INSTABILITY DETECTION AND ADAPTIVE-ADJUSTMENT FOR ACTIVE NOISE CANCELLATION SYSTEM

An active noise cancellation (ANC) system is provided with at least one loudspeaker to project anti-noise sound within a passenger cabin of a vehicle in response to receiving an anti-noise signal and at least one microphone to provide an error signal indicative of noise and the anti-noise sound within the passenger cabin. An adaptive filter controller is programmed to filter the error signal to obtain a noise reduction ratio, and to adjust a step size and/or leakage parameter based on a comparison of the noise reduction ratio to a noise threshold. A controllable filter generates the anti-noise signal based on the adjusted step size and/or leakage parameter.

TECHNICAL FIELD

The present disclosure is directed to an active noise cancellation system and, more particularly, to adjusting filter parameters to limit noise boosting and/or system instability.

BACKGROUND

Active Noise Cancellation (ANC) systems attenuate undesired noise using feedforward and/or feedback structures to adaptively remove undesired noise within a listening environment, such as within a vehicle cabin. ANC systems generally cancel or reduce unwanted noise by generating cancellation sound waves to destructively interfere with the unwanted audible noise. Destructive interference results when noise and “anti-noise,” which is largely identical in magnitude 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 undesired noise come from the engine, the exhaust system, the interaction between the vehicle's tires and a road surface on which the vehicle is traveling, and/or sound radiated by the vibration of other parts of the vehicle. Therefore, unwanted noise varies with the speed, road conditions, and operating states of the vehicle.

A Road Noise Cancellation (RNC) system is a specific ANC system implemented on a vehicle in order to minimize undesirable road noise inside the vehicle cabin. RNC systems use vibration sensors to sense road induced vibration generated from the tire and road interface that leads to unwanted audible road noise. This unwanted road noise inside the cabin is then cancelled, or reduced in level, by using loudspeakers to generate sound waves that are ideally opposite in phase and identical in magnitude to the noise to be reduced at one or more listeners' cars. Cancelling such road noise results in a more pleasurable ride for vehicle passengers, and it enables vehicle manufacturers to use lightweight materials, thereby decreasing energy consumption and reducing emissions.

An Engine Order Cancellation (EOC) system is a specific ANC system implemented on a vehicle in order to minimize undesirable engine noise inside the vehicle cabin. EOC systems use a non-acoustic sensor, such as an engine speed sensor, to generate a signal representative of the engine crankshaft rotational speed in revolutions-per-minute (RPM) as a reference. This reference signal is used to generate sound waves that are opposite in phase to the engine noise that is audible in the vehicle interior. Because EOC systems use a signal from an RPM sensor, they do not require vibration sensors.

RNC systems are typically designed to cancel broadband signals, while EOC systems are designed and optimized to cancel narrowband signals, such as individual engine orders. ANC systems within a vehicle may provide both RNC and EOC technologies. Such vehicle-based ANC systems are typically Least Mean Square (LMS) adaptive feed-forward systems that continuously adapt W-filters based on noise inputs (e.g., acceleration inputs from the vibration sensors in an RNC system) and signals of physical microphones located in various positions inside the vehicle's cabin. A feature of LMS-based feed-forward ANC systems and corresponding algorithms is the storage of the impulse response, or secondary path, between each physical microphone and each anti-noise loudspeaker in the system. The secondary path is the transfer function between an anti-noise generating loudspeaker and a physical microphone, essentially characterizing how an electrical anti-noise signal becomes sound that is radiated from the loudspeaker, travels through a vehicle cabin to a physical microphone, and becomes the microphone output signal.

The remote or virtual microphone technique is a technique in which an ANC system estimates an error signal generated by an imaginary or virtual microphone at a location where no real physical microphone is located, based on the error signals received from one or more real physical microphones. This virtual microphone technique can improve noise cancellation at a listener's ears even when no physical microphone is actually located there.

ANC systems employ modeled transfer characteristics, which estimate the various secondary paths, to adapt the W-filters. Noise cancellation performance degradation, noise gain, or actual instability can result if the modeled transfer characteristic of the secondary path stored in the ANC system differs from the actual secondary path within the vehicle. The actual secondary path may deviate from the stored secondary path model, typically measured on a “golden system” by trained engineers, when a vehicle becomes substantially different from the reference vehicle or system in terms of geometry, passenger count, luggage loading, or the like. Other differences could include or loudspeaker or microphone unit-to-unit variation, aging or failure, microphone or speaker blocking, non-identical loudspeaker replacement or wiring errors.

SUMMARY

In one embodiment, an active noise cancellation (ANC) system is provided with at least one loudspeaker to project anti-noise sound within a passenger cabin of a vehicle in response to receiving an anti-noise signal and at least one microphone to provide an error signal indicative of noise and the anti-noise sound within the passenger cabin. An adaptive filter controller is programmed to filter the error signal to obtain a noise reduction ratio, and to adjust a step size parameter based on a comparison of the noise reduction ratio to a noise threshold. A controllable filter generates the anti-noise signal based on the adjusted step size parameter.

In another embodiment, an active noise cancellation (ANC) system is provided with at least one loudspeaker to project anti-noise sound within a passenger cabin of a vehicle in response to receiving an anti-noise signal. A controller is configured to: filter an error signal indicative of noise and the anti-noise sound within the passenger cabin to obtain a noise reduction ratio, adjust a step size parameter and a leakage parameter based on a comparison of the noise reduction ratio to a noise threshold, and generate the anti-noise signal based on the adjusted step size parameter and the adjusted leakage parameter.

In yet another embodiment a method is provided for controlling stability in an active noise cancellation (ANC) system. An error signal is received from a microphone that is indicative of noise and anti-noise sound within a passenger cabin. The error signal is filtered to obtain a noise reduction ratio. An occurrence of noise boosting is detected based on a comparison of the noise reduction ratio to a noise threshold. A step size parameter is decreased in response to detection of noise boosting. An anti-noise signal, to be radiated from a loudspeaker within the passenger cabin as the anti-noise sound, is generated based on the decreased step size parameter.

DETAILED DESCRIPTION

With reference toFIG.1, a road noise cancellation (RNC) system is illustrated in accordance with one or more embodiments and generally represented by numeral100. The RNC system100is depicted within a vehicle102having one or more vibration sensors104. The vibration sensors104are disposed throughout the vehicle102to monitor the vibratory behavior of the vehicle's suspension, subframe, as well as other axle and chassis components. The RNC system100may be integrated with a broadband adaptive feed-forward active noise cancellation (ANC) system106that generates anti-noise by adaptively filtering the signals from the vibration sensors104using one or more physical microphones108. The ANC system106evaluates the signals and adaptively adjusts one or more LMS adaptation parameters, such as step size and leakage, based on instability detection to limit or eliminate noise boosting in the affected frequency ranges. The anti-noise signal may then be played through one or more loudspeakers110to become sound. S(z) represents a transfer function between a single loudspeaker110and a single microphone108.

WhileFIG.1shows a single vibration sensor104, microphone108, and loudspeaker110for simplicity purposes only, it should be noted that typical RNC systems use multiple vibration sensors104(e.g., ten or more), microphones108(e.g., four to six), and loudspeakers110(e.g., four to eight). The ANC system106may also include one or more virtual microphones112,114that are used for adapting anti-noise signal(s) that are optimized for the occupants in the vehicle102, according to one or more embodiments.

The vibration sensors104may include, but are not limited to, accelerometers, force gauges, geophones, linear variable differential transformers, strain gauges, and load cells. Accelerometers, for example, are devices whose output signal amplitude is proportional to acceleration. A wide variety of accelerometers are available for use in RNC systems. These include accelerometers that are sensitive to vibration in one, two and three typically orthogonal directions. These multi-axis accelerometers typically have a separate electrical output (or channel) for vibration sensed in their X-direction, Y-direction and Z-direction. Single-axis and multi-axis accelerometers, therefore, may be used as vibration sensors104to detect the magnitude and phase of acceleration and may also be used to sense orientation, motion, and vibration.

Noise and vibration that originates from a wheel116moving on a road surface118may be sensed by one or more of the vibration sensors104mechanically coupled to a suspension device119or a chassis component of the vehicle102. The vibration sensor104may output a noise signal X(n), which is a vibration signal that represents the detected road-induced vibration. It should be noted that multiple vibration sensors are possible, and their signals may be used separately, or may be combined. In certain embodiments, a microphone may be used in place of a vibration sensor to output the noise signal X(n) indicative of noise generated from the interaction of the wheel116and the road surface118. The noise signal X(n) may be filtered with a modeled transfer characteristic Ŝ(z), which estimates the secondary path (i.e., the transfer function between an anti-noise loudspeaker110and a physical microphone108), by a secondary path filter120.

Road noise that originates from the interaction of the wheel116and the road surface118is also transferred, mechanically and/or acoustically, into the passenger cabin and is received by the one or more microphones108inside the vehicle102. The one or more microphones108may, for example, be located in a headliner of the vehicle102, or in some other suitable location to sense the acoustic noise field heard by occupants inside the vehicle102, such as an occupant sitting on a rear seat125. The road noise originating from the interaction of the road surface118and the wheel116is transferred to the microphone108according to a transfer characteristic P(z), which represents the primary path (i.e., the transfer function between an actual noise source and a physical microphone).

The microphone108may output an error signal e(n) representing the sound present in the cabin of the vehicle102as detected by the microphone108, including noise and anti-noise. In the RNC system100, an adaptive transfer characteristic W(z) of a controllable filter126may be controlled by adaptive filter controller128, which may operate according to a known least mean square (LMS) algorithm based on the error signal e(n) and the noise signal X(n) filtered with the modeled transfer characteristic Ŝ(z) by the secondary path filter120. The controllable filter126is often referred to as a W-filter. An anti-noise signal Y(n) may be generated by the controllable filter or filters126and the vibration signal, or a combination of vibration signals X(n). The anti-noise signal Y(n) ideally has a waveform such that when played through the loudspeaker110, anti-noise is generated near the occupants' ears and the microphone108, that is substantially opposite in phase and identical in magnitude to that of the road noise audible to the occupants of the vehicle cabin. The anti-noise from the loudspeaker110may combine with road noise in the vehicle cabin near the microphone108resulting in a reduction of road noise-induced sound pressure levels (SPL) at this location. In certain embodiments, the RNC system100may receive sensor signals from other acoustic sensors in the passenger cabin, such as an acoustic energy sensor, an acoustic intensity sensor, or an acoustic particle velocity or acceleration sensor to generate error signal e(n).

The simplified RNC system schematic depicted inFIG.1shows one secondary path, represented by S(z), between the loudspeaker110and the microphone108. As previously mentioned, RNC systems typically have multiple loudspeakers, microphones and vibration sensors. Accordingly, a six-speaker, six-microphone RNC system will have thirty-six total secondary paths (i.e., 6×6). Correspondingly, the six-speaker, six-microphone RNC system may likewise have thirty-six Ŝ(z) litters (i.e., secondary path filters120), which estimate the transfer function for each secondary path. As shown inFIG.1, an RNC system will also have one W(z) filter (i.e., controllable filter126) between each noise signal X(n) from a vibration sensor (i.e., accelerometer)104and each loudspeaker110. Accordingly, a twelve-accelerometer signal, six-speaker RNC system may have seventy-two W(z) filters. The relationship between the number of accelerometer signals, loudspeakers, and W(z) filters is illustrated inFIG.2.

FIG.2is a sample schematic diagram demonstrating relevant portions of an RNC system200scaled to include R accelerometer signals [X1(n), X2(n), . . . XR(n)] from accelerometers204and L loudspeaker signals [Y1(n), Y2(n), . . . YL(n)] from loudspeakers210. Accordingly, the RNC system200may include R*L controllable filters (or W-filters)226between each of the accelerometer signals and each of the loudspeakers. As an example, an RNC system having twelve accelerometer outputs (i.e., R=12) may employ six dual-axis accelerometers or four triaxial accelerometers. In the same example, a vehicle having six loudspeakers (i.e., L=6) for reproducing anti-noise, therefore, may use seventy-two W-filters in total. At each of the L loudspeakers, R W-filter outputs are summed to produce the loudspeaker's anti-noise signal Y(n). Each of the L loudspeakers may include an amplifier (not shown). In one or more embodiments, the R accelerometer signals filtered by the R W-filters are summed to create an electrical anti-noise signal y(n), which is fed to the amplifier to generate an amplified anti-noise signal Y(n) that is sent to a loudspeaker.

The ANC system106illustrated inFIG.1may also include an engine order cancellation (EOC) system. As mentioned above, EOC technology uses a non-acoustic signal such as an engine speed signal representative of the engine crankshaft rotational speed as a reference in order to generate sound that is opposite in phase to the engine noise audible in the vehicle interior. EOC systems may utilize a narrowband feed-forward ANC framework to generate anti-noise using an engine speed signal to guide the generation of an engine order signal identical in frequency to the engine order to be cancelled, and adaptively filtering it to create an anti-noise signal. After being transmitted via a secondary path from an anti-noise source to a listening position or physical microphone, the anti-noise ideally has the same amplitude, but opposite phase, as the combined sound generated by the engine and exhaust pipes after being filtered by the primary paths that extend from the engine to the listening position and from the exhaust pipe outlet to the listening position or physical or virtual microphone position. Thus, at the place where a physical microphone resides in the vehicle cabin (i.e., most likely at or close to the listening position), the superposition of engine order noise and anti-noise would ideally become zero so that acoustic error signal received by the physical microphone would only record sound other than the (ideally cancelled) engine order or orders generated by the engine and exhaust.

Commonly, a non-acoustic sensor, for example an engine speed sensor, is used as a reference. Engine speed sensors may be, for example, Hall Effect sensors which are placed adjacent to a spinning steel disk. Other detection principles can be employed, such as optical sensors or inductive sensors. The signal from the engine speed sensor can be used as a guiding signal for generating an arbitrary number of reference engine order signals corresponding to each of the engine orders. The reference engine orders form the basis for noise cancelling signals generated by the one or more narrowband adaptive feed-forward LMS blocks that form the EOC system.

FIG.3is a schematic block diagram illustrating an example of an ANC system306, including both an RNC system300and an EOC system340. Similar to RNC system100, the RNC system300may include a vibration sensor304, physical microphone308, w-filter326, adaptive filter controller328, secondary path filter320, and loudspeaker310, consistent with operation of the vibration sensor104, physical microphone108, w-filter126, adaptive filter controller128, secondary path filter120, and loudspeaker110, respectively, discussed above.

The EOC system340may include an engine speed sensor342, which may provide an engine speed signal344(e.g., a square-wave signal) indicative of rotation of an engine crank shaft or other rotating shaft such as the drive shaft, half shafts or other shafts whose rotational rate is aligned with vibrations coupled to vehicle components that lead to noise in the passenger cabin. In some embodiments, the engine speed signal344may be obtained from a vehicle network bus (not shown). As the radiated engine orders are directly proportional to the crank shaft RPM, the engine speed signal344is representative of the frequencies produced by the engine and exhaust system. Thus, the signal from the engine speed sensor342may be used to generate reference engine order signals corresponding to each of the engine orders for the vehicle. Accordingly, the engine speed signal344may be used in conjunction with a lookup table346of Engine Speed (RPM) vs. Engine Order Frequency, which provides a list of engine orders radiated at each engine speed. The frequency generator348may take as an input the Engine Speed (RPM) and generate a sine wave for each order based on this lookup table346.

The frequency of a given engine order at the sensed Engine Speed (RPM), as retrieved from the lookup table346, may be supplied to a frequency generator348, thereby generating a sine wave at the given frequency. This sine wave represents a noise signal X(n) indicative of engine order noise for a given engine order. Similar to the RNC system300, this noise signal X(n) from the frequency generator348may be sent to an adaptive controllable filter326, or W-filter, which provides a corresponding anti-noise signal Y(n) to the loudspeaker310. As shown, various components of this narrow-band, EOC system340may be identical to the broadband RNC system300, including the physical microphone308, adaptive filter controller328and secondary path filter320. The anti-noise signal Y(n), broadcast by the loudspeaker310generates anti-noise that is substantially out of phase but identical in magnitude to the actual engine order noise at the location of a listener's ear, which may be in close proximity to a physical microphone308, thereby reducing the sound amplitude of the engine order. Because engine order noise is narrow band, the error signal e(n) may be filtered by a bandpass filter350prior to passing into the LMS-based adaptive filter controller328. In an embodiment, proper operation of the LMS adaptive filter controller328is achieved when the noise signal X(n) output by the frequency generator348is bandpass filtered using the same bandpass filter parameters.

In order to simultaneously reduce the amplitude of multiple engine orders, the EOC system340may include multiple frequency generators348for generating a noise signal X(n) for each engine order based on the Engine Speed (RPM) signal344. As an example,FIG.3shows a two order EOC system having two such frequency generators for generating a unique noise signal (e.g., X1(n), X2(n), etc.) for each engine order based on engine speed. Because the frequency of the two engine orders differ, the bandpass filters350(labeled BPF and BPF2) have different high- and low-pass filter corner frequencies. The number of frequency generators and corresponding noise-cancellation components will vary based on the number of engine orders to be cancelled for a particular engine of the vehicle. As the two-order EOC system340is combined with the RNC system300to form the ANC system306, the anti-noise signals Y(n) output from the three controllable filters326are summed and sent to the loudspeaker310as a loudspeaker signal S(n). Similarly, the error signal e(n) from the physical microphone308may be sent to the three LMS adaptive filter controllers328.

Noise cancellation performance degradation, noise gain, or actual instability may result if the modeled transfer characteristic Ŝ(z), representing an estimate of the secondary path, that is stored in the ANC system does not match the actual secondary path of the system. As previously discussed, the secondary path is the transfer function between an anti-noise generating loudspeaker and a physical microphone. Accordingly, it essentially characterizes how the electrical anti-noise signal Y(n) becomes sound that is radiated from the loudspeaker, travels through the car cabin to the physical microphone, and becomes part of the microphone output or error signal e(n) in the ANC system. The actual secondary path S(z) may deviate from the stored secondary path model Ŝ(z), which is typically measured on a “golden system” by trained engineers, when a vehicle becomes substantially different from the reference vehicle or system in terms of geometry, passenger count, luggage loading, or the like.

FIG.4is a schematic block diagram of a vehicle-based ANC system406showing many of the key ANC system parameters that may be used to adapt or adjust w-filter parameters, such as step size and/or leakage, in response to detecting instability or boosting in one or more error microphone signals to limit or eliminate noise boosting in the affected frequency ranges. For ease of explanation, the ANC system406illustrated inFIG.4is shown with components and features of an RNC system400and an EOC system440. Accordingly, the ANC system406is a schematic representation of an RNC and/or EOC system, such as those described in connection withFIGS.1-3, featuring additional system components of the ANC system406including an additional signal processing block460integrated into the error microphone signal path. Similar components may be numbered using a similar convention.

For instance, similar to ANC system106, the ANC system406may include an accelerometer or vibration sensor404, a physical microphone408, a w-filter426, an adaptive filter controller428, a secondary path filter420, and a loudspeaker410, consistent with operation of the vibration sensor104, the physical microphone108, the w-filter126, the adaptive filter controller128, the secondary path filter120, and the loudspeaker110, respectively, discussed above.FIG.4also shows the primary path P(z), the secondary path S(z), fast Fourier transform (FFT) blocks for converting signals to the frequency domain, and an inverse FFT (IFFT) block for converting signals to the time domain, in block form for illustrative purposes.

The ANC system406estimates a noise reduction ratio NRR(f) in the frequency domain in signal processing block460. The ANC system406filters the anti-noise signal y(n) by an estimated secondary path Ŝ(z), where n is the sample number, to generate an estimated anti-noise signal yŝ=(n), as shown in Equation 1:

The ANC system406converts the estimated anti-noise signal yŝ(n) to the frequency domain using an FFT to provide Yŝ(f). The ANC system406then combines Yŝ(f) with the error signal E(f) at block462to provide an estimated noise at each microphone {circumflex over (D)}(f), as shown in Equation 2:

The ANC system406then calculates the noise reduction ratio NRR(f) by subtracting the estimated error signal E(f) from the estimated noise at each microphone {circumflex over (D)}(f) at block464, as shown in Equation 3:

At block466, the ANC system406evaluates the noise reduction ratio NRR(f) to determine if the system is boosting the noise level, rather than decreasing it. The ANC system406may detect noise boosting, or instability by comparing NRR(f) to a noise threshold. At block468, the ANC system406adaptively adjusts one or more adaptive filter controller parameters, such as step size and leakage, based on the instability detection. There are three different modes for adaptively adjusting the adaptive filter controller parameters: 1) Normal Mode, where the parameter remains unchanged; 2) Attack Mode, where the parameter is decreased to maintain system stability; and 3) Release Mode, where the parameter is increased to maintain system performance. Then the adaptive filter controller428controls the w-filter426adaptation based on the adjusted w-filter parameters.

FIG.5is a flowchart depicting a method500for adjusting adaptive filter controller parameters based on ANC system instability, in accordance with one or more embodiments of the present disclosure. Various steps of the disclosed method may be carried out by the adaptive filter controller428either alone, or in combination with other components of the ANC system406.

At step502, the ANC system406compares the frequency dependent noise reduction ratio NRR(f) to a frequency dependent noise threshold value to determine if the system is boosting noise at any frequency. In one or more embodiments, the noise threshold is equal to one, and values of NRR(f) that are less than one, indicate an undesirable increase in the noise level, which is noise boosting. If the ANC system406determines that NRR(f) is less than or equal to the noise threshold, which indicates noise boosting at a frequency or in a frequency range, the ANC system406proceeds to step504and adaptively adjusts an automatic tuning step size parameter μauto(f) and/or an automatic tuning leakage parameter γauto(f) according to the Attack Mode. These parameters μauto(f) and γauto(f), are the parameters that the algorithm automatically adjusts to reduce the noise boosting at a frequency or frequencies. Then at step506, the adaptive filter controller428controls the w-filter426based on the Attack Mode adjusted w-filter adaptation parameters μauto(f) and γauto(f), which are assigned back to μ(f) and γ(f) for the adaptive filter controller428to use. In an embodiment, the NRR(f) has the same value at all frequencies.

If the ANC system406determines that the frequency dependent noise reduction ratio NRR(f) is greater than the predetermined frequency dependent noise threshold value at step502, which indicates that there is no noise boosting, it proceeds to step508and compares an automatic tuning step size parameter μauto(f) to the minimum step size parameter μmin, and/or an automatic tuning leakage parameter γauto(f) to the minimum leakage parameter γmin. The minimum step size parameter μminand the minimum leakage parameter γmindefine the minimum values of the automatic tuning step size parameter μauto(f) and the automatic tuning leakage parameter γauto(f).

If μauto(f) is less than μmin(f), or if γauto(f) is less than γmin(f), the ANC system406proceeds to step510and adaptively adjusts a step size parameter and/or a leakage parameter according to the Release Mode. Then at step506, the adaptive filter controller428controls the w-filter426based on the Release Mode adjusted adaptation parameters γauto(f) and μauto(f) which are assigned back to γ(f) and μ(f) for the adaptive filter controller428to use in updating the w-filter426. In an embodiment, the noise threshold has the same value at every frequency. In an embodiment, μmin(f) equals the predetermined, original value of μ(f) that is stored in system memory; and used when the ANC system406was powered on. In an embodiment, γmin(f) equals the predetermined, original value of μ(f) that is stored in system memory; and used when the ANC system406was powered on.

In one embodiment, the ANC system406performs the method500by adaptively adjusting the step size parameter μ(f), but not the leakage parameter γ(f). The adaptive filter controller408calculates an updated W-filter parameter (W(f, n+1)) based on a W-filter parameter at a frequency value (W(f, n)), the leakage parameter (γ(f)), the filtered reference accelerometer signal (Fx(f, n)), the estimated error signal (E(f, n)), and the updated step size parameter μ(f), which is based on an automatic tuning step size parameter, according to Equation (4):

In this step size parameter adjustment embodiment, the ANC system406calculates the new automatic tuning step size parameter (μauto(f)) at step504(Attack Mode), based on the current step size (μ(f)) and an automatic step size tuning factor (δμ) according to Equation 5. In an embodiment, a predetermined value for δμis 0.99.

At step510(Release Mode), in this step size parameter adjustment embodiment, the ANC system406calculates the automatic tuning step size parameter (μauto(f)), based on the step size (μ(f)) and the automatic step size tuning factor (δμ) according to Equation 6:

At step512(Normal Mode), in this step size parameter adjustment embodiment, the ANC system406calculates the automatic tuning step size parameter (μauto(f)), based on the previous step size parameter (μ(f)) according to Equation 7:

In another embodiment, the ANC system406performs the method500by adaptively adjusting the leakage parameter γ(f), but not the step size parameter μ(f). The ANC system406calculates the updated W-filter parameter (W(f, n+1)) based on a W-filter parameter at a frequency value (W(f, n)), the step size parameter (μ(f)), the filtered reference accelerometer signal (Fx(f, n)), the estimated error signal (E(f, n)), and the updated tuning leakage parameter γ(f), which is based on an automatic turning leakage parameter, according to Equation (8):

At step510(Release Mode), in this leakage parameter adjustment embodiment, the ANC system406calculates the automatic tuning leakage parameter (γauto(f)), based on the leakage parameter (γ(f)) and the automatic leakage tuning factor (δγ) according to Equation 10:

At step512(Normal Mode), in this leakage parameter adjustment embodiment, the ANC system406calculates the automatic tuning leakage parameter (γauto(f)), based on the previous leakage parameter (γ(f)) according to Equation 11:

In another embodiment, the ANC system406performs the method500by adaptively adjusting the step size parameter μ(f) and the leakage parameter γ(f). The ANC system406calculates an updated W-filter parameter (W(f, n+1)) based on a W-filter parameter at a frequency value (W(f, n)), the updated leakage γ(f) based on the automatic tuning leakage parameter, the updated step size μ(f) based on the automatic tuning step size parameter, the filtered reference accelerometer signal (Fx(f, n)), and the estimated error signal (E(f, n)) according to Equation 12:

In an embodiment, δγand/or δμare predetermined values stored in a look up table. In another embodiment, δγand/or δμare values determined by nominal values stored in a look up table scaled by the NRR(f) value in a predetermined way. In another embodiment, δμis scaled first, and once the value reaches a predetermined noise threshold, then the value of δγis scaled from unity in a predetermined way. In an embodiment, δγand/or δμare frequency dependent. Other methods to scale these two variables are possible. Other embodiments include a simplified version of the method500ofFIG.5that includes the Attack Mode alone, or the Attack Mode with one of the Release Mode and the Normal Mode.

FIGS.6-8are graphs illustrating the impact of the method500when the ANC system406adjusts the step size parameter and/or the leakage parameter, as compared to an existing ANC system that does not adjust the step size parameter and/or the leakage parameter.

FIG.6is a graph600that includes live curves602,604,606,608,610that illustrate the frequency response of sound pressure level measured within the vehicle at the location of the physical microphone408in decibels (dB).FIG.6Ais an enlarged view of a portion of the graph600between 480-560 Hz. The graph600includes a first curve602that illustrates sound measured when the ANC system406is off. The second curve604illustrates sound measured in the vehicle using an existing ANC system (not shown) that does not adjust the step size parameter nor the leakage parameter. The third curve606illustrates sound measured when the ANC system406is active and adjusting the step size parameter, but not the leakage parameter. The fourth curve608illustrates sound measured when the ANC system406is active and adjusting the leakage parameter, but not the step size parameter. The fifth curve610illustrates sound measured when the ANC system406is active and adjusting the step size parameter and the leakage parameter. The noise boosting shown in curve604is substantially lowered, or turned into noise cancellation, in curves606,608and610by employing an ANC system such as406that automatically adjusts adaptation parameters.

FIG.7is a graph700that includes three curves704,706, and710that illustrate the frequency response of sound pressure level within the vehicle at the location of the physical microphone408in decibels (dB). The first curve704illustrates a constant (not adjusting) step size parameter of an existing ANC system (not shown). The second curve706illustrates the automatic adjustment of the step size parameter according to the method500. The third curve710illustrates the automatic adjustment of the step size parameter and the leakage parameter according to the method500.

FIG.8is a graph800including three curves804,808,810illustrating the frequency response of sound pressure level within the vehicle at the location of the physical microphone408. The first curve804illustrates a constant (not adjusting) leakage parameter of an existing ANC system (not shown). The second curve808illustrates the automatic adjustment of the leakage parameter according to the method500. The third curve810illustrates the automatic adjustment of the step size parameter and the leakage parameter according to the method500.

Referring toFIGS.6-6A, the third curve606, the fourth curve608, and the fifth curve610, illustrate how the ANC system406switches between the Attack Mode, the Release Mode, and Normal Mode to limit noise boosting. The difference between the ANC on curves606,608,610and the ANC off curve602is indicative of the performance of each approach, where a small difference indicates higher performance. The third curve606(step size parameter adjustment) and the fifth curve610(step size and leakage parameter adjustment) overlap in the illustrated embodiment, which indicates that both approaches have similar performance within the illustrated frequency range. The second curve604, which illustrates the performance of an existing ANC system, illustrates noise boosting at 480-540 Hz.

The Attack Mode is illustrated between 520-530 Hz where the three ANC on curves606,608,610are all greater than the ANC off curve602. The ANC system406decreases the step size parameter and the leakage parameter in the Attack Mode to maintain stability and limit noise boosting. The difference between the fourth curve608(leakage parameter adjustment) and the first curve602(ANC off) is greater than the difference between the third curve606(step size parameter adjustment) and the first curve602(ANC off), which indicates that the step size parameter adjustment performs better than leakage parameter adjustment in this example in the Attack Mode.

The Release Mode is illustrated between 480-500 Hz where the three ANC on curves606,608,610are all less than the ANC off curve602. The ANC system406increases the step size parameter and the leakage parameter in the Release Mode to maintain system performance. The difference between the fourth curve608(leakage parameter adjustment) and the first curve602(ANC off) is less than the difference between the third curve606(step size parameter adjustment) and the first curve602(ANC off), which indicates that the leakage parameter adjustment performs better than the step size parameter adjustment in the Release Mode. This is Release Mode because using the lowered step size, or leakage, or both step size and leakage in the Attack Mode, reduces or eliminates noise boosting, which results in noise cancellation. This means that the value of NRR is now greater than the noise threshold, and so the step size and/or leakage values are both incremented back upward.

The Normal Mode is illustrated between 400-460 Hz where the three ANC on curves606,608,610are all approximately equal to the ANC off curve602. The ANC system406maintains δγand/or δμat unity, to return the step size parameter and the leakage parameter at their nominal values in the Normal Mode to maintain system performance.

Referring toFIG.7, the Attack Mode is illustrated between 520-530 Hz where the step size parameter is decreased and the step size curve706rises, as referenced by numeral712, to maintain stability and limit noise boosting. The Release Mode is illustrated between 480-500 Hz where the step size parameter is increased and the step size curve706falls as referenced by numeral714, to maintain system performance. The Normal Mode is illustrated between 400-460 Hz where the step size parameter does not change the step size curve706is generally flat as referenced by numeral716.

With reference toFIG.8, the Attack Mode is illustrated between 520-530 Hz where the leakage parameter is decreased and the leakage curve808rises as referenced by numeral812, to maintain stability and limit noise boosting. The Release Mode is illustrated between 480-500 Hz where the leakage parameter is increased and the leakage curve808falls as referenced by numeral814, to maintain system performance. The Normal Mode is illustrated between 400-460 Hz where the step size parameter does not change the leakage curve808is generally flat as referenced by numeral816.

FIG.9is a schematic block diagram of a vehicle-based virtual microphone (VM) ANC system906showing adaptive filter block428that contains many of the key ANC system parameters that may be used to adjust w-filter parameters, such as step size and/or leakage to optimize ANC system performance. For ease of explanation, the VM ANC system906illustrated inFIG.9is shown with components and features of an RNC system900and an EOC system940. Accordingly, the VM ANC system906is a schematic representation of an RNC and/or EOC system, such as those described in connection withFIGS.1-4, featuring additional system components of the VM ANC system906including a virtual microphone912and a virtual microphone signal processing block970. Similar components may be numbered using a similar convention.

For instance, similar to ANC system406, the VM ANC system906may include a vibration sensor904, a physical microphone908, a w-filter926, an adaptive filter controller928, a secondary path filter920, a loudspeaker910, and an instability detection and adaptive adjustment signal processing block960consistent with operation of the vibration sensor404, the physical microphone408, the w-filter426, the adaptive filter controller428, the secondary path filter420, the loudspeaker410, and the additional signal processing block460, respectively, discussed above.FIG.9also shows the primary path P(z) and secondary path S(z), as described with respect toFIG.4, in block form for illustrative purposes. In the case of an EOC system940, the vibration sensor904is replaced by an RPM sensor342, lookup table346, and frequency generator348, as described above with reference toFIG.3.

The virtual microphone912represents a microphone located at a virtual microphone location that would similarly sense all the sound at its virtual location, such as an estimate of the anti-noise signal in addition to the disturbance signal dv(n) to be cancelled, which includes road noise, engine, and exhaust noise, plus the anti-noise from the loudspeaker910, and extraneous sounds. The pressure at the virtual microphone locations is estimated from the pressure at the physical microphone locations to form an estimated error signal êv(n).

The VM ANC system906estimates the disturbance noise to be cancelled êp(n) at the physical microphone location at block948. The VM ANC system906subtracts an estimate of the anti-noise at the physical microphone location ŷp(n) from the physical error signal ep(n) to estimate the disturbance noise at the physical microphone location êp(n). The VM ANC system906then estimates the disturbance noise to be cancelled at the virtual microphone location {circumflex over (d)}v(n) at block950by convolving the estimated disturbance noise at the physical microphone location {circumflex over (d)}p(n) with the transfer function950between the physical and virtual microphone location Ŝpv(z). At block954, the VM ANC system906estimates the virtual microphone error signal êv(n) that would be present at the virtual microphone by subtracting an estimate of the anti-noise at this location ŷv(n) from the estimated disturbance noise to be cancelled at the virtual microphone location {circumflex over (d)}v(n).

Although the ANC system is described with reference to a vehicle, the techniques described herein are applicable to non-vehicle applications. For example, a room may have fixed seats which define a listening position at which to quiet a disturbing sound using reference sensors, error sensors, loudspeakers and an LMS adaptive system. Note that the disturbance noise to be cancelled is likely of a different type, such as HVAC noise, or noise from adjacent rooms or spaces. Further, a room may have occupants whose position varies with time, and the seat sensors or head tracking techniques described herein must then be relied upon to determine the position of the listener or listeners so that the 3-dimensional location of the virtual microphones can be selected.

AlthoughFIGS.1,3,4, and9show LMS-based adaptive filter controllers128,328,428, and928, respectively, other methods and devices to adapt or create optimal controllable W-filters126,326,426, and926are possible. For example, in one or more embodiments, neural networks may be employed to create and optimize W-filters in place of the LMS adaptive filter controllers. In other embodiments, machine learning or artificial intelligence may be used to create optimal W-filters in place of the LMS adaptive filter controllers.

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. In general, a processor (such as a microprocessor) receives instructions, for example from a memory, a computer-readable medium, or the like, and executes the instructions. A 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 semi-conductor storage device, or any suitable combination thereof.

For example, the steps recited in any method or process claims may be executed in any order and are not limited to the specific order presented in the claims. Equations may be implemented with a filter to minimize effects of signal noises. Additionally, the components and/or elements recited in any apparatus claims may be assembled or otherwise operationally configured in a variety of permutations and are accordingly not limited to the specific configuration recited in the claims.

Further, functionally equivalent processing steps can be undertaken in either the time or frequency domain. Accordingly, though not explicitly stated for each signal processing block in the figures, the signal processing may occur in either the time domain, the frequency domain, or a combination thereof. Moreover, though various processing steps are explained in the typical terms of digital signal processing, equivalent steps may be performed using analog signal processing without departing from the scope of the present disclosure