System and method for subband virtual path computation in active noise cancellation

Abstract

Methods and systems for a vehicle audio system are disclosed. In one example, there is provided a method for noise cancellation in a vehicle having a physical microphone configured to acquire a physical microphone signal and a plurality of virtual microphones configured to acquire a residual signal, the plurality of virtual microphones configured to acquire the physical microphone signal, and the plurality of virtual microphones configured to acquire the residual signal. The method includes processing the physical microphone signal with an adaptive weight filter to estimate a virtual auxiliary path from the physical microphone to the plurality of virtual microphones, decomposing the residual signal and the physical microphone signal into a plurality of sub-band signals, determining a sub-band gradient for each sub-band, the method further includes determining a sub-band virtual path convergence rate based on a normalized step size for each sub-band, determining a sub-band virtual path for each sub-band based on the normalized step size and the sub-band gradient, and applying a weight transformation process to each sub-band virtual path to update the adaptive weight filter and validate the sub-band virtual path.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for active noise cancellation, and more particularly, to systems and methods for computing virtual auxiliary paths used in active noise cancellation. Background Art

[0002] Active noise cancellation (ANC) technology is a method for generating sound waves that destructively interfere with unwanted sound waves. The destructively interfering sound waves can be generated by a transducer, such as a loudspeaker, to combine with the unwanted sound waves. This technology is widely used in various applications, including headphones, residential and commercial buildings, and automotive environments, to create a quieter and more comfortable acoustic experience. In automotive applications, ANC systems are particularly beneficial for reducing road noise, engine noise, and other external sounds that can penetrate the vehicle's cabin, thereby enhancing passenger comfort.

[0003] In current automotive applications, virtual microphone technology (VMT) can be used. In automotive applications, it may be desirable to eliminate noise near the driver's or passenger's ears, but placing microphones in those locations may be impractical. The location of one or more virtual microphones may be the area where noise cancellation is attempted. A VMT system in an automotive application may include one or more physical microphones placed in the vehicle cabin, and an algorithm for calculating the sound waves that can be generated by a transducer (such as a speaker in the vehicle) to create a quiet zone in the location of the one or more virtual microphones. A time-domain least mean square (LMS) algorithm is typically used to calculate the virtual path from the physical microphone to the virtual microphone.

[0004] However, the inventors herein have recognized potential issues with such systems. The LMS algorithm has inherent limitations when applied to VMT systems. Specifically, the LMS algorithm is limited in its ability to estimate high-frequency noise at the ears of passengers or drivers, which inhibits high-frequency noise cancellation. Furthermore, the LMS algorithm requires significant computing power to execute effectively, which can increase the space and power requirements of computing devices within the vehicle. Summary of the Invention

[0005] This application provides a system and method for sub-band virtual path calculation that significantly enhances the performance of virtual microphone technology (VMT) in active noise cancellation (ANC) systems, particularly in estimating high-frequency noise at the listener's ear. This application discloses a method including sub-band adaptive filtering (SAF) that addresses the computational limitations of conventional VMT systems and increases the accuracy of virtual path calculation over a wide frequency range.

[0006] In a first aspect, a method for noise cancellation in a vehicle is provided, the vehicle having a physical microphone configured to acquire a physical microphone signal correlated with a filtered noise signal within a vehicle cabin, and a plurality of virtual microphones positioned within the vehicle cabin to acquire a residual signal. The method includes processing the physical microphone signal using an adaptive weight filter to estimate a virtual auxiliary path from the physical microphone to the plurality of virtual microphones. The method includes applying a set of analysis filters to decompose the residual signal into a plurality of subband error signals and decomposing the physical microphone signal into a plurality of subband physical microphone signals. The method includes determining a subband gradient for each subband based on the subband physical microphone signal and the subband error signal, and determining a subband virtual path convergence rate based on a normalized step size for each subband. The method includes determining a subband virtual path for each subband based on the normalized step size and the subband gradient. The method includes applying a subband weight transformation process to each subband virtual path to update the adaptive weight filter and verify the subband virtual path.

[0007] In a second aspect, a noise cancellation system for a vehicle includes a physical microphone configured to acquire a physical microphone signal correlated with a filtered noise signal within a vehicle cabin, and a plurality of virtual microphones positioned within the vehicle cabin and configured to acquire a residual signal. The system includes an adaptive weight filter in electronic communication with the physical microphone signal, the adaptive weight filter configured to apply an adaptive filtering process to the physical microphone signal to estimate virtual auxiliary paths from the physical microphone to the plurality of virtual microphones. The system includes a signal processing unit in electronic communication with the physical microphone and the plurality of virtual microphones, wherein the signal processing unit includes a non-transitory memory and a processor, the non-transitory memory storing a set of analysis filters and instructions. The processor, when executing the instructions, is configured to apply the set of subband analysis filters to decompose the residual signal into a plurality of subband error signals and decompose the physical microphone signal into a plurality of subband physical microphone signals, determine a subband gradient for each subband based on the subband physical microphone signal and the subband error signal, determine a subband virtual path convergence speed based on a normalized step size for each subband, determine a subband virtual path for each subband based on the normalized step size and the subband gradient, and apply a subband weight transformation process to each subband virtual path to update an adaptive weight filter and verify the subband virtual path.

[0008] On the other hand, a method includes acquiring a physical microphone signal using a physical microphone sensor, wherein the physical microphone signal is correlated with a filtered noise signal in a vehicle cabin. The method includes processing the physical microphone signal using an adaptive weight filter to estimate a virtual auxiliary path from a physical error microphone to a plurality of virtual error microphones. The method includes acquiring a residual signal from the plurality of virtual error microphones positioned in the vehicle cabin. The method includes decomposing the physical microphone signal and the residual signal into a plurality of subband signals. The method includes calculating a subband gradient for each subband based on the decomposed physical signal and the decomposed residual signal. The method includes calculating a normalized step size for each subband based on a power contribution of the physical microphone signal. The method includes updating a set of subband virtual path weights based on the subband gradient and the normalized step size, and transforming the updated set of subband virtual path weights to the time domain using an inverse fast Fourier transform (IFFT). The method includes processing the residual signal based on the transformed subband virtual weights to reduce noise in the vehicle cabin.

[0009] In this way, the accuracy of virtual path calculation is increased while reducing computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The present disclosure may be better understood by reading the following description of non-limiting embodiments with reference to the accompanying drawings, in which:

[0011] Figure 1 shows a schematic diagram of an active noise cancellation system according to one or more embodiments of the present disclosure;

[0012] Figure 2 A schematic diagram illustrating a virtual path calculation system according to one or more embodiments of the present disclosure is shown;

[0013] Figure 3 is a graph showing a performance comparison between a conventional time-domain least mean square algorithm and the proposed sub-band virtual path calculation according to one or more embodiments of the present disclosure;

[0014] Figure 4 A schematic diagram illustrating a sub-band virtual path algorithm according to one or more embodiments of the present disclosure is shown;

[0015] Figure 5 A flow chart of a method for computing a virtual auxiliary path from a physical microphone to a virtual microphone according to one or more embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0016] In one of many exemplary embodiments, an active noise cancellation (ANC) system as described herein can reduce unwanted sounds present in an environment. Unwanted sound is any sound that annoys the listener, such as vehicle engine sound, road noise, etc., but it can also be music or other people's voices when, for example, the listener wants to make a phone call. The disclosed system and method include a VMT system that uses sub-band adaptive filtering (SAF) to calculate a virtual path from a physical microphone to a virtual microphone. The VMT system can include one or more microphones in the cabin of a vehicle that are capable of measuring sounds within the vehicle cabin. The measured sounds within the vehicle can then be processed and algorithms can be applied to it to calculate the sounds at locations of interest, such as the driver's or passenger's ears.

[0017] The following figures may illustrate various aspects of the systems and methods claimed herein. Figure 1 is a schematic diagram of a noise cancellation system. The noise cancellation system may include a plurality of error microphones, a processor, and a speaker, and is capable of detecting ambient noise within a vehicle cabin, processing the ambient noise, and determining a signal to be output to the speaker that cancels the ambient noise within the vehicle cabin. Figure 2 is a schematic diagram of a virtual path calculation system. One embodiment of a noise cancellation system in a vehicle involves the use of virtual microphones, where noise cancellation can be focused on a location where no physical microphone exists, and this location can be referred to as the location of the virtual microphone. In this case, noise detected by a physical microphone within the cabin can be processed to predict the sound at the location of the virtual microphone. The predicted sound at the location of the virtual microphone can be used to determine a signal generated by a speaker to cancel the noise at the location of the virtual microphone, and can be used to determine the remaining noise at the location of the virtual microphone after noise cancellation has been performed. The virtual path can be a process applied to the noise detected by the physical microphones within the system to calculate the noise at the location of the virtual microphone. The virtual path can be calculated by a signal processing unit and can be implemented using a sub-band adaptive filter. Figure 3 is a graph comparing the recorded sound signal with the estimated signals generated by the traditional least mean square method and the proposed sub-band virtual path algorithm at different frequencies. Figure 4 The figure below is a schematic diagram of the subband virtual path algorithm. It illustrates the process of converting the sound collected from a physical microphone into the sound received at a virtual microphone. Furthermore, it shows how the subband virtual processing algorithm splits the measured sound signal into subbands, with each subband processed separately to calculate the virtual path. Figure 5 is a description Figure 4 Flowchart of the virtual path algorithm schematically depicted in .

[0018] Go to Figure 1, which shows a block diagram of a vehicle noise cancellation system 100. The vehicle noise cancellation system 100 is configured to enhance the acoustic environment within a vehicle cabin 130 by actively reducing unwanted noise. The vehicle noise cancellation system 100 is equipped with a reference sensor 102, whose task is to acquire a reference signal related to the noise present within the vehicle cabin 130. This reference signal serves as the basis for generating a noise cancellation signal that cancels the detected noise.

[0019] The vehicle noise cancellation system 100 includes an adaptive weight filter 104 that processes a reference signal obtained by the reference sensor 102 and applies an adaptive filtering algorithm to generate a noise cancellation signal. The adaptive weight filter 104 can dynamically adjust its filtering characteristics to reduce residual signals obtained via the plurality of error microphones 108 .

[0020] The vehicle noise cancellation system 100 includes a plurality of speakers 106 strategically positioned within a vehicle cabin 130. The speakers 106 are configured to transmit noise cancellation signals into the cabin space, thereby generating an anti-noise sound field that interferes with unwanted noise to reduce or cancel the unwanted noise.

[0021] To monitor the effectiveness of the noise cancellation process, a plurality of error microphones 108 are also positioned within the vehicle cabin 130. In one example, the plurality of error microphones 108 may include one or more physical error microphones and one or more virtual error microphones, which are referenced to the Figure 2 These error microphones 108 capture a residual signal, which is the sound produced after the transmitted noise cancellation signal interacts with the original noise. The residual signal provides feedback on the performance of the vehicle noise cancellation system 100. For example, a reduction in the residual signal captured by the multiple error microphones may indicate greater effectiveness of the noise cancellation process.

[0022] The signal processing unit 110 serves as the computational center of the vehicle noise cancellation system 100. The signal processing unit 110 is in electronic communication with both the reference sensor 102 and the error microphone 108. The signal processing unit 110 houses a processor 112 and non-transitory memory 114, which together execute machine-readable instructions for canceling noise within the vehicle cabin 130. The processor 112 in the signal processing unit 110 is a hardware component designed to execute machine-executable instructions stored in the non-transitory memory 114, including instructions for computational tasks related to real-time signal processing, including adaptive filtering algorithms, subband decomposition, and gradient calculation of filter weights. The processor 112 processes data to generate a real-time noise cancellation signal that cancels unwanted noise within the vehicle cabin 130.

[0023] Non-transitory memory 114 stores machine-readable instructions and data for vehicle noise cancellation system 100, retaining this information even when the system is turned off. This memory contains firmware, software, and data structures or databases used by processor 112 and may include ROM, flash memory, or other non-volatile storage technology. Non-transitory memory 114 also stores historical data and adaptive filter coefficients used for system learning and performance enhancement.

[0024] The signal processing unit 110 of the vehicle noise cancellation system 100 uses a set of subband filters 116 to decompose the audio signal into multiple frequency subbands. The subband filters 116 are designed to divide the wide frequency range of the reference and residual signals into narrower frequency bands, allowing for noise cancellation strategies tailored to the acoustic properties of each subband. The subband filters 116 start with a prototype low-pass filter, which is then modulated to create a series of bandpass filters that cover the entire frequency range of interest.

[0025] The impulse response of each subband filter is denoted as h m , is obtained from the prototype filter by a modulation process that shifts the filter passband to the frequency range of the target subband. The impulse response of the mth subband filter is determined using a mathematical transformation that includes modulation and windowing effects. The number of subbands M and the length of each subband filter l sw is a parameter that affects the resolution and computational requirements of the subband filtering process. Subband filters 116 are applied to the reference and residual signals through filter bank analysis. This involves convolving the input signal with the impulse response of each subband filter to isolate the subband components. The output is multiple sets of subband reference signals and subband error signals, which reflect the frequency content of the original signal within each subband. By operating in the subband domain, the vehicle noise cancellation system 100 can more effectively perform noise cancellation tasks by focusing on and canceling specific frequencies of sound.

[0026] Auxiliary path filters 118 are applied to the subband reference signals to produce filtered subband reference signals. These auxiliary path filters 118 model the acoustic transfer function from the speaker 106 to the error microphone 108 within each subband. These filters reflect the characteristics of the acoustic environment of the vehicle cabin, including cabin geometry, interior materials, and the variable presence of passengers or cargo. Each of the auxiliary path filters 118 corresponds to a specific subband and processes the associated subband reference signal taking into account the frequency-dependent behavior of sound transmission (including reflection, absorption, and diffraction). As previously disclosed, the auxiliary path filters can be learned in an auxiliary path calibration process. To maintain accuracy, the system can include a calibration mechanism that adjusts the filter coefficients in response to environmental changes.

[0027] An adaptive step size determination module 120 is included in the signal processing unit 110. The adaptive step size determination module 120 adjusts the step size in the adaptive filtering algorithm of the vehicle noise cancellation system 100 on a per-subband basis. This adjustment affects the convergence rate and stability of the adaptive filter weights within the adaptive weight filter 104. The adaptive step size determination module dynamically modifies the step size in each subband based on the power contribution of the error microphone signal and the reference signal within each corresponding subband, which affects the convergence rate and stability of the adaptive filter weights. The adaptive step size determination module 120 calculates a normalized step size for each subband by evaluating factors such as the total power of the filtered reference and error signals, the individual power contributions of these signals, and a smoothness parameter related to their power. The module may also consider a power contribution parameter reflecting the maximum power within each subband. The resulting normalized step size is then applied to update the subband adaptive filter weights, seeking a balance between convergence rate and stability.

[0028] The gradient determination module 122 calculates the subband gradient for each subband based on the filtered subband reference signal and the corresponding subband error signal. This continuous real-time adjustment allows the vehicle noise cancellation system 100 to effectively adapt to changing noise conditions, thereby improving the acoustic experience within the vehicle cabin.

[0029] The sub-band adaptive weight update module 124 updates the adaptive filter weights in each sub-band based on the calculated gradient and the determined adaptive step size. The sub-band adaptive weight update module 124 ensures that the vehicle noise cancellation system 100 adapts to the noise conditions within the vehicle cabin 130 in real time.

[0030] The weight transformation module 126 integrates the updated adaptive filter weights from each subband to produce final weights in the time domain for the adaptive weight filter 104. These updated weights are then applied to the adaptive weight filter 104 to adjust the noise cancellation signal to achieve optimal noise reduction within the vehicle cabin 130.

[0031] Figure 2 1 shows a block diagram of a virtual path calculation system 200. The virtual path calculation system 200 is configured to reduce interior noise around the ears of a driver or passenger regardless of error microphone placement. In one example, the virtual path calculation system 200 can be included in the vehicle noise cancellation system 100 as part of an overall approach to reducing unwanted noise (e.g., road noise) within the vehicle cabin 130. The virtual path calculation system 200 includes a physical microphone 202 configured to acquire information about the vehicle's interior noise. Figure 1 The physical microphone signal is related to the filtered noise signal present in the vehicle cabin 130. For example, the physical microphone signal may include a residual signal, which is generated by a signal such as Figure 1The physical microphone signal is used as the basis for estimating the virtual auxiliary path from the physical microphone 202 to the plurality of virtual microphones 208. The physical microphone 202 and the plurality of virtual microphones 208 may be included in Figure 1 A non-limiting example of the error microphone 108 in the vehicle noise cancellation system 100 is shown in FIG.

[0032] The virtual path calculation system 200 includes an adaptive weight filter 204 that processes physical microphone signals obtained by a physical microphone 202 and applies a sub-band virtual path (SVP) algorithm to calculate transfer functions from the physical microphone 202 and a plurality of virtual microphones 208 (e.g., virtual auxiliary paths). Similar to the adaptive weight filter 104, the adaptive weight filter 204 is configured to adjust filtering characteristics to reduce residual signals obtained via the plurality of virtual microphones 208.

[0033] The virtual path calculation system 200 includes a plurality of virtual microphones 208 virtually positioned within the vehicle cabin 130. In one example, the plurality of virtual microphones 208 may be virtually positioned on the vehicle headrests and configured to detect the acoustic environment near the passenger's ears. In one example, the virtual microphones may include modeled representations of physical error microphones. The plurality of virtual microphones 208 record residual signals, which are the sound produced after filtering the interaction of the transmitted noise-cancelled signal with the original noise using the adaptive weight filter 204. The residual signals provide feedback on the performance of the virtual path calculation system 200.

[0034] The virtual path calculation system 200 includes the signal processing unit 110, processor 112, and non-transitory memory 114 described above with reference to the vehicle noise cancellation system 100, which together execute machine-readable instructions for calculating a virtual auxiliary path. The signal processing unit 110 is in electronic communication with both the physical microphone 202 and the plurality of virtual microphones 208. In addition to executing the reference Figure 1 In addition to the machine-executable instructions described, the processor 112 may also execute a sub-band virtual path calculation algorithm. The processor 112 processes data to estimate a transfer function from the physical microphone 202 to the virtual microphone 208 for targeted noise reduction near the ears of passengers in the vehicle cabin 130.

[0035] A set of subband filters 216 within the signal processing unit 110 of the virtual path calculation system 200 is used to decompose the audio signal into multiple frequency subbands. Similar to the subband filters 116 described above with reference to the vehicle noise cancellation system 100, the subband filters 216 are designed to divide the wide frequency range of the physical microphone signal and the residual signal into narrower frequency bands. Each subband filter within the set corresponds to a different frequency range within the residual noise spectrum within the vehicle cabin, enabling the system to estimate a virtual auxiliary path for each subband. The design of the subband filters 216 includes a prototype filter, typically a low-pass filter with a specific window function. The choice of window function, such as Hamming or Kaiser, can depend on the predetermined frequency response characteristics of each subband. This prototype filter is then modulated to create a series of bandpass filters spanning the entire frequency range of interest.

[0036] As referenced above Figure 1 As described above, the impulse response of each sub-band filter is obtained from the prototype filter, and the resolution and computational requirements of the sub-band filtering process can be adjusted by adjusting the length and number of sub-band filters. The sub-band filter 216 is applied to the physical microphone signal and the residual signal by the filter bank analysis, as described above and with reference to Figure 3 The output is multiple sets of sub-band physical microphone signals and sub-band error signals, which include the frequency content of the original signal within each sub-band. Virtual path calculation is performed on the sub-band physical microphone signals and sub-band error signals in the sub-band domain. Compared to full-band processing, this method reduces computational requirements and increases the accuracy of virtual path calculation under varying noise conditions within the vehicle cabin 130.

[0037] The step size normalization module 220 is included in the signal processing unit 110. The step size normalization module 220 adjusts the step size in the subband virtual path algorithm of the virtual path calculation system 200 on a per subband basis. This adjustment affects the convergence rate and stability of the filter weights within the adaptive weight filter 204. The step size normalization module 220 dynamically modifies the step size in each subband based on the power contribution of the error microphone signal and the physical microphone signal within each corresponding subband, which adopts the same Figure 2 A similar method to the adaptive step size determination module 120 in FIG. Figure 3 The resulting normalized step size is implemented to update the sub-band virtual path for each sub-band. This continuous real-time adjustment allows the virtual path calculation system 200 to effectively adapt to changing noise conditions in the vehicle cabin.

[0038] The gradient determination module 222 calculates the subband gradient for each subband based on the filtered subband physical microphone signal and the corresponding subband error signal.The output of the gradient determination module 122 is used to adjust the subband virtual path calculation in each subband to minimize the residual signal.

[0039] The sub-band virtual path update module 224 updates the sub-band virtual path in each sub-band based on the calculated gradient and the determined normalized step size. The sub-band virtual path update module 224 adapts the virtual path calculation system 200 to the real-time noise conditions within the vehicle cabin 130 .

[0040] The weight transformation module 226 integrates the updated subband virtual paths from each subband to produce estimated virtual auxiliary paths in the time domain for the adaptive weight filter 204. The updated subband virtual paths are then applied to the adaptive weight filter 204 to model the transfer function from the physical microphone 202 to the plurality of virtual microphones 208 for targeted noise reduction within the vehicle cabin 130.

[0041] Figure 3 Graphs 300, 310, 320, and 330 depict simulation results comparing the performance of a conventional time-domain LMS algorithm and the disclosed subband virtual path (SVP) algorithm for estimating a true virtual microphone signal. Referring to graphs 300, 310, 320, and 330, the true virtual microphone signal is referred to as the target virtual auxiliary path. The SVP algorithm demonstrates improved accuracy and performance compared to conventional methods.

[0042] In each graph, frequency in Hz is plotted on the x-axis, and sound pressure level (SPL) in dB(A) is plotted on the y-axis. Graph 300 shows a target virtual auxiliary path 302 on the driver's outer ear, a first virtual auxiliary path 304 estimated by the disclosed SVP algorithm, and a second virtual auxiliary path 306 estimated by the traditional LMS algorithm. Graph 310 shows a target virtual auxiliary path 312 on the driver's inner ear, a first virtual auxiliary path 314 estimated by the disclosed SVP algorithm, and a second virtual auxiliary path 316 estimated by the traditional LMS algorithm. Graph 320 shows a target virtual auxiliary path 322 on the passenger's outer ear, a first virtual auxiliary path 324 estimated by the disclosed SVP algorithm, and a second virtual auxiliary path 326 estimated by the traditional LMS algorithm. Graph 330 shows a target virtual auxiliary path 332 on the passenger's outer ear, a first virtual auxiliary path 334 estimated by the disclosed SVP algorithm, and a second virtual auxiliary path 336 estimated by the traditional LMS algorithm.

[0043] To enhance the overall performance and reliability of the noise cancellation system and the performance of the virtual microphone technology, the closer the estimated virtual microphone signal is to the actual virtual microphone signal, the better the noise cancellation performance achieved by the system. As shown in graphs 300, 310, 320, and 330, the virtual auxiliary paths estimated by the SVP algorithm (e.g., paths 304, 314, 324, and 334) are closer to the actual virtual microphone signals (e.g., paths 302, 312, 322, and 332). This is particularly evident in the high-frequency range, where the overall accuracy shown in graphs 300, 310, 320, and 330 is 4.6 dB. Furthermore, compared to the traditional LMS algorithm, the SVP algorithm can reduce the power consumption and computing operations of the noise reduction system.

[0044] Figure 4 is a block diagram illustrating a subband virtual path (SVP) system 400 for estimating a virtual auxiliary path using subband adaptive filtering (SAF) in an online or offline process. In one example, the SVP system 400 is used in a single-input multiple-output (SIMO) system to estimate a transfer function from one physical microphone to multiple virtual microphones. The SVP system 400 can be used with reference Figure 1 The SVP system 200 described above is the same or similar. In one example, the SVP system 400 can be used in a noise cancellation system of a vehicle, such as a vehicle described in Figure 1 The depicted vehicle noise cancellation system 100 is implemented in FIG. Signal paths are depicted in the figure by lines with arrows indicating the direction of signal transmission.

[0045] The SVP system 400 includes a physical microphone 402 configured to acquire a physical microphone signal and a plurality of virtual microphones 404 that acquire a residual signal 406. In some instances, the physical microphone 402 may include more than one physical microphone or a plurality of physical microphones. The plurality of virtual microphones 404 are virtually positioned near the ears of a listener 401 to detect the acoustic environment around them. The listener 401 may include a passenger inside a vehicle cabin (such as the vehicle cabin 130). The SVP system 400 includes a virtual auxiliary path 408. The virtual auxiliary path 408 includes an adaptive filter that represents a transfer function from the physical microphone 402 to the plurality of virtual microphones 404.

[0046] To obtain the residual signal e(n), it is expressed as:

[0047]

[0048] where e j (n) is the jth th Residual signal from the error microphone, S′ j is the signal from the selected physical microphone to the jth thThe impulse response of the virtual error microphone, r(n) is the physical microphone signal, l w is the length of the fully adaptive filter, and * is the linear convolution operator. In other words, the residual signal is obtained by linear convolution of the main signal at the physical microphone, the estimated impulse response from the physical microphone to multiple virtual microphones, and the fully adaptive filter.

[0049] For subband virtual path calculation, a set of subband analysis filters is used to decompose or split the input signal into individual subbands, each representing a different frequency range. In one embodiment, the set of subband analysis filters includes an analysis filter bank 410. In one embodiment, the analysis filter bank includes a plurality of subband filters. Each subband analysis filter of the analysis filter bank 410 is derived from the prototype filter h0 using a window-based low-pass filter. Depending on the intended purpose, different window functions are selected for the prototype filter design, such as Hamming or Kaiser windows. To generate the subband analysis filters, the following equations can be used:

[0050]

[0051] where h m It is the mth th The impulse response of the subband filter, M is the number of subbands, i is h m The i th Coefficient, i=0,1,…,l sW , and l sW is the length of the subband analysis filter. In other words, the subband analysis filter is calculated by a prototype linear phase FIR low-pass filter via complex modulation.

[0052] In order to calculate the sub-band physical microphone signal r m and subband error signal e j,m , a signal sub-banding and decomposition process is performed. This process allows the calculation of the sub-band physical microphone signals, which can be as follows:

[0053]

[0054] κ=(n-1) / D

[0055] where r m (κ) is the mth th Sub-band physical microphone signal, e j,m (κ) is the jth th Virtual error microphone channel m th Subband error signal, h m It is the mth th The impulse response of the subband analysis filter, κ is the subband index, n is the iteration, D is the decimation factor, LsW is the length of the subband adaptive filter. In other words, the signal is subbanded and the decomposition process uses an analysis filter bank to determine the number of subband signals and the signal accuracy.

[0056] In addition, based on the sub-band physical microphone signal r m and subband error signal e j,m , the subband gradient G j,m Calculated as:

[0057] G j,m (κ)=r * m (κ)e j,m (κ)

[0058] Among them G j,m (κ) is the jth th The mth error microphone channel th Subband gradient, r * m (κ) is the mth th The subband gradient calculations indicated by blocks 414a, 414b, and 414c include performing complex conjugate multiplications of the subband physical microphone signals and the subband error signals. The subband adaptive filters in each subband are adjusted based on each subband gradient calculation as part of the subband LMS process.

[0059] In order to adjust the sub-band auxiliary path convergence speed, a simple sub-band step size normalization method is applied, which adjusts the step size based only on the power contribution of the sub-band virtual microphone signal. m It can be calculated in the following equation:

[0060]

[0061] Among them U m (κ) is the mth th Normalized step size of subband, r m (κ) is the mth th sub-band physical microphone signal, and α is a constant value. In other words, the step normalization process ensures that the different sub-band signal levels converge at a uniform rate. The constant value can be adjusted based on the desired step size to avoid the step size being too small or too large. For example, the constant value can be adjusted based on the threshold normalization step size so that the normalized step size obtained by calculation exceeds the threshold normalization step size or falls within the threshold range, otherwise it may affect the stability of the system. In one example, the threshold normalization step size can be a non-zero positive threshold. This value can be determined through a calibration operation.

[0062] Therefore, the subband virtual path s′ is calculated and updated in the subband adaptive filter weight update equationj,m , which is based on the subband gradient G in the following equation j,m and normalized step size U m :

[0063] s′ j,m (κ+1)=γ m s′ j,m (κ)+U m (κ)G j,m (κ)

[0064] where s′ j,m (κ) is the jth th The mth error microphone channel th The estimated sub-band virtual path, U m (k) is the mth th The normalized step size of the subband, and γ m It is the mth th In other words, each sub-band virtual path indicated by blocks 416a, 416b, 416c is calculated and updated by the sub-band adaptive filter weight updating process to estimate the sub-band virtual path.

[0065] To obtain the time-domain estimated virtual auxiliary paths and verify the sub-band virtual paths, the SVP system 400 applies a sub-band weight transformation process 418, which transforms all sub-band virtual paths s′ into j,m The auxiliary path s′ is converted to the full-length estimate in the following equation j :

[0066] S′ j，m =FFT(s′ j，m , 2×L sW )

[0067]

[0068] F j (f)=0 f=2l sW

[0069] F j (f) = F j (2l sW -f) * f∈(2l sW , 4l sW ]

[0070] s′ j =IFFT(F j )

[0071] s′ j =s′j(1:l W )

[0072] where S′ j,m is the jth th The mth error microphone th Frequency domain subband virtual path, F j (f) is the jth th The error microphone channel f th Frequency slot, l sW is the length of the subband virtual path, M is the number of subbands, () * is complex conjugate, and s′ j is the virtual path of the full-length estimate. In other words, the computation of the virtual auxiliary path of the time-domain estimate includes applying an inverse fast Fourier transform of the frequency-domain subband virtual path to obtain an adaptive weight filter in the time domain.

[0073] Figure 5 is a flow chart of a method 500 for calculating a virtual auxiliary path from a physical microphone to a virtual microphone as part of a vehicle noise cancellation system. The method may be the same or similar to the method described with reference to the virtual path calculation system 200, which may be included in each of Figure 2 and Figure 1 Instructions for performing method 500 may be provided by a controller based on computer readable instructions stored on a memory of the controller and in conjunction with information from sensors of the vehicle system (such as those described above). Figure 1 and Figure 2 The controller may utilize the signal processing unit 110, processor 112, non-transitory memory 114, physical microphone 202, multiple virtual microphones 208, and the set of sub-band filters 216 described above to perform the above-described operations. According to the method described below, the controller may use actuators of the vehicle system (such as multiple speakers) to adjust the vehicle system operation.

[0074] At 502 , method 500 may include receiving or determining a physical microphone signal. There may be one or more physical microphones placed within a cabin of a vehicle capable of recording ambient sounds within the cabin of the vehicle at the location of the microphone.

[0075] At 504, method 500 may include processing a signal from a physical microphone using an adaptive weight filter. Applying the adaptive weight filter to the signal from the physical microphone may estimate a virtual path from the physical microphone signal to the virtual microphone. A noise cancellation signal may be emitted by a plurality of speakers or transducers within the vehicle cabin in an attempt to cancel sounds within the vehicle cabin. The noise cancellation signal may be based on the virtual path between the physical microphone and the virtual microphone. Characteristics of the adaptive weight filter may be iteratively updated to minimize residual sound acquired at the locations of the plurality of virtual microphones. The residual sound is sound that may be detected at the location of the virtual microphone after active noise cancellation has been performed on the physical microphone signal using the adaptive weight filter.

[0076] At 506, method 500 may include decomposing the residual signal into a plurality of sub-band error signals by applying a set of sub-band analysis filters. The sub-band analysis filters may separate the residual signal into individual sub-bands based on frequency. In some examples, low-pass filters and high-pass filters may be used to decompose the residual signal into a plurality of frequency sub-bands. Separating the residual signal into a plurality of frequency sub-bands allows an error signal to be associated with each sub-band.

[0077] At 508, the physical microphone signal can be decomposed into a plurality of sub-band physical microphone signals by applying a set of sub-band analysis filters to the physical microphone signal. The sub-band analysis filters can separate the physical microphone signal into the same frequency sub-bands as the residual signal, and this separation can be similarly achieved using high-pass and low-pass filters. Using the same frequency sub-bands at 506 and 508 ensures that each sub-band physical microphone signal has a corresponding sub-band error signal.

[0078] At 510, the process may be applied individually to each subband from the other subbands to determine a subband virtual path for each individual subband. Before the method continues past 510, the sub-methods within 510 may be applied to each subband created by the subband analysis filters at 506 and 508.

[0079] Within 510, method 500 includes determining subband gradients based on subband physical microphone signals and subband error signals that share the same subband at 512. The subband gradients may provide information on how weights of an adaptive weight filter may be adjusted to reduce subband signal errors.

[0080] At 514, method 500 may include determining a normalization step size based on the power contribution of the subband reference signal. The normalization step size may be adjusted from a previously determined step size based on the power contribution of the subband reference signal. The normalization step size may affect the convergence rate and stability of the adaptive filter weights, and balancing the normalization step size may be advantageous so that the adaptive filter weights converge quickly but do not change too drastically between updates.

[0081] At 516, the subband auxiliary virtual path may be updated based on the normalized step size and gradient. Figure 4 The described method updates a subband virtual path and can depend on previous iterations of the subband auxiliary virtual path, subband gradients, normalization step size, and leakage of the subband adaptive filter weights. Subband leakage can indicate the extent to which previous iterations of the auxiliary subband virtual path influence the updated subband auxiliary virtual path to limit the effect of traffic noise.

[0082] At 518, method 500 may include applying a subband weight transform to each subband virtual path. This may include transforming each subband auxiliary path into the frequency domain using a fast Fourier transform, binning the frequency domain subband auxiliary virtual paths into a plurality of bins based on the error channels of the plurality of error microphones. Applying the weight transform to each subband virtual path allows the subband to be returned to its original frequency range to generate a new full-band transfer function.

[0083] At 520, the method may further include updating weights of the adaptive weight filter based on the weight-transformed sub-band auxiliary virtual path.The weights may be updated to minimize a residual signal detected at the position of the virtual microphone.

[0084] By applying a subband adaptive structure to virtual path calculation, the present application provides several advantages. The method calculates an adaptive filter in each subband, which allows the adaptive filter to be updated over each frequency range. In addition, by using subband signal processing, the method reduces computational power consumption and computation relative to the traditional least mean square algorithm (LMS) by concentrating resources such as sound waves for elimination (e.g., anti-noise signals) on the frequency bands most affected by residual errors. In addition, the method can be used in different virtual microphone technologies or remote microphone technology structures and can be executed as an offline or online process. The technical effect of the present application is to enhance virtual microphone performance with less computational resource requirements.

[0085] The present disclosure also provides support for a method for noise cancellation in a vehicle having a physical microphone and a plurality of virtual microphones, the physical microphone being configured to acquire a physical microphone signal correlated with a filtered noise signal within a vehicle cabin, the plurality of virtual microphones being positioned within the vehicle cabin to acquire a residual signal, the method comprising: processing the physical microphone signal using an adaptive weight filter to estimate a virtual auxiliary path from the physical microphone to the plurality of virtual microphones, applying a set of analysis filters to decompose the residual signal into a plurality of subband error signals and decomposing the physical microphone signal into a plurality of subband physical microphone signals, determining a subband gradient for each subband based on the subband physical microphone signal and the subband error signals, determining a subband virtual path convergence rate based on a normalized step size for each subband, determining a subband virtual path for each subband based on the normalized step size and the subband gradient, and applying a subband weight transformation process to each subband virtual path to update the adaptive weight filter and validate the subband virtual path. In a first instance of the method, the set of analysis filters includes a plurality of subband filters derived from a prototype filter using a window-based low-pass filter, each subband filter corresponding to a different frequency range within a residual noise spectrum of the vehicle cabin. In a second example of the method, optionally including the first example, the method further comprises selecting a window function for the prototype filter based on a predetermined frequency response characteristic of each subband. In a third example of the method, optionally including one or both of the first and second examples, determining a normalization step size for each subband is based on a power contribution of the subband physical microphone signal and a constant value. In a fourth example of the method, optionally including one or more or each of the first to third examples, adjusting the constant value based on a threshold normalization step size. In a fifth example of the method, optionally including one or more or each of the first to fourth examples, determining a subband gradient for each subband comprises performing a complex conjugate multiplication of the subband physical microphone signal and a subband error signal. In a sixth example of the method, optionally including one or more or each of the first to fifth examples, the subband weight transformation process comprises performing a fast Fourier transform on each subband virtual path to obtain a frequency-domain subband virtual path. In a seventh example of the method, optionally including one or more or each of the first to sixth examples, the subband weight transformation process further includes applying an inverse fast Fourier transform to the frequency-domain subband virtual path to obtain an adaptive weight filter in the time domain. In an eighth example of the method, optionally including one or more or each of the first to seventh examples, the virtual auxiliary path is a time-domain estimated virtual auxiliary path.

[0086] The present disclosure also provides support for a noise cancellation system for a vehicle, the noise cancellation system comprising: a physical microphone configured to acquire a physical microphone signal correlated with a filtered noise signal within a vehicle cabin; a plurality of virtual microphones located within the vehicle cabin and configured to acquire a residual signal; an adaptive weight filter in electronic communication with the physical microphone signal, the adaptive weight filter configured to apply an adaptive filtering process to the physical microphone signal to estimate a virtual auxiliary path from the physical microphone to the plurality of virtual microphones; and a signal processing unit in electronic communication with the physical microphone and the plurality of virtual microphones, wherein the signal The processing unit includes a non-transitory memory storing a set of analysis filters and instructions; and a processor, wherein when executing the instructions, the processor is configured to: apply the set of subband analysis filters to decompose a residual signal into a plurality of subband error signals and decompose a physical microphone signal into a plurality of subband physical microphone signals; determine a subband gradient for each subband based on the subband physical microphone signal and the subband error signal; determine a subband virtual path convergence rate based on a normalized step size for each subband; determine a subband virtual path for each subband based on the normalized step size and the subband gradient; and apply a subband weight transformation process to each subband virtual path to update an adaptive weight filter and validate the subband virtual path. In a first example of the system, the set of analysis filters includes a plurality of subband filters derived from a prototype filter using a window-based low-pass filter, each subband filter corresponding to a different frequency range within a residual noise spectrum of a vehicle cabin. In a second example of the system, optionally including the first example, the prototype filter includes a window function selected based on a predetermined frequency response characteristic for each subband. In a third example of the system, optionally including one or both of the first and second examples, the normalization step size for each subband includes a power contribution of the subband physical microphone signal and a constant value. In a fourth example of the system, optionally including one or more or each of the first through third examples, the subband gradient for each subband includes a complex conjugate multiplication of the subband physical microphone signal and a subband error signal. In a fifth example of the system, optionally including one or more or each of the first through fourth examples, the subband weight transformation process includes performing a fast Fourier transform on each subband virtual path to obtain a frequency-domain subband virtual path. In a sixth example of the system, optionally including one or more or each of the first through fifth examples, the subband weight transformation process further includes performing an inverse fast Fourier transform on the frequency-domain subband virtual path to obtain an adaptive weight filter in the time domain. In a seventh example of the system, optionally including one or more or each of the first through sixth examples, the physical microphone signal includes a product of road noise filtered by an anti-noise signal generated by the transducer.

[0087] The present disclosure also provides support for a method comprising: obtaining a physical microphone signal using a physical microphone, wherein the physical microphone signal is correlated with a filtered noise signal in a vehicle cabin; processing the physical microphone signal using an adaptive weight filter to estimate a virtual auxiliary path from the physical microphone to a plurality of virtual microphones; obtaining a residual signal from the plurality of virtual microphones positioned in the vehicle cabin; decomposing the physical microphone signal and the residual signal into a plurality of subband signals; calculating a subband gradient for each subband based on the decomposed physical signal and the decomposed residual signal; calculating a normalized step size for each subband based on a power contribution of the physical microphone signal; updating a set of subband virtual path weights based on the subband gradient and the normalized step size; weighting the updated set of subband virtual path weights to the time domain using an inverse fast Fourier transform (IFFT); and processing the residual signal based on the transformed subband virtual weights to reduce noise in the vehicle cabin. In a first example of the method, the subband gradient for each subband comprises a complex conjugate multiplication of the subband physical microphone signal and the subband error signal. In a second example of the method, optionally including the first example, the decomposing comprises filtering the residual signal and the physical microphone signal through an analysis filter bank comprising a plurality of subband filters, each subband filter corresponding to a different frequency range within the residual noise spectrum of the vehicle cabin.

[0088] The description of the embodiments has been presented for purposes of illustration and description. Suitable modifications and variations of the embodiments may be performed in light of the above description or may be acquired through practice of the methods. For example, unless otherwise indicated, one or more of the methods described may be performed by suitable apparatus and / or combinations of apparatuses, such as those described with reference to the respective Figure 1 and Figure 2 The vehicle noise cancellation system 100 and virtual path calculation system 200 described herein are described. The method can be performed by executing stored instructions using a combination of one or more logic devices (e.g., processors) and one or more additional hardware elements, such as storage devices, memories, hardware network interfaces / antennas, switches, actuators, clock circuits, etc. The described method and associated actions can also be performed in various orders other than the order described in this application, in parallel, and / or simultaneously. Individual steps may be omitted in certain embodiments. The described system is exemplary in nature and may include additional elements and / or omit elements. The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various systems and configurations disclosed and other features, functions, and / or properties. As used herein, unless otherwise indicated, "approximately" is interpreted to mean ±5% of the range.

[0089] As used in this application, an element or step recited in the singular and followed by the word "a" or "an" should be understood as not excluding a plurality of said elements or steps, unless such exclusion is stated. In addition, reference to "one embodiment" or "an example" of the present disclosure is not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the described features. The terms "first," "second," and "third," etc. are used merely as labels and are not intended to impose numerical requirements or a particular positional order on their objects. The appended claims particularly point out subject matter believed to be novel and non-obvious from the above disclosure.

Claims

1. A method for noise cancellation in a vehicle, the vehicle having a physical microphone and a plurality of virtual microphones, the physical microphone being configured to acquire a physical microphone signal correlated with a filtered noise signal within a vehicle cabin, the plurality of virtual microphones being positioned within the vehicle cabin to acquire a residual signal, the method comprising: processing the physical microphone signal using an adaptive weight filter to estimate a virtual auxiliary path from the physical microphone to the plurality of virtual microphones; applying a set of analysis filters to decompose the residual signal into a plurality of sub-band error signals and to decompose the physical microphone signal into a plurality of sub-band physical microphone signals; determining a subband gradient for each subband based on the subband physical microphone signal and the subband error signal; Determining the sub-band virtual path convergence speed based on the normalized step size of each sub-band; determining a subband virtual path for each subband based on the normalized step size and the subband gradient; as well as A subband weight transformation process is applied to each subband virtual path to update the adaptive weight filter and validate the subband virtual path.

2. The method of claim 1 , wherein the set of analysis filters comprises a plurality of sub-band filters derived from a prototype filter using a window-based low-pass filter, each sub-band filter corresponding to a different frequency range within the residual noise spectrum of the vehicle cabin.

3. The method of claim 2, wherein the method further comprises selecting a window function for the prototype filter based on a predetermined frequency response characteristic of each subband. The method of claim 1 , wherein determining the normalization step size for each sub-band is based on power contributions of the sub-band physical microphone signals and a constant value. The method of claim 4 , wherein the constant value is adjusted to exceed a threshold normalization step size.

6. The method of claim 1, wherein the subband gradient for each subband comprises performing a complex conjugate multiplication of the subband physical microphone signal and the subband error signal. 7 . The method of claim 1 , wherein the subband weight transformation process comprises performing a fast Fourier transform on each subband virtual path to obtain a frequency-domain subband virtual path.

8. The method of claim 7, wherein the subband weight transformation process further comprises applying an inverse fast Fourier transform to the frequency-domain subband virtual path to obtain the adaptive weight filter in the time domain.

9. The method of claim 1, wherein the virtual auxiliary path is a time-domain estimated virtual auxiliary path.

10. A noise cancellation system for a vehicle, the noise cancellation system comprising: a physical microphone configured to acquire a physical microphone signal correlated with a filtered noise signal within a vehicle cabin; a plurality of virtual microphones positioned within the vehicle cabin and configured to acquire a residual signal; an adaptive weight filter in electronic communication with the physical microphone signal and configured to apply an adaptive filtering process to the physical microphone signal to estimate a virtual auxiliary path from the physical microphone to the plurality of virtual microphones; and a signal processing unit in electronic communication with the physical microphone and the plurality of virtual microphones, wherein the signal processing unit comprises: a non-transitory memory storing a set of analysis filters and instructions; and A processor, wherein the processor, when executing the instructions, is configured to: applying the set of subband analysis filters to decompose the residual signal into a plurality of subband error signals and to decompose the physical microphone signal into a plurality of subband physical microphone signals; determining a subband gradient for each subband based on the subband physical microphone signal and the subband error signal; Determining the sub-band virtual path convergence speed based on the normalized step size of each sub-band; determining a subband virtual path for each subband based on the normalized step size and the subband gradient; and A subband weight transformation process is applied to each subband virtual path to update the adaptive weight filter and validate the subband virtual path.

11. The noise cancellation system of claim 10, wherein the set of analysis filters comprises a plurality of sub-band filters derived from a prototype filter using a window-based low-pass filter, each sub-band filter corresponding to a different frequency range within the residual noise spectrum of the vehicle cabin.

12. The noise cancellation system of claim 11, wherein the prototype filter comprises a window function, the window function being selected based on a predetermined frequency response characteristic of each subband.

13. The noise cancellation system of claim 10, wherein the normalization step size for each sub-band comprises a power contribution of the sub-band physical microphone signal and a constant value.

14. The noise cancellation system of claim 10, wherein the subband gradient for each subband comprises a complex conjugate multiplication of the subband physical microphone signal and the subband error signal.

15. The noise cancellation system of claim 10 , wherein the subband weight transformation process comprises a fast Fourier transform (FFT) on each subband virtual path to obtain a frequency domain subband virtual path, wherein the subband weight transformation process further comprises an inverse fast Fourier transform (IFFT) on the frequency domain subband virtual path to obtain the adaptive weight filter in the time domain, or The physical microphone signal comprises a product of road noise filtered by an anti-noise signal generated by a transducer.

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