Divergence of adaptive filters for automotive noise cancellation

Abstract

To provide a divergent controllable filter to restore proper operation of a noise canceling system in on-vehicle noise cancellation.SOLUTION: In a system 100, a microphone 112 of a vehicle 102 outputs an error signal e(n) indicating noise existing in a vehicle interior of the vehicle detected by the microphone. An anti-noise signal Y(n) is produced by an adaptive filter formed by a controllable filter 118 and an adaptive filter controller 120 based on an identified transmission characteristic W(z) and a vibration signal or a combination of vibration signals X(n). A processor 128 collects data from a vibration sensor 108 and the microphone 112 and optionally processes it to build a database or map containing data and / or parameters used by the vehicle 102.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to active noise cancellation, and more particularly to mitigating the effects of adaptive filter divergence in engine decompression and / or road noise cancellation systems. [Background technology]

[0002] Active noise control (ANC) systems use feed-forward and feedback structures to adaptively eliminate unwanted noise in a listening environment, such as in a vehicle cabin, and attenuate the unwanted noise. ANC systems generally cancel or reduce unwanted noise by generating cancellation sound waves to destructively interfere with the unwanted audible noise. Destructive interference occurs when a noise and an "anti-noise" that is nearly identical in amplitude but opposite in phase to the noise reduce the sound pressure level (SPL) at a location. In a vehicle cabin listening environment, potential sources of unwanted noise are sounds radiated by the engine, interactions between the vehicle's tires and the surface of the road the vehicle is moving over, and / or vibrations of other parts of the vehicle. Thus, the unwanted noise varies depending on the speed, road conditions, and operating state of the vehicle.

[0003] Road noise cancellation (RNC) systems are specialized ANC systems implemented in vehicles to minimize unwanted traffic noise inside the vehicle cabin. RNC systems use vibration sensors to detect road-induced vibrations arising from the tire-road interface that result in unwanted audible traffic noise. This unwanted traffic noise inside the vehicle cabin is then ideally canceled or reduced in level by using speakers to generate sound waves that are opposite in phase and identical in amplitude to the noise being canceled at one or more listeners' ears. Canceling such road noise leads to a more pleasant ride for vehicle passengers, which allows vehicle manufacturers to use lighter weight materials, thereby reducing energy consumption and reducing emissions.

[0004] An Engine Over-Command (EOC) system is a specialized ANC system implemented in vehicles to minimize undesirable interior vehicle noise emanating from narrowband acoustic and vibration emissions from the vehicle engine and exhaust system. EOC systems use a non-acoustic signal, such as a revolutions per minute (RPM) sensor, as a reference to generate a reference signal representative of engine speed. This reference signal is used to generate sound waves that are opposite in phase to the engine noise heard inside the vehicle. Because the EOC system uses data from the RPM sensor, the EOC system does not require a vibration sensor.

[0005] RNC systems are typically designed to cancel broadband signals. EOC systems, on the other hand, are designed and optimized to cancel narrowband signals, such as individual engine operating commands. ANC systems in vehicles may provide both RNC and EOC technologies. Such vehicle-based ANC systems are typically least mean square (LMS) adaptive feedforward systems that continuously adapt a W filter based on noise inputs (e.g., acceleration inputs from vibration sensors in the RNC system) and signals of error microphones located at various locations inside the vehicle's cabin. ANC systems are susceptible to instability or divergence of the adaptive W filter. When the W filter is adapted to an LMS system, one or more of the W filters may diverge rather than converge to minimize pressure at the error microphone location. Generally, the first tap of an adaptive W filter represented in the time domain has a high amplitude, and the amplitude decreases to zero at later taps. However, the adaptive W filter may not have this property if it diverges. Divergence of the adaptive filter may lead to wideband or narrowband noise boost or other undesirable behavior of the ANC system. Summary of the Invention [Means for solving the problem]

[0006] In one or more exemplary embodiments, a method for controlling stability in an active noise cancellation (ANC) system is provided. The method may include receiving filter coefficients corresponding to at least one controllable filter from an adaptive filter controller. The method may further include calculating a parameter based on an analysis of at least a portion of the filter coefficients, detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold, and modifying a characteristic of the at least one controllable filter that has diverged.

[0007] Implementations may include one or more of the following features: The controllable filter may include a plurality of coefficients. The parameter may be a sum of absolute values ​​of at least a portion of the coefficients of the at least one controllable filter. The parameter may be a maximum value of at least a portion of the coefficients of the at least one controllable filter.

[0008] Furthermore, detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold value may include detecting a divergence of the at least one controllable filter when the parameter exceeds a threshold value. The threshold value may be a dynamic threshold value calculated from a statistical analysis of a parameter calculated from filter coefficients of one or more previous adaptations of the at least one controllable filter. The threshold value may be an average value of the parameter taken from multiple previous adaptations of the at least one controllable filter multiplied by a gain factor.

[0009] Further, detecting divergence of the at least one controllable filter based on a comparison of the parameter to a threshold value may include comparing a parameter from a current adaptation of the at least one controllable filter to an average value of the same parameter from one or more previous adaptations of the at least one controllable filter, and detecting divergence of the one or more controllable filters when a difference between the parameter from the current adaptation of the at least one controllable filter and the average value from one or more previous adaptations of the at least one controllable filter exceeds a threshold value.

[0010] Modifying the characteristics of the at least one controllable filter that has diverged may include ceasing operation of at least one of the ANC system and the at least one controllable filter that has diverged. Alternatively, modifying the characteristics of the at least one controllable filter that has diverged may include resetting filter coefficients of the at least one controllable filter to zero and allowing the at least one controllable filter to re-adapt, or modifying the characteristics of the at least one controllable filter that has diverged may include resetting filter coefficients of the at least one controllable filter to a set of filter coefficient values ​​stored in a memory of the ANC system.

[0011] Furthermore, modifying the characteristic of the at least one controllable filter that has diverged may include increasing a leakage value of the adaptive filter controller in response to detecting the divergence of the at least one controllable filter. The leakage value of the adaptive filter controller may increase at a diverged frequency of the at least one controllable filter. Furthermore, the method may further include decreasing the leakage value of the adaptive filter controller when a highest amplitude filter coefficient of the at least one controllable filter falls below a predetermined threshold.

[0012] One or more additional embodiments may be directed to an ANC system including at least one controllable filter configured to generate an anti-noise signal based on an adaptive transfer characteristic and a noise signal received from a sensor. The adaptive transfer characteristic of the at least one controllable filter may be characterized by a set of filter coefficients. The ANC system may further include an adaptive filter controller and a divergence controller in communication with at least the adaptive filter controller. The adaptive filter controller may include a processor and memory programmed to adapt the set of filter coefficients based on the noise signal and an error signal received from a microphone located in the vehicle cabin. The divergence controller may include a processor and memory programmed to receive a set of filter coefficients corresponding to a current adaptation of the adaptive transfer characteristic of the at least one controllable filter, calculate a parameter based on an analysis of at least a portion of the set of filter coefficients, and detect a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold value.

[0013] Implementations may include one or more of the following features: The threshold may be a predetermined static threshold programmed for the ANC system. The divergence controller may be programmed to detect divergence of the at least one controllable filter when a difference between a parameter calculated from a current adaptation of the at least one controllable filter and an average value of the same parameter from one or more previous adaptations of the at least one controllable filter exceeds a threshold. The divergence controller may be further programmed to increase a leakage value of the adaptive filter controller in response to detecting divergence of the at least one controllable filter.

[0014] One or more additional embodiments may be directed to a computer program product embodied in a non-transitory computer-readable medium programmed for active noise cancellation (ANC). The computer program product may include instructions for receiving from an adaptive filter controller a set of filter coefficients corresponding to a current adaptation of at least one controllable filter, calculating parameters based on an analysis of at least a portion of the filter coefficients, detecting a divergence of the at least one controllable filter based on a comparison of the parameters to a threshold value, and modifying an adaptive transfer characteristic of the at least one controllable filter during a current adaptation in response to detecting the divergence of the at least one controllable filter.

[0015] Implementations may include one or more of the following features: A computer program product is provided in which the instructions for detecting a divergence of the at least one controllable filter may include detecting a divergent frequency of the at least one controllable filter in a time domain, and the instructions for modifying the adaptive transfer characteristic may include resetting the divergent frequency of the at least one controllable filter to zero, attenuating a filter coefficient at the divergent frequency, or increasing a leakage value of the adaptive filter controller at the divergent frequency, in the time domain. Further, the instructions for detecting a divergence of the at least one controllable filter may include detecting a divergent frequency of the at least one controllable filter in a frequency domain, and the instructions for modifying the adaptive transfer characteristic may include notching out the divergent frequency in the frequency domain using an error signal received from the microphone and a filter coefficient from a previous adaptation of the at least one controllable filter stored in the memory. For example, the present application provides the following: (Item 1) 1. A method for controlling stability of an active noise cancellation (ANC) system, comprising: receiving filter coefficients corresponding to the at least one controllable filter from an adaptive filter controller; calculating parameters based on an analysis of at least a portion of the filter coefficients; detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold; modifying the characteristics of said at least one controllable filter that have diverged; The above method. (Item 2) 2. The method of claim 1, wherein the controllable filter includes a plurality of coefficients, and the parameter is the sum of absolute values ​​of at least a portion of the coefficients of the at least one controllable filter. (Item 3) 13. The method of claim 1, wherein the controllable filter comprises a plurality of coefficients, and the parameter is the maximum value of at least a portion of the coefficients of the at least one controllable filter. (Item 4) 2. The method of claim 1, wherein detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold value comprises detecting a divergence of the at least one controllable filter when the parameter exceeds the threshold value. (Item 5) 13. The method of claim 1, wherein the threshold is a dynamic threshold calculated from a statistical analysis of the parameters calculated from filter coefficients of one or more previous adaptations of the at least one controllable filter. (Item 6) 13. The method of claim 1, wherein the threshold is an average value of the parameter taken from a number of previous adaptations of the at least one controllable filter multiplied by a gain factor. (Item 7) detecting divergence of the at least one controllable filter based on a comparison of the parameter to a threshold value; comparing said parameter from a current adaptation of said at least one controllable filter against an average value of the same parameter from one or more previous adaptations of said at least one controllable filter; detecting a divergence of the at least one controllable filter when a difference between the parameters from the current adaptation of the at least one controllable filter and the average values ​​from the one or more previous adaptations of the at least one controllable filter exceeds a threshold value; The method according to any one of the above items, comprising: (Item 8) 2. The method of claim 1, wherein modifying the characteristics of the at least one controllable filter that has diverged comprises deactivating operation of at least one of the ANC system and the at least one controllable filter that has diverged. (Item 9) 2. The method of claim 1, wherein correcting the characteristics of the at least one controllable filter that has diverged comprises resetting the filter coefficients of the at least one controllable filter to zero and allowing the at least one controllable filter to readapt. (Item 10) 2. The method of claim 1, wherein correcting the characteristics of the at least one controllable filter that has diverged comprises resetting the filter coefficients of the at least one controllable filter to a set of filter coefficient values ​​stored in a memory of the ANC system. (Item 11) 20. The method of claim 19, wherein modifying a characteristic of the at least one controllable filter that has diverged comprises increasing a leakage value of the adaptive filter controller in response to detecting a divergence of the at least one controllable filter. (Item 12) 13. The method of claim 1, wherein the leakage value of the adaptive filter controller increases at the divergent frequencies of the at least one controllable filter. (Item 13) 13. The method of claim 1, further comprising: decreasing the leakage value of the adaptive filter controller when a highest amplitude filter coefficient of the at least one controllable filter falls below a predetermined threshold. (Item 14) An active noise cancellation (ANC) system, at least one controllable filter configured to generate an anti-noise signal based on an adaptive transfer characteristic and a noise signal received from a sensor, the adaptive transfer characteristic of the at least one controllable filter being characterized by a set of filter coefficients; an adaptive filter controller including a processor and a memory, the adaptive filter controller being programmed to adapt the set of filter coefficients based on the noise signal and an error signal received from a microphone located within the vehicle cabin; a divergence controller in communication with at least the adaptive filter controller, receiving said set of filter coefficients corresponding to a current adaptation of said adaptive transfer characteristic of said at least one controllable filter; calculating parameters based on an analysis of at least a portion of said set of filter coefficients; detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold; the divergence controller, comprising a processor and memory programmed to: The active noise cancellation (ANC) system comprises: (Item 15) The ANC system described in the preceding item, wherein the threshold is a predetermined static threshold programmed for the ANC system. (Item 16) 2. The ANC system of claim 1, wherein the divergence controller is programmed to detect divergence of the at least one controllable filter when a difference between the parameter calculated from the current adaptation of the at least one controllable filter and an average value of the same parameter from one or more previous adaptations of the at least one controllable filter exceeds the threshold. (Item 17) 4. The ANC system of claim 1, wherein the divergence controller is further programmed to increase a leakage value of the adaptive filter controller in response to detecting a divergence of the at least one controllable filter. (Item 18) 1. A computer program product embodied in a non-transitory computer readable medium programmed for active noise cancellation (ANC), the computer program product comprising instructions, the instructions comprising: receiving, from an adaptive filter controller, a set of filter coefficients corresponding to a current adaptation of the at least one controllable filter; calculating parameters based on an analysis of at least a portion of the filter coefficients; detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold; modifying an adaptive transfer characteristic of the at least one controllable filter during the current adaptation in response to detecting a divergence of the at least one controllable filter; The above computer program product causes the computer to perform the above steps. (Item 19) the instructions for detecting divergence of the at least one controllable filter include detecting a divergent frequency of the at least one controllable filter in a time domain; the instructions to modify the adaptive transfer characteristic include resetting, in the time domain, the divergent frequencies of the at least one controllable filter to zero, attenuating the filter coefficients at the divergent frequencies, or increasing a leakage value of the adaptive filter controller at the divergent frequencies. A computer program product as described above. (Item 20) the instructions for detecting divergence of the at least one controllable filter include detecting a divergent frequency of the at least one controllable filter in a frequency domain; the instructions for modifying the adaptive transfer characteristic include using, in the frequency domain, an error signal received from a microphone and filter coefficients from a previous adaptation of the at least one controllable filter stored in a memory, to notch out the divergent frequencies. 2. A computer program product according to any one of the preceding items. (Summary) An active noise cancellation (ANC) system may include an adaptive filter divergence detector for detecting divergence of one or more controllable filters as they adapt based on various time-domain or frequency-domain amplitude characteristics. Upon detection of divergence of the controllable filters, the ANC system may be deactivated or a particular speaker may be muted. Alternatively, the ANC system may correct the diverged controllable filters to restore proper operation of the noise cancellation system. This may include adjusting a leakage value of an adaptive filter controller. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is an environmental block diagram of a vehicle having an active noise control (ANC) system including road noise cancellation (RNC), in accordance with one or more embodiments of the present disclosure. [Diagram 2] FIG. 2 is a sample schematic diagram showing relevant portions of an RNC system sized to include R accelerometer signals and L speaker signals. [Diagram 3] FIG. 1 is a sample schematic block diagram of an ANC system including an Engine Over Control (EOC) system and an RNC system. [Figure 4] 1 is a look-up table of sample frequencies for each engine operating command for a given RPM in an EOC system. [Diagram 5] FIG. 1 is a schematic block diagram illustrating an ANC system including a divergence controller in accordance with one or more embodiments of the present disclosure. [Figure 6]1 is a flowchart illustrating a method for detecting correct divergence of an adaptive filter in an ANC system in accordance with one or more embodiments of the present disclosure. [Figure 7] 11 is a graphical representation of an analysis of a controllable filter in the frequency domain using a threshold in accordance with one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Where necessary, detailed embodiments of the present invention are disclosed herein, but it should be understood that the disclosed embodiments are merely examples of the present invention that may be embodied in various and alternative forms. The figures are not necessarily to scale, and some features may be exaggerated or minimized to show details of specific components. Therefore, specific structural and functional details disclosed herein should not be construed as limiting, but merely as representative criteria for teaching those skilled in the art to variously utilize the present invention.

[0018] Any one or more of the controllers or devices described herein include computer-executable instructions that may be compiled or interpreted from computer programs created using a variety of programming languages ​​and / or technologies. Generally, a processor (such as a microprocessor) receives instructions from, for example, a memory, a computer-readable medium, etc., and executes the instructions. The processing unit includes a non-transitory computer-readable storage medium capable of executing instructions of a software program. The computer-readable storage medium may be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof.

[0019] FIG. 1 shows a Road Noise Cancellation (RNC) system 100 for a vehicle 102 having one or more vibration sensors 108. The vibration sensors are placed throughout the vehicle 102 to monitor the vibration behavior of the vehicle suspension, sub-wheels, as well as other axle and chassis components. The RNC system 100 may be integrated with a wideband feedforward and feedback active noise control (ANC) framework or system 104 that uses one or more microphones 112 and generates anti-noise by adaptive filtering of signals from the vibration sensors 108. The anti-noise signal may then be played through one or more speakers 124. S(z) represents the transfer function between a single speaker 124 and a single microphone 112. It should be noted that while FIG. 1 shows a single vibration sensor 108, microphone 112, and speaker 124 for simplicity only, a typical RNC system uses multiple vibration sensors 108 (e.g., 10 or more), microphones 112 (e.g., 4-6), and speakers 124 (e.g., 4-8).

[0020] The vibration sensor 108 may include, but is not limited to, an accelerometer, a force gauge, a geophone, a linear variable differential transformer, a strain gauge, and a load cell. An accelerometer, for example, is a device whose output signal amplitude is proportional to acceleration. A variety of accelerometers are available for use in the RNC system. These include accelerometers that are sensitive to vibrations in one, two, or three, usually orthogonal directions. These multi-axis accelerometers usually have separate electrical outputs (or channels) for vibrations sensed in their X, Y, and Z directions. Thus, single-axis and multi-axis accelerometers may be used as vibration sensors 108 to detect the magnitude and phase of acceleration, and may be used to sense orientation, motion, and vibration.

[0021] Noise and vibration resulting from the wheels 106 moving on the road surface 150 may be sensed by one or more vibration sensors 108 mechanically coupled to the suspension system 110 or chassis components of the vehicle 102. The vibration sensor 108 may output a noise signal X(n), which is a vibration signal representative of the detected road-induced vibration. It is noted that multiple vibration sensors are contemplated, and their signals may be used separately or combined in various ways known to those skilled in the art. In certain embodiments, a microphone may be used instead of a vibration sensor to output a noise signal X(n) indicative of noise resulting from the interaction of the wheels 106 and the road surface 150. The noise signal X(n) may be filtered by a secondary path filter 122 using a modeled transfer characteristic S'(z) that estimates the secondary path (i.e., the transfer function between the anti-noise speaker 124 and the error microphone 112).

[0022] Traffic noise resulting from the interaction of the wheels 106 and the road surface 150 is also transmitted mechanically and / or acoustically into the passenger compartment and picked up by one or more microphones 112 inside the vehicle 102. The one or more microphones 112 may be located, for example, in the headrest 114 of the seat 116, as shown in FIG. 1. Alternatively, the one or more microphones 112 may be located in the headliner of the vehicle 102, or in some other suitable location, to detect the acoustic noise field heard by the occupants inside the vehicle 102. The traffic noise resulting from the interaction of the road surface 150 and the wheels 106 is transmitted to the microphone 112 according to a transfer characteristic P(z) that represents the first-order path (i.e., the transfer function between the actual noise source and the error microphone).

[0023] The microphone 112 may output an error signal e(n) representative of the noise present in the vehicle 102 cabin as detected by the microphone 112. In the RNC system 100, the adaptive transfer characteristic W(z) of the controllable filter 118 may be controlled by an adaptive filter controller 120, which may operate according to a known least mean square (LMS) based on the error signal e(n) and the noise signal X(n) filtered with the transfer characteristic S'(z) modeled by the filter 122. The controllable filter 118 is often referred to as a W filter. The LMS adaptive filter controller 120 may provide an integrated cross spectrum configured to update the transfer characteristic W(z) filter coefficients based on the error signal e(n). The process of adapting or updating W(z) leading to improved noise cancellation is called convergence. Convergence refers to the creation of a W filter that minimizes the error signal (e)n for a given input signal, which is controlled by a step size that governs the speed of adaptation. The step size is a scaling factor that determines how quickly the algorithm converges to minimize e(n) by limiting the change in the magnitude of the W filter coefficients upon each update of the controllable W filter 118.

[0024] The anti-noise signal Y(n) may be generated by an adaptive filter formed by the controllable filter 118 and adaptive filter controller 120 based on the identified transfer characteristic W(z) and the vibration signal, or combination of vibration signals X(n). The anti-noise signal Y(n) ideally has a waveform such that, when played through the speaker 124, an anti-noise that is substantially in phase with and identical in amplitude to the traffic noise heard by an occupant of the vehicle's cabin is generated at the occupant's ears and near the microphone 112. The anti-noise from the speaker 124 combines with the traffic noise in the cabin near the microphone 112, resulting in a reduction in the traffic noise induced sound pressure level (SPL) at this location. In certain embodiments, the RNC system 100 may receive sensor signals from other acoustic sensors in the passenger compartment, such as acoustic energy sensors, acoustic intensity sensors, or acoustic particle velocity or acceleration sensors, to generate the error signal e(n).

[0025] While the vehicle 102 is operating, the processor 128 may collect and optionally process data from the vibration sensor 108 and microphone 112 to build a database or map containing data and / or parameters for use by the vehicle 102. The collected data may be stored locally in the storage device 130 or in the cloud for future use by the vehicle 102. Examples of types of data related to the RNC system 100 that may be useful to store locally in the storage device 130 include, but are not limited to, optimal W filter, W filter threshold, initial W filter, W filter gain coefficient, amount of leakage increment and decrement, accelerometer or microphone spectrum or time dependent signals, and engine SPL versus torque and RPM. In one or more embodiments, the processor 128 and storage device 130 may be integrated with one or more system controllers, such as the adaptive filter controller 120.

[0026] As mentioned above, a typical RNC system may use several vibration sensors, microphones, and speakers to detect the solid-borne vibration behavior of the vehicle and generate anti-noise. The vibration sensor may be a multi-axis accelerometer with multiple output channels. For example, a triaxial accelerometer usually has separate electrical outputs for vibrations detected in its X-, Y-, and Z-directions. A typical configuration of an RNC system may have, for example, six error microphones, six speakers, and twelve channels of acceleration signals resulting from four triaxial accelerometers or six biaxial accelerometers. Thus, the RNC system also includes multiple S'(z) filters (i.e., secondary path filters 122) and multiple W(z) filters (i.e., controllable filters 118).

[0027] The simplified RNC system schematic shown in FIG. 1 shows one secondary path, denoted S(z), between each speaker 124 and each microphone 112. As mentioned above, an RNC system typically has multiple speakers, microphones, and vibration sensors. Thus, a six speaker, six microphone RNC system has a total of 36 secondary paths (i.e., 6x6). Correspondingly, a six speaker, six microphone RNC system may similarly have 36 S'(z) filters (i.e., stored secondary path filters 122) that estimate transfer functions for each secondary path. As shown in FIG. 1, the RNC system also has one W(z) filter (i.e., controllable filter 118) between each noise signal X(n) from the vibration sensor (i.e., accelerometer) 108 and each speaker 224. Thus, a twelve accelerometer signal, six speaker RNC system may have 72 W(z) filters. The relationship between the number of accelerometer signals, speakers, and W(z) filters is shown in Figure 2.

[0028] FIG. 2 shows R accelerometer signals [X 1 (n), X 2 (n),...X R (n)] and L speaker signals [Y 1 (n), Y 2 (n),...Y LFIG. 1 is a sample schematic diagram showing relevant portions of an RNC system 200 sized to include a plurality of R*L controllable filters (or W filters) 218 ​​between each of the accelerometer signals and each of the speakers. As an example, an RNC system having 12 accelerometer outputs (i.e., R=12) may utilize six biaxial accelerometers or four triaxial accelerometers. Thus, in the same example, a vehicle having six speakers (i.e., L=6) for reproducing anti-noise may use a total of 72 W filters. At each of the L speakers, the R W filter outputs are summed to generate the speaker's anti-noise signal Y(n). Each of the L speakers may include an amplifier (not shown). In one or more embodiments, the R accelerometer signals filtered by the R W filters are summed to produce an electrical anti-noise signal y(n) that is sent to an amplifier to generate an amplified anti-noise signal Y(n) that is sent to the speaker.

[0029] The ANC system 104 shown in FIG. 1 may include an engine operating command cancellation (EOC) system. As described above, EOC technology uses a non-acoustic signal, such as an RPM signal representing engine speed, as a reference to generate a sound that is opposite in phase to the engine noise heard inside the vehicle. A common EOC system utilizes a narrowband feedforward ANC framework to generate an anti-noise using the RPM signal to guide the generation of an engine operating command signal that is identical in frequency to the engine operating command being cancelled, and adaptively filters it to create the anti-noise signal. After being transmitted from the anti-noise source to the listening position or error microphone via a secondary path, the anti-noise ideally has the same amplitude but opposite phase as the combined sound generated by the engine and exhaust pipe and filtered by the primary path extending from the engine to the listening position and from the exhaust pipe outlet to the listening position. Thus, where the error microphone resides in the passenger compartment (i.e., most likely at or near the listening position), the superposition of engine operating command noise and anti-noise would ideally be zero, such that the acoustic error signal received by the error microphone records only sounds other than one or more engine operating commands generated by the engine and exhaust (which would ideally be deactivated).

[0030] Typically, a non-acoustic sensor, such as an RPM sensor, is used as the reference. The RPM sensor may be, for example, a Hall effect sensor located adjacent to a rotating steel disk. Other detection principles, such as optical or inductive sensors, may also be used. The signal from the RPM sensor may be used as a guide signal to generate any number of reference engine operating command signals corresponding to respective engine operating commands. The reference engine operating commands form the basis of noise cancellation signals generated by one or more narrowband adaptive feedforward LMS blocks forming the EOC system.

[0031] 3 is a schematic block diagram illustrating an example of an ANC system 304 including both an RNC system 300 and an EOC system 340. Similar to the RNC system 100, the RNC system 300 may include elements 308, 312, 318, 320, 322, and 324, which correspond to the operation of elements 108, 112, 118, 120, 122, and 124, respectively, described above. The EOC system 340 may include an RPM sensor 342, which may provide an RPM signal 344 (e.g., a square wave signal) indicative of the rotation of the engine drive shaft or other rotating shaft, which in turn indicates the rotational speed of the engine. In some embodiments, the RPM signal 344 may be obtained from a vehicle network bus (not shown). The RPM signal 344 represents the frequency generated by the engine and exhaust system, since the issued engine operating commands are directly proportional to the drive shaft RPM. In this manner, the signal from the RPM sensor 342 may be used to generate a reference engine operating command signal corresponding to each of the vehicle's engine operating commands. Thus, the RPM signal 344 may be used in conjunction with an RPM vs. engine command frequency look-up table 346 that provides a list of engine commands to issue at each engine RPM.

[0032] 4 illustrates an example EOC cancellation tuning table 400 that may be used to generate the lookup table 346. The example table 400 shows the frequency (in cycles / second) for each engine operating command for a given RPM. In the example shown, four engine operating commands are shown. The LMS algorithm takes the RPM as an input and generates a sine wave for each command based on this lookup table 400. As mentioned above, the associated RPM of the table 400 may be the drive shaft RPM.

[0033] 3, the frequency of a given engine operating command at the sensed RPM retrieved from the lookup table 346 may be provided to a frequency generator 348, which generates a sine wave at the given frequency. This sine wave represents a noise signal X(n) indicative of the engine operating command noise for the given engine operating command. Similar to the RNC system 300, this noise signal X(n) from the frequency generator 348 may be sent to an adaptive controllable filter 318, or W filter, which provides a corresponding anti-noise signal Y(n) to the loudspeaker 324. As shown, various components of this narrowband EOC system 340 may be identical to the wideband RNC system 300, including the error microphone 312, the adaptive filter controller 320, and the secondary path filter 322. The anti-noise signal Y(n) broadcast by the speaker 324 generates an anti-noise that is substantially out of phase with, but identical in amplitude to, the actual engine operating command noise at the listener's ear, which may be in close proximity to the error microphone 312, thereby reducing the audio amplitude of the engine operating command. Because the engine operating command noise is narrowband, the error microphone signal e(n) may be filtered by bandpass filters 350, 352 before being passed to the LMS-based adaptive filter controller 320. In one embodiment, proper operation of the LMS adaptive filter controller 320 is achieved when the noise signal X(n) output by the frequency generator 348 is bandpass filtered using the same bandpass filter parameters.

[0034] To simultaneously reduce the amplitude of multiple engine operating commands, the EOC system 340 may include multiple frequency generators 348 for generating a noise signal X(n) for each engine operating command based on the RPM signal 344. As an example, FIG. 3 illustrates a method for generating a unique noise signal (e.g., X(n)) for each engine operating command based on the engine speed. 1 (n), X 23 shows a second order EOC system having two such frequency generators for generating an error signal e(n) from the error microphone 312. Because the frequencies of the two engine operating commands are different, the bandpass filters 350, 352 (named BPF and BPF2, respectively) have different high-pass and low-pass corner frequencies. The number of frequency generators and corresponding noise cancellation components will ultimately vary based on the number of engine operating commands for a particular engine of the vehicle. When the second order EOC system 340 is combined with the RNC system 300 to form the ANC system 304, the anti-noise signals Y(n) output from the three controllable filters 318 are summed and sent to the speaker 324 as the speaker signal S(n). Similarly, the error signal e(n) from the error microphone 312 may be sent to the three LMS adaptive filter controllers 320.

[0035] One major factor that may lead to instability or reduced noise cancellation performance in an ANC system occurs when the adaptive W filters diverge during adaptation by a feedforward LMS system. When the adaptive W filters converge properly, the sound pressure level (and associated error signal e(n)) at the error microphone location is minimized. However, if one or more of these adaptive W filters diverge, noise boosting may occur instead. Generally, the first tap of an adaptive W filter has a high amplitude, which decreases to zero in later taps. However, if an LMS ANC system diverges, one or more W filters may not have this property. Thus, systems and methods may be utilized to detect and control adaptive filter divergence to maintain the performance and stability of the ANC system. Briefly, the W filter values ​​(i.e., adaptive filter coefficients) may be compared to predefined thresholds in either the time domain or the frequency domain. If the W filter values ​​exceed these thresholds, divergence mitigation may be utilized to prevent noise boosting or other undesirable behavior. Emission mitigation may include, for example, muting the ANC system, resetting the divergent W filter to a zero state or some other stored state, adding leakage at frequencies that include the divergent frequency, etc.

[0036] FIG. 5 is a schematic block diagram of a vehicle-based ANC system 500 illustrating many of the important ANC system parameters that may be used to detect adaptive W filter divergence and optimize ANC system performance. For ease of explanation, the ANC system 500 shown in FIG. 5 is illustrated with the components and features of an RNC system, such as the RNC system 100. However, the ANC system 500 may include an EOC system, such as that shown and described in connection with FIG. 3. Thus, the ANC system 500 is a schematic diagram of an RNC and / or EOC system, such as those described in connection with FIGS. 1-3, featuring additional system components. Similar components may be numbered using similar conventions. For example, similar to the RNC system 100, the ANC system 500 may include elements 508, 510, 512, 518, 520, 522, and 524 that are consistent with the operation of elements 108, 110, 112, 118, 120, 122, and 124, respectively, described above.

[0037] As shown, the ANC system 500 may further include a divergence controller 562 disposed along the path between the controllable filter 518 and the adaptive filter controller 520. The divergence controller 562 may include a processor and memory (not shown) programmed to detect the divergence of the controllable filter 518. This may include calculating parameters by analyzing samples from the adaptive filter values ​​(e.g., filter coefficients) in either or both the time domain or the frequency domain. To this end, FIG. 5 explicitly shows Fast Fourier Transform (FFT) blocks 564, 566 and an Inverse Fast Fourier Transform (IFFT) block 568 for converting signals between the time domain and the frequency domain. Thus, the variable names in FIG. 5 are slightly modified from those shown in FIGS. 1-3. Uppercase variables represent signals in the frequency domain. Meanwhile, lowercase variables represent signals in the time domain. The letter "n" indicates a sample in the time domain. Meanwhile, the letter "k" indicates a bin in the frequency domain. The diagram of Figure 5 further illustrates the presence of multiple signals, showing R reference signals, L speaker signals, and M error signals. The following table provides a detailed explanation of the various symbols and variables in Figure 5. [Table 1]

[0038] Similar to FIG. 1, a noise signal x from a noise input such as a vibration sensor 508 r [n] is the transfer characteristic modeled using the stored estimate of the secondary path by the secondary path filter 522 described above.

number

[0039] The divergence controller 562 calculates the time-domain filter coefficients w for each adaptation of the controllable filter 518 generated by the LMS adaptive filter controller 520. r、l [n] and / or the frequency domain filter coefficients w r、l[k]. Additionally, the divergence controller 562 may calculate one or more parameters by analyzing the filter coefficients. If divergence of one or more controllable filters is detected, the divergence controller 562 may send a signal, such as an adjustment signal, back to the adaptive filter controller 520 instructing the adaptive filter controller to modify the characteristics of at least one controllable filter 518 that has diverged. For example, in either an RNC system or an EOC system, a response to detection of divergence of a controllable W filter 518 may be for the divergence controller 562 to use, for example, a previously stored adjusted W filter to substitute for the diverged W filter value. Other responses to detection of W filter divergence by the divergence controller 562 may include replacing the controllable filter 518 with a filter consisting of zeros, which effectively resets the controllable filter. Other divergence mitigation actions by the divergence controller 562 may include adding leakage at frequencies that include the diverged frequency, attenuating some or all of the W filter coefficients, or reducing the step size to lower the risk of future divergence events.

[0040] The divergence controller 562 may be a dedicated controller for detecting diverged controllable W filters or may be integrated with another controller or processor in the ANC system, such as the LMS controller 520. Alternatively, the divergence controller 562 may be integrated into another controller or processor in the vehicle 102 that is separate from the other components in the ANC system 500.

[0041] 5 explicitly shows an ANC system with both time and frequency domain processing, an ANC system may be implemented with only time domain processing, in which case the secondary path estimates are stored in the time domain and the LMS updates also occur in the time domain. In one embodiment, divergence detection by divergence controller 562 may also occur in the time domain. In another embodiment, an FFT of the time domain W filter can enable divergence detection by calculating parameters from the frequency domain W filter.

[0042] 6 is a flow chart illustrating a method 600 for mitigating the effects of a divergent or mis-adapted controllable W filter in an ANC system 500. Various steps of the disclosed method may be performed by the divergence controller 562 either alone or in conjunction with other components of the ANC system.

[0043] In step 610, the divergence controller 562 calculates the divergence in the time domain (i.e., w r、l [n]) and / or frequency domain (i.e., w r、l [k]) of one or more controllable filters 518. To this end, a group of samples of time or frequency domain filter coefficients output from the adaptive filter controller 520 may be received by the divergence controller 562. In one embodiment, the controllable W filter may consist of 128 taps in the time domain. In alternative embodiments, there may be more or fewer filter taps. The filter values ​​or coefficients may be received from the LMS adaptive filter controller 520 and may represent the current adaptation of the controllable filters 518. As mentioned above, each controllable filter 518 is adapted continuously by the adaptive filter controller 520, with the rate of change limited by a step size. The update rate of the controllable filters 518 is determined by the step size of the input signal x. r [k,n] and e m The [k,n] may be set by the sample rate and block length of the data. Divergence controller 562 may receive these updated W filter coefficients for each controllable filter.

[0044] At step 620, an analysis of the W filter data may be performed and one or more parameters may be calculated in either the time domain or the frequency domain. There are several ways to detect divergence or mis-adaptation in the time domain version of the controllable W filter based on the analysis of the filter coefficients. In one embodiment, the parameter calculated by the divergence controller 562 may be the sum of absolute values ​​of the taps of the entire controllable W filter. In another embodiment, the parameter calculated by the divergence controller 562 may be the sum of absolute values ​​of the taps of a later portion of the controllable W filter, such as the second half or the last quarter of the coefficients of the controllable filter. In yet another embodiment, the parameter may be the maximum of the individual tap values ​​of at least a portion of the controllable filter, such as the second half (or the last quarter) to determine whether any exceed a predetermined amplitude. The controllable filter characteristic parameters may also be calculated in the frequency domain. Parameters calculated in the frequency domain may include, for example, a phase shift over a frequency range. In another embodiment, the parameter calculated by the divergence controller 562 may be the sum of all or a portion of the W filter coefficients. In yet another embodiment, the parameter may be a maximum value of the W filter coefficients over at least a portion of the frequency range of the controllable W filter. In various embodiments, these sums or maxima may be calculated using the real, imaginary, or magnitude of the W filter coefficients.

[0045] Step 620 may also include storing the parameter(s) and / or the current W filter value for use in performing future W filter analysis. In one embodiment, the parameter(s) or W filter data from the W filter immediately preceding the current W filter may be stored. In another embodiment, a statistical analysis may be performed on the parameters obtained from multiple previous W filters (e.g., to determine a threshold). For example, a short-term or long-term average of the parameters obtained from multiple preceding W filters may be calculated and stored as a dedicated parameter for use in step 630, either as a threshold or to obtain a difference from the current W filter for comparison against the threshold. In certain of these embodiments, a predetermined gain margin may be added to the average value (or other statistical value) calculated from multiple preceding W filters to form the threshold. This may include adding a gain margin of 20%, 50%, or 100% to the average value or other statistical value. In this manner, the average value from multiple preceding W filters may be multiplied by a gain factor (e.g., 120%, 150%, 200%, etc.) to obtain a threshold value. In other embodiments, other gain factors are also contemplated. Additionally, one or more controllable W-filters may be stored for future use in mitigating W-filter divergence.

[0046] In step 630, the calculated parameters from the current controllable W filter may be directly compared to the corresponding thresholds. If the parameters from the current W filter exceed the thresholds, the divergence controller 562 may conclude that a divergence or misadaptation has been detected. If the parameters from the current W filter do not exceed the thresholds, the divergence controller 562 may conclude that a divergence or misadaptation has not been detected. For example, the divergence controller 562 may calculate the highest amplitude frequency domain W filter coefficient values ​​or the average of the absolute values ​​of the last tenths of the time domain filter taps of the W filter and compare the peak amplitude or sum against the corresponding thresholds to determine whether a divergence event or a misadaptation has occurred. As another example, divergence may be detected if the phase difference between the beginning and end of a frequency range exceeds a threshold.

[0047] Instead, the calculated parameters from the current W filter may be compared against a statistical value (e.g., average value) of the same parameters from one or more previous W filters, as described above. The difference between the parameters and the statistical value of the current W filter may then be compared against a threshold value, which may be referred to as the W threshold. If the difference exceeds the threshold, the divergence controller 562 may conclude that divergence or misadaptation has been detected. If the difference does not exceed the threshold, the divergence controller 562 may conclude that divergence has not occurred. For example, in one embodiment, the divergence controller 562 may calculate the average of the absolute values ​​of the last sixth of the time-domain filter taps of the W filter and compare it to the previous W filter's average of the absolute values ​​of the last tenth of the time-domain filter taps of the W filter, noting that any difference exceeding a predetermined threshold may indicate divergence of the W filter.

[0048] In one or more embodiments, the threshold may be a predetermined static threshold set and may be programmed by a trained engineer during tuning of the ANC system and its corresponding algorithm. In an alternative embodiment, the threshold may be a dynamic threshold calculated from a statistical analysis of parameters obtained with one or more previous W filters as described above with respect to step 620. For example, the threshold may be a short-term or long-term average value of the parameters taken from multiple previous W filters. Furthermore, the average value may be expanded by a gain factor as described above to establish the dynamic threshold. In yet another embodiment, the threshold may simply be the value of the parameter from a previous W filter, which may be multiplied by a gain factor.

[0049] Referring to step 640, when a threshold is exceeded, indicating adjustable filter divergence, the method may proceed to step 650. At step 650, mitigation measures may be applied to the divergent controllable W filter to minimize noise boost in the vehicle cabin or reduce the effect of the W filter divergence. However, when a W filter is not detected, the method may omit any mitigation and return to step 610 so that the process can be repeated with new W filter coefficients corresponding to the next filter adaptation.

[0050] In step 650, divergence mitigation may be applied to either or both of the divergent or misadapted W filters in the time domain or frequency domain. In general, this may involve modifying the characteristics of at least one controllable filter 518 in which divergence has been detected. Such characteristics may be modified in part based on or in response to an adjustment signal sent from the divergence controller 562 to the adaptive filter controller 520. In certain embodiments, countermeasures may be applied to the entire W filter or only to certain frequencies of the W filter in the frequency domain. Mitigation methods that may be applied to the entire controllable W filter (in either the time domain or the frequency domain) may include resetting the filter coefficients of one or more W filters to allow them to re-adapt, or setting the filter coefficients to a set of filter coefficient values ​​stored in a memory of the ANC system. The set of filter coefficient values ​​stored in the memory may include filter coefficient values ​​from a W filter in a known good state, such as a W filter tuned by a trained engineer or obtained from a controllable filter before divergence was detected. For example, the controllable filter may use the filter coefficients it had, such as 10 seconds or 1 minute before the divergence, and be reset. Alternatively, the controllable W filter may be reset to an initial state, such as when the ANC system 500 was powered on. Another mitigation technique may simply disable or mute the operation of the ANC system when a divergence is detected. In an embodiment, only the divergent W filter may be disabled or set to zero and not allowed to adapt when a divergence is detected. In an embodiment, the amplitude of all filter steps, or the magnitude of all frequency domain filter coefficients, may be reduced when a divergence is detected. The characteristics of the controllable W filter may be modified directly, such as by setting the filter coefficients to a particular value. Alternatively, the characteristics of the controllable W filter 518 may be modified indirectly. For example, the value of leakage at all frequencies may be increased by the adaptive filter controller 520 in response to an adjustment signal from the divergence controller 562 when a divergence is detected.

[0051] Countermeasures applied solely in a frequency domain manner may include attenuating the W filter coefficient at or near the divergence frequency, or adding or increasing the leakage value at or near the divergence frequency. In one embodiment of mitigation applied in the frequency domain, the divergence controller 562 calculates the leakage of the input signal x r [n] and e m The unstable divergent frequencies identified in step 630 may be adaptively notched out by adding a notch filter or band-reject filter at [n] or its frequency domain equivalent. This may prevent the adaptive filter controller 520 from increasing the amplitude of the W filter in the problematic frequency range in future operation of the ANC system 500. This may be optionally achieved by resetting the W filter as outlined above, or by using leakage at these unstable divergent frequencies or all frequencies.

[0052] As mentioned above, in one or more additional embodiments, the leakage value may be increased in the LMS adaptive filter controller 520 when divergence is detected, such as when the highest amplitude W filter coefficient exceeds a predetermined threshold. Increasing the leakage value of the adaptive filter controller 520 may decrease the amplitude of the controllable W filter 518. This leakage value may be continuously increased by a predetermined amount with each iteration through the process flow shown in FIG. 6 while the highest amplitude W filter coefficient still exceeds the predetermined threshold. Once the highest amplitude W filter coefficient no longer exceeds the predetermined threshold, the leakage value may be decreased by a predetermined amount during subsequent iterations through the process flow shown in FIG. 6 as long as the highest amplitude W filter coefficient does not exceed the predetermined threshold. Decreasing the leakage value of the adaptive filter controller 520 may increase the amplitude of the controllable W filter 518. In this manner, the leakage value of the adaptive filter controller may be continuously adjusted up or down based on the magnitude of the filter coefficient with respect to the threshold.

[0053] In one embodiment, leakage is increased for all W filters of the ANC system 500 when the highest amplitude W filter coefficient of any of the W filters exceeds a predetermined threshold. In another embodiment, leakage is increased for all W filters for a particular speaker when the highest amplitude W filter coefficient of any of the W filters associated with that speaker exceeds a predetermined threshold. The LMS controller 520 may be instructed to increase or decrease the leakage value in response to receiving an adjustment signal from the divergence controller 562. As described above, adjusting the leakage value for one or more of the controllable filters 518 may indirectly affect the magnitude of the W filter coefficients. For example, increasing leakage may generally decrease the magnitude of the filter coefficients. Conversely, decreasing leakage may generally increase the magnitude of the filter coefficients.

[0054] As mentioned above, there is one controllable W filter for each combination of speaker 512 and noise input (e.g., each engine operating command or vibration sensor). Thus, a 12 accelerometer, 6 speaker RNC system would have 72 W filters (i.e., 12x6=72), and a 5 engine operating command, 6 speaker EOC system would have 30 W filters (i.e., 5x6=30). The method 600 shown in FIG. 6 may be executed after every new set of W filters is calculated, or less frequently, to reduce the computational power required and thereby save CPU cycles.

[0055] 7 shows an example analysis of frequency domain threshold comparison. ANC system 500 may store a set of limit values ​​(i.e., W thresholds) for each controllable filter. Under normal operating conditions, all controllable W filter points are below the W threshold. Under divergent operating conditions or misadaptation conditions, one or more coefficients of the W filter exceed the W threshold. Divergence controller 562 may detect and indicate which W filters and / or which bins of the W filters exceed the W threshold so that adaptive filter controller 520 or divergence controller 562 may apply countermeasures.

[0056] 1, 3, and 5 show LMS-based adaptive filter controllers 120, 320, and 520, respectively, other methods and apparatus for adapting or creating optimal controllable W filters 118, 318, and 518 are possible. For example, in one or more embodiments, a neural network may be utilized to create and optimize the W filter instead of an LMS adaptive filter controller. In other embodiments, machine learning or artificial intelligence may be used to create an optimal W filter instead of an LMS adaptive filter controller.

[0057] In the foregoing specification, the inventive subject matter has been described with reference to certain exemplary embodiments. However, various modifications and changes may be made without departing from the scope of the inventive subject matter as set forth in the claims. The specification and drawings are exemplary rather than restrictive, and modifications are intended to be included within the scope of the inventive subject matter. The scope of the inventive subject matter should therefore be determined by the claims and their legal equivalents, and not merely by the examples described.

[0058] For example, the steps recited in any method or process claim may be performed in any order and are not limited to the particular order set forth in the claims. Equations may be implemented with filters to minimize the effects of signal noise. Additionally, the components and / or elements recited in any apparatus claim may be grouped together in various combinations or otherwise configured for operation and therefore are not limited to the particular configurations recited in the claims.

[0059] Those skilled in the art will appreciate that functionally equivalent processing steps can be performed in either the time domain or the frequency domain. Thus, although not specifically and explicitly described for each signal processing block in the figures, particularly in FIGS. 1-3, signal processing can occur in either the time domain, the frequency domain, or a combination thereof. Additionally, although various processing steps are described in general terms of digital signal processing, equivalent steps may be performed using analog signal processing without departing from the scope of the present disclosure.

[0060] Benefits, advantages, and solutions to problems have been described above with respect to specific embodiments. However, any benefit, advantage, solution to a problem, or any element that can enable or make more pronounced any particular benefit, advantage, or solution, is not to be construed as a critical, required, or essential feature or component of any or all of the claims.

[0061] The terms "comprise," "comprises," "comprising," "having," "including," "includes," or any variation thereof, are intended to refer to a non-exclusive inclusion, such that a process, method, article, composition, or apparatus that includes a list of elements may include not only those elements listed, but also other elements not expressly listed or inherent in such process, method, article, composition, or apparatus. Other combinations and / or modifications of the above-described structure, arrangement, application, proportions, elements, materials, or components used in the practice of the inventive subject matter, in addition to those not expressly listed, may be varied without departing from the general principles of the same, or may be otherwise specifically adapted to a particular environment, manufacturing specifications, design parameters, or other operating requirements.

Claims

1. 1. A method for controlling stability of an active noise cancellation (ANC) system, the method comprising: receiving filter coefficients corresponding to the at least one controllable filter from an adaptive filter controller; calculating parameters based on an analysis of at least a portion of the filter coefficients; detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold, the threshold being a dynamic threshold calculated from an average value of the parameter calculated from filter coefficients of one or more previous adaptations of the at least one controllable filter; modifying the characteristics of the at least one controllable filter that has diverged; A method comprising:

2. The method of claim 1 , wherein the controllable filter comprises a plurality of coefficients, and the parameter is a sum of absolute values ​​of at least a portion of the coefficients of the at least one controllable filter.

3. The method of claim 1 , wherein the controllable filter comprises a plurality of coefficients, and the parameter is a maximum value of at least a portion of the coefficients of the at least one controllable filter.

4. The method of claim 1 , wherein detecting a divergence of the at least one controllable filter based on a comparison of the parameter to a threshold comprises detecting a divergence of the at least one controllable filter when the parameter exceeds the threshold.

5. The method of claim 1 , wherein the threshold is an average value of the parameter taken from multiple previous adaptations of the at least one controllable filter multiplied by a gain factor.

6. detecting divergence of the at least one controllable filter based on a comparison of the parameter to a threshold value; comparing said parameter from a current adaptation of said at least one controllable filter against an average value of the same parameter from one or more previous adaptations of said at least one controllable filter; detecting a divergence of the at least one controllable filter when a difference between the parameters from the current adaptation of the at least one controllable filter and the average value from the one or more previous adaptations of the at least one controllable filter exceeds a threshold value; The method of claim 1 , comprising:

7. 2. The method of claim 1, wherein modifying a characteristic of the at least one controllable filter that has diverged comprises deactivating at least one of the ANC system and the at least one controllable filter that has diverged.

8. 2. The method of claim 1, wherein correcting the characteristics of the at least one controllable filter that has diverged comprises resetting the filter coefficients of the at least one controllable filter to zero and allowing the at least one controllable filter to re-adapt.

9. 2. The method of claim 1, wherein correcting a characteristic of the at least one controllable filter that has diverged comprises resetting the filter coefficients of the at least one controllable filter to a set of filter coefficient values ​​stored in a memory of the ANC system.

10. 2. The method of claim 1, wherein modifying a characteristic of the at least one controllable filter that has diverged comprises increasing a leakage value of the adaptive filter controller in response to detecting a divergence of the at least one controllable filter.

11. The method of claim 10 , wherein the leakage value of the adaptive filter controller increases at the divergent frequency of the at least one controllable filter.

12. The method of claim 10 , further comprising: decreasing the leakage value of the adaptive filter controller when a highest amplitude filter coefficient of the at least one controllable filter falls below a predetermined threshold.

13. 1. An active noise cancellation (ANC) system, comprising: at least one controllable filter configured to generate an anti-noise signal based on an adaptive transfer characteristic and a noise signal received from a sensor, the adaptive transfer characteristic of the at least one controllable filter being characterized by a set of filter coefficients; an adaptive filter controller including a processor and a memory, the adaptive filter controller being programmed to adapt the set of filter coefficients based on the noise signal and an error signal received from a microphone located within the vehicle cabin; a divergence controller in communication with at least the adaptive filter controller, the divergence controller comprising: receiving the set of filter coefficients corresponding to a current adaptation of the adaptive transfer characteristic of the at least one controllable filter; calculating parameters based on an analysis of at least a portion of the set of filter coefficients; detecting a divergence of the at least one controllable filter when a difference between the parameter calculated from the current adaptation of the at least one controllable filter and an average value of the same parameter from one or more previous adaptations of the at least one controllable filter exceeds a threshold value; a divergence controller including a processor and memory programmed to: An ANC system comprising:

14. The ANC system of claim 13 , wherein the threshold is a predetermined static threshold programmed for the ANC system.

15. The ANC system of claim 13 , wherein the divergence controller is further programmed to increase a leakage value of the adaptive filter controller in response to detecting a divergence of the at least one controllable filter.

16. 1. A computer program product embodied in a non-transitory computer readable medium programmed for active noise cancellation (ANC), the computer program product comprising instructions, the instructions comprising: receiving, from an adaptive filter controller, a set of filter coefficients corresponding to a current adaptation of the at least one controllable filter; calculating parameters based on an analysis of at least a portion of the filter coefficients; detecting a divergence of the at least one controllable filter when a difference between the parameter calculated from the current adaptation of the at least one controllable filter and an average value of the same parameter from one or more previous adaptations of the at least one controllable filter exceeds a threshold value; modifying an adaptive transfer characteristic of the at least one controllable filter during the current adaptation in response to detecting a divergence of the at least one controllable filter; A computer program product that causes

17. the instructions for detecting a divergence of the at least one controllable filter include detecting a divergent frequency of the at least one controllable filter in a time domain; the instructions to modify the adaptive transfer characteristic include resetting, in the time domain, filter coefficients of a controllable filter corresponding to the divergent frequency to zero, attenuating the filter coefficients at the divergent frequency, or increasing a leakage value of the adaptive filter controller at the divergent frequency.

17. A computer program product according to claim 16.

18. and wherein the instructions for detecting divergence of the at least one controllable filter include detecting a divergent frequency of the at least one controllable filter in a frequency domain. the instructions for modifying the adaptive transfer characteristic include using, in the frequency domain, an error signal received from a microphone and filter coefficients from a previous adaptation of the at least one controllable filter stored in a memory to notch out an input signal corresponding to the divergent frequencies.

17. A computer program product according to claim 16.

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