System and method for adjusting active noise cancellation tuning parameters

Abstract

In at least one embodiment, an active noise cancellation (ANC) system is provided. The system includes at least one audio signal source, at least one speaker, at least one microphone, and at least one controller. The at least one controller is programmed to receive the first error signal and the second error signal and to provide an estimated impulse response based at least on the first error signal and the second error signal. The at least one controller is programmed to select a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate an anti-noise signal, and selecting a first plurality of pre-stored tuning parameters from a plurality of pre-stored sets of tuning parameters based on the first pre-stored impulse response.

Description

Technical Field

[0001] Various aspects disclosed herein generally relate to systems and methods for adjusting active noise cancellation (ANC) tuning parameters. These and other aspects will be discussed in greater detail herein. Background Art

[0002] Active noise cancellation (ANC) systems attenuate unwanted noise using a feed-forward structure and a feedback structure to adaptively remove unwanted noise within a listening environment, such as within a vehicle cabin. ANC systems cancel or reduce unwanted noise by generating cancellation sound waves to destructively interfere with unwanted audible noise. ANC systems installed on vehicles that minimize noise within a vehicle cabin include road noise cancellation (RNC) systems that minimize unwanted road noise and engine order cancellation (EOC) systems that minimize unwanted engine noise within a vehicle cabin.

[0003] Typically, ANC systems use digital signal processing and digital filtering techniques. For example, a noise sensor (such as a microphone, accelerometer, or revolutions per minute (RPM) sensor) outputs an electrical reference signal that represents the interfering noise signal generated by the noise source. This reference signal is fed to an adaptive filter. The filtered reference signal is then supplied to a sound actuator (such as a speaker), which generates a compensating sound field that, ideally, may be opposite in phase to the noise signal but has almost the same amplitude. This compensating sound field eliminates or reduces the noise signal in the listening environment.

[0004] An RNC system is a specific ANC system installed on a vehicle to minimize unwanted road noise in the vehicle cabin. An RNC system uses a vibration sensor to sense road-induced vibrations generated by the tire and road interface, which cause unwanted audible road noise. Eliminating this road noise makes the ride more pleasant for vehicle passengers, and it enables vehicle manufacturers to use lightweight materials, thereby reducing energy consumption and reducing emissions. An EOC system is a specific ANC system installed on a vehicle to minimize unwanted engine noise in the vehicle cabin. An EOC system uses a non-acoustic sensor (such as an engine speed sensor) to generate a signal representing the engine crankshaft speed in revolutions per minute (RPM) as a reference. RNC systems are generally designed to eliminate broadband signals, while EOC systems are designed and optimized to eliminate narrowband signals, such as individual engine orders. An ANC system in a vehicle can provide both RNC technology and EOC technology.

[0005] The residual noise signal can be measured using a microphone to provide an error signal to an adaptive unit of an adaptive filter, wherein the filter coefficients (also referred to as parameters) of the adaptive filter are modified so as to generate a norm of the error signal. The adaptive unit of the adaptive filter can use a digital signal processing method, such as least mean square (LMS), filter-x least mean square (FxLMS), improved filter-x least mean square (MFxLMS) or other techniques to reduce the error signal.

[0006] When applying many variants of the LMS algorithm, such as the FxLMS algorithm and the MFxLMS algorithm, an estimated model representing the sound transmission path from the loudspeaker to the microphone is used. This sound transmission path is often referred to as the secondary path of the ANC system. In contrast, the sound transmission path from the noise source to the microphone is often referred to as the primary path of the ANC system. The secondary path transfer function represented in the time domain is often referred to as an impulse response or IR.

[0007] The way the estimated secondary path transfer function matches the actual secondary path transfer function affects the stability of the ANC system. A changing secondary path transfer function can have a negative impact on the ANC system because the actual secondary path transfer function no longer matches the "a priori" estimated secondary path transfer function used in the FxLMS or MFxLMS algorithm when it changes. The estimated model of the secondary path is typically measured once and approximated during the production tuning process, and during the production tuning process, the secondary path transfer function is estimated for a "nominal" sound scene (i.e., one occupant, windows closed, seats in default positions). However, the secondary path can change for a variety of different reasons, such as changes in the number of occupants, seat position, and items in the listening environment. These differences between the stored estimated secondary path and the actual secondary path can result in insufficient performance of the noise cancellation system, or even cause the adaptive filter to diverge, resulting in undesirable sounds in the listening environment, commonly referred to as noise enhancement. Summary of the invention

[0008] In at least one embodiment, an active noise cancellation (ANC) system is provided. The system includes at least one audio signal source, at least one speaker, at least one microphone, and at least one controller. The at least one speaker projects an anti-noise sound in a cabin of a vehicle in response to receiving an anti-noise signal. The at least one microphone provides a first error signal indicating noise in the cabin, the audio signal, and the anti-noise sound, and a second error signal indicating an estimated anti-noise signal. The at least one controller is programmed to receive the first error signal and the second error signal, and to provide an estimated impulse response based at least on the first error signal and the second error signal. The at least one controller is programmed to select a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate the anti-noise signal, and to select a first plurality of pre-stored tuning parameters from a plurality of pre-stored sets of tuning parameters based on the first pre-stored impulse response. The at least one controller is programmed to tune the adaptive filter using the first plurality of pre-stored tuning parameters to generate the anti-noise signal.

[0009] In at least another embodiment, an active noise cancellation (ANC) system is provided. The system includes at least one audio signal source, at least one speaker, at least one microphone, and at least one controller. The at least one speaker projects an anti-noise sound in a cabin of a vehicle in response to receiving an anti-noise signal. The at least one microphone provides a first error signal indicating noise in the cabin, the audio signal, and the anti-noise sound, and a second error signal indicating an estimated anti-noise signal. The at least one controller is programmed to receive the first error signal and the second error signal, and to provide an estimated impulse response based at least on the first error signal and the second error signal. The at least one controller is also programmed to select a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate the anti-noise signal, and to select a first plurality of tuning parameters based at least on one of a constant offset applied to one or more baseline tuning parameters and a variable offset value applied to the one or more baseline tuning parameters. The at least one controller is also programmed to tune the adaptive filter using the first plurality of tuning parameters to generate the anti-noise signal.

[0010] In at least another embodiment, a method for performing active noise cancellation (ANC) is provided. The method includes generating an audio signal for transmission in a cabin of a vehicle using at least one audio signal source, and transmitting an anti-noise sound in the cabin of the vehicle via at least one speaker in response to receiving an anti-noise signal. The method also includes providing a first error signal indicating noise in the cabin, the audio signal, and the anti-noise sound and a second error signal indicating an estimated anti-noise signal, and receiving the first error signal and the second error signal by at least one controller. The method also includes providing an estimated impulse response based on the first error signal and the second error signal, and selecting a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate the anti-noise signal. The method also includes selecting a first plurality of pre-stored tuning parameters from a plurality of pre-stored sets of tuning parameters based on the first pre-stored impulse response, and tuning the adaptive filter using the first plurality of pre-stored tuning parameters to generate the anti-noise signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Embodiments of the present disclosure are particularly pointed out in the appended claims. However, other features of the various embodiments will become more apparent and will be best understood by referring to the following detailed description in conjunction with the accompanying drawings, in which:

[0012] Figure 1 An example of an FxLMS active noise cancellation (ANC) system is generally described;

[0013] Figure 2 An example describing the MFxLMS ANC system in general;

[0014] Figure 3 A system for performing secondary path switching using impulse response (IR) fingerprinting is generally described;

[0015] Figure 4 generally depicts a state diagram (or method) for performing secondary path switching using IR fingerprinting according to one embodiment;

[0016] Figure 5 A first system for adjusting ANC tuning parameters according to one embodiment is generally described;

[0017] Figure 6 generally describing a second system for adjusting ANC tuning parameters according to one embodiment;

[0018] Figure 7generally depicts a third system for adjusting ANC tuning parameters according to one embodiment;

[0019] Figure 8 depicts a method of executing a second system for adjusting ANC tuning parameters according to one embodiment; and

[0020] Fig. 9 A method of executing a third system for adjusting ANC tuning parameters is depicted according to one embodiment. DETAILED DESCRIPTION

[0021] As required, detailed embodiments of the present invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely illustrative of the present invention that may be embodied in various alternative forms. The drawings are not necessarily drawn to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, the specific structural and functional details disclosed herein should not be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to employ the present invention in various ways.

[0022] Adaptive feed-forward ANC algorithms such as engine order cancellation (EOC) and road noise cancellation (RNC) include a static estimate of the secondary path impulse response (IR) between the speaker and the error microphone. In one example, the IR has been measured in one or more predetermined pre-production vehicles to create an IR estimate that is stored in each of the large number of production vehicles produced. This IR and the fact that the cabin configuration of any particular vehicle will change during operation can result in a mismatch between the actual secondary path IR and the stored secondary path IR. This mismatch can result in a degradation of noise cancellation performance and, in some cases, can also result in undesirable noise enhancement.

[0023] As described herein, the disclosed systems and methods use an estimate of the actual cabin configuration to intelligently update the static secondary path IR to maintain noise cancellation performance. The estimated cabin configuration can be derived using a variety of methods, including generating a test signal and measuring the response through a microphone or any other adaptive model of the cabin acoustics. The estimated IR is processed with an IR matching algorithm such as fingerprint recognition technology to obtain the closest match from a set of pre-measured IRs. The disclosed systems and methods are used to determine whether, when, and in what manner the secondary path IR can be estimated and updated. The various aspects disclosed herein provide, among other things, a variety of techniques for detecting changes in cabin acoustics and safely updating the static secondary path IR to achieve better noise cancellation performance for a variety of cabin acoustic configurations.

[0024] Figure 1An example of an active noise cancellation (ANC) system 100 is generally depicted. The ANC system 100 may be a filtered x least mean square (FxLMS) based ANC system. The system 100 includes a noise source 102 and a primary noise signal d[n] that passes through an airborne or structure-borne transfer path 104 (or primary path) (or primary path 104) having a primary path transfer function P(z). P(z) represents the transfer characteristics of the signal path between the noise source 102 and the error microphone 106. An adaptive filter 108 is a transfer function W(z) with an adaptive unit 110 (or adaptive filter controller 110) that calculates a set of filter coefficients (also referred to as parameters) of the adaptive filter 108. An actual secondary path (or transfer function) 112 is S(z) (or is characterized by the transfer function), downstream of the adaptive filter 108. The transfer function S(z) represents the airborne and electrical signal path between a loudspeaker that radiates the compensation signal and a microphone location in a listening environment. The anti-noise signal y'[n] includes the transfer characteristics of all components downstream of the adaptive filter 108, including, for example, amplifiers, DACs, speakers, acoustic transmission paths, microphones, and ADCs. The estimated secondary path system 114 has a modeled or measured transfer function It represents the actual secondary path transfer function S(z) and is used by the adaptation unit 110 to calculate the filter coefficients of the transfer function W(z) of the adaptive filter 108. The main path 104 and the actual secondary path 112 represent the physical properties of the listening environment. The transfer function W(z) and Implemented in a digital signal processor.

[0025] The noise source 102 provides a signal to the primary path 104, which provides an interfering noise signal d[n] at the error microphone 106. The noise source 102 also provides a reference signal x[n] to the adaptive filter 108, which applies a phase and amplitude offset and outputs a filtered anti-noise signal y[n] to the actual secondary path transfer function 112, which outputs a signal y'[n] that destructively interferes with the primary noise signal d[n] at the error microphone location. The reference signal x[n] may originate from a source related to the primary noise source 102, such as an engine RPM or a microphone or an accelerometer. The measurable residual signal represents the error signal e[n] for the adaptive unit 110. Estimated Secondary Path Transfer Function Used to calculate the updated filter coefficients. This compensates for the decorrelation between the anti-noise signal y[n] and the filtered anti-noise signal y'[n], which is caused by the transfer function that characterizes the secondary path. Secondary path transfer function A reference signal x[n] characterizing the noise source 102 is also received and a filtered reference signal x′[n] is provided to the adaptation unit 110 .

[0026] Estimating the secondary path transfer function The quality of affects the stability of the system 100. Estimating the secondary path transfer function Deviations from the actual secondary path transfer function S(z) affect the convergence and stability behavior of the adaptive unit 110. Unstable behavior or suboptimal noise cancellation may be caused by changes in environmental conditions in the listening environment, which cause changes in the actual secondary path transfer function S(z). For example, when the listening environment is a vehicle cabin, the environmental conditions may change when a window is opened, a seat position is adjusted, or by adding objects or passengers to one or more seats in the listening environment.

[0027] The second topology of the noise cancellation system is shown in the block diagram Figure 2 As shown in FIG. 1 , the second topology is similar to Figure 1 The filter arrangement shown, but includes an additional adaptive filter arrangement in parallel with the secondary path system. Figure 2 is an improved filtered x LMS (MFxLMS) feedforward noise cancellation system 200. A reference signal x[n] is filtered by a first estimated secondary path filter 114 and an adaptive filter 108 having a transfer function W(z). The coefficients of the first estimated secondary path filter 114 are referred to as active filter coefficients. The dynamic system also includes a second adaptive filter 208 that filters the reference signal x[n] using the transfer function W(z) to generate an anti-noise signal y[n]. The anti-noise signal y[n] is filtered by the actual secondary path transfer function S(z) or 112. The signal y'[n] is the audible anti-noise at the error microphone 106 after being filtered by the actual secondary path transfer function S(z) 112. The filtered anti-noise signal y'[n] is combined with the primary noise d[n] filtered by the actual primary path transfer function P(z) 104 at the error microphone.

[0028] In the electrical domain, the noise-rejecting signal y[n] is given by the second secondary path transfer characteristic 214 is filtered and subtracted from the error signal e[n] at adder 216 (or microphone). This produces an estimated noise signal d^_p[n] at the error microphone 106. The estimated noise signal d^_p[n] is combined with the signal filtered by the adaptive filter 108 at adder 218 to generate an internal error signal g[n]. The internal error signal g[n] is the input of the adaptive unit 110.

[0029] In practice, the secondary path estimate IR is estimated only once for a listening environment with optimal conditions. For a vehicle cabin listening environment, this occurs only during the production tuning process, after the vehicle leaves the production facility. Furthermore, the estimated secondary path IR represents the listening environment in a nominal configuration. For example, when the listening environment is a vehicle cabin, the nominal configuration is when the vehicle is parked, not moving, with only one driver, no other passengers, and all windows, doors, sunroof, and trunk are fully closed.

[0030] The secondary path estimation process involves playing a test signal from each speaker to excite the electroacoustic path, followed by a deconvolution step. These secondary path estimates are then maintained for the life of the vehicle. Mismatches between the actual IR and the stored IR occur when the acoustic environment within the listening environment changes during operation, such as when the vehicle is moving with one or more windows partially open and multiple passengers or objects on the seats.

[0031] In a live listening environment, The stored IR of may be different from the actual IR of the secondary path S(z), and this mismatch may eventually lead to degradation of ANC performance or divergence of the W(z) filter, resulting in infinite noise enhancement in the passenger compartment. When it is better matched to S(z), the resulting estimate of d^_p[n] more accurately represents the dominant noise signal present in the listening environment, and the adaptive filter W(z) is more likely to avoid divergence. In addition, when When better matching S(z), more aggressive conditioning methods can be used to improve noise cancellation performance, since the risk of divergence has been reduced.

[0032] The systems and methods disclosed herein can improve the accuracy of stored estimates, thereby replacing the stored estimates in real time on-line in an almost imperceptible manner during system operation after a new secondary path IR is calculated. These methods are respectively applied to Figure 1 and Figure 2 and MFxLMS systems described in , and can be used to calculate without generating test signals In addition, it is also possible to find a unique Understand and determine if, when and how to change Parameters. In addition, the disclosed systems and methods calculate and update the stored estimates in a manner that is barely perceptible to a listener in the listening environment. Any updates made to the transfer function coefficients are inaudible to a listener in the listening environment. The updates are so slight, gradual, or subtle that the listener's senses cannot detect or affect it, making it unnoticed.

[0033] Using the conventional implementation of ANC system, The IR is estimated only once during the production tuning process. Generally speaking, the IR represents S(z) for a nominal cabin scenario (e.g., one driver, windows and doors closed, sunroof closed, etc.) and remains constant thereafter for the life of the vehicle. The IR estimation process performed during the tuning process may involve playing a test signal (e.g., broadband noise or a sine sweep) to excite and characterize the electro-acoustic path, followed by a deconvolution step to determine the IR coefficients.

[0034] In general, once production adjustments are completed (e.g., when the vehicle software is finalized and the vehicle has been sold and is operated by the customer), IR(S(z)) may differ from the estimated These reasons may involve changes in occupancy, temperature, seat position or window position, etc. This mismatch may lead to a degradation of the ANC performance and may eventually cause the W(z) filter to diverge, causing noise enhancement. One strategy to mitigate the effects of divergence is to tune the ANC algorithm in a more conservative way. This may include setting an adjustable limiter that counteracts the noise output or reducing the step size of the ANC algorithm or increasing the leakage of the ANC system. However, these methods may undesirably cause a degradation in the noise cancellation performance of the ANC system.

[0035] Solve S(z) and One approach to the mismatch problem can include simply re-measuring the IR and loading an estimated secondary path whose IR better matches the actual secondary path S(z) However, this approach may not be ideal because it involves subjecting the customer, seated in the vehicle, to a series of audible and unpleasant test signals over the life of the vehicle. The closer the match with S(z), the more accurately the error feedback signal e[n] (or g[n]) represents the noise cancellation performance in the vehicle cabin. Under these conditions, the adaptive filter W(z) is less likely to be misadjusted. In addition, due to Better matching of S(z) allows more aggressive tuning to be used (leading to better noise cancellation) as there is less risk of divergence.

[0036] Re-estimate and change during runtime Several factors need to be considered when evaluating the IR. For the disclosed system, the ANC system should include a method to re-estimate the method (for example, this would be referred to as utilizing Update the value of ). The measurement system can be embodied in many ways. This can include various implementations such as:

[0037] Generates a non-intrusive, sub-audible sine sweep or other test signal at the loudspeaker output and measures the response at the microphone to derive

[0038] Use speaker output (e.g., audio, vehicle prompts, chimes) and microphone measurements to derive Adaptive filter;

[0039] Export some or all secondary paths Deep learning or system identification techniques; and / or

[0040] By playing only pink noise or a sine sweep from each speaker of interest in a manner that is audible to the occupants of a passenger vehicle or in a manner that is less intrusive to the vehicle occupants because no one is in the vehicle when the test signals are played. From these test signals received at the microphones of interest, various methods can be used to derive

[0041] For the updated estimated Such an update may also occur at least once and at any time after the vehicle is purchased. One or more of the above implementations may be implemented, for example, by combining Figure 4 The state machine is described in more detail.

[0042] Figure 3 A system 300 for performing secondary path switching using impulse response (IR) fingerprinting according to one embodiment is generally depicted. The system 300 includes at least one controller 303 (hereinafter controller 303) and a memory 304. The controller 303 is configured to perform secondary path impulse response estimation to provide The system 300 includes at least one speaker 311 (hereinafter speaker) that generates anti-noise sound (e.g., within a cabin of a vehicle 313) in response to the anti-noise signal y[n]. The system 300 can utilize a variety of techniques to provide Provides an estimate of the current value of Various techniques for estimating the current value of are listed in U.S. Serial No. 17 / 976,048 (the '048 application), filed on October 28, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0043] For providing The system 300 includes an alternative audio signal source 305 that provides an additional signal to each speaker 311 of interest. The system 300 includes at least one speaker 311 (hereinafter, speaker) that generates an anti-noise sound (e.g., in a cabin of a vehicle 313) in response to the anti-noise signal y[n]. The signal e[n] or Provided to controller 303 Generally speaking, the system 300 performs Any "on-site" measurements or estimates may include This additional unwanted noise is not present in the original pre-production characterization of the The field measurement is not as good as the original, pre-production measurement of S(z) at the time of the initial measurement. Accurate in-situ S(z) representation. These additional noise sources present when the vehicle is in use include any one or more of engine noise, motor noise, wind noise, music noise, noise from phone calls or conversations in the vehicle, HVAC noise, traffic noise, urban landscape noise, or noise from other nearby vehicles intruding into the vehicle cabin. Fingerprint recognition (or An estimate of ) is essential to produce optimal noise cancellation. One goal of fingerprinting methods is to use noise to select a predetermined, pre-stored or measured Each of the selected corresponds to different cabin configurations that best represent the current cabin configuration of the vehicle. This produces optimal noise cancellation and provides stable system performance.

[0044] Figure 4 A state diagram (or method) 400 for performing secondary path switching using IR fingerprinting is generally depicted according to one embodiment. In state 402, the controller 303 sets the algorithm, IR fingerprinting, and matching conditions for performing secondary path switching to initial conditions. In operation 404, the controller 303 remains idle and runs checks. For example, the controller 303 may set the secondary acoustic transfer function The estimation of the IR estimation algorithm (or secondary path IR re-estimation) remains inactive until a predetermined condition has been met. For example, in operation 404, the controller 303 can continuously monitor the level of each group of signals (as discussed in more detail below) to determine when to enable or activate the secondary path IR re-estimation algorithm. In addition to monitoring the level of each group of signals, the controller 303 can also determine whether there is enough spectral content and signal to noise (SNR) in the audio signal. Once these conditions have been met, the method 400 proceeds to 412. In one embodiment, one or more of these quality checks can be omitted. In one embodiment, different quality checks (such as confidence scores) can be obtained as described in operation 428, which can alleviate the need for one or more conditions or quality checks. Operation 428 will be described in more detail below.

[0045] In operation 406, the controller 303 determines whether to initialize an adaptive secondary path (ASP). ASP corresponds to a process that performs online secondary path estimation in a multiple input / multiple output (MIMO) environment. If the ASP process can identify a closer match to the secondary path, this aspect can improve cancellation performance and the secondary path will be utilized.

[0046] If this condition is true, the method 400 moves to operation 408. If not, the method 400 moves back to operation 404. In operation 408, the controller 303 determines whether an increasing true audio (TA) error has occurred. The TA error corresponds to the difference between the audio signal predicted at the error microphone 106 and the actual audio signal at the error microphone 106. If this difference is increasing, the TA error indicates that the model of the secondary path is inaccurate.

[0047] If this condition is true, the method 400 moves to operation 410. If not, the method 400 moves back to operation 404.

[0048] In operation 410, the controller 303 determines whether there is sufficient audio content and whether the SNR of the audio signal is above a predetermined level. It is desirable that the level of any test signal be as low as possible so that the passengers cannot hear the test signal. However, the level of the test signal needs to be high enough so that the test signal can be detected at the error microphone 106. This aspect provides a range of optimal amplitudes for the test signal. If this condition is true, the method 400 moves to operation 412. If not, the method 400 moves back to operation 404 because the poor signal-to-noise ratio has caused the test signal to be too noisy to be used to reliably estimate

[0049] In operation 412, the controller 303 prepares for processing. In other words, the controller 303 activates the secondary path IR re-estimation algorithm. In operation 414, the controller 303 is committed to identifying the current audio configuration in the vehicle (or the cabin in the vehicle). In operation 416, the controller 303 continues to determine whether the audio content is sufficient in terms of spectral density and SNR. In general, the adaptive filter 108 is adjusted only when the audio content is sufficiently flat over the required bandwidth. To achieve this aspect, the system 300 and method 400 can use spectral descriptors such as spectral flatness to determine whether the audio content allows the adaptive filter to converge correctly.

[0050] Regarding the SNR level of the error microphone 106 in the vehicle, if the background noise in the vehicle is much louder than the audio played in the car, such noise may dominate the adaptive filter estimate (or secondary path estimate ). Therefore, in this case, if the audio is below the background noise of the vehicle, this condition may indicate a secondary path estimate In addition, with respect to the error level on microphone 106, the overall level in the microphone (eg, the ANC microphone) may also be used to determine the estimated secondary path For example, if the microphone exhibits a persistently low amplitude, this condition may indicate that the estimated secondary path If the controller 303 determines that the audio content is adequate in terms of spectral density and SNR, the method 400 moves to operation 418. If not, the method 400 moves to operation 420.

[0051] In operation 420, the controller 303 determines whether the amount of audio received has been insufficient and has exceeded a predetermined amount of time. If this condition is true, the method 400 moves to operation 424 and terminates. If not, the method 400 moves back to operation 414. In addition, in operation 414, the controller 303 monitors one or more signals from the secondary path IR re-estimation algorithm to determine whether convergence has been achieved. In general, the error microphone 106 provides an output that indicates has converged to an acceptable error in S(z). Additionally or alternatively, a signal derived from this error (such as a gradient value from an adaptive filter using a gradient descent method) may be performed Online estimation of .

[0052] In operation 418, the controller 303 determines whether the audio content exhibits a small stable gradient. For example, the controller 303 can compare the gradient on the audio content to a predetermined gradient value. A high gradient in the system 300 can indicate that the filter (e.g., ) has not yet converged to the IR on the actual secondary path S(z). Since the gradient is usually a vector quantity, the L2 norm can be used to determine the estimated secondary path (e.g., ) filter convergence (or convergence of the adaptive filter). When the L2 norm is continuously low (or below a predetermined gradient value), this may indicate that the estimated secondary path Therefore, if this condition is true (e.g., the gradient of the audio content is less than the predetermined gradient value), the method 400 moves to operation 422. If not (e.g., the gradient of the audio content is greater than the predetermined gradient value), the method 400 moves back to operation 414.

[0053] In general, one may wish to ensure that the secondary path estimates reliable and can be used to utilize secondary path estimation To update the secondary acoustic transfer function S(z), the adaptive unit 110 can use the secondary path estimation To calculate the filter coefficients of the transfer function W(z) of the adaptive filter 108. In general, although the system 300 can reliably generate But this may not mean The secondary path estimate of the system 300 generates a robust or reliable fingerprint. The system 300 is configured as a default when the system 300 is started. Alternatively, the last determined during the last operation of the vehicle 313 may be used. The possible candidates are determined based on the selection that can minimize the performance degradation in the largest number of possible cabin configurations. The controller 303 also includes instructions for executing an adaptive filter (or deep learning, or system identification algorithm) that iteratively solves for the secondary path based on a reference component of the signal output from the microphone 307 and the reception of the signal by all error microphones 106 through the actual secondary path S(z) 112.

[0054] There are many reasons why this may be unreliable. For example, if the estimated secondary path is obtained by an adaptive filter of controller 303, the adaptive filter may be incorrectly adjusted or include coefficients with values ​​approaching infinity (or provide a divergent filter) for various reasons. If the system is obtained through deep learning or system identification, the estimate may be inaccurate if the system has not been trained and tested with as many real-world acoustic combinations as possible. The coefficients can be used for fingerprint identification and update The reliability of these coefficients was assessed before deriving them.

[0055] A high error in system 300 may indicate that the filter estimating the secondary path deviates from the actual IR of the secondary path S(z). Large errors can indicate that convergence has not occurred, leading to Therefore, to achieve this, the state machine (or method 400) can compare the current error to an adjustable threshold, and if the error is below the threshold, the method 400 can conclude that for this metric, Generally speaking, the controller 303 monitors the error of the microphone 216 output.

[0056] In general, due to the secondary path estimation One or more of these methods can be used to estimate, and therefore validate, the secondary path estimates The specific process can vary. Method 400 provides a method for using many possible processes that have the advantages of validating the secondary path estimate based on adaptive system identification. Any combination of the following techniques may be used. Such an example can be seen in method 400, which includes operations 406, 408, 410, 416, 418, and 420 between operation 404 (e.g., idle) and operation 414 (e.g., run IR estimation).

[0057] In operation 422 , the controller 303 disables or deactivates the secondary path IR re-estimation algorithm and initializes the IR fingerprinting and matching process.

[0058] In operation 424, the controller 303 starts or triggers the matching algorithm to perform IR fingerprinting. The fingerprint of one or more IRs is derived in this operation. In general, the method 400 and system 300 provide, for example, fingerprinting IRs to dynamically switch secondary path parameters in an online single-input single-output (SISO) or MIMO ANC system, although Figure 1 and Figure 2 A simplified schematic diagram of a SISOANC system is shown. Fingerprinting involves, among other things, applying signal processing techniques to estimate an impulse response from at least one or each of the systems 300. Extracting a unique signature. Each IR in a multi-channel ANC system displays unique properties about the electro-acoustic path. These properties take into account signal processing components such as analog-to-digital (A / D) and digital-to-analog (D / A) converters, amplifiers, speakers, microphones, and the acoustic path itself. The response of the electro-acoustic path (i.e., the frequency response of the electro-acoustic path) is largely affected by the acoustic properties of the vehicle cabin. For a typical vehicle cabin, the frequency response shows peaks and valleys at various frequencies. In addition, the amplitude and frequency of the peaks and valleys can vary depending on a variety of factors such as temperature, seat configuration, number of passengers in the vehicle, etc. It is these variations in the frequency response that provide the opportunity to extract a unique fingerprint from the IR.

[0059] Can be estimated from production vehicle IR measurements or different secondary paths The estimation technique obtains a library of pre-measured IRs for various configurations of the vehicle cabin. Another approach may involve using secondary path estimation The estimation algorithm itself derives a pre-measured library of IRs based on various acoustic cabin configurations. Additionally, the controller 303 may assign each cabin acoustic configuration in this combination to a label (library 1, library 2, etc.) or by using a look-up table (LUT) 320. The pre-measured library of IRs may be stored as pre-stored IRs in a memory 322 (or LUT 320) associated with the controller 303 to be used with the estimated impulse response. One or more of the .

[0060] IR measured in a production vehicle can provide a more accurate fingerprint for multiple cabin configurations because it uses a test signal to stimulate all frequencies. Another method is to use The IR estimation algorithm itself derives the IR from various acoustic combinations.Finally, each cabin acoustic configuration in this library is assigned a label (library 1, library 2, etc.).

[0061] Once the estimate has been IR extracts all fingerprint features, the system 300 and method 400 can estimate the secondary path A match is performed with an existing database of fingerprints (or predetermined (or pre-stored) secondary path estimates) from various cabin configurations to find the closest match. Finally, the IR coefficients from the closest matching configuration are loaded into the adaptive filter of the system 300 by the adaptive unit 110. The system 300 performs the above method of estimating the impulse response and determining if there is a match with the pre-stored impulse response for each speaker and microphone pair in the vehicle.

[0062] The matching process performed by system 300 may involve controller 303 calculating a distance metric, determining a winner (e.g., the IR value that matches the closest pre-stored IR), estimating secondary paths, and determining the best match for the selected IR. The confidence level of the match between the closest matching IR value and various pre-stored IR values ​​and the margin of victory (MOV) between determining the closest matching IR value and multiple pre-stored IRs.

[0063] In operation 424, the controller 303 calculates a distance metric to determine a secondary path estimate IR, The closest match to a library of previously stored IRs. Examples of distance metrics may include the Euclidean norm, the Lp norm, or variations thereof such as, for example, Hausdorff distance and normalized misalignment. Distance metrics are discussed in more detail in the '048 application.

[0064] In operation 426, the controller 303 determines whether the match is complete. For example, the controller 303 builds a voting matrix that matches the closest IR value relative to the previously stored closest IR stored in the memory. If this condition is true (e.g., the voting matrix has been built), the method 400 moves to operation 428. If not, the method 400 moves back to operation 424.

[0065] In operation 428, the controller 303 establishes a confidence metric by considering various aspects of the voting matrix to determine the final winning IR configuration. The controller 303 determines whether the IR candidate exhibits a confidence level greater than a predetermined value (e.g., 50%). If this condition is met, the method 400 moves to operation 432. If not, the method 400 moves back to operation 412.

[0066] In operation 430, the controller 303 determines the secondary path estimate The controller 303 determines whether the MOV is above a predetermined MOV threshold (e.g., 20%). If the MOV is above the predetermined MOV threshold, the method 400 moves to operation 432. If not, the method 400 moves back to operation 412.

[0067] In operation 432, the controller 303 determines whether the selected IR timeslot is different from the current timeslot. If the selected IR timeslot is the same as the current IR timeslot, then the best fit IR is currently being used by the ANC system 300 and there is no need to change to a different IR. If this condition is true, the method 400 moves to operation 434 to perform an IR switch. If not, the method 400 moves back to operation 412. In operation 434, the controller 303 performs or performs an IR switch. For example, the controller 303 performs a triggering IR switch mechanism. In this case, the controller 303 performs an IR switch mechanism to utilize the same IR as the current IR timeslot. Measuring matching new parameters starts from the old The switching of the parameters may be abrupt, but is possible, and may temporarily degrade the ANC performance. To avoid this, the IR switching mechanism includes a smooth turnaround method to minimize any audible artifacts. The time constant of the turnaround may be adjustable or variable, but may be done in the range of, for example, 100 ms to several seconds. Another embodiment may include using the adaptive filter 108 coefficients to instantly update the IR of the secondary estimation path. It is generally recognized that one aspect may involve utilizing The new parameters of the measurement match, rather than corresponding to the impulse response measurement (or measurement) parameters.

[0068] In general, the controller 303 may perform one of the following options when performing the IR switching mechanism. In a first option, the controller 303 updates all coefficients of the adaptive filter 108 via the adaptive unit 110. For example, the LMS parameters of the step size and leakage may be temporarily modified so that the adaptive filter 108 of the ANC system may not respond when an unexpected transient occurs. In a second option, the controller 303 performs a slope (average) update of all coefficients of the adaptive filter 108 over a short period of time via the adaptive unit 110. In this case, the coefficients are gradually updated using, for example, an averaging filter to incorporate the coefficients from the secondary path estimate. The current value of the coefficient is mixed into the secondary path estimate The averaging can be linear or exponential, and the total update time can be adjustable (for example, the total update time can range from 40ms to 5 seconds).

[0069] In operation 436, the controller 303 determines whether the coefficients provided by the adaptive unit 110 to the adaptive filter 108 have been correctly loaded. For example, the controller 303 monitors the state of the ANC after the switch occurs in operation 434. If the appropriate coefficients have been correctly loaded, the method 400 moves to operation 438. If not, the method 400 moves back to operation 434.

[0070] In operation 438, the controller 303 executes a wait state. For example, the controller 303 determines whether the selected IR (or new IR) is stable. Generally, after the update is complete, the controller 303 monitors the effect of the newly updated parameters. If the controller 303 detects divergence, or the parameters from the new measurement are The error signal with these new IRs seems to be too high or higher than the previous one before the coefficient swap If the problem is not detected, the controller 303 can quickly revert to the previous parameters because the parameters are stored rather than deleted. However, if no problem is detected, the controller 303 can make the estimated The estimated coefficients of the adaptive filter 108 are kept frozen for a long period of time (e.g., 5-30 minutes), and then the IR estimation algorithm is resumed and the same logic described above is repeated to decide whether, when, and how to update again. This process can continue to run throughout the vehicle ignition cycle. If the selected IR (or estimated secondary path ) is stable, the method 400 moves to operation 440. If not, the method 400 moves back to operation 434. In operation 440, the controller 303 determines whether the timer of the waiting state has expired. The controller 303 uses a timer and waits for the timer to expire to determine whether the state of the selected IR (or new IR) is stable. If the state of the selected IR is stable after the time expires, the method 400 moves to operation 404. If not, the method 400 moves back to operation 438.

[0071] Generally speaking, aspects disclosed herein provide distance measurements (or misalignments) that are obtained by estimating IR and the actual IR Sp(z). This can be defined by the following equation:

[0072] The equations can then be converted to a logarithmic scale. It should be appreciated that the disclosed system can also perform the above equations in the time domain. This is generally referred to as a relative modeling error. An example of such a relative modeling error is generally described in Sun et al., "A Novel Online Secondary Path Modeling Method with Auxiliary Noise Power Scheduling Strategy for Narrowband Active Noise Control Systems" (November 29, 2017), which is incorporated herein by reference in its entirety. The controller 303 provides various IR A vector of distances to the IRs stored in the distance matrix.

[0073] Use the following Figures 4 to 6 According to the embodiments disclosed in the disclosure, the disclosed system provides a method for modifying ANC tuning online. For example, by adopting a "conservative" tuning that can be used before determining the secondary path, such as Figure 6 to Figure 7 The disclosed embodiments set forth in Figure 3 and Figure 4 and the IR fingerprinting concepts generally set forth in the '048 application. Now, after successful detection, using the embodiments disclosed below, a more "aggressive" tuning can be calculated / loaded in real time and used for the duration of the vehicle ignition cycle.

[0074] As combined Figure 3 and Figure 4 The illustrated embodiments disclose, for example, a system for adaptively changing the secondary path of an ANC system. Previous feed-forward ANC algorithms for EOC and RNC typically include a static estimate of the secondary path between the speaker and the error microphone. In general, typically about five pre-production vehicles may need to have their IR measured to create a single IR estimate that is stored in the thousands of production vehicles that are later produced. This estimate and the fact that the cabin configuration of the vehicle can change during operation can cause a mismatch between the actual secondary path IR and the stored secondary path IR. This mismatch results in reduced noise cancellation performance and, in some cases, noise enhancement.

[0075] The '048 application describes, among other things, a method for intelligently updating a static secondary path IR using an estimate of the actual cabin configuration to maintain noise cancellation performance. The estimated cabin configuration may be derived using one of several methods, including generating a test signal and measuring the response through a microphone or any other adaptive model of the cabin acoustics. The estimated IR may be processed through a fingerprinting algorithm to find the closest match from a library of pre-measured IRs. Additionally, the '048 application discloses systems and methods for determining whether, when, and how to estimate and update a secondary path IR. Overall, embodiments of the '048 application provide a variety of techniques for detecting changes in cabin acoustics and safely updating a static secondary path IR to achieve better noise cancellation performance for multiple cabin acoustic configurations.

[0076] Figure 5A first system 500 for adjusting ANC tuning parameters according to one embodiment is generally depicted. The first system 500 includes at least one controller 502 (hereinafter controller 502) to select a corresponding IR. The controller 502 includes a secondary path IR selection block 510, a library IR block 512, an active IR index block 514, a selector block 516, a filter setting block 518, and a core adaptive filter block 520 (or adaptive filter 520). The secondary path selector 510 includes an adaptive music compensation canceller (AMIC) 511 or an adaptive music compensation filter. The AMIC 511 uses a music signal as a test signal and an ANC error microphone 106 to estimate the secondary path IR between all speakers 311 and the microphone 106. The first system 500 verifies that the music signal has sufficient audio content to be considered a qualified test signal. In addition, the first system 500 employs additional signal processing to ensure that the unique IRs of all speakers 311 and the microphone 106 can be obtained using the audio test signal. The controller 502 uses the music signal to calculate new coefficients for the AMIC 511 in real time and can copy them into the estimated secondary path of the ANC system. A supervisory unit (not shown) manages the enabling and disabling of the AMIC as needed to calculate and copy coefficients.

[0077] The controller 502 receives signals from the error microphone 106, the speaker 311, and the vehicle data bus 504. In general, the vehicle data bus 504 includes any information indicating that the sound within the vehicle has changed. Examples of various aspects that illustrate that the acoustics of the vehicle have changed generally involve obtaining information about the state or various components within the vehicle. Such information may correspond to information related to the state of a window (e.g., up, down, or percentage open), the state of a trunk open / closed, the state of a seat position (e.g., a change in seat position), or the number of occupants in the vehicle, which may be obtained via a seat weight sensor for occupancy detection. The existing secondary path selection block 510 includes a compensation block 511. The relevance of blocks 510, 511, 512, 514, 516, 518, and 520 will be discussed in more detail below.

[0078] The controller 502 receives signals from the error microphone 106, the speaker 311, and the vehicle data bus 504. The secondary path selector block 510 receives signals from the error microphone 106, the speaker 311, and the vehicle data bus 504, and selects the corresponding stored IR from the library IR block 512. The controller 502 may be combined with the above Figure 3 and Figure 4The corresponding stored IR is selected in the manner described. The active IR index block 514 provides the corresponding stored IR to the selector block 516. The selector block 516 applies various filter settings as described in the filter setting block 518 to the stored IR as described in the selector block 516. The various filter settings (or tuning values) that can be applied correspond to the adaptive step filter μ, leakage (Y), maximum output level, etc. It can be appreciated that these filter settings may only correspond to default values, which can remain unchanged during the ignition cycle regardless of the type of stored IR used. Although this implementation in terms of using the default filter settings may be useful and practical, this may hinder or limit the ANC performance. Regarding the maximum output level relative to the ANC, one known problem is the concept of when the adaptive filter diverges. In this case, the anti-noise output begins to increase the gain exponentially, and then a loud and unpleasant noise may be heard in the cabin. Therefore, most ANC applications may include a limiter that will not allow the ANC anti-noise to exceed the "maximum output level" after tuning. Typically, this maximum output tuning level is set to be slightly higher than the amount determined to be a normal anti-noise output.

[0079] For example, a constant tuning value may generally be derived as a minimum value to achieve a stable ANC system or performance (e.g., a converged adaptive filter). However, such a constant tuning value may be described as "conservative". In other words, a tuning may be utilized that can manage large variations in the acoustic path (e.g., manifested as high feedback errors) and prevent the adaptive filter 108 from diverging. As is well known, the divergence of the adaptive filter 108 may cause a large enhanced sound in the vehicle cabin. However, by maintaining a "conservative" tuning of the tuning value, this may result in an ANC performance that may be lower than its true potential. This aspect may be frustrating to original equipment manufacturers (OEMs) because these OEMs want to obtain the best or optimal cancellation performance while also ensuring that the adaptive filter does not diverge.

[0080] like Figure 6 and Figure 7 The aspects set forth in provide, inter alia, that additional tuning parameters beyond the stored IR are no longer constant and that these tuning parameters may be based on selections of the ASP system (or the selected IR).

[0081] Figure 6 A second system 600 for adjusting ANC tuning parameters according to one embodiment is generally depicted. The system 600 may be similar to the system described in reference Figure 3 The system 600 includes the controller 502 as well as the error microphone 106, the speaker 311 and the vehicle data bus 504. All references to the controller 502 hereafter may also be performed as follows. Figure 3The operation of controller 303 is described. Controller 502 includes secondary path IR selection block 510, library IR block 512, active IR index block 514, filter setting block 518, and core adaptive filter block 520. In addition, controller 502 includes tuning parameter block 602 and adaptive tuning selection block 616.

[0082] In general, the tuning parameter block 602 stores a plurality of tuning parameters that can be applied based on which stored IR is selected. In one example, the tuning parameter block 602 can store a first tuning parameter setting 602a, a second tuning parameter setting 602b, a third tuning parameter setting 602c, and a fourth tuning parameter setting 602d. The first tuning parameter setting 602a can correspond to a conservative set of tuning parameters for the adaptive filter 108. In general, when a stored IR has not yet been selected, the controller 502 can apply the first tuning parameter setting 602, and this setting 602a can be considered a default setting until a stored IR is selected. Once the actual vehicle cabin acoustics are determined and the controller 502 is in a position to select a corresponding stored IR to match the vehicle cabin acoustics, the controller 502 can apply any one of the parameter settings 602b, 602c, or 602d based on how aggressive tuning the corresponding stored IR needs to be. Thus, in this regard, it can be appreciated that the selected IR has a corresponding ideal set of parameter settings 602a, 602b, 602c, and 602d, and the controller 502 selects corresponding or appropriate tuning parameter settings 602a, 602b, 602c, and 602d that are consistent with the selected IR to maximize performance. In general, it should be appreciated that the system 600 may require some time (e.g., a few seconds or minutes, etc.) for the controller 502 to confidently select a new stored IR. While waiting for the vehicle cabin acoustics to stabilize, the controller 502 utilizes any one of the tuning parameter settings 602a, 602b, 602c, and 602d as a corresponding default setting (or conservative setting), and the first tuning parameter setting 602 may be selected based on its provision of an unnoticeable conservative effect on the vehicle occupants. After selecting the stored IR for a predetermined amount of time, the controller 502 may utilize any one of the parameter settings 602a, 602b, 602c, or 602d to tune the adaptive filter 108. In this regard, cancellation performance may be lower, but conservative tuning parameter settings can ensure stability of the various cabin acoustics. After stabilization, the controller 502 selects the desired parameter settings 602a, 602b, 602c, and 602d that are optimal for the selected IR. The specific setting parameters 602a, 602b, 602c, and 602d that are consistent with the selected stored IR can be determined during a pre-production build of the vehicle and selected to ensure optimal ANC performance.

[0083] Once the system 600 selects the appropriate secondary path (or has stored the IR), the controller 502 can load any of the first, second, third, or fourth tuning parameters 602a, 602b, 602c, 602d, with the goal of maximizing the ANC performance. When the tuning parameters are more responsive, maximized ANC performance (or performance gain) can be achieved because it can be guaranteed that the feedback error originating from the error in the secondary path has been minimized. Different levels of tuning parameters can be adjusted, such as the adaptive step filter μ, leakage (Y), maximum output level, etc. In one example, the following may correspond to various conservative tunings: step size = 0.005, leakage 0.001, and maximum output = -30dB, etc. The following example may correspond to various aggressive tunings: step size of 0.1 to 0.5, leakage = 0.0001 (which corresponds to less leakage) and maximum output = -10dB (which allows more output). Leakage (or leakage value) generally corresponds to a "forgetting factor" in an adaptive filter. The leakage value is the amount removed from the LMS update equation to prevent divergence. A high leakage value provides a high chance of stabilizing the adaptive filter. However, a high leakage value also provides low cancellation because a high leakage value limits the LMS updates in the adaptive filter. In the context of system 600, low leakage values ​​are more desirable, however, system 600 can only use lower leakage values ​​for which the acoustic space in the vehicle is known or characterized.

[0084] In addition, leakage can correspond to a stabilization method that changes the update formula in the adaptive filter so that the mean square error and norm of the filter taps are minimized. This condition can prevent the filter coefficients from growing unbounded in numerically ill conditions. In general, the leakage value is proportional to the amount of cancellation provided by the adaptive filter.

[0085] Regarding the operation of the system 600, the controller 502 receives signals from the error microphone 106, the speaker 311, and the vehicle data bus 504. In particular, the secondary path selector block 510 receives signals from the error microphone 106, the speaker 311, and the vehicle data bus 504, and selects the corresponding stored IR from the library IR block 512. The controller 502 may be used in conjunction with the above Figure 3 and Figure 4The active IR index block 514 provides the corresponding stored IR to the adaptive tuning selection block 516. The tuning parameter block 602 stores the first tuning parameter 602a, the second tuning parameter 602b, the third tuning parameter 602c, and the fourth tuning parameter 602d. If the controller 502 determines that the acoustics within the vehicle cabin have not yet stabilized, the adaptive tuning selection block 616 selects the first tuning parameter 602a and applies these parameters by default in the filter setting block 518. The controller 502 can then store these tuning parameters as set forth in the first tuning parameter setting 602a in the filter setting block 518. Figure 3 In the adaptive filter controller 110 described above, the adaptive filter controller 110 controls the Figure 3 Also shown is the adaptive filter 108.

[0086] If the controller 502 determines that the new stored IR can be used with a high degree of confidence, the adaptive tuning selector block 516 selects any of the first, second, third, or fourth tuning parameters 602b, 602c, and 602d that corresponds favorably to the new stored IR and applies these parameters in the filter setting block 518. In general, each of the tuning parameters 602a, 602b, 602c, 602d can correspond to a particular step size, leakage, and / or maximum output. It should be recognized that the number of occupants detected in the vehicle, the window open / closed (e.g., the window is 50% open or closed, etc.), the trunk open / closed state, the seat position state (e.g., a change in seat position) generally affect the manner in which the controller 502 selects the stored IR. Each of the tuning parameters 602a, 602b, 602c, 602d can include different degrees of conservative and aggressive values. The controller 502 may then store the tuning parameters as set forth in the second, third, or fourth tuning parameter settings 602b, 602c, or 602d in the adaptive filter controller 110 so that the adaptive filter controller 110 controls the adaptive filter 108 accordingly.

[0087] The following example below illustrates one way in which the disclosed embodiments may be implemented. Generally, production vehicles (e.g., sedans) include seats positioned in rows that can be folded up so that the driver or passenger can store large items in the trunk and vehicle cabin. The vehicle may include a speaker in the form of a subwoofer located in the trunk in most cases, and the vehicle's overall sound system may also be equipped with an ANC system. In this case, the ANC algorithm is currently performing conservative tuning (e.g., step size low <0.05, high leakage >0.01), and low maximum output <-30dB). The reason the ANC system performs this conservative tuning is that there may be no vehicle bus messages when the rear seats are folded up. Therefore, in this regard, even if this condition is not common, the ANC tuning may always have to be prepared for the scenario. If the tuning is not conservative, the ANC system may diverge and become unstable, resulting in ANC enhancement.

[0088] However, with an ASP system as described herein, the starting (or initial) tuning can be aggressive (e.g., step size high > 0.1, low leakage < 0.001, and the maximum output can be set higher (e.g., < -10 dB). These settings can achieve higher performance and ensure system stability because the ASP (e.g., the disclosed system 500, 600, 700) assures the vehicle that the rear seats are not folded up. Then, if the rear seats are folded up, the system 500, 600, 700 detects this condition and switches the secondary path to a different tuning that is less aggressive but also less conservative than the above case without the ASP. After switching, the tuning can be considered to be between the conservative and aggressive settings.

[0089] Figure 7 A third system 700 for adjusting ANC tuning parameters is generally depicted according to one embodiment. The third system 700 shares similar aspects as described in conjunction with the second system 600. However, the third system 700 may not require the tuning parameters block 602. In fact, the controller 502 may calculate various tuning parameters (e.g., LMS settings) of the adaptive filter controller 110 to be applied in real time. This may be advantageous because this aspect may not require the controller 502 to consume a large amount of embedded processor memory compared to the second system 600. The controller 502 may run the real-time calculations as a background task to minimize the impact on normal execution time.

[0090] In one example, the controller 502 can perform real-time calculations by utilizing a constant offset (e.g., a single adjustable value added to each tuning table). For example, the controller 502 can add a predetermined step value (e.g., 0.05) to all LMS (or adaptive filter controller 110) step entries (which can typically be a table of 256 entries). Similarly, the controller 502 can add a predetermined leakage value, such as -0.01, to all LMS leakage entries (and large tables).

[0091] In another example, the controller 502 can perform real-time calculations by utilizing a variable offset / lookup table offset. In this approach, the controller 502 can add an offset to the step or leakage table, no longer as a single constant value as mentioned above, but as a variable value calculated by the controller 502. For example, the controller 502 can utilize a simple linear or polynomial equation to determine the variable to be applied to the step or leakage table (e.g., or any of the tuning parameters). For example, the controller 502 can utilize a descending line of step sizes so that the step values ​​for low frequencies are higher, while the step values ​​for high frequencies increase by smaller amounts. The tuning parameters can include a slope and a y-intercept (e.g., the controller 502 can implement a step equation such as, for example, y=-0.001x+0.09, where x is the tuning table index (i.e., a lower index means a lower frequency) 0.

[0092] The controller 502 can also run the eigenvalue (λ) equation in real time to calculate or apply variable tuning parameters. An example of the eigenvalue equation is described in "Signal Processing for Active Control" (SJ Elliott, 2001). In essence, the eigenvalue vector v of the linear transformation T can be a non-zero vector, and the direction does not change when T is applied to a non-zero vector. Applying the linear transformation T to the eigenvalue vector can make the eigenvalue vector only scale the scalar value λ, called the eigenvalue. In this application, the controller 502 can derive the eigenvalue between the selected IR and the actual music compensation filter coefficient. In general, the music compensation filter reflects the actual cabin sound level, and the selected stored IR is the best match. By using the eigenvalue, the controller 502 can adopt a new optimization group of LMS steps and leakage values, which may be similar to the variable offset and lookup table calculation method. The difference is that the controller 502 can use the eigenvalue as the calculation basis for the new tuning value of the adaptive filter controller 110 and / or the adaptive filter 108.

[0093] In general, aspects disclosed herein can remove the "conservative" tuning of the adaptive filter 108 and / or adaptive filter controller 110, and can allow tuning for production vehicles in a more "aggressive" manner, thereby enabling improved ANC performance. This can occur upon detecting that the disclosed system has correctly identified the correct secondary path (or the correct stored IR) that matches the actual vehicle acoustics. Once the correct secondary path or stored IR has been identified, the disclosed embodiments modify the basic "conservative" tuning with an "aggressive" value, thereby making the ANC adaptive filter more responsive, as it can be ensured that feedback errors originating from errors in the secondary path have been minimized.

[0094] Additionally, the disclosed embodiments can implement "conservative" tuning of the amplifier / host at startup. Essentially, such an amplifier / host uses normal "conservative" tuning values ​​until the appropriate secondary path selected by the adaptive secondary path algorithm (or stored IR) is selected with high confidence. As described above, the disclosed embodiments can utilize one system (e.g., system 700) that is optimized for memory consumption, and another system (e.g., system 600) that is optimized for execution time.

[0095] As described above, the system 600 can be execution time optimized. Thus, if the end application has a large amount of memory, but the processor speed is reduced, all "aggressive" data can be stored in memory only. When the system 600 detects a change in the impulse response, the adaptive filter controller 110 (and therefore the adaptive filter 108) will be fully loaded with the data stored in memory.

[0096] As also described above, the system 700 can be memory-optimal. Thus, in this implementation, the controller 502 can calculate the tuning values ​​in an "aggressive" manner, and then the tuning values ​​can be calculated in real time. This has the benefit of reducing memory consumption because multiple data tables do not need to be stored. This implementation does not require loading pre-stored data, but can calculate a more ideal set of tuning data based on active IR.

[0097] Figure 8 Depicts a method for performing at least system 300 and system 600 (see also Figure 3 and Figure 6 ) to adjust ANC tuning parameters.

[0098] In operation 802 , the audio source 305 provides an audio signal (a[n]) within a cabin of the vehicle 313 .

[0099] In operation 804 , the speaker 311 generates anti-noise sound (eg, within a cabin of the vehicle 313 ) in response to the anti-noise signal y′[n].

[0100] In operation 806, the microphone 106 provides a first error signal (e[n]) indicative of the noise (d[n]), the audio signal (a[n]), and the anti-noise sound (y[n]) within the cabin and a second error signal (y[n]) indicative of an estimated anti-noise signal.

[0101] In operation 808, the controller 303 and / or 502 receives the first error signal (e[n]) and the second error signal (y^[n]), and provides an estimated impulse response based on the first error signal (e[n]) and the second error signal (y^[n]).

[0102] In operation 810, the controller 303 and / or 502 estimates the impulse response based on the A first pre-stored impulse response is selected from a plurality of pre-stored impulse responses (eg, IR1, IR2, IR3) (see 512) to filter one or more reference signals at the adaptive filter 108 to generate an anti-noise signal y'[n].

[0103] In operation 812, the controller 303 and / or 502 selects a first plurality of pre-stored tuning parameters 602a, 602b, 602c, or 602d from a plurality of pre-stored sets of tuning parameters 602a, 602b, 602c, 602d based on a first pre-stored impulse response (eg, IR1, IR2, or IR3).

[0104] In operation 814 , the controller 303 and / or 502 tunes the adaptive filter 108 using the first plurality of pre-stored tuning parameters 602 a , 602 b , 602 c , or 602 d to generate the anti-noise signal y′[n].

[0105] Fig. 9 Depicts a method for performing at least system 300 and system 600 (see also Figure 3 and Figure 6 ) to adjust ANC tuning parameters.

[0106] In operation 902 , the audio source 305 provides an audio signal (a[n]) within a cabin of the vehicle 313 .

[0107] In operation 904 , the speaker 311 generates anti-noise sound (eg, within a cabin of the vehicle 313 ) in response to the anti-noise signal y′[n].

[0108] In operation 906, the microphone 106 provides a first error signal (e[n]) indicative of the noise (d[n]), the audio signal (a[n]), and the anti-noise sound (y[n]) within the cabin and a second error signal (y^[n]) indicative of an estimated anti-noise signal.

[0109] In operation 908, the controller 303 and / or 502 receives the first error signal (e[n]) and the second error signal (y^[n]), and provides an estimated impulse response based on the first error signal (e[n]) and the second error signal (y^[n]).

[0110] In operation 910, the controller 303 and / or 502 estimates the impulse response based on the A first pre-stored impulse response is selected from a plurality of pre-stored impulse responses (eg, IR1, IR2, IR3) (see 512) to filter one or more reference signals at the adaptive filter 108 to generate an anti-noise signal y'[n].

[0111] In operation 912, the controller 303 and / or 502 selects a first plurality of pre-stored tuning parameters 602a, 602b, 602c, or 602d based at least on one of a constant offset applied to one or more baseline tuning parameters and a variable offset value applied to one or more baseline tuning parameters.

[0112] Although exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the present invention. On the contrary, the words used in the specification are words of description rather than limitation, and it should be understood that multiple changes can be made without departing from the spirit and scope of the present invention. In addition, the features of various implementation embodiments can be combined to form other embodiments of the present invention.

Claims

1. An active noise cancellation (ANC) system, comprising: at least one audio signal source for providing an audio signal in a cabin of the vehicle; at least one speaker for projecting anti-noise sound within the cabin of the vehicle in response to receiving the anti-noise signal; at least one microphone for providing a first error signal indicative of noise within the cabin, the audio signal, and the anti-noise sound and a second error signal indicative of an estimated anti-noise signal; as well as At least one controller programmed to: receiving the first error signal and the second error signal; and providing an estimated impulse response based at least on the first error signal and the second error signal; selecting a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate the anti-noise signal, and selecting a first plurality of pre-stored tuning parameters from a plurality of pre-stored sets of tuning parameters based on the first pre-stored impulse response; as well as The adaptive filter is tuned using the first plurality of pre-stored tuning parameters to generate the anti-noise signal.

2. The ANC system of claim 1, wherein the first plurality of pre-stored tuning parameters correspond to default tuning parameters.

3. The ANC system of claim 2, wherein the at least one controller is further programmed to tune the adaptive filter using the default tuning parameters for a predetermined amount of time to allow the adaptive filter to stabilize after executing the adaptive filter using the first pre-stored impulse response.

4. The ANC system of claim 3, wherein when the predetermined amount of time expires, the at least one controller selects a second plurality of pre-stored tuning parameters from the plurality of pre-stored sets of tuning parameters.

5. The ANC system of claim 1, wherein the first plurality of pre-stored tuning parameters comprises at least one of a step size, a leakage value, and a maximum output level of the adaptive filter.

6. The ANC system of claim 5, wherein the step size of the adaptive filter corresponds to a constant value that keeps the adaptive filter stable at a maximum possible convergence speed.

7. The ANC system of claim 5, wherein the leakage value is proportional to an amount of cancellation provided by the adaptive filter.

8. The ANC system of claim 5, wherein the maximum output level corresponds to a limit that ANC noise will not exceed when the anti-noise signal is generated.

9. An active noise cancellation (ANC) system comprising: at least one audio signal source for providing an audio signal in a cabin of the vehicle; at least one speaker for projecting anti-noise sound within the cabin of the vehicle in response to receiving the anti-noise signal; at least one microphone for providing a first error signal indicative of noise within the cabin, the audio signal, and the anti-noise sound and a second error signal indicative of an estimated anti-noise signal; as well as At least one controller programmed to: receiving the first error signal and the second error signal; and providing an estimated impulse response based at least on the first error signal and the second error signal; selecting a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate the anti-noise signal, and selecting a first plurality of tuning parameters based on at least one of a constant offset applied to one or more baseline tuning parameters and a variable offset value applied to the one or more baseline tuning parameters, and The adaptive filter is tuned using the first plurality of tuning parameters to generate the anti-noise signal.

10. The ANC system of claim 9, wherein the at least one controller is further programmed to execute a linear or polynomial equation to determine the variable offset value applied to the one or more baseline tuning parameters.

11. The ANC system of claim 9, wherein the at least one controller is further programmed to execute one or more eigenvalue-based equations to generate the variable offset value.

12. The ANC system of claim 9, wherein the first plurality of tuning parameters comprises at least one of a step size, a leakage value, and a maximum output level of the adaptive filter.

13. The ANC system of claim 12, wherein the step size of the adaptive filter corresponds to a constant value that stabilizes the adaptive filter at a maximum possible convergence speed.

14. The ANC system of claim 12, wherein the leakage value is proportional to an amount of cancellation provided by the adaptive filter.

15. The ANC system of claim 12, wherein the maximum output level corresponds to a limit that ANC noise will not exceed when the anti-noise signal is generated.

16. A method of performing active noise cancellation (ANC), the method comprising: generating, using at least one audio signal source, an audio signal for transmission within a cabin of a vehicle; transmitting an anti-noise sound in the cabin of the vehicle via at least one speaker in response to receiving an anti-noise signal; providing a first error signal indicative of noise within the cabin, the audio signal, and the anti-noise sound and a second error signal indicative of an estimated anti-noise signal; receiving, by at least one controller, the first error signal and the second error signal; as well as providing an estimated impulse response based on the first error signal and the second error signal; selecting a first pre-stored impulse response from a plurality of pre-stored impulse responses based on the estimated impulse response to filter one or more reference signals at an adaptive filter to generate the anti-noise signal, selecting a first plurality of pre-stored tuning parameters from a plurality of pre-stored sets of tuning parameters based on the first pre-stored impulse response; as well as The adaptive filter is tuned using the first plurality of pre-stored tuning parameters to generate the anti-noise signal.

17. The method of claim 16, wherein the first plurality of pre-stored tuning parameters comprises at least one of a step size, a leakage value, and a maximum output level of the adaptive filter.

18. The ANC system of claim 17, wherein the first plurality of pre-stored tuning parameters includes at least one of a step size, a leakage value, and a maximum output level of the adaptive filter.

19. The ANC system of claim 17, wherein the step size of the adaptive filter corresponds to a constant value that stabilizes the adaptive filter at a maximum possible convergence speed.

20. The ANC system of claim 17, wherein the leakage value is proportional to an amount of cancellation provided by the adaptive filter.

Images