Patent Description:
Active Noise Control (ANC) systems attenuate undesired noise using feedforward and 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, combine to 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 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 vibrations 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 speakers to generate sound waves that are ideally opposite in phase and identical in magnitude to the noise to be reduced at the typical location of one or more listeners' ears. 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.

RNC systems are typically Least Mean Square (LMS) adaptive feed-forward systems that continuously adapt W-filters based on both acceleration inputs from the vibration sensors located in various positions around a vehicle's suspension system, subframe and body, and on signals of microphones located in various positions inside the vehicle's cabin. Certain driving events, such as driving over train tracks, hitting a pothole, and driving over a speedbump, induce signals in both the accelerometers and the microphones. Consequently, the LMS RNC system will adapt the W-filters to attempt to more optimally cancel these signals, which have a different spectral character than that of the surrounding pavement. However, these types of events are transients, and are not indicative of most of the road that the vehicle is traveling on. Therefore, when the W-filters are adapted based on these transient, non-stationary events, the RNC is worsened for a period of time after the events. This is because the RNC system needs to re-adapt to re-converge to the correct W-filters to optimally cancel the steady-state or pseudo-steady state road surface.

Publication <CIT> discloses an active noise control (ANC) system including a speaker and one or more processors. The one or more processors implement an adaptive sub band filtered reference control algorithm that applies thresholds to reference and error feedback signal paths such that, in response to a series of broadband non-Gaussian impulsive reference signals indicative of road noise in the vehicle having an audible frequency range of <NUM> to <NUM>, weight coefficients defining an adaptive filter of the control algorithm converge and permit the ANC system to partially cancel the road noise via output of the speaker. The control algorithm is based on the use of M-estimators in which a cost function is minimized. Further active noise cancellation systems are known from publications <CIT> and <CIT>.

Various aspects of the present disclosure relate to protecting a road noise cancellation (RNC) system from mis-adapting in response to non-stationary, transient events. Several detection and mitigation systems and/or methods are disclosed that prevent mis-adaptation of the RNC system's controllable filters.

In a first aspect, the present invention provides a method for preventing mis-adaptation in a feed-forward road noise cancellation, RNC, system as defined in independent claim <NUM>. The method includes adjusting an adaptive transfer characteristic based on a noise signal received from a vibration sensor, an error signal received from a microphone located in a cabin of a vehicle, and an adaptation parameter. The method further includes generating an anti-noise signal, to be radiated by a speaker as anti-noise within the cabin of the vehicle, based in part on the adaptive transfer characteristic. The method further includes receiving at least one sensor signal from at least one sensor and detecting a non-stationary event based on signal parameters sampled from a frame of the at least one sensor signal. The method also includes modifying the adaptation parameter for a duration of the frame in response to detecting the non-stationary event. The controllable filter is controlled according to a least mean square, LMS, algorithm, wherein modifying the adaption parameter includes reducing a step size of the LMS algorithm. Detecting a non-stationary event comprises analyzing sensor data within the frame by calculating the fast Fourier transform of the frame. The sensor is a vibration sensor and the at least one sensor signal is the noise signal.

Implementations may include one or more of the following features. Modifying an adaptation parameter may include reducing a rate of adaptation of one or more controllable filters. Modifying an adaptation parameter may include pausing adaptation of one or more controllable filters by reducing a rate of adaptation of the controllable filters to zero.

In a second aspect, the present invention provides an RNC system as defined in independent claim <NUM>. The RNC system includes a sensor adapted to generate a sensor signal on at least one output channel in response to an input. The RNC system also includes a controllable filter adapted to generate an anti-noise signal, the anti-noise signal to be radiated by a speaker as anti-noise within a cabin of a vehicle, based in part on an adaptive transfer characteristic. The RNC system further includes an adaptive filter controller, including a processor and memory, programmed to control the adaptive transfer characteristic of the controllable filter based on a noise signal received from a vibration sensor, an error signal received from a microphone located in the cabin of the vehicle, and an adaptation parameter. The RNC system further includes a signal analysis controller, including a processor and memory, programmed to: detect a non-stationary event based on parameters sampled from a current frame of the sensor signal; and modify the adaptation parameter in response to detecting a non-stationary event. The adaptation parameter determines a rate of change of the adaptive transfer characteristic, also called the step size, for the controllable filter. The controllable filter is controlled according to an LMS algorithm, wherein the signal analysis controller is programmed to modify the adaption parameter by reducing a step size of the LMS algorithm. Detecting a non-stationary event comprises analyzing sensor data within the frame by calculating the fast Fourier transform of the frame.

The sensor is the vibration sensor and the sensor signal is the noise signal.

Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention, the scope of the present invention being defined by the appended claims.

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.

<FIG> shows a road noise cancellation (RNC) system <NUM> for a vehicle <NUM> having one or more vibration sensors <NUM>. The vibration sensors are disposed throughout the vehicle <NUM> to monitor the vibratory behavior of the vehicle's suspension, subframe, as well as other axle and chassis components. The RNC system <NUM> may be integrated with a broadband feed-forward and feedback active noise control (ANC) framework or system <NUM> that generates anti-noise by adaptive filtering of the signals from the vibration sensors <NUM> using one or more microphones <NUM>. The anti-noise signal may then be played through one or more speakers <NUM>. S(z) represents a transfer function between a single speaker <NUM> and a single microphone <NUM>. While <FIG> shows a single vibration sensor <NUM>, microphone <NUM>, and speaker <NUM> for simplicity purposes only, it should be noted that typical RNC systems use multiple vibration sensors <NUM> (e.g., <NUM> or more), speakers <NUM> (e.g., <NUM> to <NUM>), and microphones <NUM> (e.g., <NUM> to <NUM>).

The vibration sensors <NUM> may 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 voltage 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 vibrations sensed in their X-direction, Y-direction and Z-direction. Single-axis and multi-axis accelerometers, therefore, may be used as vibration sensors <NUM> to detect the magnitude and phase of acceleration and may also be used to sense orientation, motion, and vibration.

Noise and vibrations that originate from a wheel <NUM> moving on a road surface <NUM> may be sensed by one or more of the vibration sensors <NUM> mechanically coupled to a suspension device <NUM> or a chassis component of the vehicle <NUM>. The vibration sensor <NUM> may 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 various ways known by those skilled in the art. In certain examples not forming part of the claimed invention, 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 wheel <NUM> and the road surface <NUM>. The noise signal X(n) may be filtered with a modeled transfer characteristic S'(z), which estimates the secondary path (i.e., the transfer function between an anti-noise speaker <NUM> and an error microphone <NUM>), by a secondary path filter <NUM>.

Road noise that originates from interaction of the wheel <NUM> and the road surface <NUM> is also transferred, mechanically and/or acoustically, into the passenger cabin and is received by the one or more microphones <NUM> inside the vehicle <NUM>. The one or more microphones <NUM> may, for example, be located in a headrest <NUM> of a seat <NUM> as shown in <FIG>. Alternatively, the one or more microphones <NUM> may be located in a headliner of the vehicle <NUM>, or in some other suitable location to sense the acoustic noise field heard by occupants inside the vehicle <NUM>. The road noise originating from the interaction of the road surface <NUM> and the wheel <NUM> is transferred to the microphone <NUM> according to a transfer characteristic P(z), which represents the primary path (i.e., the transfer function between an actual noise source and an error microphone).

The microphones <NUM> may output an error signal e(n) representing the noise present in the cabin of the vehicle <NUM> as detected by the microphones <NUM>. In the RNC system <NUM>, an adaptive transfer characteristic W(z) of a controllable filter <NUM> is controlled by adaptive filter controller <NUM>. The adaptive filter controller <NUM> operates according to a known least mean square (LMS) algorithm based on the error signal e(n) and the noise signal X(n), which is optionally filtered with the modeled transfer characteristic S'(z) by the filter <NUM>. The controllable filter <NUM> is often referred to as a W-filter. An anti-noise signal Y(n) may be generated by an adaptive filter formed by the controllable filter <NUM> and the adaptive filter controller <NUM> based on the identified transfer characteristic W(z) and 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 speaker <NUM>, anti-noise is generated near the occupants' ears and the microphone <NUM> 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 speaker <NUM> may combine with road noise in the vehicle cabin near the microphone <NUM> resulting in a reduction of road noise-induced sound pressure levels (SPL) at this location.

While the vehicle <NUM> is under operation, a processor <NUM> may collect and optionally processes the data from the vibrations sensors <NUM> and the microphones <NUM> to construct a database or map containing data and/or parameters to be used by the vehicle <NUM>. The data collected may be stored locally at a storage <NUM>, or in the cloud, for future use by the vehicle <NUM>. Examples of the types of data related to the RNC system <NUM> that may be useful to store locally at storage <NUM> include, but are not limited to, frequency dependent leakage and step size, accelerometer or microphone spectra or time dependent signals, other acceleration characteristics including spectral and time dependent properties, and microphone-based acoustic performance data. In addition, the processor <NUM> may analyze the vibration sensor and microphone data and extract key features to determine a set of parameters to be applied to the RNC system <NUM>. The set of parameters may be selected when triggered by an event. In one or more embodiments, the processor <NUM> and storage <NUM> may be integrated with one or more RNC system controllers, such as the adaptive filter controller <NUM>.

As previously described, typical RNC systems may use several vibration sensors, microphones and speakers to sense structure-borne vibratory behavior of a vehicle and generate anti-noise. The vibrations sensors may be multi-axis accelerometers having multiple output channels. For instance, triaxial accelerometers typically have a separate electrical output for vibrations sensed in their X-direction, Y-direction, and Z-direction. A typical configuration for an RNC system may have, for example, <NUM> error microphones, <NUM> speakers, and <NUM> channels of acceleration signals coming from <NUM> triaxial accelerometers or <NUM> dual-axis accelerometers. Therefore, the RNC system will also include multiple S'(z) filters (i.e., secondary path filters <NUM>) and multiple W(z) filters (i.e., controllable filters <NUM>).

The simplified RNC system schematic depicted in <FIG> shows one secondary path, represented by S(z), between each speaker <NUM> and each microphone <NUM>. As previously mentioned, RNC systems typically have multiple speakers, microphones and vibration sensors. Accordingly, a <NUM>-speaker, <NUM>-microphone RNC system will have <NUM> total secondary paths (i.e., <NUM> x <NUM>). Correspondingly, the <NUM>-speaker, <NUM>-microphone RNC system may likewise have <NUM>'(z) filters (i.e., secondary path filters <NUM>), which estimate the transfer function for each secondary path. As shown in <FIG>, an RNC system will also have one W(z) filter (i.e., controllable filter <NUM>) between each noise signal X(n) from a vibration sensor (i.e., accelerometer) <NUM> and each speaker <NUM>. Accordingly, a <NUM>-accelerometer signal, <NUM>-speaker RNC system may have <NUM> W(z) filters. The relationship between the number of accelerometer signals, speakers, and W(z) filters is illustrated in <FIG>.

<FIG> is a sample schematic diagram demonstrating relevant portions of an RNC system <NUM> scaled to include R accelerometer signals [X<NUM>(n), X<NUM>(n),. XR(n)] from accelerometers <NUM> and L anti-noise speaker signals [Y<NUM>(n), Y<NUM>(n),. YL(n)] for speakers <NUM>. Accordingly, the RNC system <NUM> may include R*L controllable filters (or W-filters) <NUM> between each of the accelerometer signals and each of the speakers. As an example, an RNC system having <NUM> accelerometer outputs (i.e., R=<NUM>) may employ <NUM> dual-axis accelerometers or <NUM> triaxial accelerometers. In the same example, a vehicle having <NUM> speakers (i.e., L=<NUM>) for reproducing anti-noise, therefore, may use <NUM> W-filters in total. At each of the L speakers, R W-filter outputs are summed to produce the speaker's anti-noise signal Y(n). Each of the L speakers may include an amplifier (not shown). In one or more embodiments, the R accelerometer signals filtered by the R W-filters are summed to 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 speaker.

As set forth above, RNC systems are susceptible to mis-adaptation due to non-stationary events, such as driving over train tracks, hitting a pothole, driving over a speedbump or a crack or patch in the road. If the LMS system adapts the W-filters based on non-stationary signals, the RNC performance may be degraded in the time period immediately afterward because these non-stationary signals are transient in nature, and have a different spectral character than that of the steady-state road surface. Adaptation of the LMS system with non-stationary inputs is described as mis-adaptation, due to the degraded noise cancellation performance that can result following the non-stationary input. Mis-adaptation of the W-filters in response to non-stationary, transient events may be prevented by detecting such events and mitigating their effect on the LMS adaptation algorithm.

To detect a non-stationary event, such as driving over train tracks or hitting a pothole, the noise signal(s) X(n) output from one or multiple accelerometers in the RNC system may be evaluated. The noise signal X(n) of each accelerometer channel may be an analog or digital signal. Evaluation of the time history of these output signals may identify non-stationary, transient events when they occur. For instance, driving over a pothole may cause a relatively high amplitude, short duration pulse to appear on an accelerometer output. It is likely that this high amplitude (i.e., possibly full-scale), short-duration signal will appear on more than one of the X-, Y-, and Z-direction output channels of more than one accelerometer, perhaps during different frames.

<FIG> is a schematic block diagram representing an RNC system <NUM>, in accordance with one or more embodiments of the present disclosure. The RNC system <NUM> may be a Filtered-X Least Mean Squares (FX-LMS) RNC system, as understood by those of ordinary skill in the art. Similar to RNC system <NUM>, the RNC system <NUM> may include elements <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>, consistent with operation of elements <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>, respectively, discussed above. In one or more embodiments, a music signal M(n) from a music playback device <NUM>, such as the head unit (not shown) may be combined with the anti-noise signal Y(n) to be amplified and sent to the speaker <NUM>. <FIG> also shows the primary path P(z) and secondary path S(z), as described with respect to <FIG>, in block form. As shown, the RNC system <NUM> may further include one or more signal analysis controllers <NUM>. Each signal analysis controller <NUM> may include a processor and memory (not shown), such as processor <NUM> and storage <NUM>, programmed to detect non-stationary events, including impulsive events that are contained within the time dependent noise signal X(n) and/or the error signal e(n). This may include computing parameters by analyzing time samples from a frame of the noise signal X(n). Accordingly, the signal analysis controller <NUM> may be disposed along the path between the vibration sensor <NUM> and the adaptive filter (i.e., the controllable filter <NUM> and the adaptive filter controller <NUM>). In an alternate embodiment shown in <FIG>, a signal analysis controller <NUM>' may be disposed along the path between the vibration sensor <NUM> and the adaptive filter controller <NUM>, not acting on the signal into the controllable filter <NUM>. In other embodiments, a signal analysis controller <NUM> may be disposed along the path between the microphones <NUM> and the adaptive filter controller <NUM>. The signal analysis controller <NUM> may be a dedicated controller for detecting non-stationary signals or may be integrated with another controller or processor in the RNC system, such as the LMS adaptive filter controller <NUM>. Alternatively, the signal analysis controller <NUM> may be integrated into another controller or processor within vehicle <NUM> that is separate from the other components in the RNC system.

In response to detecting a non-stationary event, the RNC system <NUM> may slow adaptation of some or all of the controllable filters <NUM>, or pause adaptation altogether, for the duration of the frame in which the event is detected. The LMS algorithm's step size controls the rate of adaptation. A smaller step-size slows the adaptation of the controllable filters <NUM> based on the acceleration and microphone inputs. Reducing the step size for the duration of a frame results in the controllable filters <NUM> changing less than they otherwise would due to the presence of these nonstationary inputs. Reducing the step-size to zero effectively pauses the adaption, by preventing adaptation of the controllable filters <NUM> based on these nonstationary signals for the duration of the frame. Other, equivalent methods to pause adaptation for the duration of the frame may be employed, such as a repetition of the previous frame's controllable filter(s) <NUM> rather than updating the controllable filter(s) based on an input frame containing a non-stationary event.

Alternatively, the signal analysis controller <NUM> may generate an adjusted noise signal X'(n) or adjusted error signal e'(n) in response to detecting a non-stationary event, as depicted in <FIG>. Accordingly, the controllable filter <NUM> may be configured to generate the anti-noise signal Y(n) based on the adjusted noise signal X'(n) and the adaptive transfer characteristic W(z) as controlled by the LMS adaptive filter controller <NUM>. The adjusted noise signal X'(n) may modify the anti-noise signal Y(n) to be radiated by the speaker <NUM> as anti-noise in a manner that reduces the effect of the non-stationary event on the anti-noise. The adjusted error signal e'(n) and/or noise signal X'(n) may also prevent the controllable filter <NUM> from mis-adapting due to a non-stationary or transient event. If a non-stationary event is not detected, the signal analysis controller <NUM> may not adjust the noise signal X(n) and/or error signal e'(n) such that the noise signal X(n) and/or error signal e(n) may be passed through to the controllable filter <NUM>, and/or LMS block <NUM>.

<FIG> is a flowchart depicting a method <NUM> for preventing mis-adaptation of controllable filters in an RNC system due to non-stationary events. Various steps of the disclosed method may be carried out by the signal analysis controller <NUM>, either alone, or in conjunction with other components of the RNC system. Moreover, certain descriptions of the method may be explained in connection with detecting a non-stationary event based on the noise signal from a vibration sensor <NUM>. However, non-stationary events may be detected by a similar signal analysis applied to error signals e(n) received from a microphone <NUM>, such as what may occur when a passenger rubs or strikes a microphone or during speech inside the passenger cabin or due to wind or other incident airflow. In certain embodiments, non-stationary events included in the noise signal X(n) originating from microphones or other sensor types than accelerometers may be detected by the signal analysis controller <NUM>.

At step <NUM>, the RNC system <NUM> receives sensor signals, such as noise signals X(n) from at least one vibration sensor <NUM> and/or error signals e(n) from at least one microphone <NUM>. The RNC system <NUM> may also 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 this end, a group of samples of time data from an output channel of a vibration sensor <NUM> or a microphone <NUM> may be received by the signal analysis controller <NUM>. The group of samples of time data may form one digital signal processing (DSP) frame. In an embodiment, <NUM> time samples of the output from a sensor (i.e., vibration sensor <NUM> or microphone <NUM>) may form a single DSP frame. In alternate embodiments, greater or fewer time samples may compose a single frame.

At step <NUM>, an analysis of the sensor data within a frame is performed. In various embodiments, this analysis may include calculating, extracting or otherwise obtaining one or more parameters from each frame of sensor data sampled from, for example, the noise signal X(n). According to the claimed invention, the signal analysis controller <NUM> calculates the fast Fourier transform (FFT) of the frame to form a frequency domain representation of the sensed vibrational input from the vibration sensor <NUM>. The analysis may further include evaluating the FFT in one or multiple frequency ranges, or in individual frequency bins. For instance, non-stationary, transient events are typically a short duration impulse, which in the frequency domain is a very broadband signal. Thus, the acceleration character of many non-stationary events in the frequency domain is quite different than the acceleration character of the road in steady-state. Obtaining and analyzing a parameter from the frame such as a level of one or more frequency ranges may therefore enable detection of a non-stationary event. In other examples, the analysis could also include computing parameters such as the total energy within the DSP frame or the peak or highest amplitude of all the time samples within the frame. Because the amplitude of the acceleration signal created by a non-stationary event detected by a vibration sensor (such as an accelerometer) can be much higher amplitude than the acceleration signal created by traversing a predominant road surface, analyzing these parameters may also enable detection.

Step <NUM> may also include storing the parameter(s) or sensor data of a current frame for use in analyzing future frames of sensor data. In an embodiment, the parameter(s) or sensor data from the frame immediately prior to a current frame may be stored. In another embodiment, a statistical analysis may be performed on the parameters obtained from multiple prior frames of sensor data to determine a threshold. For instance, a short- or long-term average of a parameter obtained from multiple preceding frames may be calculated and stored as its own parameter for use in step <NUM>, either as a threshold or to obtain a difference from the current frame for comparison to a threshold. In certain of these embodiments, a predetermined gain margin may be added to the average value (or other statistical value) calculated from multiple preceding frames to form a threshold. This may include adding a gain margin of <NUM>%, <NUM>% or <NUM>% to the average value, or other statistical value. Thus, the average value from multiple preceding frames may be multiplied by a gain factor (e.g., <NUM>%, <NUM>%, <NUM>%, etc.,) to obtain the threshold. In other embodiments, other gain factors are possible. In another embodiment, a threshold may be calculated using data from other sensors in the RNC system using any combination of the aforementioned threshold-deriving techniques. Additionally, a threshold may be derived by analyzing the current frame or a past frame or frames of sensor data from any, or combinations of any, noise signals from other vibration sensors.

At step <NUM>, the parameter computed from the current frame of sensor data may be compared directly to a corresponding threshold. If the parameter from the current frame exceeds the threshold, the signal analysis controller <NUM> may conclude a non-stationary event has been detected. If the parameter from the current frame does not exceed the threshold, the signal analysis controller <NUM> may conclude that no nonstationary event has been detected. For instance, the signal analysis controller <NUM> may compute the energy in the current frame or a peak amplitude of the current frame and compare the energy value or peak amplitude to a corresponding threshold to determine whether a nonstationary event has occurred.

Alternatively, the parameter computed from the current frame of sensor data may be may be compared to a statistical value (e.g., average value) of the same parameter from one or more previous frames of sensor data obtained from either the same noise signal, one or more noise signals from other vibration sensors, or any combination thereof, as previously described. The difference between the current frame's parameter and the statistical value may then be compared to a threshold. If the difference exceeds the threshold, the signal analysis controller <NUM> may conclude a non-stationary event has been detected. If the difference does not exceed the threshold, the signal analysis controller <NUM> may conclude that a non-stationary event has not been detected. For example, in an embodiment, the signal analysis controller <NUM> may compute the energy in the current frame and compare it to the energy in a previous frame, noting that any difference exceeding a predetermined threshold may be indicative of a non-stationary signal, such as hitting a pothole. In another embodiment, the FFT of a current frame of the noise signal output from a vibration sensor may be calculated and compared to the FFT of the previous frame, noting that a change on the level of one or more FFT bins beyond a predetermined threshold may also be indicative of a non-stationary signal.

In one or more embodiments, the threshold may be a predetermined static threshold set and programmed by trained engineers during the tuning of the RNC system and its corresponding algorithms. In alternate embodiments, the threshold may be a dynamic threshold computed from a statistical analysis of the parameter obtained in one or more preceding frames as discussed above with regard to step <NUM>. For instance, the threshold may be a short- or long-term average value of a parameter taken from multiple preceding frames. Moreover, the average value may be enhanced by a gain factor, as previously discussed, to establish the dynamic threshold. In yet another embodiment, the threshold may simply be the value of the parameter from the previous frame of time data, which may also be multiplied by a gain factor.

The signal analysis controller <NUM> may also apply temporal thresholding in conjunction with the aforementioned variants of amplitude thresholding at step <NUM>. For example, some impulsive, non-stationary events induce a high amplitude output signal with a duration of <NUM> to <NUM>. Thus, temporal thresholding may further aid in the detection of nonstationary events. For instance, when the amplitude of samples in the current frame exceeds an amplitude threshold for less than a predetermined temporal threshold, an impulsive, non-stationary event may be detected.

Referring to step <NUM>, when a non-stationary event is detected, the method proceeds to step <NUM> in which an adaptation parameter in the LMS algorithm is modified to prevent the RNC system from mis-adapting or diverging due to the non-stationary event. In an embodiment, the method may proceed to step <NUM> in which the sensor signal itself is modified in attempt to mask, reduce or eliminate the non-stationary event and prevent mis-adaptation. However, when a non-stationary event is not detected, the method may skip any adaptation parameter or signal modification and return to step <NUM> so the process can repeat with a new frame of sensor data. In an embodiment, both steps <NUM> and <NUM> can be executed in effort to prevent mis-adaptation.

At step <NUM>, upon detection of a non-stationary event, an adaptation parameter is modified. According to the claimed invention, the LMS algorithm's step size is reduced. The LMS algorithm's step-size controls the rate of adaptation. A smaller step-size slows the adaptation of the controllable filters <NUM> based on the acceleration and microphone sensor inputs. In one or more embodiments, the signal analysis controller <NUM> may inform the LMS controller <NUM> when a non-stationary event is detected so that they LMS controller may reduce the step size of its adaptation algorithm for the duration of the frame or of the nonstationary event. Reducing the step size for the duration of this frame may result in one or more of the controllable filters <NUM> changing less than they otherwise would have due to the presence of these non-stationary inputs. During this frame, the controllable filters that do not receive noise signal X(n) containing a nonstationary event may use an unmodified step size. In certain embodiments, adaptation of one or more controllable filters may be paused altogether by reducing the step size to zero for the duration of the frame, or by other techniques known to those of ordinary skill in the art.

In an alternative embodiment at step <NUM>, sensor signal itself may be modified to mask the non-stationary event and prevent mis-adaption based on transient, non-stationary events. One technique may be to simply deactivate or mute RNC for the duration of the current DSP frame, resulting in the lack of anti-noise output signals Y(n) to some or all the speakers <NUM> in the RNC system <NUM>. In certain embodiments, it may be possible to mute certain speakers that have medium to high amplitude controllable filters <NUM> for the particular noise signal X(n).

Because RNC systems typically have multiple feedforward vibration sensors, there are response options that are not available to simpler ANC systems, such as those employed in headphones. For example, if the frame containing the non-stationary event is simply zeroed, then no anti-noise related to this impulsive event will be radiated into the passenger cabin. Likewise, if this were an ANC headphone, then no anti-noise at all would be present during that frame. This may lead to an undesirable impression that ANC momentarily turned off (for the duration of that frame) and then resumed after the frame. The sudden discontinuity at the beginning or end of the DSP frame could also create the impression of undesirable pops and clicks coming from the speaker. Methods of temporal smoothing known to those skilled in the art of DSP may be applied to the samples at the start and the end of the current frame of data to prevent this. Alternately, smoothing or changes to the sample values just preceding or just following the current DSP frame can be made to prevent the audible pops and clicks. It is possible to replace the current frame of data with a signal that has near zero amplitude to eliminate or reduce audible pops and clocks at the beginning and/or end of the frame. In an embodiment, the data in the current frame can be replaced by samples that contain the averaged values of one or more previous frames that also eliminate or reduce audible pops and clicks.

The RNC system <NUM> may not exhibit this same undesirable behavior if a current frame of the feed-forward noise signal from one vibration sensor is zeroed. This is because the anti-noise radiated from each speaker <NUM> is made up of signals from multiple vibration sensor outputs. For instance, in an RNC system that employs <NUM> dual-axis accelerometers or <NUM> triaxial accelerometers, there will be <NUM> accelerometer output X(n) signals. In the case of <NUM> dual-axis accelerometers, zeroing the current frame containing parameters indicative of a nonstationary event would result in the reduction accelerometer signals used in creating the total anti-noise radiated from a particular speaker from <NUM> to <NUM>. Thus, this may result in the decrease in anti-noise amplitude of <NUM> dB (i.e., <NUM>/<NUM>), as compared to the complete muting of anti-noise to the speaker or to all the speakers for the duration of the frame.

In certain embodiments, more sophisticated solutions are possible, wherein only during the duration of the nonstationary event is the acceleration signal zeroed. This may further shorten the duration of the reduced anti-noise, which, in turn, may further mask the nonstationary event. Other techniques are possible, such as repeating the last frame of the output noise signal from the vibration sensor, rather than zeroing it. In various embodiments, any aforementioned mitigation technique, or combinations of techniques, may be accompanied by a reduced playback level during all or a portion of the current frame. This may be accomplished by reducing any, or combinations of any, W(z) filter amplitude, or by additional attenuation blocks (not shown) that reduce the level of one or more X'(n) or Y(n).

In the event that the non-stationary event is not totally eliminated in the adjusted noise signal X'(n) and/or the adjusted error signal e'(n), an additional measure can be undertaken to expedite re-adaptation to improve RNC performance on the surrounding pavement more quickly. In an embodiment, the step size can be increased for the one or more adjustable W-filters whose adjusted noise signal X'(n) contained a non-stationary event. The duration of this step size increase can be for one or more frames, or until the system has re-adapted to restore the pre-nonstationary event noise cancelling performance. In an embodiment, leakage can be increased for a duration of one or more frames in an effort more quickly to reduce the effect of the mis-adaptation on the W-filters.

In the foregoing specification, the present invention has been described with reference to specific exemplary embodiments. Various modifications and changes may be made, however, without departing from the scope of the present invention as set forth in the claims. The specification and figures are illustrative, rather than restrictive. Accordingly, the scope of the present invention is defined by the appended claims.

For example, the steps recited in any method or process claims may be executed in any order. 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.

Those of ordinary skill in the art understand that 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 invention as defined by the appended claims.

Benefits, advantages and solutions to problems have been described above with regard to particular embodiments. However, any benefit, advantage, solution to problems or any element that may cause any particular benefit, advantage or solution to occur or to become more pronounced are not to be construed as critical, required or essential features or components of any or all the claims.

Claim 1:
A method (<NUM>) for preventing mis-adaptation in a feed-forward road noise cancellation, RNC, system, the method comprising:
adjusting an adaptive transfer characteristic of a controllable filter (<NUM>) based on a noise signal received from a sensor, an error signal received from a microphone (<NUM>) located in a cabin of a vehicle (<NUM>), and an adaptation parameter;
generating an anti-noise signal based in part on the adaptive transfer characteristic, the anti-noise signal to be radiated by a speaker (<NUM>) as anti-noise within the cabin of the vehicle (<NUM>);
receiving (<NUM>) at least one sensor signal;
detecting (<NUM>) a non-stationary event based on signal parameters sampled from a frame of the at least one sensor signal; and
modifying (<NUM>) the adaptation parameter for a duration of the frame in response to detecting the non-stationary event,
wherein the controllable filter (<NUM>) is controlled according to a least mean square, LMS, algorithm, wherein modifying the adaptation parameter includes reducing a step size of the LMS algorithm,
wherein detecting a non-stationary event comprises analyzing sensor data within the frame by calculating the fast Fourier transform, FFT, of the frame, and
wherein the sensor is a vibration sensor (<NUM>) and the at least one sensor signal is the noise signal.