algorithms are conditioned using a programmable filter, B G  (z), which is programmed such that the ERP signal can mimic the cancellation performance at DRP. Additionally, B G  (z), can be programmed to optimize performance during low frequency transients and high frequency distractors in the environment. 
     It will be appreciated that the embodiments of  FIGS. 4A-B  are example implementations, and that the approaches disclosed therein can be modified for adaptive versions of feedback, feedforward and hybrid ANC solutions. In some embodiments, instead of adapting a gain element, a purposefully constrained filter element can be adapted. The computed gain can have an additional non-linear processing to further increase the robustness. 
     Referring to  FIG. 5 , embodiments of a transient activity detector (TAD)  500  are illustrated. In operation, the TAD  500  detects changes in the sound environment and causes an update process to be temporarily halted when sudden/intermittent noise activity is detected. As a result, the unwanted adaptation artefacts in the anti-noise signal (e.g., artefacts that might result from rapid adaptation) are minimized. Examples of transient events might include talking by the headset wearer, honking car horns, head movements, and other similar sound events. A separate set of TAD calculations may be performed on the inputs from each microphone in an ANC system (e.g., a total of 4 microphones in a headset including left error microphone, left outside microphone, right error microphone, right outside microphone). Each of the four microphones may be enabled or disabled independently. 
     An embodiment of transient activity detection processing for a microphone is illustrated in  FIG. 5 . A detection state machine  514  is used to assert and de-assert the “detect” output. In various embodiments, the detect output will be asserted when the smoothed instantaneous magnitude (output A from the LPF  506 ) is greater than the scaled average noise magnitude (C in disclosure). After the smoothed instantaneous magnitude A falls below the scaled average noise magnitude C, a release delay counter will cause the detect output to persist for a programmable period of time before being de-asserted. 
     In the illustrated embodiment, audio samples  502  from a microphone (e.g., reference microphone or error microphone) are received and fed through an absolute value block  504  followed by a low pass filter  506  to generate the smoothed instantaneous magnitude A. In one embodiment, the output A comprises an average magnitude of the audio samples  502  over a certain period of time and is representative of an instantaneous noise value. The value A is provided to a detect state machine  514 , and to a low pass filter  508  with saturation which has an output B representing an average of the A values over a second period of time (i.e., average noise magnitude). A programmable scale factor defines a threshold for detecting transients (e.g., 5 times the average noise magnitude) and is multiplied at component  516  by the average noise magnitude to produce a second input C to the detect state machine  514 . 
     In one embodiment, if the smoothed instantaneous noise magnitude A is greater than the scaled average noise magnitude C, then the detect state machine  514  is operable to instruct the adaptation processing (e.g., adaptation block  220  of  FIG. 2 ) to stop. In various embodiments, the adaptation will freeze until the instantaneous noise magnitude A is below the scaled average noise magnitude C. Referring to  FIG. 2 , when the adaptation is stopped, filter  202  and gain adjust  204  will continue to modify the noise input x(n) using the most recent weights and gain values. In some embodiments, a programmable release delay counter is operable to maintain the detect output for a programmable period of time before being de-asserted. Further, attack and release component  512  is operable to control how quickly the low pass filter  508  rises and falls in response to the instantaneous noise magnitude A. A programmable attack time constant defines a time it takes for the average noise magnitude to rise when the instantaneous noise is greater than the average noise magnitude B. A programmable release time constant defines a time it takes for the average noise magnitude B to fall when the instantaneous noise magnitude A is lower than the average noise magnitude B. 
     The foregoing disclosure is not intended to limit the present disclosure to the precise forms or particular fields of use disclosed. As such, it is contemplated that various alternate embodiments and/or modifications to the present disclosure, whether explicitly described or implied herein, are possible in light of the disclosure. Having thus described embodiments of the present disclosure, persons of ordinary skill in the art will recognize that changes may be made in form and detail without departing from the scope of the present disclosure. Thus, the present disclosure is limited only by the claims.