Systems and methods for converting noise cancellation systems

Abstract

A vehicle-implemented noise cancellation system includes: a noise cancellation system disposed in the vehicle, the noise cancellation system including an adaptive filter that is adjusted based on a reference signal and an error signal, the adaptive filter outputting a noise cancellation signal that, when converted by a speaker into a noise cancellation audio signal, cancels road noise in at least one zone within a cabin of the vehicle; and an adjustment module configured to change a power of the noise cancellation signal or an adaptation rate of the adaptive filter from a first value to a second value through at least one intermediate value between the first and second values ​​based on a time-varying signal indicative of a signal-to-noise ratio of the reference signal relative to a first standard.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. patent application serial number 16 / 782,676, filed on February 5, 2020, and entitled “Systems and Methods for Transitioning a Noise-Cancellation System,” which is incorporated herein by reference in its entirety. Background Art

[0003] The present disclosure generally relates to systems and methods for transitioning a noise cancellation output signal or adaptation rate from a first value to a second value. Various examples relate to systems and methods for smoothly transitioning a noise cancellation or adaptation rate from a first value to a second value. Summary of the Invention

[0004] All examples and features mentioned below can be combined in any technically possible way.

[0005] In one aspect, a vehicle-implemented noise cancellation system includes: a noise cancellation system disposed in the vehicle, the noise cancellation system including an adaptive filter that is adjusted based on a reference signal and an error signal, the adaptive filter outputting a noise cancellation signal that, when converted by a speaker into a noise cancellation audio signal, cancels road noise in at least one zone within a cabin of the vehicle; and an adjustment module configured to change the power of the noise cancellation signal or the adaptation rate of the adaptive filter from a first value to a second value through at least one intermediate value between the first value and the second value based on a comparison of a time-varying signal indicative of a signal-to-noise ratio of the reference signal with a first standard.

[0006] In one example, the time-varying signal is at least one of: the speed of the vehicle, the power of the reference signal, the revolutions per minute of the vehicle's engine, the gear of the vehicle's engine, and a similarity measure between outputs of at least two of the reference sensor signals.

[0007] In one example, the first criterion is at least one fixed threshold.

[0008] In one example, the first criterion is at least one variable threshold, and a variation of the at least one variable threshold is based on a second time-varying signal indicative of a signal-to-noise ratio of the reference signal.

[0009] In one example, the intermediate value is determined according to a predetermined function of the time-varying signal.

[0010] In one example, the predetermined function is a linear function.

[0011] In one example, the predetermined function is a logarithmic function.

[0012] According to another aspect, a vehicle-implemented noise cancellation system includes: a noise cancellation system disposed in the vehicle, the noise cancellation system including an adaptive filter that is adjusted based on a reference signal and an error signal, the adaptive filter outputting a noise cancellation signal that, when converted by a speaker into a noise cancellation audio signal, cancels road noise in at least one zone within a cabin of the vehicle; and an adjustment module configured to change the power of the noise cancellation signal or the adaptation rate of the adaptive filter from a first value to a second value based on a comparison of a time-varying input indicating a state of the vehicle or a relationship measure between two or more reference sensors with a first standard.

[0013] In one example, the state of the vehicle is at least one of: a speed of the vehicle, revolutions per minute of an engine of the vehicle, a gear of an engine of the vehicle.

[0014] In one example, the first criterion is at least one fixed threshold.

[0015] In one example, the first criterion is at least one variable threshold, and a variation of the at least one variable threshold is based on a second time-varying signal indicative of a signal-to-noise ratio of the reference signal.

[0016] According to another aspect, a computer-implemented method for smoothly transitioning a vehicle-implemented noise cancellation system from an off state to an on state includes: receiving input indicating a signal-to-noise ratio of a reference sensor of the noise cancellation system; comparing a value of a signal to a first threshold, wherein if the value of the signal is less than the first threshold, setting a power of the noise cancellation signal or an adaptation rate of the noise cancellation system to a first value, wherein if the value of the signal is greater than the first threshold, performing the following steps: comparing the value of the signal to a second threshold, wherein if the value of the signal is greater than the second threshold, setting the power or adaptation rate of the noise cancellation to a second value, wherein if the signal is greater than the first threshold and less than the second threshold, setting the power or adaptation rate of the noise cancellation signal to an intermediate value, wherein the second threshold is greater than the first threshold.

[0017] In one example, the input is at least one of: a speed of the vehicle, a power of a reference signal, rpm of an engine of the vehicle, a gear of an engine of the vehicle, and a similarity measure between outputs of at least two reference sensors.

[0018] In one example, the value of the intermediate value is determined according to a predetermined function of the input.

[0019] In one example, the predetermined function is a linear function.

[0020] In one example, the predetermined function is a logarithmic function.

[0021] In one example, the values ​​of the first threshold and the second threshold are determined based on a second signal indicating a signal-to-noise ratio of a reference sensor.

[0022] In one example, the computer-implemented method further comprises the following steps: receiving a second input indicating a signal-to-noise ratio of a reference sensor; comparing the value of the second signal with a third threshold, wherein if the value of the signal is less than the third threshold, setting the first threshold to a first threshold value, wherein if the value of the second signal is greater than the third threshold, performing the following steps: comparing the value of the second signal with a fourth threshold, wherein if the value of the second signal is greater than the fourth threshold, setting the first threshold to a second threshold value, wherein if the second signal is greater than the third threshold and less than the fourth threshold, setting the first threshold to an intermediate value, wherein the second threshold is greater than the first threshold.

[0023] The details of one or more implementations are discussed in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the various aspects.

[0025] Figure 1 Depicted is a schematic diagram of a noise cancellation system according to an example.

[0026] Figure 2 Depicted is a block diagram of a noise cancellation system according to one example.

[0027] Figure 3A Depicted is a flow chart of a method for transitioning a noise cancellation signal from a first value to a second value, according to one example.

[0028] Figure 3B Depicted is a flow chart of a method for transitioning a noise cancellation signal from a first value to a second value, according to one example.

[0029] Figure 3C Depicted is a flow chart of a method for transitioning an adaptation rate of an adaptive filter from a first value to a second value, according to one example.

[0030] Figure 3D Depicted is a flow chart of a method for changing a threshold to shift a noise cancellation signal or adaptation rate, according to one example.

[0031] Figure 4A A graph depicts combined power spectral density of multiple reference sensors according to an example.

[0032] Figure 4B A graph depicting average power spectral density of multiple reference sensors according to an example.

[0033] Figure 5 Depicted is a graph of transitioning the gain of a noise cancellation signal and a step size from a first value to a second value according to one example.

[0034] Figure 6A Depicted is a flow chart of a method for smoothly transitioning a noise cancellation signal from a first value to a second value, according to one example.

[0035] Figure 6B Depicted is a flow chart of a method for smoothly transitioning an adaptation rate of an adaptive filter from a first value to a second value, according to one example.

[0036] Figure 6C Depicted is a flow chart of a method for smoothly transitioning a noise cancellation signal or adaptation rate from a first value to a second value, according to one example.

[0037] Figure 7 Depicted is a graph of transitioning the gain of a noise cancellation signal and a step size from a first value to a second value according to one example.

[0038] Figure 8 Depicted is a graph of smoothly changing a threshold to transition the gain of a noise cancellation signal and a step size from a first value to a second value according to one example. DETAILED DESCRIPTION

[0039] Adaptive noise cancellation systems employ at least one reference signal from a reference sensor to generate a noise cancellation signal. If the noise cancellation system is deployed in a vehicle, the reference sensor is typically an accelerometer operably mounted to the vehicle for detecting vibrations in the chassis, which are converted by the chassis into what passengers perceive as road noise. In some situations, such as at low speeds, the chassis vibrations are insufficient to generate an output that would cause the noise cancellation system to adapt in a manner that better cancels noise in the vehicle cabin (in other words, the signal-to-noise ratio is too low to cause the adaptive filter to adapt). In these situations, the noise cancellation system adapts to the accelerometer's noise floor rather than the vehicle's chassis vibrations, which can degrade the noise cancellation system's performance or add noise to the output of speakers in the vehicle.

[0040] Various examples described in this disclosure relate to a vehicle-implemented noise cancellation system that reduces or cuts off a noise cancellation audio signal and / or slows or stops adaptation of the noise cancellation system when an accelerometer's signal-to-noise ratio (SNR) is too low to allow the noise cancellation system to adapt to better cancel noise in the vehicle cabin. In some of these examples, the road noise cancellation system smoothly transitions from an off state to an on state when road noise in the cabin increases from zero or a negligible amount to an amount detectable by the accelerometer. The smooth transition from the off state to the on state may include smoothly adjusting the gain of the noise cancellation audio signal from zero to one through at least one intermediate value. In addition to or in lieu of transitioning the gain from zero to one, the smooth transition from the off state to the on state may also include smoothly transitioning the noise cancellation system from a non-adapting state to a state adapted to the accelerometer output.

[0041] For the purpose of illustration, Figures 1 to 2 An example of a vehicle-implemented noise cancellation system is briefly described. Figure 1 is a schematic diagram of an exemplary noise cancellation system 100. Noise cancellation system 100 can be configured to destructively interfere with undesirable sounds in at least one cancellation zone 102 within a predefined volume 104, such as a vehicle cabin. At a high level, one example of noise cancellation system 100 can include a reference sensor 106, an error sensor 108, an actuator 110, and a controller 112.

[0042] In one example, the reference sensor 106 is configured to generate a noise signal 114 that is representative of an undesired sound or a source of an undesired sound within the predefined volume 104. For example, Figure 1 As shown, reference sensor 106 may be an accelerometer or a plurality of accelerometers mounted and configured to detect vibrations transmitted through vehicle structure 116. Vibrations transmitted through vehicle structure 116 are converted by the structure into undesirable sounds within the vehicle cabin (perceived as road noise), and thus the accelerometers mounted to the structure provide signals representative of the undesirable sounds.

[0043] The actuators 110 may be, for example, speakers distributed at discrete locations around the perimeter of a predefined volume. In one example, four or more speakers may be positioned within a vehicle cabin, with each of the four speakers located within a respective door of the vehicle and configured to project sound into the vehicle cabin. In alternative examples, the speakers may be located within headrests or elsewhere within the vehicle cabin.

[0044] Noise cancellation signal 118 may be generated by controller 112 and provided to one or more speakers in the predefined volume, which convert the noise cancellation signal 118 into acoustic energy (i.e., sound waves). Because the acoustic energy generated by noise cancellation signal 118 is approximately 180° out of phase with the undesired sound within cancellation zone 102 and therefore destructively interferes with the undesired sound, the combination of the sound waves generated from noise cancellation signal 118 and the undesired noise in the predefined volume results in cancellation of the undesired noise, which is perceived by a listener in the cancellation zone.

[0045] Because noise cancellation cannot be equal throughout a predefined volume, the noise cancellation system 100 is configured to produce maximum noise cancellation within one or more predefined cancellation zones 102 within the predefined volume. Noise cancellation within the cancellation zone can reduce undesirable sounds by approximately 3 dB or more (although different amounts of noise cancellation may occur in different examples). Furthermore, noise cancellation can cancel sounds within a certain frequency range, such as frequencies less than approximately 350 Hz (although other ranges are possible).

[0046] An error sensor 108, positioned within the predefined volume, generates an error sensor signal 120 based on detection of residual noise resulting from a combination of the sound waves generated from the noise cancellation signal 118 and undesirable sounds within the cancellation zone. Error sensor signal 120 is provided as feedback to controller 112 and represents the residual noise that was not canceled by the noise cancellation signal. Error sensor 108 may be, for example, at least one microphone mounted within the vehicle cabin (e.g., on the roof, headrests, pillars, or elsewhere within the cabin).

[0047] It should be noted that the cancellation zone may be located away from the error sensor 108. In this case, the error sensor signal 120 may be filtered to represent an estimate of the residual noise in the cancellation zone. In either case, the error signal will be understood to represent the residual undesired noise in the cancellation zone.

[0048] In one example, the controller 112 may include a non-transitory storage medium 122 and a processor 124. In one example, the non-transitory storage medium 122 may store program code that, when executed by the processor 124, implements the various filters and algorithms described below. The controller 112 may be implemented in hardware and / or software. For example, the controller may be implemented by a SHARC floating-point DSP processor, but it should be understood that the controller 112 may be implemented by any other processor, FPGA, ASIC, or other suitable hardware.

[0049] Go to Figure 2, shows a block diagram of an example of a noise cancellation system 100 that includes multiple filters implemented by a controller 112. As shown, the controller may define a filter including W adapt The control system of the filter 126 and the adaptive processing module 128.

[0050] W adapt The filter 126 is configured to receive the noise signal 114 of the reference sensor 106 and generate a noise cancellation signal 118. As described above, the noise cancellation signal 118 is input to the actuator 110 where it is converted into a noise cancellation audio signal that destructively interferes with the undesired sounds in the predefined cancellation zone 102. adapt Filter 126 may be implemented as any suitable linear filter, such as a multiple-input multiple-output (MIMO) finite impulse response (FIR) filter. adapt The filter 126 employs a set of coefficients that define the noise cancellation signal 118 and can be adjusted to accommodate the changing behavior of the vehicle in response to road inputs (or other inputs in the non-vehicle noise cancellation environment).

[0051] The adjustment of the coefficients may be performed by an adaptive processing module 128 that receives the error sensor signal 120 and the noise signal 114 as inputs and uses these inputs to generate a filter update signal 130. The filter update signal 130 is a filter signal generated by the W adapt The filter coefficients implemented in the filter 126 are updated. adapt The noise cancellation signal 118 produced by the filter 126 will minimize the error sensor signal 120 and, therefore, minimize the undesired noise in the cancellation zone.

[0052] W at time step n can be updated according to the following formula adapt The coefficients of filter 126 are:

[0053]

[0054] in is an estimate of the physical transfer function between the actuator 110 and the noise cancellation zone 102, yes , e is the error signal, and x is the output signal of the reference sensor 106. In the update formula, the output signal x of the reference sensor is divided by the norm of x, expressed as ‖x‖2.

[0055] In an application, the total number of filters is typically equal to the number of reference sensors (M) multiplied by the number of loudspeakers (N). Each reference sensor signal is filtered N times, and then each loudspeaker signal is obtained as the sum of the M signals (each sensor signal is filtered by the corresponding filter).

[0056] The noise cancellation system 100 also includes an adjustment module 132 configured to vary the power of the noise cancellation signal 118 and the adaptive filter W as implemented by the adaptive processing module 128 in response to signals received from the reference sensor 106 or input from the engine computer unit 134. adapt At least one of the adaptation rates of the filter 126. The adjustment module may be configured according to FIG. Figure 8 This can be achieved by one of the various methods described.

[0057] same, Figure 1 and Figure 2 The noise cancellation system 100 is provided merely as an example of such a system. This system, variations of this system, and other suitable noise cancellation systems may be used within the scope of the present disclosure. For example, while Figures 1 to 2 The system has been described in conjunction with a least mean square filter (LMS), but in other examples, a different type of filter may be implemented, such as one implemented using a recursive least squares (RLS) filter.

[0058] Figure 3 to Figure 8 A flow chart and associated graphs depict a computer-implemented method for adjusting the output and / or adaptation of a vehicle-implemented noise cancellation system when the SNR of an accelerometer is too low to allow the noise cancellation system to adapt in a manner that better cancels noise in the vehicle cabin. Figure 8 The computer-implemented method described may be executed by a controller (such as the controller 112) or a controller suitable for executing the method in conjunction with FIG. Figure 8 The described methods may be implemented on any computing device.

[0059] Figure 3AA high-level flow chart depicts a method for adjusting the output and adaptation of a noise cancellation system implemented in a vehicle. Steps 302-306 generally entail receiving a time-varying input representing the signal-to-noise ratio of at least one reference sensor and transitioning the power of the noise cancellation signal and the adaptation rate of the noise cancellation system from a first level to a second level (e.g., from an off state to an on state) based on a comparison of the input to a standard. In one example, and as will be described below, the standard can be a fixed or variable threshold against which the input is compared. If the value of the input is below the threshold, which generally indicates that the SNR of the reference sensor is too low to cause the adaptive filter to adapt, the noise cancellation signal and / or adaptation is set to an off state. If the input is above the threshold, the noise cancellation signal and / or adaptation is set to an on state.

[0060] At step 304, an input is received indicating a signal-to-noise ratio of a reference sensor. For purposes of this disclosure, a reference sensor is any sensor within a predefined volume that generates a noise signal representative of an undesired sound or source of an undesired sound and is used to update the adaptive filter of the noise cancellation system.

[0061] The input indicative of the signal-to-noise ratio of at least one reference sensor may be any signal (or set of signals) that has a positive correlation with the signal-to-noise ratio of the reference sensor in the context of the vehicle. Examples of such signals include signals related to the state of the vehicle, such as the speed of the vehicle, the revolutions per minute of the vehicle's engine, or the gear of the vehicle's engine, all of which generally increase with improvement in the signal-to-noise ratio of the reference sensor. These inputs of the state of the vehicle may be received via the vehicle's CAN bus from an engine computer unit (e.g., Figure 2 The engine computer unit 134 shown receives it.

[0062] Furthermore, the input indicating the signal-to-noise ratio of at least one reference sensor can be the result of preliminary processing of the output reference sensor. For example, the input can be the power of the noise signal output by the reference sensor. In this example, the input requires a preliminary step of determining the power of the sensor signal, such as by determining the power spectral density of the sensor signal or the average of the power spectral density across frequency and / or time. Any suitable method for determining the power spectral density of the reference sensor can be used for this preliminary step. For example, the combined PSD of multiple reference sensors can be defined as follows.

[0063]

[0064] where PSD(x,n) is the combined power spectral density of all reference sensor signals at time n, N ref is the total number of reference sensors used for road noise cancellation (alternatively, a subset of reference sensors may be used), and w j,kis the weight associated with the jth reference sensor and the kth frequency bin. j,k Determine which reference sensors and which frequency intervals to consider. In other words, reference sensor outputs can be weighted differently, and / or certain frequencies can be weighted differently based on relevance. For example, a frequency range of interest can be used. Road noise is typically below 400 Hz, and therefore, in one example, only power below 400 Hz is used.

[0065] That is, the PSD estimate of the jth reference sensor at frequency bin k and time index n can be calculated as:

[0066]

[0067] where X j(n,k) is the frequency domain value of the jth accelerometer at frequency bin k and time index n, and α is the forgetting factor. This is provided merely as an example of a method for finding the PSD for a given reference sensor, and thus, in alternative examples, any other suitable method for finding the PSD may be used.

[0068] As mentioned above, the time-varying input can be the combined (i.e., summed) PSD of multiple reference sensors. An example of the combined PSD of multiple accelerometers is Figure 4A The PSD is shown in a graph across various vehicle states and road surfaces, including: vehicle off, input 0 mph, smooth road 5 mph, smooth road 10 mph, gravel road 5 mph, and gravel road 10 mph. In this example, an amplitude that varies with frequency can be used. Alternatively, the PSD can be averaged across frequencies or a range of frequencies to yield a single power value, which can be estimated based on a standard. Alternatively, the power of each frequency bin of the PSD can be compared to a standard, which will be described in conjunction with FIG3 .

[0069] In an alternative example, multiple PSDs can be averaged over frequency. Figure 4B 3 for various vehicle states and road surfaces, including: vehicle in the off state, input of 0 mph, smooth road at 5 mph, smooth road at 10 mph, gravel road at 5 mph, and gravel road at 10 mph. In an alternative example, the PSD of a single reference sensor can be used. In yet another example, the method described in conjunction with FIG. 3 can be repeated for each of these reference sensors, each time using a value associated with the PSD of a different reference sensor. In other words, the method described in conjunction with FIG. 3 can be repeated for each individual reference sensor, with each iteration of the method comparing the PSD of that individual sensor to a standard.

[0070] Instead of (or in addition to) relying on the power of the reference sensor signals, a value indicating a measure of similarity between the reference sensor signals can be used. Such a measure of similarity includes, for example, coherence or correlation between the reference sensor signals. Because there is no similarity between the noise floors of the various reference sensors, the similarity measure between the sensors will be approximately zero when the vehicle is stationary. In contrast, when the vehicle is in motion, there will be some measurable similarity between the reference sensor signals because the vibrations in the vehicle cabin are correlated. Therefore, the similarity measure between the reference sensor signals will be positively correlated with the signal-to-noise ratio of the reference sensor signals because there will generally be some similarity between the reference sensor signals when there is signal output rather than just noise.

[0071] For example, coherence is a measure of the linear relationship between reference sensors. Because the noise output of each reference sensor is uncorrelated, the coherence between the reference sensors will be approximately zero when the vehicle is stationary. However, once the vehicle begins to move and vibrations are transmitted through the vehicle chassis, the coherence between the sensors will reach a certain positive value because the vibrations at different points in the vehicle will be correlated. In theory, if the vibrations transmitted through the vehicle are the same, the coherence between the reference sensors will be equal to one. However, because the wheels of a vehicle do not vibrate in the same way, and because vibrations are not transmitted in the same way through the vehicle, the coherence between the reference sensors will be somewhere between zero and one when the vehicle is moving.

[0072] In one example, the cumulative coherence between multiple reference sensors can be expressed as:

[0073]

[0074] where w s,l,k Determine the calculation set {x} s Multi-coherence between the single reference sensor l Which reference sensor sets and which range of frequency bins are considered when determining the frequency bins. A subset of frequencies (e.g., below 400 Hz) may be used.

[0075] Similarly, correlation between two or more sensors may be used. Typically, coherence is more desirable because coherence is normalized; however, it will be appreciated that any suitable similarity measure may be used as input.

[0076] Return to Figure 3AAt step 304, based on a comparison of the input representing the SNR of the reference sensor with the standard, the gain of the noise cancellation signal is transitioned from a first value (e.g., zero) to a second value (e.g., one), thereby causing the power of the noise cancellation signal to transition from the first value to the second value. In alternative examples, the standard may be a fixed threshold or a variable threshold. Thus, upon determining that the input is above the fixed or variable threshold, the power of the noise cancellation signal is transitioned from the first value to the second value.

[0077] In one example, the power can be changed from a first value to a second value by changing the gain of the noise cancellation signal. This is shown by the following formula:

[0078] b(n)=G input (n) b in (n) (5)

[0079] where b in(n) is the road noise cancellation signal generated by the adaptive filter, and G input(n) is the gain, which is calculated as follows:

[0080]

[0081] In other words, when the value of the variable input (labeled as INP 1(n) ) is less than or equal to threshold I1, the gain is set to 0, and thus, the noise cancellation signal is turned off, while when the time-varying input is above threshold I1, the gain is set to 1, and the noise cancellation signal is sent to the speaker without attenuation. The power of the noise cancellation signal thus varies from zero to a second value representing an unattenuated noise cancellation signal. In an alternative embodiment, the gain may be set to a value that will produce a noise cancellation signal of negligible power (i.e., power that is not noticeable to the user). Typically, the unattenuated noise cancellation signal will be a value that produces the maximum allowable cancellation of the noise signal. However, in another example, the first value may be a predetermined non-zero value. Even if the noise level is too low to adapt the adaptive filter, the noise cancellation signal can still be played, with the unadapted adaptive filter behaving like a fixed filter (having a predetermined or previously stored set of coefficients). In this case, the first value may be a small gain value that results in cancellation of minor road noise in the vehicle cabin during low-speed driving on most road surfaces.

[0082] Typically, threshold I1 is set to a minimum value at which the noise cancellation signal is generated. In the example of a vehicle speed input, threshold I1 would be set to a speed value at which road noise is present in the vehicle cabin (e.g., 10 mph) that can be canceled by the noise cancellation audio signal. It will be appreciated that the threshold value will depend on the type of input selected (e.g., vehicle speed, coherence, etc.).

[0083] Figure 3B An exemplary flow chart for step 304 is shown, in which the input is compared to a threshold. At step 310, the input (described in conjunction with step 302) is compared to a fixed threshold (e.g., a vehicle speed of ten miles per hour). This is represented by a conditional box that asks whether the input exceeds the threshold. If the answer to this condition is no, then at step 312, the gain of the noise cancellation signal is set to a first value (e.g., zero or a negligible amount); and if the answer to this condition is yes, then at step 314, the noise cancellation signal is set to a second value (e.g., the noise cancellation signal is set to a gain of one).

[0084] Return to Figure 3A Simultaneously with step 304 or at some point thereafter, an adaptation rate of the noise cancellation system, typically updated by the adaptive module, is transitioned from a first value (e.g., zero) to a second value (e.g., one) based on a comparison of an input representing the SNR of the reference sensor with a standard. In one example, this can be achieved by changing the step size gain of an update formula used by the adaptive processing module to update the adaptive filter. When the step size is zero, the adaptive processing module will not update the coefficients of the adaptive filter. When the step size gain is one, the adaptation rate is set to a certain optimal level for updating the coefficients of the adaptive filter.

[0085] In one example, the adaptation rate of the noise cancellation filter may be varied according to the following formula:

[0086] μ(n)=μ0·μ input (n) (7)

[0087] where μ0 is the maximum allowed step size of the adaptive filter, and μ input (n) is the input-dependent step size gain, which can be calculated as follows:

[0088]

[0089] In this example, the step size gain is zero when the input is less than or equal to the threshold I1 and is equal to one when the input is greater than the threshold I1. Therefore, the adaptive filter stops adapting when the input is below the threshold and starts adapting the adaptive filter when the input is above the threshold.

[0090] Figure 3CAn exemplary flow chart of step 306 of method 300 is shown. At step 316, an input signal is received and compared to a threshold. If the input signal (e.g., vehicle speed) is less than the threshold (e.g., 10 mph), then at step 318, the step size gain is set to a first value (e.g., zero); however, if the signal is greater than the threshold, then at step 320, the step size gain is set to a second value (e.g., one).

[0091] Figure 5 A graph showing the gain of the noise cancellation signal and step size as a function of vehicle speed (an exemplary input) is shown. As shown, the gain is set to 0 until the vehicle speed reaches a threshold I1 where the gain of both the noise cancellation signal and step size are set to one.

[0092] Generally speaking, adaptation occurs simultaneously with the generation of the noise cancellation signal, and therefore, the threshold used to begin adaptation is the same as the threshold used to begin generating the noise cancellation signal. Starting adaptation of the adaptive filter before the generation of the noise cancellation audio signal is undesirable because the update formula relies on an error signal that assumes the noise cancellation system is fully operational. In other words, if adaptation begins before the generation of the noise cancellation audio signal, the update formula will be updated even if the noise cancellation audio signal is playing but fails to cancel any undesired sounds in the vehicle cabin, and the adaptive filter will be incorrectly updated. However, in various alternative embodiments, adapting the adaptive filter can occur at some point after the generation of the noise cancellation signal begins. In one example, the input can be compared to a different, higher threshold. For example, if the input is vehicle speed, adaptation can begin at a speed higher than the speed at which the noise cancellation audio signal begins to be generated. In a simpler example, adaptation of the adaptive filter can begin at a predetermined time interval (e.g., one second) after the generation of the noise cancellation signal begins, rather than relying on a threshold.

[0093] It will be appreciated that, before the adaptive filter is adapted, it will behave like a fixed filter. In this case, the coefficients of the (fixed) adaptive filter may be set to some default coefficient values ​​that produce road noise cancellation for most road surfaces, or to some previously stored coefficient set.

[0094] Combine Figures 3A to 3CThe examples described above compare an input to a fixed threshold. However, in some cases, the fixed threshold may not adequately capture the actual SNR of the reference sensor (even if the input is correlated with the SNR of the reference sensor). For example, while an input of vehicle speed may accurately represent road noise under most road conditions, the input will not represent road noise under rough road conditions (e.g., if the vehicle is traveling over cobblestones). Therefore, a second input, such as the power of the reference sensor, may be analyzed to determine the threshold against which the first input is analyzed. In other words, the threshold against which the first signal (e.g., the speed of the vehicle) is compared may itself be determined by comparing the second input (e.g., the power of the reference sensor) to the second threshold, as follows:

[0095]

[0096] Among them, INP 2(n) is the second input, I 1max The first threshold value of the threshold I1 and I 1min The second threshold value of threshold I1, I var1 is the variance threshold (i.e., the threshold against which the second input is compared to determine the change in the first threshold). Typically, the first threshold value I 1max The second threshold value I 1min , the subscripts "max" and "min" refer to the maximum value that the threshold is set to, rather than the maximum and minimum possible values ​​of the threshold.) More specifically, the variance threshold I var1 It can be set so that on a paved road surface, the power of the reference sensor is insufficient to move the first threshold to a lower value I 1min It is set so that under rough road conditions, the second input INP 2(n) Will exceed the variance threshold I var1 , and thus the first threshold is set to the second threshold value Under normal driving conditions, the first input INP 1(n) will be compared with the first threshold value I 1max Under rough road conditions, the first input INP 1(n) The second threshold value I 1min This compensates for instances where the first threshold does not adequately represent the signal-to-noise ratio of the reference sensor.Because the second input is a different type of input than the first input, the second threshold will typically be different from the first threshold.

[0097] Figure 3D Describes the changes Figure 3B and Figure 3C Flowchart of method 322 for adjusting the threshold to adapt to different road conditions. Figure 3D The method 322 is described as being executed before the step of comparing the first input to the first threshold; however, due to the combination of Figure 3B and Figure 3C The methods described typically loop over multiple samples. Figure 3D The steps can be found in Figure 3B and Figure 3C Run after the steps.

[0098] At step 324, a second input is received. This input may be a combination of Figure 3A One of the inputs described in step 302, however, must be a different type of input than the inputs compared to the thresholds in steps 304 and / or 306. For example, if vehicle speed is used in step 304, then a similarity measure between reference sensor signals, such as power, may be used for the second input.

[0099] At step 326, the second input is compared to the variance threshold at condition block 326. If the second input is below the variance threshold, then the threshold is maintained at the first threshold value at step 328. However, if the second input is above the variance threshold, then the first threshold is set to the second threshold value at step 330. The second threshold value is typically less than the first threshold value because a higher value of the second input indicates a second condition (e.g., a rough road condition) that may increase noise in the vehicle cabin.

[0100] The above method takes into account the case where the SNR of the reference sensor is too low to update the adaptive filter. However, suddenly turning on the noise cancellation signal may be abrupt and jarring to the user. Therefore, combined with Figure 6A Methods for smoothly transitioning a noise cancellation signal and / or an adaptation rate from a first value (eg, an off state) to a second value (eg, an on state) are described.

[0101] Combined with Figure 3A As with the method described above, at step 602, an input is received indicating the SNR of at least one reference sensor. The input may be any input related to the signal-to-noise ratio of the at least one reference sensor. Examples of such inputs have been described in conjunction with step 302.

[0102] At step 304, based on a comparison of the input representing the reference sensor with the standard, the power of the noise cancellation signal is smoothly transitioned from a first value (e.g., zero) to a second value (e.g., a gain). The smooth transition requires passing through at least one intermediate value between the first value and the second value, but it is contemplated that the power of the noise cancellation signal may transition through multiple intermediate values ​​on its way from the first value to the second value. The values ​​of the intermediate values ​​may be fixed or may be determined by a function.

[0103] For example, the power can be changed from a first value to a second value by changing the gain of the noise cancellation signal. This is shown by the following formula:

[0104] b(n)=G input (n) b in (n) (10)

[0105] where b in(n) is the road noise cancellation signal generated by the adaptive filter, and G input(n) is the gain, which is calculated as follows:

[0106]

[0107] When the value of the time-varying input INP 1(n) Below or equal to the first threshold value I1, the gain is therefore set to 0, and thus the noise cancellation signal is turned off (or alternatively, set to a negligible value or some other predetermined value), while when the time-varying input is above the second threshold value I2, the gain is set to unity. However, when the input is between the first and second thresholds, the noise cancellation signal gain is defined by a formula that changes linearly between the first and second values. Thus, in this example, the gain changes linearly between the first and second values, thereby smoothly transitioning the noise cancellation signal from the off state to the on state.

[0108] In another example, the intermediate value can be a fixed value. For example, rather than setting the intermediate value according to a linear formula, the intermediate value can be a fixed value between the first value and the second value (e.g., a gain of 0.5). In yet another example, a different function, such as a logarithmic function, can define the intermediate value.

[0109] Figure 6B An exemplary flow chart depicts step 604, in which an input is compared to at least two thresholds and, if the input is between a first threshold and a second threshold, the input is set to an intermediate value. At step 608, the input (an example of which was described in conjunction with step 302) is compared to a first threshold (e.g., a vehicle speed of ten miles per hour). This is represented by conditional block 608, which inquires whether the input exceeds the first threshold. If the value of the input is less than the first threshold, then at step 610, the noise cancellation signal is set to a first value. In one example, the first value may be zero or a negligible value (i.e., a value that would result in playback of the noise cancellation audio signal imperceptible to the user). However, in alternative examples, the first value may be a predetermined non-zero value. As described above, even if the noise level is too low to adapt the adaptive filter, the noise cancellation signal can still be played; the adaptive filter, which has not yet adapted, behaves like a fixed filter (having a predetermined or previously stored set of coefficients). In this case, the first value may be some small gain value that results in cancellation of minor road noise in the vehicle cabin during driving at low speeds on most road surfaces.

[0110] If the input value is above the first threshold, then at step 612, the input is compared to a second threshold value. This is represented by conditional box 612 asking whether the input exceeds the second threshold. If the input is above the second threshold, then at step 616, the gain of the noise cancellation signal is set to a second value (e.g., one), which results in the noise cancellation audio signal being played at a level that produces optimal cancellation. However, if the noise cancellation signal is below the second threshold, then at step 614, the gain of the noise cancellation signal is set to a value according to a predetermined function (e.g., a linear function as disclosed in formula (11), or a logarithmic function). As described above, in an alternative example, the intermediate value can be a predetermined value (e.g., a 0.5 gain value).

[0111] Return to Figure 6A At step 606, the adaptation rate may also be smoothly transitioned from a first value (e.g., zero) to a second value (e.g., one) based on a comparison of the input representing the SNR of the reference sensor with the standard. In one example, this may be achieved by changing the step size gain of the update formula used by the adaptive processing module to update the adaptive filter. When the step size gain is zero, the adaptive processing module will not update the coefficients of the adaptive filter. When the step size gain is one, the adaptation rate is typically set to some optimal level for updating the coefficients of the adaptive filter. Again, the smooth transition requires passing through at least one intermediate value between the first value and the second value, but it is contemplated that the adaptation rate may transition through multiple intermediate values ​​on its way from the first value to the second value. The values ​​of the intermediate values ​​may be fixed or may be determined by a function.

[0112] In one example, the adaptation rate of the noise cancellation filter may be varied according to the following formula:

[0113] μ(n)=μ0·μ input (n) (12)

[0114] where μ0 is the maximum allowed step size of the adaptive filter, and μ input (n) is the input-dependent step size gain, which can be calculated as follows:

[0115]

[0116] Thus, when the input is less than or equal to the third threshold value, the step size gain is set to zero (causing adaptation to stop). When the input is greater than the fourth threshold value, the step size gain is set to one. When the value of the input is between the third threshold value and the fourth threshold value, the step size gain is determined by the linear function shown in formula (13). Thus, as the input value increases, the step size ramps linearly from the first value to the second value. In an alternative example, the intermediate value can be determined by a different function, such as a logarithmic function. In yet another example, the intermediate value can be a fixed value (e.g., 0.5).

[0117] Generally speaking, the third threshold is equal to or higher than the second threshold used in step 612 (and described in Equation 11) to ensure that the noise-canceling audio signal is played at the optimal volume before adaptation of the adaptive filter begins. This ensures that the adaptive filter is not updated with an incorrect error signal. In one example, if some compensation for an incorrect error signal is provided, the third threshold can be set to a value lower than the second threshold. For example, the error signal can be minimized by a gain value less than one, where the error signal gain value is determined by the value of the gain of the noise-canceling signal.

[0118] Figure 6C A flowchart illustrating an exemplary implementation of step 616 is shown. At step 618, the input (an example of which was described in conjunction with step 302) is compared to a first threshold (e.g., a vehicle speed of 20 miles per hour). This is indicated by conditional block 618, which inquires whether the input exceeds a third threshold. If the input value is less than the third threshold, then at step 620, the step size gain is set to a first value by adjusting the gain of the adaptation rate.

[0119] If the input value is above the third threshold, then at step 622, the input is compared to a fourth threshold value. This is represented by a conditional box 622 that asks whether the input exceeds the fourth threshold. If the input is above the fourth threshold, then at step 626, the step size is set to a second value (e.g., an optimal step size) by adjusting the gain to a second value (e.g., one). However, if the input is below the second threshold, then at step 624, the gain of the step size is set to a certain value according to a predetermined function (e.g., a linear function as disclosed in formula (13), or a logarithmic function). In an alternative example, the intermediate value can be a predetermined value (e.g., a 0.5 gain value).

[0120] Figure 6B and Figure 6C The flowcharts of each illustrate a single example of a computer-implemented method that will be run in a loop to achieve a smooth transition of the noise cancellation signal and the adaptation rate, respectively. In practice, to transition from a first value to a second value via an intermediate value, Figure 6B and Figure 6CThe method will need to be looped at least three times to set the gain to the first value, the intermediate value, and the second value respectively.

[0121] Figure 7 A graph depicts the gain of the noise cancellation signal and step size for the vehicle speed input according to equations (11) and (13). As shown, at a first threshold I1, the gain of the noise cancellation signal increases linearly until reaching a second threshold I2. Similarly, at a third threshold I3, the gain of the step size increases linearly until reaching a fourth threshold I4. In this example, and as described above, the third threshold is generally higher than or equal to the second threshold.

[0122] In another example, to achieve a smooth transition, the noise cancellation output signal or the step size gain of the adaptive filter can follow a predetermined sequence to transition from a first value to a second value. For example, once the input exceeds a certain threshold, the noise cancellation system can initiate a predetermined sequence of smooth transitions from a first value through at least one predetermined intermediate value to a second value based on a single instance of exceeding the threshold. The predetermined sequence of values ​​can follow a predetermined function, such as a linear function or a logarithmic function.

[0123] This example may be useful for inputs that have large discrete jumps in value rather than continuous outputs or small steps of value. For example, if the input is gear, which typically has only five or six values, then the vehicle being in a certain gear (e.g., second gear) may be set as a threshold. Using a higher gear as the next threshold in a smooth transition function (e.g., Equation (11) or Equation (13)) would not be useful because the time between successive gears would be too large to produce a transition that the user would perceive as smooth. Thus, once the vehicle enters a predetermined gear, the noise cancellation system may be programmed to transition the noise cancellation signal and / or adaptation rate from a first value through at least one intermediate value to a second value without waiting for an additional gear change. This may follow Figure 7 The line of the graph shown is, but only for example triggered by a single threshold. However, this example is not limited to inputs with large discrete jumps and can be used for any type of input that is indicative of the signal-to-noise ratio of a reference sensor.

[0124] In addition, for combining Figures 6A to 6C The described smoothly transitioning thresholds can smoothly transition between threshold values. As described in conjunction with FIG. 6D , the threshold value can transition from a first threshold value to a second threshold value to compensate for certain instances where the input (e.g., vehicle speed) cannot adequately capture the SNR of the reference sensor. However, the threshold value, similar to the noise cancellation signal and the adaptation rate, can smoothly transition from the first threshold value to the second threshold value. In other words, the threshold value can transition between the first value and the second value via at least one intermediate value. In one example, the threshold values ​​can each be adjusted according to the following formula:

[0125]

[0126] Among them I i(n) It can be any threshold from I1 to I4. is the maximum value to which a given threshold is set, Is the minimum value for which a given threshold is set, the first variance threshold I var1 is the first threshold against which the second input is compared, and the second variance threshold I var2 is the second threshold against which the second input is compared.

[0127] The operation is similar to formula (11) and (13). When the second input is lower than the first variance threshold I var1 When the given threshold is set to its maximum threshold value When the second input is higher than the second variance threshold I var2 When the given threshold is set to its minimum threshold value And when the second input is between the first variance threshold and the second variance threshold, the given threshold is determined by the value of the second input at the maximum threshold value. and minimum threshold value In this way, the threshold against which the first input is compared can be smoothly changed from a maximum value to a minimum value.

[0128] As described in conjunction with FIG6D , the second input is not the same type of input as the first input. For example, if the first input is vehicle speed, the second input may be another type of input, such as the power of a reference sensor or the coherence of a reference sensor. In addition, the variance threshold (e.g., I var1 , I var2 ) may vary for each different threshold I1-I4, or may be the same for each threshold I1-I4.

[0129] Figure 8 A graph of formula (14) is depicted, where the first input is the vehicle speed and the second input is the power of the reference sensor. As shown in the figure, although the PSD is less than the first variance threshold I var1 , but the first threshold is linearly transformed to the second variance threshold I based on the power of the reference sensor var2 Down The previous maintenance

[0130] Of course, the function that determines the intermediate value need not be determined by a linear function, but can be a logarithmic or any other suitable function. Furthermore, the intermediate value can be a constant value between the maximum and minimum values ​​(e.g., halfway between the maximum and minimum values). Furthermore, the smooth transition need not be determined by a piecewise formula, but can be pre-programmed to smoothly transition over a period of time when the second input exceeds the first value.

[0131] In combination Figures 3A to 8 In each of the examples described above, rather than using only a single input (e.g., a first input or a second input), multiple inputs can be used to determine when to transition the noise cancellation signal or adaptation rate, or to determine when these thresholds should be used to determine when a transition occurs. Multiple inputs can be used by combining the inputs using a logical "and" or "or" function. For example, rather than using vehicle speed, a certain gear and an engine RPM above a given threshold can be used to determine when to set the noise cancellation signal, adaptation rate, or a specific threshold for transition to a certain value. Alternatively, a logical "or" function can be used. In other words, the first threshold can be a certain vehicle speed or a certain engine RPM value.

[0132] For purposes of this disclosure, any examples of formulas for determining values ​​(eg, formulas for determining intermediate values) may be implemented as lookup tables whose values ​​are determined by the formulas or may be calculated in real time.

[0133] The functions or parts thereof described herein, as well as various modifications thereof (hereinafter referred to as "functions") may be implemented at least in part via a computer program product, for example, a computer program tangibly embodied in an information carrier, such as one or more non-transitory machine-readable media or storage devices, for execution, or controlling the operation of one or more data processing apparatuses, such as a programmable processor, a computer, multiple computers and / or programmable logic components.

[0134] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed on one computer or executed on multiple computers distributed at one site or multiple sites and interconnected by a network.

[0135] The actions associated with implementing all or part of the functionality may be performed by one or more programmable processors executing one or more computer programs to perform the functionality of the calibration process. All or part of the functionality may be implemented as special-purpose logic circuitry, such as an FPGA and / or an ASIC (application-specific integrated circuit).

[0136] Processors suitable for executing a computer program include, for example, both general-purpose and special-purpose microprocessors, and any one or more processors of any type of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory, or both. The components of a computer include a processor for executing instructions and one or more memory devices for storing instructions and data.

[0137] Although several inventive embodiments have been described and illustrated herein, a person of ordinary skill in the art will readily conceive of a variety of other devices and / or structures for performing the functions described herein and / or obtaining one or more of the results and / or advantages described herein, and each of such variations and / or modifications is considered to be within the scope of the inventive embodiments described herein. More generally, a person of ordinary skill in the art will readily understand that all parameters, dimensions, materials, and configurations described herein are intended to be exemplary, and that actual parameters, dimensions, materials, and / or configurations will depend on one or more specific applications in which the teachings of the present invention are used. A person of ordinary skill in the art will recognize or be able to ascertain, using only routine experimentation, many equivalents to the specific inventive embodiments described herein. Therefore, it should be understood that the above embodiments are presented by way of example only, and that within the scope of the appended claims and their equivalents, inventive embodiments may be practiced in a manner other than that specifically described and claimed. The inventive embodiments of the present disclosure relate to each individual feature, system, article, material, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, and / or methods is included within the scope of the invention of the present disclosure if such features, systems, articles, materials, and / or methods are not mutually inconsistent.

Claims

1. A noise cancellation system implemented in a vehicle, comprising: a noise cancellation system disposed in a vehicle, the noise cancellation system including an adaptive filter that is adjusted based on a reference signal and an error signal, the adaptive filter outputting a noise cancellation signal that, when converted by a speaker into a noise cancellation audio signal, cancels road noise in at least one zone within a cabin of the vehicle; as well as an adjustment module configured to change one or both of the power of the noise cancellation signal and the adaptation rate of the adaptive filter from a first value to a second value through at least one intermediate value between the first and second values, wherein changing one or both of the power of the noise cancellation signal and the adaptation rate of the adaptive filter is based on a comparison of a time-varying signal indicative of a signal-to-noise ratio of the reference signal with a first criterion.

2. The vehicle-implemented noise cancellation system of claim 1 , wherein the time-varying signal is at least one of: a speed of the vehicle, a power of the reference signal, revolutions per minute of an engine of the vehicle, a gear of the engine of the vehicle, and a similarity measure between outputs of at least two of the reference sensor signals. 3 . The vehicle-implemented noise cancellation system of claim 1 , wherein the first criterion is at least one fixed threshold. 4 . The vehicle-implemented noise cancellation system of claim 1 , wherein the first criterion is at least one variable threshold, a variation of the at least one variable threshold being based on a second time-varying signal indicative of the signal-to-noise ratio of the reference signal.

5. The vehicle-implemented noise cancellation system of claim 1, wherein the intermediate value is determined based on a predetermined function of the time-varying signal. 6 . The vehicle-implemented noise cancellation system of claim 5 , wherein the predetermined function is a linear function.

7. The vehicle-implemented noise cancellation system of claim 5, wherein the predetermined function is a logarithmic function.

8. A computer-implemented method for smoothly transitioning a vehicle-implemented noise cancellation system from an off state to an on state, comprising: receiving an input indicative of a signal-to-noise ratio of a reference sensor of the noise cancellation system; comparing the value of the input to a first threshold, wherein if the value of the input is less than the first threshold, setting one or both of the power of the noise cancellation signal and the adaptation rate of the noise cancellation system to a first value, wherein if the value of the input is greater than the first threshold, performing the following steps: The value of the input is compared to a second threshold, wherein if the value of the input is greater than the second threshold, one or both of the power of the noise cancellation and the adaptation rate are set to a second value, wherein if the input is greater than the first threshold and less than the second threshold, one or both of the power of the noise cancellation signal and the adaptation rate are set to an intermediate value, wherein the second threshold is greater than the first threshold.

9. The computer-implemented method of claim 8, wherein the input is at least one of: a speed of the vehicle, a power of a reference signal, revolutions per minute of an engine of the vehicle, a gear of the engine of the vehicle, and a similarity measure between outputs of at least two reference sensors.

10. The computer-implemented method of claim 8, wherein the value of the intermediate value is determined according to a predetermined function of the input. The computer-implemented method of claim 10 , wherein the predetermined function is a linear function.

12. The computer-implemented method of claim 10, wherein the predetermined function is a logarithmic function.

13. The computer-implemented method of claim 8, wherein the values ​​of the first threshold and the second threshold are determined based on a second input indicative of a signal-to-noise ratio of the reference sensor.

14. The computer-implemented method of claim 8, further comprising the steps of: receiving a second input indicative of a signal-to-noise ratio of the reference sensor; comparing the value of the second input to a third threshold, wherein if the value of the second input is less than the third threshold, setting the first threshold to a first threshold value, wherein if the value of the second input is greater than the third threshold, performing the following steps: The value of the second input is compared with a fourth threshold, wherein if the value of the second input is greater than the fourth threshold, the first threshold is set to a second threshold value, wherein if the second input is greater than the third threshold and less than the fourth threshold, the first threshold is set to an intermediate value, wherein the second threshold is greater than the first threshold.

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