A noise reduction method, device, system, noise-reducing headrest, and vehicle

Abstract

This application provides a noise reduction method, device, system, noise-reducing headrest, and vehicle, belonging to the technical field of vehicle noise reduction. The method includes multi-phase resampling of an acquired noise signal with a first sampling rate to obtain a target noise signal with a second sampling rate, and generating an inverted sound wave signal based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate. This application aims to reduce the time delay of the noise reduction process and improve its real-time performance.

Description

Technical Field

[0001] This application relates to the technical field of vehicle noise reduction, specifically to a noise reduction method, device, system, noise-reducing headrest, and vehicle. Background Technology

[0002] As users' demands for driving experience increase, vehicle quietness has become one of the key indicators for measuring driving quality. Therefore, vehicle noise reduction technologies, such as Active Noise Control (ANC), are showing increasing value due to their unique working principle and significant effects. The core of ANC is to generate an anti-phase sound wave with the opposite phase and equal amplitude to the noise. Therefore, the entire processing process must have low latency. High latency will destroy the stability of the control loop and may cause the noise reduction system to oscillate, producing a piercing howl or other unstable noise.

[0003] To reduce system latency and accurately capture and cancel high-frequency noise, high sampling rates are typically used to acquire signals. However, high sampling rates also mean that the noise reduction system has to process a massive number of data points, resulting in a large time delay for the noise reduction system and affecting the real-time performance of the noise reduction process. Summary of the Invention

[0004] This application provides a noise reduction method, device, system, noise-reducing headrest, and vehicle, aiming to reduce the time delay of the noise reduction process and improve the real-time performance of the noise reduction process.

[0005] In a first aspect, embodiments of this application provide a noise reduction method, the method comprising: The noise signal with a first sampling rate is subjected to multiphase resampling to obtain a target noise signal with a second sampling rate, and an antiphase acoustic signal is generated based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate.

[0006] Optionally, the method further includes: The acquired error signal with the first sampling rate is subjected to multiphase resampling to obtain the target error signal with the second sampling rate.

[0007] Optionally, the process of performing multiphase resampling on either the noise signal or the error signal includes: The signal is decomposed into multiple signal subsequences according to a preset sampling multiple, wherein the sampling multiple is determined by the first sampling rate and the second sampling rate; The anti-aliasing filter is decomposed into multiple anti-aliasing sub-filters according to the sampling factor; Based on the phase mapping relationship between the plurality of anti-aliasing sub-filters and the plurality of signal sub-sequences, the corresponding signal sub-sequences are filtered by each sub-filter to obtain the filtering result sub-sequences corresponding to each of the plurality of signal sub-sequences; Based on the filtering result subsequences corresponding to the multiple signal subsequences, a target noise signal or target error signal with a second sampling rate is obtained by combining them.

[0008] Optionally, generating an antiphase acoustic signal based on the target noise signal includes: A reference signal is determined based on the target noise signal; Based on the reference signal, an antiphase acoustic signal is generated using an adaptive filter.

[0009] Optionally, determining the reference signal based on the target noise signal includes: Based on the antiphase acoustic wave signal, the feedback signal is determined through a preset feedback neutralization filter; The reference signal is determined based on the difference between the target noise signal and the feedback signal.

[0010] Optionally, the feedback neutralization filter is determined through offline modeling and / or online modeling.

[0011] Optionally, the method further includes: Based on the target error signal, determine the human ear error signal corresponding to the virtual microphone; The coefficients of the adaptive filter are updated based on the human ear error signal.

[0012] Optionally, based on the target error signal, determining the human ear error signal corresponding to the virtual microphone includes: Based on the target error signal and the first reverse acoustic wave signal arriving at the acquisition module of the error signal, determine the first actual noise signal corresponding to the acquisition module of the error signal; Based on the observation transfer function between the error signal acquisition module and the virtual microphone, and the first actual noise signal, the second actual noise signal corresponding to the virtual microphone is determined; The human ear error signal corresponding to the virtual microphone is determined based on the second actual noise signal corresponding to the virtual microphone and the second reverse sound wave signal arriving at the virtual microphone.

[0013] Optionally, the process of determining the first reverse acoustic wave signal arriving at the error signal acquisition module includes: Based on the reverse acoustic wave signal and the first transfer function, the first reverse acoustic wave signal arriving at the error signal acquisition module is determined, where the first transfer function is the transfer function between the reverse acoustic wave signal transmitting device and the error signal acquisition module.

[0014] Optionally, the process of determining the second reverse acoustic signal arriving at the virtual microphone includes: Based on the reverse acoustic wave signal and the second transfer function, the second reverse acoustic wave signal arriving at the virtual microphone is determined, where the second transfer function is the transfer function between the reverse acoustic wave signal transmitting device and the virtual microphone.

[0015] Optionally, the method further includes: After determining the anti-phase acoustic signal with the second sampling rate, the anti-mirror filter is decomposed into multiple anti-mirror sub-filters according to the preset sampling multiple. The anti-phase acoustic wave signal with the second sampling rate is filtered by multiple anti-image sub-filters, and the filtering results are arranged in order to obtain the target anti-phase acoustic wave signal with the first sampling rate. The target antiphase acoustic wave signal is emitted.

[0016] In a second aspect, embodiments of this application provide an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the noise reduction method as described in the first aspect of the embodiments.

[0017] Thirdly, embodiments of this application provide a noise reduction system, the noise reduction system comprising: The noise signal acquisition module is used to acquire a noise signal with a first sampling rate; The calculation module is used to perform multiphase resampling on the noise signal to obtain a target noise signal with a second sampling rate, and to generate an antiphase acoustic signal based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate.

[0018] Fourthly, this application provides a noise-reducing headrest, wherein the noise-reducing headrest is provided with the noise-reducing system described in the third aspect of the embodiment.

[0019] Fifthly, embodiments of this application provide a vehicle for performing the noise reduction method described in the first aspect of the embodiment, or including the electronic device described in the second aspect of the embodiment, or including the noise reduction system described in the third aspect of the embodiment, or including the noise reduction headrest described in the fourth aspect of the embodiment.

[0020] Beneficial effects: This method obtains a target noise signal with a second sampling rate by performing multiphase resampling on the acquired noise signal with a first sampling rate, and generates an inverse acoustic signal based on the target noise signal. The first sampling rate is greater than the second sampling rate. By performing multiphase resampling on the noise signal, the computational load and power consumption can be reduced without losing information, and the processing delay can be reduced while ensuring computational efficiency, thereby reducing the time delay of the noise reduction process and improving the real-time performance of the noise reduction process. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the noise reduction method proposed in an embodiment of this application; Figure 2 This is a schematic diagram of the feedback neutralization filter principle proposed in one embodiment of this application; Figure 3 This is a schematic diagram illustrating the principle of determining human ear error signals according to an embodiment of this application; Figure 4 This is a schematic diagram of multiphase resampling proposed in an embodiment of this application; Figure 5 This is a simulation diagram of multiphase resampling proposed in an embodiment of this application; Figure 6 This is a simulation diagram of a feedback neutralization filter provided in an embodiment of this application; Figure 7 This is a schematic diagram of a noise reduction system provided in an embodiment of this application; Figure 8 This is a schematic diagram of a noise-reducing headrest provided in one embodiment of this application; Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application; Figure 10 This is a schematic diagram of a readable storage medium proposed in an embodiment of this application; Figure 11 This is a schematic diagram of a computer program product proposed in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] As users' demands for driving experience increase, vehicle quietness has become one of the key indicators for measuring driving quality. Therefore, vehicle noise reduction technologies, such as Active Noise Control (ANC), are showing increasing value due to their unique working principle and significant effects.

[0026] However, there is currently no effective noise reduction system for actively controlling wind noise inside the carriage. The main reason is that wind noise has many sources and changes rapidly, making it difficult to collect a reference signal with strong coherence. If the reference microphone is moved near the noise reduction area, although the coherence of the reference signal can be improved, this introduces delay and acoustic feedback problems.

[0027] Specifically, the core of ANC lies in generating an antiphase sound wave that is out of phase and has the same amplitude as the noise. Therefore, the entire processing link must have extremely low latency. High latency will destroy the stability of the control loop and may cause the noise reduction system to oscillate, producing a harsh howling sound or other unstable noise.

[0028] In practical applications, in order to reduce system latency and accurately capture and cancel high-frequency noise, a very high sampling rate is required when acquiring reference microphone and error microphone signals. However, a high sampling rate also means that the noise reduction system has to process a massive number of data points, which places extremely high demands on the computing power, power consumption and real-time performance of the noise reduction system. It is impractical and inefficient to directly perform complex filtering and adaptive algorithm calculations at a high sampling rate. Therefore, it is necessary to downsample the high sampling rate signal.

[0029] Therefore, in the ANC system, the original signal with a high sampling rate is first downsampled, and then the output is upsampled. During the downsampling process, the signal is first subjected to anti-aliasing filtering at a high sampling rate to prevent high-frequency information from aliasing when the sampling rate is reduced. Then the filtered signal is sampled. At this time, a large number of calculation results are discarded, resulting in unnecessary computational waste. During the upsampling process, the low-sampled signal is first padded with zeros and then anti-mirror filtering is performed. However, the zero-padding process also increases the computational load of anti-mirror filtering.

[0030] Therefore, in order to reduce the time delay of the noise reduction process and improve the real-time performance of the noise reduction process, this application provides a noise reduction method.

[0031] Reference Figure 1 The diagram illustrates a flowchart of a noise reduction method provided in an embodiment of this application. Specifically, the method may include the following steps: S101: Perform multiphase resampling on the acquired noise signal with a first sampling rate to obtain a target noise signal with a second sampling rate, and generate an antiphase acoustic signal based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate.

[0032] In actual implementation, the acquisition module for collecting noise signals can be selected according to the needs of the actual application. For example, the noise signal acquisition module can be one or more reference microphones. The noise signal collected by the reference microphone represents the spatial noise at the location of the reference microphone, such as wind noise, road noise, and other noises. For example, the reference microphone can be deployed on the vehicle near the user's head, which can enhance the coherence and accuracy of the noise collected near the user's ear.

[0033] To reduce system latency and accurately capture and cancel high-frequency noise, a high sampling rate is usually required when acquiring noise signals inside a vehicle. However, a high sampling rate leads to a large computational load and a high delay in the noise reduction process.

[0034] Therefore, in this embodiment of the application, the noise signal with a higher first sampling rate is subjected to multiphase resampling to obtain the target noise signal with a second sampling rate. Multiphase resampling can split a single filter into a group of parallel, short sub-filters. Each sub-filter is only responsible for calculating the data at a specific phase position in the output sample sequence. This can reduce the computational load and power consumption without losing information, and reduce the processing delay while ensuring computational efficiency, thereby reducing the time delay of the noise reduction process and improving the real-time performance of the noise reduction process.

[0035] In actual implementation, the magnitudes of the first sampling rate and the second sampling rate can be set according to the needs of the actual application, and this application embodiment does not impose any restrictions.

[0036] In one feasible implementation, the method further includes: The acquired error signal with the first sampling rate is subjected to multiphase resampling to obtain the target error signal with the second sampling rate.

[0037] In actual implementation, the acquisition module for collecting error signals can be selected according to the needs of the actual application. For example, the error signal acquisition module can be one or more physical microphones. The physical microphones acquire error signals mainly to monitor and provide feedback on the residual noise effect near the human ear after noise reduction control. For example, the physical microphones can be deployed on the vehicle near the user's head to improve the monitoring effect of residual noise near the human ear.

[0038] In one feasible implementation, the process of performing multiphase resampling on either the noise signal or the error signal may include the following steps: A1: Decompose the signal into multiple signal subsequences according to the preset sampling multiple.

[0039] The sampling multiple is determined by the first sampling rate and the second sampling rate; for example, when the first sampling rate is set to 48k and the second sampling rate is set to 6k, the sampling multiple M = 48 / 6 = 8.

[0040] Taking a sampling multiple of M=8 as an example, the original signal It is decomposed into 8 signal subsequences, as shown below: ,

[0041] in, to These are the original signals. The sampled data subsequence with phase difference k=0 to k=7; in actual implementation, the signal subsequence to Zeros need to be added before the signal for alignment.

[0042] In this embodiment, the phase difference refers to the phase difference in time; specifically, on the time axis at a high sampling rate, Compare One sampling clock cycle later, Compare Two sampling clock cycles later, and so on.

[0043] A2: Decompose the anti-aliasing filter into multiple anti-aliasing sub-filters according to the sampling factor.

[0044] Specifically, the coefficients of the anti-aliasing filter are determined according to the sampling factor M. It is split into M anti-aliasing sub-filters.

[0045] Taking a sampling multiple of M=8 as an example, the anti-aliasing filter It is also decomposed into 8 anti-aliasing sub-filters according to phase, as shown below: , .

[0046] A3: Based on the phase mapping relationship between the plurality of anti-aliasing sub-filters and the plurality of signal sub-sequences, the corresponding signal sub-sequences are filtered by each sub-filter to obtain the filtering result sub-sequences corresponding to each of the plurality of signal sub-sequences.

[0047] Specifically, each anti-aliasing sub-filter corresponds to the original signal. In the actual implementation process, multiple anti-aliasing sub-filters and multiple signal sub-sequences with different phases can be pre-established in phase mapping relationship.

[0048] For example, anti-aliasing sub-filter Corresponding signal subsequence , to The filter needs to be reversed to obtain the correct filtered output, i.e., the anti-aliasing sub-filter. Corresponding signal subsequence Anti-aliasing sub-filter Corresponding signal subsequence Anti-aliasing sub-filter Corresponding signal subsequence Anti-aliasing sub-filter Corresponding signal subsequence Anti-aliasing sub-filter Corresponding signal subsequence Anti-aliasing sub-filter Corresponding signal subsequence Anti-aliasing sub-filter Corresponding signal subsequence .

[0049] Then, each sub-filter filters its corresponding signal sub-sequence, resulting in a filtered sub-sequence for each signal sub-sequence. For example, the signal sub-sequence... to signal subsequence The respective filtered result subsequences are denoted as follows: .

[0050] A4: Based on the filtering result subsequences corresponding to the multiple signal subsequences, a target noise signal or target error signal with a second sampling rate is obtained by combining them.

[0051] Specifically, after filtering each signal subsequence sequentially to obtain its corresponding filtered result subsequence, the elements at corresponding positions are added together and combined to obtain the downsampled output.

[0052] Taking a sampling multiple of M=8 as an example, after obtaining the filtered result subsequence Then, the elements at corresponding positions are added together, and the combined downsampled output is shown below: .

[0053] When using multiphase resampling to downsample noise and error signals, the length of each signal subsequence and anti-aliasing sub-filter is 1 / M, which reduces the overall computational load to 1 / M of the original. This reduces computational load and power consumption without losing information, and reduces processing latency while ensuring computational efficiency, thereby reducing the time delay of the noise reduction process and improving the real-time performance of the noise reduction process.

[0054] Furthermore, considering that when noise signals are collected inside a vehicle, the anti-phase acoustic wave signal transmitting device, such as a secondary speaker, emits an anti-phase acoustic wave signal, which can affect the noise signal acquisition module, such as a reference microphone, especially when the secondary speaker and the reference microphone are deployed close to each other, the anti-phase acoustic wave signal played by the secondary speaker can be picked up by the reference microphone through feedback paths such as air or vehicle body structure, resulting in acoustic feedback. This is a harmful acoustic coupling that can seriously damage the stability of the noise reduction process.

[0055] Therefore, in view of the pollution problem caused by acoustic feedback, this application also provides a process for suppressing acoustic feedback.

[0056] Reference Figure 2 The diagram illustrates the principle of the feedback neutralization filter provided in this application embodiment. In one feasible implementation, the process of generating an anti-phase acoustic signal based on the target noise signal may include the following steps: B1: Determine the reference signal based on the target noise signal.

[0057] The reference signal characterizes the target noise signal by filtering out the feedback signal introduced by the secondary loudspeaker.

[0058] Specifically, when determining the reference signal based on the target noise signal, it can be based on the anti-phase acoustic wave signal. The feedback signal is determined by a preset feedback neutralization filter. .

[0059] Feedback neutralization filters can use the modeling results of the feedback path to estimate the feedback components introduced by secondary speakers in real time.

[0060] In actual implementation, the feedback path can be modeled based on the output signal of the secondary speaker and the input signal of the reference microphone to obtain the feedback neutralization filter.

[0061] For example, the feedback neutralization filter can be determined through offline modeling and / or online modeling.

[0062] In the offline modeling approach, white noise can be played by the secondary speaker before noise reduction begins, and the reference microphone is deployed. Simultaneously, the reference microphone collects the signal from the secondary speaker. Then, a feedback neutralization filter is established, using the white noise signal as input and outputting an estimated feedback signal. The feedback neutralization filter can be iterated using the Least Mean Squares (LMS) algorithm, with the iteration error being the difference between the estimated feedback signal and the actual reference microphone signal.

[0063] In the online modeling approach, during the noise reduction process, an additional random signal unrelated to the original noise can be added to the anti-phase acoustic wave signal played by the secondary speaker. The reference microphone will receive the feedback signal, and a feedback neutralization filter will be established. The random signal played is used as input, and the estimated feedback signal is output. The least mean square algorithm can be used to iterate the feedback neutralization filter, and the iteration error is the difference between the estimated feedback signal and the actual reference microphone signal.

[0064] In practical implementation, since the deployment positions of the secondary speaker and reference microphone on the vehicle are fixed, i.e., the feedback paths of the secondary speaker and reference microphone are fixed, a feedback neutralization filter with fixed coefficients can be determined through modeling. This allows for a stable estimation of the feedback signal from the secondary speaker to the reference microphone through the feedback path. .

[0065] In actual implementation, the feedback component introduced by the secondary speaker can also be determined in other ways, and this application embodiment does not impose any restrictions.

[0066] Determine the feedback signal Then, based on the target noise signal With the feedback signal The difference is used to determine the reference signal. For example, the reference signal The calculation formula is as follows: .

[0067] B2: Based on the reference signal, an antiphase acoustic signal is generated through an adaptive filter.

[0068] Determining the reference signal Then, an antiphase acoustic signal can be generated using an adaptive filter.

[0069] By target noise signal Feedback signal introduced by the secondary speaker Filtering out the noise allows us to obtain a more representative reference signal. Based on reference signal Generating an inverted acoustic signal ensures stable convergence of the adaptive filtering and noise reduction process.

[0070] In actual implementation, the algorithm used by the adaptive filter can be selected according to the needs of the actual application. This application embodiment does not impose any restrictions and any feedforward adaptive filtering algorithm that includes reference signal and error signal can be used.

[0071] Furthermore, the main function of the error signal is to reflect a noise reduction effect near the human ear. However, since the error signal acquisition module, such as a physical microphone, can only accurately sense the acoustic conditions at its installation point, using a regular physical microphone can only ensure a good noise reduction effect at the physical microphone installation point, and cannot effectively guarantee the best effect at the passenger's actual listening position (especially the moving head area). If a large number of physical microphones are to be deployed in order to obtain better spatial coverage, this will significantly increase the system hardware cost, wiring complexity and integration difficulty, and also increase potential failure points.

[0072] Therefore, this application also provides a process for updating an adaptive filter based on human ear error signals, which may specifically include the following steps: C1: Determine the human ear error signal corresponding to the virtual microphone based on the target error signal.

[0073] Reference Figure 3 This diagram illustrates the principle of determining the human ear error signal according to an embodiment of this application. Specifically, determining the human ear error signal corresponding to the virtual microphone based on the target error signal includes the following sub-steps: C11: Based on the target error signal and the first reverse acoustic wave signal arriving at the acquisition module of the error signal, determine the first actual noise signal corresponding to the acquisition module of the error signal.

[0074] In practical implementation, the first transfer function between the anti-phase acoustic wave signal transmitting device, such as a secondary loudspeaker, and the error signal acquisition module, such as a physical microphone, can be pre-modeled. First transfer function This describes the characteristics of the entire physical acoustic channel, from the emission of an anti-phase sound wave signal from a secondary speaker through the physical space (including cavities, ear canals, air, and reflections) to its final reception by a physical microphone.

[0075] During the noise reduction process, the secondary speaker emits a reverse sound wave signal. It will pass through the real secondary function A physical microphone can transmit backsound signals. and the first transfer function The first reverse acoustic wave signal arriving at the acquisition module of the error signal is estimated and calculated. .

[0076] Since the error signal collected by the physical microphone includes both noise and reverse sound wave signals, it is possible to determine the target error signal. The first reverse acoustic wave signal arriving at the acquisition module of the error signal The first actual noise signal corresponding to the physical microphone is calculated. The formula is as follows: .

[0077] C12: Based on the observation transfer function between the error signal acquisition module and the virtual microphone, and the first actual noise signal, determine the second actual noise signal corresponding to the virtual microphone.

[0078] In practical implementation, the relative position of the virtual microphone to the error signal acquisition module, such as the physical microphone, can be predefined, and then the observation transfer function between the virtual microphone and the physical microphone can be modeled. .

[0079] In determining the first actual noise signal Then, based on the first actual noise signal and observation transfer function The second actual noise signal corresponding to the virtual microphone is calculated. .

[0080] C13: Determine the human ear error signal corresponding to the virtual microphone based on the second actual noise signal corresponding to the virtual microphone and the second reverse sound wave signal arriving at the virtual microphone.

[0081] In practical implementation, the second transfer function between the anti-phase acoustic wave signal transmitting device, such as a secondary loudspeaker, and the virtual microphone can be pre-modeled. .

[0082] During the noise reduction process, the secondary speaker emits a reverse sound wave signal. It will pass through the real secondary function A virtual microphone can be achieved based on the reverse acoustic signal. Second transfer function Determine the second reverse acoustic signal arriving at the virtual microphone. .

[0083] Finally, based on the second actual noise signal corresponding to the virtual microphone... and the second reverse acoustic signal reaching the virtual microphone The human ear error signal corresponding to the virtual microphone is calculated. The formula is shown below: .

[0084] C2: Update the coefficients of the adaptive filter based on the human ear error signal.

[0085] Using human ear error signals The coefficients involved in the adaptive filter update can be moved to a position closer to the virtual microphone of the human ear, which can enhance the noise reduction effect.

[0086] In one feasible implementation, the method further includes: After determining the anti-phase acoustic signal with the second sampling rate, the anti-mirror filter is decomposed into multiple anti-mirror sub-filters according to a preset sampling multiple; the anti-phase acoustic signal with the second sampling rate is filtered by the multiple anti-mirror sub-filters respectively, and the filtering results are arranged in order to obtain the target anti-phase acoustic signal with the first sampling rate; the target anti-phase acoustic signal is emitted.

[0087] For example, after obtaining the target noise signal and the target error signal through the above multiphase downsampling, the process of determining the reference signal and the human ear error signal is carried out at the second sampling rate. Finally, the inverted acoustic wave signal obtained by the adaptive filter is also at the second sampling rate. It is also necessary to reconstruct the low sampling rate inverted acoustic wave signal to a sufficiently high sampling rate to cover the frequency band of the noise.

[0088] Therefore, after obtaining the anti-phase acoustic wave signal with the second sampling rate, it is necessary to upsample to obtain the target anti-phase acoustic wave signal emitted by the secondary loudspeaker.

[0089] Specifically, the anti-mirror filter is decomposed into multiple anti-mirror sub-filters according to a preset sampling multiple. The anti-phase acoustic signal with the second sampling rate obtained in the process of adopting the embodiment of this application does not need to be interpolated again. It can be directly filtered by multiple anti-mirror sub-filters. The upsampled output, i.e. the target anti-phase acoustic signal, can be obtained by arranging the filtering results in order.

[0090] For example, taking a sampling factor of M=8 as an example, the anti-mirror filter Decomposed into 8 anti-mirror sub-filters based on phase: ,

[0091] The antiphase acoustic signal with the second sampling rate does not require interpolation. It can be directly passed through eight anti-image sub-filters. Then, the eight filtered results are arranged in order to obtain the target antiphase acoustic signal.

[0092] Reference Figure 4 The diagram illustrates a multiphase resampling method provided in this application embodiment. Compared with traditional downsampling and upsampling methods, the multiphase resampling process used in this application embodiment is simpler. Unlike traditional methods that first perform anti-aliasing filtering on the signal at a high sampling rate and then sample the filtered signal, this method avoids unnecessary computational waste. Furthermore, it does not perform zero padding during upsampling, which can significantly reduce computational load, reduce the delay in the noise reduction process, and enhance the real-time performance of the noise reduction process.

[0093] Reference Figure 5 The diagram shows a simulation of multiphase resampling provided in the embodiments of this application. The simulation shows that multiphase resampling can save nearly 80% of the processing time compared with traditional downsampling and upsampling methods, and has higher computational efficiency.

[0094] Reference Figure 6 The diagram shows a simulation of the feedback neutralization filter provided in the embodiments of this application. Compared with the case where there is acoustic feedback, the noise reduction after neutralizing the feedback signal is significantly improved based on the feedback neutralization filter provided in the embodiments of this application.

[0095] Reference Figure 7 The diagram illustrates a noise reduction system according to an embodiment of this application. Specifically, the noise reduction system includes: The noise signal acquisition module 100 is used to acquire a noise signal with a first sampling rate; The calculation module 200 is used to perform multiphase resampling on the noise signal to obtain a target noise signal with a second sampling rate, and to generate an antiphase acoustic signal based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate.

[0096] Optionally, the system further includes: The error signal acquisition module is used to acquire the error signal with a first sampling rate; The calculation module is also used to perform multiphase resampling on the acquired error signal with a first sampling rate to obtain a target error signal with a second sampling rate.

[0097] Optionally, the process of performing multiphase resampling on either the noise signal or the error signal includes: The signal is decomposed into multiple signal subsequences according to a preset sampling multiple, wherein the sampling multiple is determined by the first sampling rate and the second sampling rate; The anti-aliasing filter is decomposed into multiple anti-aliasing sub-filters according to the sampling factor; Based on the phase mapping relationship between the plurality of anti-aliasing sub-filters and the plurality of signal sub-sequences, the corresponding signal sub-sequences are filtered by each sub-filter to obtain the filtering result sub-sequences corresponding to each of the plurality of signal sub-sequences; Based on the filtering result subsequences corresponding to the multiple signal subsequences, a target noise signal or target error signal with a second sampling rate is obtained by combining them.

[0098] Optionally, the computing module is further configured to: A reference signal is determined based on the target noise signal; Based on the reference signal, an antiphase acoustic signal is generated using an adaptive filter.

[0099] Optionally, determining the reference signal based on the target noise signal includes: Based on the antiphase acoustic wave signal, the feedback signal is determined through a preset feedback neutralization filter; The reference signal is determined based on the difference between the target noise signal and the feedback signal.

[0100] Optionally, the feedback neutralization filter is determined through offline modeling and / or online modeling.

[0101] Optionally, the computing module is further configured to: Based on the target error signal, determine the human ear error signal corresponding to the virtual microphone; The coefficients of the adaptive filter are updated based on the human ear error signal.

[0102] Optionally, based on the target error signal, determining the human ear error signal corresponding to the virtual microphone includes: Based on the target error signal and the first reverse acoustic wave signal arriving at the acquisition module of the error signal, determine the first actual noise signal corresponding to the acquisition module of the error signal; Based on the observation transfer function between the error signal acquisition module and the virtual microphone, and the first actual noise signal, the second actual noise signal corresponding to the virtual microphone is determined; The human ear error signal corresponding to the virtual microphone is determined based on the second actual noise signal corresponding to the virtual microphone and the second reverse sound wave signal arriving at the virtual microphone.

[0103] Optionally, the process of determining the first reverse acoustic wave signal arriving at the error signal acquisition module includes: Based on the reverse acoustic wave signal and the first transfer function, the first reverse acoustic wave signal arriving at the error signal acquisition module is determined, where the first transfer function is the transfer function between the reverse acoustic wave signal transmitting device and the error signal acquisition module.

[0104] Optionally, the process of determining the second reverse acoustic signal arriving at the virtual microphone includes: Based on the reverse acoustic wave signal and the second transfer function, the second reverse acoustic wave signal arriving at the virtual microphone is determined, where the second transfer function is the transfer function between the reverse acoustic wave signal transmitting device and the virtual microphone.

[0105] Optionally, the computing module is further configured to: After determining the anti-phase acoustic signal with the second sampling rate, the anti-mirror filter is decomposed into multiple anti-mirror sub-filters according to a preset sampling multiple; the anti-phase acoustic signal with the second sampling rate is filtered by the multiple anti-mirror sub-filters respectively, and the filtering results are arranged in order to obtain the target anti-phase acoustic signal with the first sampling rate. The system also includes an anti-phase acoustic signal transmitting device for emitting the target anti-phase acoustic signal.

[0106] Reference Figure 8 The diagram shows a noise-reducing headrest provided in an embodiment of this application. The noise-reducing headrest is equipped with the noise-reducing system described in the embodiment of this application and performs the noise-reducing method described in the embodiment of this application.

[0107] Specifically, the acquisition module used to acquire noise signals can be one or more reference microphones. In this embodiment, two reference microphones are used as an example. The two reference microphones can be installed on both sides of the headrest and face outwards to facilitate the acquisition of noise signals near the user's head.

[0108] The anti-phase acoustic signal transmitting device can select one or more secondary loudspeakers. In this embodiment, two secondary loudspeakers are used as an example. The two secondary loudspeakers are at an angle of 30°-45° with the axis and are directed towards the human ear.

[0109] The acquisition module used to acquire error signals can be one or more physical microphones. In this embodiment, two physical microphones are used as an example. The two physical microphones can be installed on the front of the headrest and located inside the two secondary speakers, facing forward, to estimate the noise near the human ear.

[0110] The virtual microphone can be set to be located 5-10cm in front of the physical microphone.

[0111] The headrest contains a computing module, which, for example, may include a DSP, memory, DAC, and power amplifier, to run algorithm programs and drive the speaker to emit control signals.

[0112] The noise-reducing headrest provided in this application embodiment can replace ordinary vehicle seat headrests, thereby providing localized noise reduction for the user's head area and enhancing the noise reduction effect.

[0113] Reference Figure 9 The diagram illustrates an electronic device provided in an embodiment of this application. The electronic device includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the various processes of the noise reduction method embodiments described above and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0114] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0115] Reference Figure 10 The diagram illustrates a readable storage medium provided in an embodiment of this application. The readable storage medium stores a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the noise reduction method embodiments described above and achieve the same technical effect. To avoid repetition, the details will not be repeated here.

[0116] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0117] Reference Figure 11The diagram illustrates a computer program product provided in an embodiment of this application, including a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the various processes of the noise reduction method embodiment described above and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0118] This application also provides a vehicle for performing the various processes of the noise reduction method embodiments described above, or including the electronic equipment described in this application embodiment, or including the noise reduction system described in this application embodiment, or including the noise reduction headrest described in this application embodiment, and achieving the same technical effect. To avoid repetition, it will not be described again here.

[0119] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0121] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. The description of the embodiments above is only for the purpose of helping to understand the method and core idea of ​​this application. Those skilled in the art can make many forms under the guidance of this application without departing from the spirit and scope of protection of the claims, and all of these are within the protection scope of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A noise reduction method, characterized in that, The method includes: The acquired noise signal with a first sampling rate is subjected to multiphase resampling to obtain a target noise signal with a second sampling rate, and an antiphase acoustic signal is generated based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate.

2. The method according to claim 1, characterized in that, The method further includes: The acquired error signal with the first sampling rate is subjected to multiphase resampling to obtain the target error signal with the second sampling rate.

3. The method according to claim 2, characterized in that, The process of performing multiphase resampling on either the noise signal or the error signal includes: The signal is decomposed into multiple signal subsequences according to a preset sampling multiple, wherein the sampling multiple is determined by the first sampling rate and the second sampling rate; The anti-aliasing filter is decomposed into multiple anti-aliasing sub-filters according to the sampling factor; Based on the phase mapping relationship between the plurality of anti-aliasing sub-filters and the plurality of signal sub-sequences, the corresponding signal sub-sequences are filtered by each sub-filter to obtain the filtering result sub-sequences corresponding to each of the plurality of signal sub-sequences; Based on the filtering result subsequences corresponding to the multiple signal subsequences, a target noise signal or target error signal with a second sampling rate is obtained by combining them.

4. The method according to claim 1, characterized in that, Generating an antiphase acoustic signal based on the target noise signal includes: A reference signal is determined based on the target noise signal; Based on the reference signal, an antiphase acoustic signal is generated using an adaptive filter.

5. The method according to claim 4, characterized in that, Determining a reference signal based on the target noise signal includes: Based on the antiphase acoustic wave signal, the feedback signal is determined through a preset feedback neutralization filter; The reference signal is determined based on the difference between the target noise signal and the feedback signal.

6. The method according to claim 5, characterized in that, The feedback neutralization filter was determined through offline and / or online modeling.

7. The method according to claim 2, characterized in that, The method further includes: Based on the target error signal, determine the human ear error signal corresponding to the virtual microphone; The coefficients of the adaptive filter are updated based on the human ear error signal.

8. The method according to claim 7, characterized in that, Based on the target error signal, determine the human ear error signal corresponding to the virtual microphone, including: Based on the target error signal and the first reverse acoustic wave signal arriving at the acquisition module of the error signal, determine the first actual noise signal corresponding to the acquisition module of the error signal; Based on the observation transfer function between the error signal acquisition module and the virtual microphone, and the first actual noise signal, the second actual noise signal corresponding to the virtual microphone is determined; The human ear error signal corresponding to the virtual microphone is determined based on the second actual noise signal corresponding to the virtual microphone and the second reverse sound wave signal arriving at the virtual microphone.

9. The method according to claim 8, characterized in that, The process of determining the first reverse acoustic wave signal arriving at the error signal acquisition module includes: Based on the reverse acoustic wave signal and the first transfer function, the first reverse acoustic wave signal arriving at the error signal acquisition module is determined, where the first transfer function is the transfer function between the reverse acoustic wave signal transmitting device and the error signal acquisition module.

10. The method according to claim 8, characterized in that, The process of determining the second reverse acoustic wave signal arriving at the virtual microphone includes: Based on the reverse acoustic wave signal and the second transfer function, the second reverse acoustic wave signal arriving at the virtual microphone is determined, where the second transfer function is the transfer function between the reverse acoustic wave signal transmitting device and the virtual microphone.

11. The method according to any one of claims 1-10, characterized in that, The method further includes: After determining the anti-phase acoustic signal with the second sampling rate, the anti-mirror filter is decomposed into multiple anti-mirror sub-filters according to the preset sampling multiple. The anti-phase acoustic wave signal with the second sampling rate is filtered by multiple anti-image sub-filters, and the filtering results are arranged in order to obtain the target anti-phase acoustic wave signal with the first sampling rate. The target antiphase acoustic wave signal is emitted.

12. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the noise reduction method as described in any one of claims 1-11.

13. A noise reduction system, characterized in that, The noise reduction system includes: The noise signal acquisition module is used to acquire a noise signal with a first sampling rate; The calculation module is used to perform multiphase resampling on the noise signal to obtain a target noise signal with a second sampling rate, and to generate an antiphase acoustic signal based on the target noise signal, wherein the first sampling rate is greater than the second sampling rate.

14. A noise-reducing headrest, characterized in that, The noise-reducing headrest is provided with the noise-reducing system as described in claim 13.

15. A vehicle, characterized in that, The vehicle is used to perform the noise reduction method according to any one of claims 1-11, or includes the electronic device according to claim 12, or includes the noise reduction system according to claim 13, or includes the noise reduction headrest according to claim 14.

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