Adaptive enhancement of the road noise cancellation system

Abstract

A road noise cancellation (RNC) system may include a signal analysis controller for detecting non-stationary transient events based on sensor signals having spectral or temporal characteristics that are significantly different from steady-state road or cabin noise. Upon detecting such non-stationary events, the RNC system may modify the sensor signals to mask the non-stationary events, thereby preventing the RNC system's adaptive filters from erroneously adapting due to the transient non-stationary events. Alternatively, the RNC system may pause or slow down or suspend adaptation of its controllable filters for the duration of a frame that includes the non-stationary events.

Description

Technical Field

[0001] The present disclosure relates to road noise cancellation and, more particularly, to detecting non-stationary events in a feed-forward road noise cancellation system to minimize misadaptation. Background Art

[0002] Active noise control (ANC) systems attenuate undesirable noise using feed-forward and feedback structures to adaptively remove undesirable noise within a listening environment, such as within a vehicle cabin. ANC systems typically cancel or reduce unwanted noise by generating canceling sound waves that destructively interfere with unwanted audible noise. Destructive interference occurs when noise and "anti-noise" (which is approximately the same 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 undesirable noise come from the engine, the interaction between the vehicle's tires and the road surface on which the vehicle is traveling, and / or the sound radiated by vibrations of other parts of the vehicle. Therefore, unwanted noise varies with the speed, road conditions, and operating state of the vehicle.

[0003] 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. An RNC system uses vibration sensors to sense road-induced vibrations generated by the tire and road interface that result in unwanted audible road noise. The level of this unwanted road noise inside the vehicle cabin is then canceled or reduced by using speakers to generate sound waves that are ideally opposite in phase and equal in magnitude to the noise to be reduced at the typical location of one or more listener ears. Cancelling this road noise can result in a more pleasant ride experience for vehicle occupants and enables automakers to use lightweight materials, thereby reducing energy consumption and emissions.

[0004] The RNC system is typically a least mean square (LMS) adaptive feedforward system that continuously adjusts the W filter based on both acceleration inputs from vibration sensors located in various locations around the vehicle suspension system, subframe, and body, and signals from microphones located in various locations inside the vehicle compartment. Certain driving events (such as driving over rails, hitting potholes, and driving over acceleration strips) induce signals in both the accelerometer and the microphone. Therefore, the LMS RNC system debugs the W filter to try to better eliminate these signals with spectral characteristics that are different from the spectral characteristics of the surrounding road surface. However, these types of events are transient and do not indicate most of the roads that the vehicle is traveling. Therefore, when the W filter is debugged based on these transient non-stationary events, the RNC deteriorates within a certain period of time after the event. This is because the RNC system needs to re-adapt to converge to the correct W filter again to best eliminate steady-state or pseudo-steady-state road surfaces. Summary of the invention

[0005] Various aspects of the present disclosure are directed 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 to prevent mis-adaptation of a controllable filter of an RNC system.

[0006] In one or more illustrative embodiments, a method for preventing misadaptation in a feedforward road noise cancellation (RNC) system is provided. The method may include 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 adaptive parameter. The method may also include 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 may also include receiving at least one sensor signal from at least one sensor; and detecting a non-stationary event based on a signal parameter sampled from a frame of the at least one sensor signal. The method may also include modifying the adaptive parameter for a duration of the frame in response to detecting the non-stationary event.

[0007] Implementations may include one or more of the following features. The sensor may be a vibration sensor or a microphone and the sensor signal may be a noise signal. The sensor may be a microphone and the sensor signal may be an error signal. Detecting a non-stationary event based on signal parameters sampled from a frame of at least one sensor signal may include comparing at least one signal parameter of a current frame of each sensor signal with a threshold; and detecting the non-stationary event when the at least one signal parameter exceeds the threshold. The signal parameter may be a peak amplitude of the sensor signal sampled in the frame. The signal parameter may be an energy value for each frame. The threshold may be a predetermined static threshold programmed for the RNC system. The threshold may be a dynamic threshold calculated based on a statistical analysis of the at least one signal parameter in one or more previous frames of the sensor signal. Modifying an adaptive parameter may include reducing an adaptive rate of one or more controllable filters. Modifying an adaptive parameter may include pausing the adaptation of the controllable filter by reducing the adaptive rate of the one or more controllable filters to zero. Modifying an adaptive parameter may include deactivating the RNC system for the duration of the frame.

[0008] One or more additional embodiments may be directed to an RNC system comprising a sensor adapted to generate a sensor signal on at least one output channel in response to an input. The RNC system may also include a controllable filter adapted to generate an anti-noise signal based in part on an adaptive transfer characteristic, the anti-noise signal to be radiated by a speaker as anti-noise within a cabin of a vehicle. The RNC system may also include an adaptive filter controller comprising a processor and a memory, the adaptive filter controller configured 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 adaptive parameter. The RNC system may also include a signal analysis controller comprising a processor and a memory, the signal analysis controller being programmed to: detect a non-stationary event based on a parameter sampled from a current frame of the sensor signal; and modify the adaptive parameter in response to detecting a non-stationary event. The adaptive parameter may determine a rate of change, also referred to as a step size, of the adaptive transfer characteristic of the controllable filter.

[0009] Implementations may include one or more of the following features. The signal analysis controller is programmed to modify the adaptive parameter by reducing the adaptation rate of the controllable filter. The sensor may be a vibration sensor or a pressure sensor and the sensor signal may be a noise signal. The sensor may be the microphone and the sensor signal may be the error signal. The signal analysis controller may be programmed to detect non-stationary events based on parameters sampled from a current frame of the sensor signal by comparing at least one signal parameter of a current frame of each sensor signal to a threshold value.

[0010] One or more additional embodiments may be directed to a computer program product embodied in a non-transitory computer-readable medium programmed for road noise cancellation (RNC). The computer program product may include instructions for: receiving a sensor signal from at least one sensor; detecting a non-stationary event based on a signal parameter sampled from a frame of the at least one sensor signal; and modifying an anti-noise signal to be radiated as anti-noise by a speaker within a cabin of a vehicle during the duration of the frame in response to detecting the non-stationary event.

[0011] Implementations may include one or more of the following features. The computer program product, wherein the instructions for detecting a non-stationary event based on signal parameters sampled from frames of at least one sensor signal may include comparing at least one signal parameter of a current frame of each sensor signal to a threshold. The computer program product, wherein the instructions for modifying an anti-noise signal may include zeroing a frame of a sensor signal that includes a parameter indicative of the non-stationary event. The computer program product, wherein the instructions for modifying an anti-noise signal may include replacing a frame that includes a parameter indicative of the non-stationary event with a previous frame from the same sensor signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a block diagram of a vehicle having a road noise cancellation (RNC) system according to one or more embodiments of the present disclosure;

[0013] Figure 2 is a sample schematic diagram showing the relative positions of the RNC system, which is scaled to include R accelerometer signal and L loudspeaker signal;

[0014] Figure 3A is a schematic block diagram showing an RNC system including a signal analysis controller according to one or more embodiments of the present disclosure;

[0015] Figure 3B is a schematic block diagram showing an alternative RNC system including a signal analysis controller; and

[0016] Figure 4 is a flow chart describing a method for preventing mis-adaptation of a controllable filter due to non-stationary events in an RNC system according to one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] As required, detailed embodiments of the present invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely examples of the present invention that may be embodied in various forms and 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 those skilled in the art to employ the present invention in different ways.

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

[0019] Figure 1 A road noise cancellation (RNC) system 100 is shown for a vehicle 102 having one or more vibration sensors 108. The vibration sensors are arranged throughout the vehicle 102 to monitor the vibration behavior of the vehicle's suspension and subframe and other axle and chassis components. The RNC system 100 can be integrated with a broadband feedforward and feedback active noise control (ANC) framework or system 104 that generates anti-noise by adaptively filtering the signals from the vibration sensors 108 using one or more microphones 112. The anti-noise signal can then be played through one or more speakers 124. S(z) represents the transfer function between a single speaker 124 and a single microphone 112. Although only for simplicity Figure 1 A single vibration sensor 108, microphone 112, and speaker 124 are shown, but it should be noted that typical RNC systems use multiple vibration sensors 108 (eg, 10 or more), speakers 124 (eg, 4 to 8), and microphones 112 (eg, 4 to 6).

[0020] The vibration sensor 108 may include, but is not limited to, accelerometers, dynamometers, geophones, linear differential transformers, strain gauges, and load cells. For example, an accelerometer is a device whose output voltage is proportional to acceleration. A wide variety of accelerometers can be used in RNC systems. These include accelerometers that are sensitive to vibrations in one, two, and three generally orthogonal directions. These multi-axis accelerometers typically have separate electrical outputs (or channels) for the vibrations sensed in their X, Y, and Z directions. Therefore, single-axis and multi-axis accelerometers can be used as vibration sensors 108 to detect the magnitude and phase of acceleration, and can also be used to sense orientation, motion, and vibration.

[0021] Noise and vibration originating from the wheels 106 moving on the road surface 150 may be sensed by one or more of the vibration sensors 108 mechanically coupled to the suspension device 110 or chassis components of the vehicle 102. The vibration sensor 108 may output a noise signal X(n), which is a vibration signal representing the detected road-induced vibration. It should be noted that multiple vibration sensors are also possible, and their signals may be used individually or may be combined in various ways known to those skilled in the art. In certain embodiments, a microphone may be used in place of a vibration sensor to output a noise signal X(n) indicating the noise generated from the interaction of the wheels 106 with the road surface 150. 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 the anti-noise speaker 124 and the error microphone 112) through the secondary path filter 122.

[0022] Road noise originating from the interaction of the wheels 106 with the road surface 150 is also mechanically and / or acoustically transmitted into the passenger compartment and received by one or more microphones 112 within the vehicle 102. The one or more microphones 112 may be located, for example, in an armrest 114 of a seat 116, such as a Figure 1 Alternatively, one or more microphones 112 may be located in the roof trim of the vehicle 102 or in some other suitable location to sense the acoustic noise field heard by occupants inside the vehicle 102. Road noise originating from the interaction of the road surface 150 with the wheels 106 is transferred to the microphone 112 according to a transfer characteristic P(z) representing the primary path (i.e., the transfer function between the actual noise source and the error microphone).

[0023] The microphone 112 may output an error signal e(n) representing the cabin noise present in the vehicle 102 detected by the microphone 112. In the RNC system 100, the adaptive transfer function W(z) of the controllable filter 118 may be controlled by an adaptive filter controller 120. The adaptive filter controller 120 may operate 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 by a filter 122 with a modeled transfer characteristic S'(z). The controllable filter 118 is generally referred to as a W filter. The anti-noise signal Y(n) may be generated by an adaptive filter formed by the controllable filter 118 and the adaptive filter controller 120 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 124, anti-noise is generated near the occupant's ear and microphone 112 that is substantially opposite in phase and equal in magnitude to the road noise audible to the occupant of the vehicle cabin. The anti-noise from the speaker 124 may combine with the road noise in the vehicle cabin near the microphone 112, resulting in a reduction in the road noise induced sound pressure level at this location.

[0024] When the vehicle 102 is in operation, the processor 128 can collect and optionally process data from the vibration sensor 108 and the microphone 112 to build a database or map containing data and / or parameters to be used by the vehicle 102. The collected data can be stored locally at the storage device 130 or in the cloud for future use by the vehicle 102. Examples of data types related to the RNC system 100 that can be used to be stored locally at the storage device 130 include, but are not limited to, frequency-dependent leakage and step length, accelerometer or microphone spectrum or time-dependent signals, other acceleration characteristics including frequency and time-dependent properties, and microphone-based acoustic performance data. In addition, the processor 128 can analyze the vibration sensor and microphone data and extract key features to determine a set of parameters to be applied to the RNC system 100. The set of parameters can be selected when triggered by an event. In one or more embodiments, the processor 128 and the storage device 130 can be integrated with one or more RNC system controllers such as the adaptive filter controller 120.

[0025] As previously described, a typical RNC system may use several vibration sensors, microphones, and speakers to sense the structurally transmitted vibration behavior of the vehicle and generate anti-noise. The vibration sensor may be a multi-axis accelerometer with multiple output channels. For example, a three-axis accelerometer typically has separate electrical outputs for the vibrations sensed in its X-direction, Y-direction, and Z-direction. A typical configuration of an RNC system may have, for example, 6 microphones, 6 speakers, and 12 channels of acceleration signals from 4 three-axis accelerometers or 6 two-axis accelerometers. Therefore, the RNC system will also include multiple S'(z) filters (i.e., secondary path filters 122) and multiple W(z) filters (i.e., controllable filters 118).

[0026] Figure 1 The simplified RNC system schematically depicted in FIG. 1 shows one secondary path represented by S(z) between each speaker 124 and each microphone 112. As previously mentioned, an RNC system typically has multiple speakers, microphones, and vibration sensors. Thus, a 6-speaker 6-microphone RNC system would have a total of 36 secondary paths (i.e., 6×6). Accordingly, the 6-speaker 6-microphone RNC system may also have 36 S'(z) filters (i.e., secondary path filters 122) that estimate the transfer function of each secondary path. As shown in FIG. Figure 1 As shown, the RNC system will also have one W(z) filter (i.e., controllable filter 118) between each noise signal X(n) from the vibration sensor (i.e., accelerometer) 108 and each speaker 124. Thus, a 12 accelerometer signal 6 speaker RNC system may have 72 W(z) filters. The relationship between the number of accelerometer signals, speakers, and W(z) filters is shown in Figure 2 Shown in.

[0027] Figure 2 is scaled to include R accelerometer signals [X 1 (n), X 2 (n),…,X R (n)] and the L anti-noise speaker signals [Y 1 (n), Y 2 (n),…,Y L(n)] is a sample schematic diagram of relevant portions of an RNC system 200 for controlling an accelerometer signal. Thus, the RNC system 200 may include R×L controllable filters (or W filters) 218 ​​located between each of the accelerometer signals and each of the speakers. For example, an RNC system with 12 accelerometer outputs (i.e., R=12) may employ 6 dual-axis accelerometers or 4 tri-axis accelerometers. In the same example, therefore, a vehicle with 6 speakers (i.e., L=6) for regenerative anti-noise may use a total of 72 W filters. At each of the L speakers, the R W filter outputs are accumulated to produce an anti-noise signal Y(n) for the speaker. 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 accumulated to produce 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 the speaker.

[0028] As described above, the RNC system is susceptible to misadaptation due to non-stationary events such as driving over rails, hitting potholes, driving over acceleration strips or cracks or patches in the road. If the LMS system tunes the W filter based on non-stationary signals, the RNC performance may degrade in the immediate time period thereafter because these non-stationary signals are transient in nature and have spectral characteristics that are different from the spectral characteristics of the steady-state road surface. Due to the degraded noise cancellation performance that can result after non-stationary inputs, the adaptation of the LMS system under non-stationary inputs is described as misadaptation. Misadaptation of the W filter in response to non-stationary transient events can be prevented by detecting such events and mitigating their impact on the LMS adaptive algorithm.

[0029] In order to detect non-stationary events, such as driving over a rail or hitting a pothole, one or more noise signals X(n) output from one or more accelerometers in the RNC system may be evaluated. The noise signal X(n) for 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 as they occur. For example, driving over a pothole may result in a relatively high amplitude, short duration pulse appearing on the accelerometer output. It is possible that this high amplitude, short duration signal will appear on more than one of the X, Y, and Z direction output channels of more than one accelerometer during different frames.

[0030] Figure 3A3 is a schematic block diagram representing an RNC system 300 according to one or more embodiments of the present disclosure. As will be appreciated by one of ordinary skill in the art, the RNC system 300 may be a filtered X least mean square (FX-LMS) RNC system. Similar to the RNC system 100, the RNC system 300 may include elements 308, 310, 312, 318, 320, 322, and 324 that operate in accordance with the elements 108, 110, 112, 118, 120, 122, and 124 discussed above, respectively. In one or more embodiments, a music signal M(n) from a music playback device 360, such as a head unit (not shown), may be combined with an anti-noise signal Y(n) to be amplified and radiated to a speaker 324. FIG. 3 also illustrates in block form a primary path P(z) and a secondary path S(z), as shown with respect to FIG. Figure 1 As described. As shown, the RNC system 300 may also include one or more signal analysis controllers 362. Each signal analysis controller 362 may include a processor and memory (not shown) (such as the processor 128 and the storage device 130) programmed to detect non-stationary events including impulse events contained in the time-dependent noise signal X(n) and / or the error signal e(n). This may include calculating parameters by analyzing time samples of frames from the noise signal X(n). Therefore, the signal analysis controller 362 may be set along the path between the vibration sensor 308 and the adaptive filter (i.e., the controllable filter 318 and the adaptive filter controller 320). In Figure 3B In the illustrated alternative embodiment, the signal analysis controller 362' may be disposed along the path between the vibration sensor 308 and the adaptive filter controller 320 so as not to act on the signal entering the controllable filter 318. In other embodiments, the signal analysis controller 362 may be disposed along the path between the microphone 312 and the adaptive filter controller 320. The signal analysis controller 362 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 320. Alternatively, the signal analysis controller 362 may be integrated into another controller or processor within the vehicle 102 that is separate from other components in the RNC system.

[0031] In response to detecting a non-stationary event, the RNC system 300 may slow down some or all of the adaptation of the controllable filter 318, or completely suspend adaptation, for the duration of the frame in which the event is detected. The step size of the LMS algorithm controls the rate of adaptation. A smaller step size slows down the adaptation of the controllable filter 318 based on acceleration and microphone input. Reducing the step size over the duration of a frame causes the change of the controllable filter 318 to be less than what they would otherwise be due to the presence of these non-stationary inputs. Reducing the step size to zero effectively suspends adaptation by preventing the adaptation of the controllable filter 318 based on these non-stationary signals during the duration of the frame. Other equivalent methods of suspending adaptation over the duration of a frame may also be used, such as repeating one or more controllable filters 318 of a previous frame instead of updating one or more controllable filters based on an input frame containing a non-stationary event.

[0032] Alternatively, the signal analysis controller 362 may generate an adjusted noise signal X'(n) or an adjusted error signal e'(n) in response to detecting a non-stationary event, as depicted in FIG. 3 . Thus, the controllable filter 318 may be configured to generate an anti-noise signal Y(n) based on the adjusted noise signal X'(n) and the adaptive transfer characteristic W(z) controlled by the LMS adaptive filter controller 320. The adjusted noise signal X'(n) may modify the anti-noise signal Y(n) to be radiated as anti-noise by the speaker 324 in a manner that reduces the effect of the non-stationary event on the anti-noise. The adjusted error signal e'(n) and / or the noise signal X'(n) may also prevent the controllable filter 318 from being misadapted due to non-stationary or transient events. If a non-stationary event is not detected, the signal analysis controller 362 may not adjust the noise signal X(n) and / or the error signal e'(n) so that the noise signal X(n) and / or the error signal e'(n) may be passed to the controllable filter 318 and / or the LMS block 320.

[0033] Figure 4 4 is a flow chart describing a method 400 for preventing misadaptation of a controllable filter due to non-stationary events in an RNC system. The various steps of the disclosed method may be performed by a signal analysis controller 362 alone or in combination with other components of the RNC system. In addition, certain descriptions of the method may be explained in conjunction with detecting non-stationary events based on a noise signal from a vibration sensor 308. However, non-stationary events may be detected by similar signal analysis applied to the error signal e(n) received from the microphone 312, such as when a passenger scratches or hits the microphone or during speech inside the passenger compartment or due to wind or other incident airflow. In certain embodiments, non-stationary events included in noise signals X(n) originating from other sensor types other than microphones or accelerometers may be detected by the signal analysis controller 362.

[0034] At step 410, the RNC system 300 may receive sensor signals, such as a noise signal x(n) from at least one vibration sensor 308 and / or an error signal e(n) from at least one microphone 312. The RNC system 300 may also receive sensor signals from other acoustic sensors in the passenger compartment, such as an acoustic energy sensor, an acoustic intensity sensor, or an acoustic particle velocity or accelerometer sensor. To this end, a group of time data samples from the output channel of the vibration sensor 308 or the microphone 312 may be received by the signal analysis controller 362. The group of time data samples may form a digital signal processing (DSP) frame. In one embodiment, 128 time samples from the output of the sensor (i.e., the vibration sensor 308 or the microphone 312) may form a single DSP frame. In an alternative embodiment, more or fewer time samples may constitute a single frame.

[0035] At step 420, analysis of the sensor data within the frame may be performed. In various embodiments, such analysis may include calculating, extracting, or otherwise obtaining one or more parameters from each sensor data frame sampled from, for example, a noise signal X(n). In one example, the signal analysis controller 362 may calculate a fast Fourier transform (FFT) of the frame to form a frequency domain representation of the sensed vibration input from the vibration sensor 308. The analysis may also include evaluating the FFT in one or more frequency ranges or in a single frequency bin. For example, non-stationary transient events are typically short-duration pulses that are extremely wideband signals in the frequency domain. Therefore, the acceleration characteristics of many non-stationary events in the frequency domain are very different from the acceleration characteristics of the road in a steady state. Therefore, obtaining and analyzing parameters from the frame (such as the level of one or more frequency ranges) may enable detection of non-stationary events. In other examples, the analysis may also include calculating parameters such as the total energy within a DSP frame or the peak or highest amplitude of all time samples within a frame. Since the amplitude of the acceleration signal generated by a non-stationary event detected by a vibration sensor (such as an accelerometer) may be much higher than the amplitude of the acceleration signal generated by crossing the mainstream road surface, analyzing these parameters may also enable detection.

[0036] Step 420 may also include storing one or more parameters or sensor data of the current frame for use in analyzing future sensor data frames. In one embodiment, one or more parameters or sensor data from the frame just before the current frame may be stored. In another embodiment, a statistical analysis may be performed on the parameters obtained from multiple previous sensor data frames in order to determine the threshold. For example, the short-term or long-term average of the parameters obtained from multiple previous frames may be calculated and stored as its own parameter for use in step 430, as a threshold or for obtaining a difference with the current frame for comparison with the threshold. In some of these embodiments, a predetermined gain margin may be added to the average value (or other statistical value) calculated from multiple previous frames to form a threshold. This may include adding a gain margin of 20%, 50%, or 100% to the average value or other statistical value. Therefore, the average value from multiple previous frames may be multiplied by a gain factor (e.g., 120%, 150%, 200%, etc.) to obtain a threshold. In other embodiments, other gain factors are also possible. In another embodiment, the threshold may be calculated using data from other sensors in the RNC system, using any combination of the aforementioned threshold derivation techniques. Additionally, the threshold value may be derived by analyzing the current frame or one or more past frames of sensor data in light of any noise signals from other vibration sensors, or any combination thereof.

[0037] At step 430, the parameters calculated from the current sensor data frame may be directly compared to the corresponding thresholds. If the parameters from the current frame exceed the thresholds, the signal analysis controller 362 may infer that a non-stationary event has been detected. If the parameters from the current frame do not exceed the thresholds, the signal analysis controller 362 may infer that no non-stationary events have been detected. For example, the signal analysis controller 362 may calculate the energy in the current frame or the peak amplitude of the current frame and compare the energy value or the peak amplitude to the corresponding thresholds to determine whether a non-stationary event has occurred.

[0038] Alternatively, the parameter calculated from the current frame of sensor data may be compared to a statistic (e.g., an average) of the same parameter from one or more previous frames of sensor data obtained from the same noise signal, one or more noise signals from other vibration sensors, or any combination thereof, as previously described. The difference between the parameter of the current frame and the statistic before may then be compared to a threshold. If the difference exceeds the threshold, the signal analysis controller 362 may infer that a non-stationary event has been detected. If the difference does not exceed the threshold, the signal analysis controller 362 may infer that a non-stationary event has not occurred. For example, in one embodiment, the signal analysis controller 362 may calculate the energy in the current frame and compare it to the energy in the previous frame, noting that any difference exceeding a predetermined threshold may indicate a non-stationary signal, such as hitting a pothole. In another embodiment, the FFT of the current frame of the noise signal output from the vibration sensor may be calculated and compared to the FFT of the previous frame, noting that changes in the level of one or more FFT bins exceeding a predetermined threshold may also indicate a non-stationary signal.

[0039] In one or more embodiments, the threshold value can be a predetermined static threshold value set and programmed by a trained engineer during the tuning of the RNC system and its corresponding algorithm. In an alternative embodiment, the threshold value can be a dynamic threshold value calculated from a statistical analysis of the parameters obtained in one or more previous frames, as discussed above with respect to step 420. For example, the threshold value can be a short-term or long-term average value of the parameters obtained from a plurality of previous frames. In addition, as discussed previously, the average value can be increased by a gain factor to establish a dynamic threshold value. In another embodiment, the threshold value can be simply the value of the parameter from the previous time data frame, which value can also be multiplied by a gain factor.

[0040] The signal analysis controller 362 may also apply a time threshold conversion in conjunction with the aforementioned variation of the amplitude threshold conversion at step 430. For example, some impulsive non-stationary events include high amplitude output signals having a duration of 1 ms to 100 ms. Thus, the time threshold conversion may also assist in the detection of non-stationary events. For example, an impulsive non-stationary event may be detected when the sample amplitude in the current frame exceeds the amplitude threshold by less than a predetermined time threshold.

[0041] Referring to step 440, when a non-stationary event is detected, the method may proceed to step 450, where the adaptive parameters in the LMS algorithm are modified to prevent the RNC system from mis-adapting or deviating due to the non-stationary event. In one embodiment, the method may proceed to step 460, where the sensor signal itself is modified to attempt to shield, reduce or eliminate the non-stationary event and prevent mis-adaptation. However, when no non-stationary event is detected, the method may skip any adaptive parameter or signal modification and return to step 410 so that the process can be repeated with a new sensor data frame. In one embodiment, both steps 450 and 460 may be performed to prevent mis-adaptation.

[0042] At step 450, when a non-stationary event is detected, the adaptive parameters may be modified. In particular, the step size of the LMS algorithm may be reduced. The step size of the LMS algorithm controls the rate of adaptation. A smaller step size slows down the adaptation of the controllable filter 318 based on the acceleration and microphone sensor inputs. In one or more embodiments, the signal analysis controller 362 may notify the LMS controller 320 when a non-stationary event is detected so that the LMS controller may reduce the step size of its adaptive algorithm for the duration of the frame or non-stationary event. Reducing the step size for the duration of this frame may cause one or more of the controllable filters 318 to change less than they would otherwise due to the presence of these non-stationary inputs. During this frame, the controllable filter that does not receive the noise signal X(n) containing the food toilet may use an unmodified step size. In certain embodiments, the adaptation of one or more controllable filters may be completely suspended 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.

[0043] In an alternative embodiment at step 460, the sensor signal itself may be modified to mask non-stationary events and prevent false adaptation based on transient non-stationary events. One technique may be to simply deactivate or mute the RNC for the duration of the current DSP frame, resulting in a lack of anti-noise output signals Y(n) to some or all speakers 324 in the RNC system 300. In some embodiments, it may be possible to mute certain speakers having medium to high amplitude controllable filters 318 for specific noise signals X(n).

[0044] Since RNC systems typically have multiple feed-forward vibration sensors, response options 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, no anti-noise associated with this impulse event will be radiated into the passenger compartment. Likewise, if this is an ANC headphone, no anti-noise at all will be present during the frame. This can lead to the undesirable impression that the ANC is temporarily turned off (during the duration of the frame) and then restored after the frame. Sudden discontinuities at the beginning or end of a DSP frame can also create the impression of undesirable clicks from the speakers. Methods of time smoothing known to those skilled in the art of DSP can be applied to samples at the beginning and end of the current data frame to prevent this. Alternatively, the sample values ​​just before or after the current DSP frame are smoothed or changed to prevent audible clicks. It is possible to replace the current data frame with a signal close to zero amplitude to reduce audible clicks at the beginning and / or end of the frame. In one embodiment, the data in the current frame can be replaced by samples containing the average of one or more previous frames, which also eliminates or reduces audible clicks.

[0045] If the current frame of the feedforward noise signal from one vibration sensor is zeroed, the RNC system 300 may not exhibit this same undesirable behavior. This is because the anti-noise radiated from each speaker 324 is composed of signals from multiple vibration sensor outputs. For example, in an RNC system employing 6 dual-axis accelerometers or 4 triaxial accelerometers, there will be 12 accelerometer output X(n) signals. In the case of 6 dual-axis accelerometers, zeroing the current frame containing parameters indicating non-stationary events will result in the accelerometer signals used in creating the total anti-noise radiated from a particular speaker being reduced from 12 to 10. Therefore, this can result in a 1.5dB (i.e., 10 / 12) reduction in anti-noise amplitude compared to complete muting of the anti-noise of the speaker or all speakers during the continuous application of the frame.

[0046] In certain embodiments, more complex solutions are also possible, where the acceleration signal is zeroed only during the duration of the non-stationary event. This can shorten the continued application of reduced anti-noise, which in turn can also mask the non-stationary event. Other techniques are also possible, such as repeating the last frame of the output noise signal from the vibration sensor instead of zeroing it. In various embodiments, any of the foregoing mitigation techniques or combinations of techniques can be accompanied by a reduced playback level during all or a portion of the current frame. This can be achieved by reducing any W(z) filter amplitude or combination thereof or by an additional attenuation block (not shown) that reduces the level of one or more X'(n) or Y(n).

[0047] In the event that the non-stationary event is not completely eliminated in the adjusted noise signal X'(n) and / or the adjusted error signal e'(n), additional measures may be taken to accelerate re-adaptation to improve the RNC performance of the surrounding road surface more quickly. In one embodiment, the step size of one or more adjustable W filters where the adjusted noise signal X'(n) contains non-stationary events may be increased. The duration of this step size increase may be used for one or more frames, or to guide the system to re-adapt to restore the previous non-stationary event noise elimination performance. In one embodiment, leakage may be increased for the duration of one or more frames to more quickly reduce the impact of misadaptation on the W filter.

[0048] In the foregoing description, the inventive subject matter has been described with reference to specific exemplary embodiments. However, various modifications and changes may be made without departing from the scope of the inventive subject matter as set forth in the claims. The present description and drawings are illustrative rather than restrictive, and modifications are intended to be included within the scope of the inventive subject matter. Therefore, the scope of the inventive subject matter should be determined by the appended claims and their legal equivalents, rather than by the examples described alone.

[0049] For example, the steps recited in any method or method claim may be performed in any order and are not limited to the specific order presented in the claim. The equations may be implemented with filters to minimize the effects of signal noise. Additionally, the components and / or elements recited in any apparatus claim may be assembled or otherwise operatively configured in a variety of arrangements and are therefore not limited to the specific configuration recited in the claim.

[0050] It will be appreciated by those skilled in the art that functionally equivalent processing steps may be performed in the time domain or the frequency domain. Thus, although not explicitly stated for each signal processing block in the accompanying drawings, signal processing may occur in the time domain, the frequency domain, or a combination thereof. Furthermore, although various processing steps are typically explained in terms of digital signal processing, equivalent steps may be performed using analog signal processing without departing from the scope of the present disclosure.

[0051] The benefits, other advantages and solutions to problems have been described above with respect to specific embodiments. However, any benefit, advantage, solution to a problem, or any element that may make any particular benefit, advantage or solution to a problem appear or become more significant, should not be construed as a key, required or essential feature or part of any or all claims.

[0052] The terms "comprise," "comprises," "comprising," "having," "including," "includes," or any variation thereof, are intended to refer to a non-exclusive inclusion such that a process, method, article, composition, or apparatus that includes a list of elements includes not only those elements that are recited, but may include other elements not expressly listed or inherent to such process, method, article, composition, or apparatus. Other combinations and / or modifications of the above-described structures, arrangements, applications, proportions, elements, materials, or components for practicing the present subject matter, except those not specifically recited, may be varied or otherwise specifically adapted according to a particular environment, manufacturing specification, design parameters, or other operating requirements without departing from the general principles of the present subject matter.

Claims

1. A method for preventing misadaptation in a feed-forward road noise cancellation RNC system, the method comprising: adjusting an adaptive transfer characteristic based on a noise signal received from a sensor, an error signal received from a microphone located in a cabin of the vehicle, and an adaptive parameter; generating an anti-noise signal based in part on the adaptive transfer characteristic, the anti-noise signal to be radiated by a speaker into the cabin of the vehicle as anti-noise; receiving at least one sensor signal from at least one sensor; detecting a non-stationary event based on signal parameters sampled from frames of the at least one sensor signal; as well as modifying the adaptive parameters for a duration of the frame in response to detecting the non-stationary event; wherein the signal parameter is the amplitude of the sensor signal sampled in the frame; Wherein, detecting a non-stationary event based on signal parameters sampled from a frame of the at least one sensor signal comprises: comparing at least one amplitude of samples in the current frame of each sensor signal to an amplitude threshold; and A non-stationary event is detected when at least one amplitude of samples in the current frame exceeds the amplitude threshold for less than a predetermined time threshold. 2 . The method of claim 1 , wherein the sensor is a vibration sensor and the sensor signal is the noise signal.

3. The method of claim 1, wherein the sensor is the microphone and the sensor signal is the error signal.

4. The method of claim 1, wherein the amplitude threshold is a predetermined static threshold programmed for the RNC system.

5. The method of claim 1, wherein the amplitude threshold is a dynamic threshold calculated based on a statistical analysis of the at least one signal parameter in one or more previous frames of the sensor signal.

6. The method of claim 1, wherein modifying the adaptive parameters comprises: The adaptation rate of one or more controllable filters is reduced.

7. The method of claim 1, wherein modifying the adaptive parameters comprises: Adaptation of one or more controllable filters is suspended by reducing the adaptation rate of the controllable filters to zero.

8. The method of claim 1, wherein modifying the adaptive parameters comprises: The RNC system is deactivated for the duration of the frame.

9. A road noise cancellation RNC system, comprising: a sensor adapted to generate a sensor signal on at least one output channel in response to an input; a controllable filter adapted to generate an anti-noise signal based in part on the adaptive transfer characteristic, the anti-noise signal to be radiated by the speaker into a cabin of the vehicle as anti-noise; an adaptive filter controller comprising a processor and a memory, the adaptive filter controller being configured to control the adaptive transfer characteristic of the controllable filter based on a noise signal received from a sensor, an error signal received from a microphone located in the cabin of the vehicle, and an adaptive parameter; and A signal analysis controller, the signal analysis controller comprising a processor and a memory, the signal analysis controller being programmed to: detecting a non-stationary event based on signal parameters sampled from a current frame of the sensor signal; and modifying the adaptive parameters in response to detecting a non-stationary event; wherein the adaptive parameter determines the rate of change of the adaptive transfer characteristic of the controllable filter; wherein the signal parameter is the amplitude of the sensor signal sampled in the frame; Wherein, detecting a non-stationary event based on signal parameters sampled from a frame of the at least one sensor signal comprises: comparing at least one amplitude of samples in the current frame of each sensor signal to an amplitude threshold; and A non-stationary event is detected when at least one amplitude of samples in the current frame exceeds the amplitude threshold for less than a predetermined time threshold.

10. The RNC system of claim 9, wherein the signal analysis controller is programmed to modify the adaptation parameter by reducing an adaptation rate of the controllable filter.

11. The RNC system of claim 9, wherein the sensor is a vibration sensor and the sensor signal is the noise signal.

12. The RNC system of claim 9, wherein the sensor is the microphone and the sensor signal is the error signal.

13. The RNC system of claim 9, wherein the signal analysis controller is programmed to detect non-stationary events based on parameters sampled from a current frame of the sensor signals by comparing at least one signal parameter of the current frame of each sensor signal to an amplitude threshold.

14. A computer program product embodied in a non-transitory computer readable medium programmed for a road noise cancellation RNC, the computer program product comprising instructions for: receiving a sensor signal from at least one sensor; detecting a non-stationary event based on signal parameters sampled from frames of at least one sensor signal; as well as modifying, in response to detecting the non-stationary event, an anti-noise signal to be radiated by a speaker as anti-noise within a cabin of the vehicle for a duration of the frame; in, The signal parameter is the amplitude of the sensor signal sampled in the frame; Wherein, detecting a non-stationary event based on signal parameters sampled from a frame of the at least one sensor signal comprises: comparing at least one amplitude of samples in a current frame of each sensor signal to an amplitude threshold; and A non-stationary event is detected when at least one amplitude of samples in the current frame exceeds the amplitude threshold for less than a predetermined time threshold.

15. The computer program product of claim 14, wherein the instructions for modifying the anti-noise signal comprise: The frame containing the parameter indicative of the non-stationary event is zeroed.

16. The computer program product of claim 14, wherein the instructions for modifying the anti-noise signal comprise: The frame containing the parameter indicative of the non-stationary event is replaced with a previous frame from the same sensor signal.

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