Active noise reduction processing method and active noise reduction processing system in intelligent household electrical appliance
By introducing signal fusion technology and fixed controllers into smart home appliances, a SIMO system architecture is formed, which solves the problems of high computational complexity and poor stability of existing active noise cancellation technologies. This achieves low-cost, high-efficiency noise cancellation and stability, thereby improving the user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-02
- Publication Date
- 2026-03-10
AI Technical Summary
Existing active noise cancellation technologies in smart home appliances suffer from problems such as large computational load, poor system stability, insufficient device adaptability, and high production costs.
By employing signal fusion technology, a single-input multiple-output (SIMO) system architecture is formed. Signals are collected from multiple reference microphones and fused, and a fixed controller is used for noise reduction, which reduces computational complexity and computational resource requirements, enabling calibration-free standardized production.
While ensuring noise reduction performance, the system's computational complexity and cost were reduced, the signal-to-noise ratio and system stability were improved, anti-interference capabilities were enhanced, production and maintenance processes were simplified, and user experience was improved.
Smart Images
Figure CN121640979A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of smart home appliance technology, and in particular to an active noise reduction processing method and system for smart home appliances. Background Technology
[0002] Currently, active noise cancellation technology in smart home appliances (such as range hoods) mainly adopts a multiple-input multiple-output (MIMO) feedforward control scheme. This scheme uses multiple reference sensors and multiple speakers for independent control. For a MIMO system with I reference channels and J speaker channels, I×J independent controllers are required, and the total computational complexity is O(I×J×L). The system generates anti-phase sound waves through the speakers to cancel the original noise, which has a good noise reduction effect in the low-frequency range. To ensure noise reduction performance, the existing scheme requires personalized parameter tuning for each product, adopting a "one machine, one standard" production strategy.
[0003] However, existing MIMO active noise reduction solutions generally suffer from problems such as large computational load, poor system stability, insufficient device adaptability, and high production costs. Summary of the Invention
[0004] The technical problem to be solved by this disclosure is to overcome the above-mentioned defects in the prior art and to provide an active noise reduction processing method and active noise reduction processing system for smart home appliances.
[0005] This disclosure solves the above-mentioned technical problems through the following technical solution:
[0006] In a first aspect, this disclosure provides an active noise cancellation method for smart home appliances, wherein the smart home appliances are equipped with M reference microphones, N speakers and F preset controllers, and each speaker corresponds to one preset controller; wherein M, N and F are all greater than 1 and are integers, and F ≥ N;
[0007] The active noise reduction processing method includes:
[0008] M initial reference channel signals are acquired based on each of the reference microphones;
[0009] The M initial reference channel signals are fused to obtain a single target reference channel signal;
[0010] The target reference channel signal is processed by different preset controllers to obtain N target output signals after noise reduction, and then output by N speakers respectively.
[0011] Optionally, the step of fusing the M initial reference channel signals to obtain a single target reference channel signal includes:
[0012] The M initial reference channel signals are fused using a direct averaging fusion method to obtain a single target reference channel signal.
[0013] Optionally, the step of fusing the M initial reference channel signals to obtain a single target reference channel signal includes:
[0014] The M initial reference channel signals are synchronously aligned, and the M initial reference channel signals after alignment are fused to obtain a single target reference channel signal.
[0015] Optionally, the active noise reduction method further includes:
[0016] In response to acquiring K initial reference channel signals, the K initial reference channel signals are fused to obtain a single target reference channel signal; wherein 1 < K < M and is an integer.
[0017] Optionally, the preset controller is a fixed controller.
[0018] Optionally, the fixed controller is a fixed controller based on Wiener filtering design.
[0019] In a second aspect, this disclosure provides an active noise cancellation system for smart home appliances, wherein the smart home appliances are equipped with M reference microphones, N speakers and F preset controllers, and each speaker corresponds to one preset controller; wherein M, N and F are all greater than 1 and are integers, and F ≥ N;
[0020] The active noise reduction system includes:
[0021] The signal fusion processing module is used to fuse M initial reference channel signals acquired by each of the reference microphones to obtain a single target reference channel signal.
[0022] The target reference channel signal is processed by different preset controllers to obtain N target output signals after noise reduction, and then output by N speakers respectively.
[0023] Optionally, the signal fusion processing module is further configured to perform fusion processing on the M initial reference channel signals using a direct averaging fusion method to obtain a single target reference channel signal.
[0024] Optionally, the signal fusion processing module is further configured to synchronously align the M initial reference channel signals and perform fusion processing on the aligned M initial reference channel signals to obtain a single target reference channel signal.
[0025] Optionally, the signal fusion processing module is further configured to, in response to acquiring K initial reference channel signals, perform fusion processing on the K initial reference channel signals to obtain a single target reference channel signal; wherein 1 < K < M and is an integer.
[0026] Optionally, the preset controller is a fixed controller.
[0027] Alternatively, a fixed controller based on Wiener filtering design.
[0028] In a third aspect, this disclosure provides a smart home appliance, the smart home appliance including the active noise cancellation system as described in the second aspect.
[0029] Optionally, the smart home appliance includes a range hood.
[0030] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.
[0031] The positive and progressive effects of this disclosure are as follows:
[0032] This solution introduces signal fusion technology to fuse signals from multiple reference channels corresponding to multiple reference microphones, forming a single-input multiple-output (SIMO) system architecture. While ensuring the core noise reduction performance of the system, it effectively reduces the computational complexity, improving processing efficiency and reducing system computing power requirements while maintaining the accuracy and reliability of active noise reduction processing. This avoids unnecessary consumption of computing resources, enabling implementation on a low-cost embedded platform. Simultaneously, it effectively suppresses irrelevant interference signals such as wind noise, solving the problem of single-channel solutions being susceptible to environmental interference, significantly improving the signal-to-noise ratio, effectively enhancing the overall anti-interference capability of the system, and significantly improving the system's stability in complex environments. At the most critical user perception location (ear measurement point), it achieves excellent noise reduction, ensuring a good user experience. Furthermore, it effectively mitigates the impact of differences in individual sensor characteristics, solving the problem of performance degradation caused by device parameter deviations, improving the system's robustness and adaptability to changes in device parameters, and achieving calibration-free standardized production. Attached Figure Description
[0033] Figure 1 This is a flowchart of the active noise reduction processing method according to Embodiment 1 of this disclosure;
[0034] Figure 2 This is a first flowchart of the active noise reduction processing method according to Embodiment 2 of this disclosure;
[0035] Figure 3 This is a two-flowchart of the active noise reduction processing method of Embodiment 2 of this disclosure;
[0036] Figure 4 This is a schematic diagram of the first process architecture of active noise reduction processing in Embodiment 2 of this disclosure;
[0037] Figure 5 This is a schematic diagram of the second process architecture for active noise reduction processing in Embodiment 2 of this disclosure;
[0038] Figure 6 This is a schematic diagram showing the comparison of the average spectrum before and after noise reduction in Embodiment 2 of this disclosure;
[0039] Figure 7 This is a schematic diagram comparing the average noise reduction amount before and after noise reduction in Embodiment 2 of this disclosure;
[0040] Figure 8 This is a schematic diagram of the active noise reduction processing system provided in Embodiment 3 of this disclosure. Detailed Implementation
[0041] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.
[0042] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.
[0043] Example 1
[0044] The smart home appliance in this embodiment includes M reference microphones, N speakers, and F preset controllers, with each speaker corresponding to one preset controller; wherein M, N, and F are all greater than 1 and are integers, and F ≥ N. That is, the number of preset controllers may not be the same as the number of speakers, but preferably, the number of preset controllers is the same as the number of speakers.
[0045] Smart home appliances include, but are not limited to, range hoods. Additionally, smart home appliances also include J error microphones.
[0046] M, N, and J can be set or adjusted according to actual needs. For example, M is 4, N is 2, and J is 4, thus forming a Multiple-Input Multiple-Output (MIMO) system architecture.
[0047] Specifically, in this embodiment, the smart home appliance uses multiple reference microphones for signal acquisition, i.e., a multi-channel redundancy design. By using spatial diversity, a more stable reference channel signal is obtained to ensure the reliability of subsequent signal processing results, thereby ensuring the final processing effect of active noise reduction.
[0048] like Figure 1 As shown, the active noise reduction processing method in smart home appliances in this embodiment includes:
[0049] S101. Based on the acquisition of M initial reference channel signals from each reference microphone;
[0050] S102. Perform fusion processing on the M initial reference channel signals to obtain a single target reference channel signal;
[0051] Assume the noise signal for each reference channel corresponding to each reference microphone is If the interference signals such as wind noise are nᵢ(n), then the noise signal for each reference channel is... Where n represents discrete time, usually s(n) represents the value of an s signal at time n; when the noise signals of each reference channel are highly correlated while the wind noise signals are uncorrelated, the fused interference power is reduced to: .
[0052] Specifically, through the signal fusion processing mechanism, when the noise signals of each channel are highly correlated while the interference signals are uncorrelated, the SNR (signal-to-noise ratio) of the fused reference channel signals acquired by each reference microphone can be significantly improved as follows: This means that the 4-channel system can achieve an improvement of 6.0dB in interference suppression, thereby effectively suppressing irrelevant interference signals such as wind noise, solving the problem that single-channel solutions are susceptible to environmental interference, and achieving a significant improvement in signal-to-noise ratio.
[0053] Furthermore, the introduction of multi-channel signal fusion can effectively reduce the sensitivity to the characteristics of a single sensor (i.e., the reference microphone), and the system's sensitivity to microphone sensitivity deviation can be reduced by more than 60%.
[0054] Furthermore, compared to Multiple-Input Multiple-Output (MIMO), fusing and processing multiple reference channel signals is equivalent to forming a Single-Input Multiple-Output (SIMO) system architecture. This significantly reduces the computational complexity of the entire system from O(M×N×J) to O(N×J), thereby reducing the computational power requirements of the system. This makes it possible to achieve high-performance active noise cancellation on low-cost embedded platforms. Specifically, for a system with 4 reference microphones and 2 speakers, the system computational power requirement can be reduced to about 25%.
[0055] Furthermore, by integrating multi-channel signals, the robustness of the system is effectively improved, the personalized calibration process is eliminated, and production efficiency can be increased by 30%. Among these features, it supports standardized production without calibration, thereby effectively reducing the requirements for installation accuracy. No recalibration is required after component replacement, significantly reducing the complexity of after-sales service.
[0056] S103. Different preset controllers are used to process the target reference channel signal to obtain N target output signals after noise reduction, and then output them through N speakers respectively.
[0057] In this embodiment, signal fusion technology is introduced to fuse the signals from multiple reference channels corresponding to multiple reference microphones, thus forming a single-input multiple-output (SIMO) system architecture. This effectively reduces the system's computational complexity while ensuring the core noise reduction performance. It also improves processing efficiency and reduces system computing power requirements, avoiding unnecessary consumption of computing resources and enabling implementation on a low-cost embedded platform. Simultaneously, it effectively suppresses irrelevant interference signals such as wind noise, solving the problem of single-channel solutions being susceptible to environmental interference, significantly improving the signal-to-noise ratio, effectively enhancing the overall anti-interference capability of the system, and significantly improving the system's stability in complex environments. At the most critical user perception location (ear measurement point), it achieves excellent noise reduction, ensuring a good user experience. Furthermore, it effectively mitigates the impact of differences in individual sensor characteristics, solving the problem of performance degradation caused by device parameter deviations, improving the system's robustness and adaptability to changes in device parameters, and achieving the effect of calibration-free standardized production.
[0058] Example 2
[0059] The active noise cancellation method in the smart home appliance of this embodiment is a further improvement on Embodiment 1, specifically:
[0060] In a feasible solution, such as Figure 2 As shown, step S102 includes:
[0061] S1021. Using a direct averaging fusion method, the M initial reference channel signals are fused to obtain a single target reference channel signal.
[0062] The formula corresponding to the direct average fusion method is: ;in, The initial reference channel signal corresponding to the i-th reference microphone, This refers to the target reference channel signal after fusion processing.
[0063] In this scheme, direct averaging fusion is used to fuse the initial reference channel signals in multiple reference channels. While ensuring the accuracy of the fusion result, it has the advantages of low computational load and high processing effect, thereby further ensuring the performance of the system's active noise reduction processing.
[0064] In a feasible solution, such as Figure 3 As shown, step S102 includes:
[0065] S1022. Synchronously align M initial reference channel signals and fuse the aligned M initial reference channel signals to obtain a single target reference channel signal.
[0066] Preferably, the M initial reference channel signals after synchronization and alignment are fused using a direct averaging fusion method to obtain a single target reference channel signal.
[0067] The calculation formula for synchronization alignment is as follows: ;in, Determined through cross-correlation analysis; This is the initial reference channel signal corresponding to the i-th reference microphone. for The signal after alignment processing.
[0068] In real-world scenarios, some microphones are farther from the noise source, so the signals they receive will be delayed compared to the microphone closest to the noise source. This solution aims to eliminate this delay through synchronization alignment.
[0069] In this scheme, the sampling delay between different sensors is eliminated by synchronously aligning the M initial reference channel signals, thus ensuring the accuracy of the fused target reference channel signal.
[0070] In one feasible solution, the active noise reduction method further includes:
[0071] In response to the acquisition of K initial reference channel signals, the K initial reference channel signals are fused to obtain a single target reference channel signal; where 1 < K < M and is an integer.
[0072] In this solution, even when one or more reference microphones fail, the initial reference channel signals corresponding to each of the remaining available reference microphones can still be fused to obtain a single target reference channel signal for the corresponding scenario. This allows the system to maintain basic noise reduction functions, support calibration-free standardized production processes, and further improve the system's active noise reduction performance, ensuring a better user experience.
[0073] In one feasible solution, the preset controller is a fixed controller.
[0074] Preferably, the fixed controller is a fixed controller based on Wiener filtering; specifically, the optimal controller parameters are obtained by solving the regularized Wiener-Hough equations. Where γ is the regularization parameter. The autocorrelation matrix of the x signal is represented by... This represents the cross-correlation vector between the error signal and the reference signal. Furthermore, in adaptive filter theory, determining controller parameters based on the Wiener-Hough equations is a mature and readily available technique, and therefore will not be elaborated upon here.
[0075] Specifically, in this embodiment, considering that the fan noise spectrum characteristics are relatively stable when the range hood is working at a specific speed, an offline-designed fixed controller is used instead of an adaptive controller to avoid convergence and stability issues.
[0076] This solution addresses the issues of high production costs and complex maintenance associated with traditional solutions through a fixed controller design and calibration-free production process, significantly reducing the total product lifecycle cost. No recalibration is required after component replacement, significantly reducing after-sales service complexity. Furthermore, reduced component consistency requirements lower procurement costs. The simplified production process further reduces overall production costs. These cost reductions in both production and maintenance effectively enhance the overall system performance and market competitiveness of the product.
[0077] Specifically, the active noise reduction processing method of this embodiment will be described with M = 4 and N = 2:
[0078] like Figure 4 and 5 As shown, the active noise reduction process in this embodiment is as follows:
[0079] The four reference microphones respectively acquired the corresponding initial reference channel signal 1, initial reference channel signal 2, initial reference channel signal 3, and initial reference channel signal 4;
[0080] Synchronization alignment processing is performed on initial reference channel signal 1, initial reference channel signal 2, initial reference channel signal 3, and initial reference channel signal 4;
[0081] The initial reference channel signals 1, 2, 3, and 4, after synchronization and alignment processing, are directly averaged and fused to obtain a single target reference channel signal.
[0082] Fixed controller 1 and fixed controller 2 are used to process the target reference channel signal to obtain the corresponding target output signal, which is then output through the corresponding speaker 1 and speaker 2 to complete the active noise reduction process of the signal, ensuring high-quality output of the target output signal and satisfying the user's experience.
[0083] Furthermore, the active noise reduction effect of this embodiment will be illustrated in detail with actual experimental examples:
[0084] (1) Experimental platform
[0085] The test environment was the kitchen of a trial user, which was equipped with a range hood featuring active noise cancellation. The active noise cancellation system in this range hood can directly average and fuse reference channel signals from different microphones; and the noise cancellation controller is designed as a fixed controller referencing a Wiener filter design.
[0086] The system employs a high-performance floating-point DSP (Digital Signal Processing) as its core processing unit. The reference microphones consist of four high signal-to-noise ratio MEMS (Micro-Electro-Mechanical Systems) microphones, positioned on the same horizontal plane inside the range hood's duct. The error microphones are four identical MEMS microphones positioned at the air inlet. All microphones are protected by a waterproof, sound-permeable membrane, sound-absorbing cotton, and a protective housing. The secondary source consists of two 3.5-inch speakers (with rear cavities) installed inside the range hood's duct. In addition to the error microphones, two monitoring points (Monitoring Point 1 and Monitoring Point 2) were set up in the test scenario. A precision sound level meter (such as the Rion ML-53 sound level meter) was used to evaluate the noise reduction effect. Monitoring Point 1 is located on the central axis of the cooktop, 50cm from the wall, at a height of 1.35m. Monitoring Point 2 is located on the front of the range hood, 70cm from the wall, at a height of 1.50m.
[0087] (2) Experimental results
[0088] After turning on the active noise reduction system of the range hood, the noise reduction effect at the air inlet / measuring point was measured under the high setting of the range hood; see Table 1 below for the measured noise reduction amount (noise reduction performance, average value).
[0089] Table 1
[0090]
[0091] And, such as Figure 6 The figure shows the comparison of the average spectrum before and after noise reduction; the horizontal axis represents frequency (in Hz), and the vertical axis represents amplitude (in dB); the red line corresponds to the signal state before noise reduction, the green line corresponds to the signal state after noise reduction by the MIMO controller, and the blue line corresponds to the signal state after noise reduction by the SIMO controller.
[0092] like Figure 7 As shown, this is a comparison of the average noise reduction before and after noise reduction; the horizontal axis represents frequency (in Hz), and the vertical axis represents amplitude (in dB); the dashed line corresponds to the zero reference line, the green line corresponds to the average noise reduction of the signal under the MIMO controller, and the blue line corresponds to the average noise reduction of the signal under the SIMO controller.
[0093] in, Figure 6 and Figure 7 In Chinese, a 4x2 MIMO controller represents a traditional MIMO controller (four inputs and two outputs), while a 1x2 SIMO controller represents a 1x2 SIMO controller (single input and two outputs).
[0094] Experimental results show that at the air inlet location, the noise reduction of the two active noise cancellation schemes based on MIMO and SIMO controllers differs by 1.3 dBA; at a slightly farther measurement point on the stove, the difference is only 0.1 dBA; and at the measurement point closest to the user's ear, the noise reduction effects of the two schemes are essentially identical. This demonstrates that the SIMO controller-based active noise cancellation scheme, while significantly reducing computing power by 75%, achieves the same noise reduction effect as the MIMO controller-based scheme at key user-perceived locations. This fully verifies the engineering practical value of the signal fusion strategy adopted in this embodiment and provides a feasible technical path for the promotion of active noise cancellation technology in the cost-sensitive home appliance sector.
[0095] Example 3
[0096] The smart home appliance in this embodiment includes M reference microphones, N speakers, and F preset controllers, with each speaker corresponding to one preset controller; wherein M, N, and F are all greater than 1 and are integers, and F ≥ N. That is, the number of preset controllers may not be the same as the number of speakers, but preferably, the number of preset controllers is the same as the number of speakers.
[0097] like Figure 8 As shown, the active noise cancellation system in this embodiment of the smart home appliance includes:
[0098] Signal fusion processing module 1 is used to fuse M initial reference channel signals acquired by each reference microphone to obtain a single target reference channel signal.
[0099] Different preset controllers 2 are used to process the target reference channel signal to obtain N target output signals after noise reduction, and then output through N speakers respectively.
[0100] The preset controller can be part of the active noise cancellation system, or it can be used as an auxiliary active noise cancellation system in smart home appliances to complete the active noise cancellation process; the specific configuration can be determined or adjusted according to the actual situation.
[0101] In this embodiment, signal fusion technology is introduced to fuse the signals from multiple reference channels corresponding to multiple reference microphones, thus forming a single-input multiple-output (SIMO) system architecture. This effectively reduces the system's computational complexity while ensuring the core noise reduction performance. It also improves processing efficiency and reduces system computing power requirements, avoiding unnecessary consumption of computing resources and enabling implementation on a low-cost embedded platform. Simultaneously, it effectively suppresses irrelevant interference signals such as wind noise, solving the problem of single-channel solutions being susceptible to environmental interference, significantly improving the signal-to-noise ratio, effectively enhancing the overall anti-interference capability of the system, and significantly improving the system's stability in complex environments. At the most critical user perception location (ear measurement point), it achieves excellent noise reduction, ensuring a good user experience. Furthermore, it effectively mitigates the impact of differences in individual sensor characteristics, solving the problem of performance degradation caused by device parameter deviations, improving the system's robustness and adaptability to changes in device parameters, and achieving the effect of calibration-free standardized production.
[0102] Example 4
[0103] The active noise cancellation method in the smart home appliance of this embodiment is a further improvement on Embodiment 1, specifically:
[0104] In one feasible scheme, the signal fusion processing module 1 is also used to perform fusion processing on the M initial reference channel signals using a direct averaging fusion method to obtain a single target reference channel signal.
[0105] In one feasible scheme, the signal fusion processing module 1 is also used to synchronously align M initial reference channel signals and perform fusion processing on the aligned M initial reference channel signals to obtain a single target reference channel signal.
[0106] In one feasible scheme, the signal fusion processing module 1 is further configured to, in response to the acquisition of K initial reference channel signals, perform fusion processing on the K initial reference channel signals to obtain a single target reference channel signal; wherein, 1 < K < M and is an integer.
[0107] In one feasible solution, the preset controller is a fixed controller.
[0108] In one feasible approach, a fixed controller is designed based on Wiener filtering.
[0109] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.
[0110] Example 5
[0111] The smart home appliance in this embodiment includes the active noise cancellation system in embodiment 3 or 4.
[0112] Smart home appliances include, but are not limited to, range hoods.
[0113] The smart home appliance in this embodiment integrates the aforementioned active noise cancellation system. While ensuring the core noise cancellation performance of the system, it effectively reduces the computational complexity of the system. It maintains the accuracy and reliability of active noise cancellation processing, while also effectively improving processing efficiency, reducing system computing power requirements, and avoiding unnecessary consumption of computing resources, enabling implementation on a low-cost embedded platform. Simultaneously, it effectively suppresses irrelevant interference signals such as wind noise, solving the problem of single-channel solutions being susceptible to environmental interference, significantly improving the signal-to-noise ratio, effectively enhancing the overall anti-interference capability of the system, and significantly improving the system's working stability in complex environments. At the most critical user perception location (ear measurement point), it achieves excellent noise reduction effects, ensuring a good user experience. Furthermore, it effectively mitigates the impact of differences in individual sensor characteristics, solving the problem of performance degradation caused by device parameter deviations, improving the system's robustness and adaptability to changes in device parameters, and achieving the effect of calibration-free standardized production, thereby effectively improving the overall product performance of the smart home appliance.
[0114] In this embodiment, the smart home appliance can be controlled using a voice module, which is equipped with a controller, a voice receiving module, and a voice parsing module. The voice receiving module receives user commands, and the voice parsing module parses the commands. Based on the parsed commands, the controller controls the smart home appliance to perform corresponding operations, thereby realizing intelligent control of the smart home appliance and improving the user experience.
[0115] The smart home appliances in this embodiment can also adopt other smart interaction functions, such as gesture interaction and fingerprint recognition. The specific settings or adjustments can be made according to actual needs to further improve the intelligence level of smart home appliances and bring a better user experience.
[0116] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.
Claims
1. An active noise reduction processing method in a smart home appliance, characterized by, The intelligent household appliance is provided with M reference microphones, N loudspeakers and F preset controllers, each of the loudspeakers corresponding to one of the preset controllers; wherein M, N and F are all greater than 1 and are integers, and F is greater than or equal to N. The active noise reduction processing method comprises: M initial reference channel signals are acquired based on each of the reference microphones; M initial reference channel signals are fused to obtain a single target reference channel signal; The target reference channel signal is processed by different preset controllers to obtain N target output signals after noise reduction processing, and is output by N loudspeakers respectively. 2.The method of claim 1, wherein, The step of fusing M initial reference channel signals to obtain a single target reference channel signal comprises: M initial reference channel signals are fused by a direct average fusion method to obtain a single target reference channel signal. 3.The method of claim 1, wherein, The step of fusing M initial reference channel signals to obtain a single target reference channel signal comprises: M initial reference channel signals are aligned synchronously, and the aligned M initial reference channel signals are fused to obtain a single target reference channel signal. 4.The method of claim 1, wherein, The active noise reduction processing method further comprises: In response to acquiring K initial reference channel signals, the K initial reference channel signals are fused to obtain a single target reference channel signal; wherein 1 < K < M and K is an integer. 5.The method of claim 1-4, wherein, The preset controller is a fixed controller. 6.The method of claim 5, wherein, The fixed controller is a fixed controller based on Wiener filtering design.
7. An active noise reduction processing system in a smart home appliance, characterized by, The intelligent household appliance is provided with M reference microphones, N loudspeakers and F preset controllers, each of the loudspeakers corresponding to one of the preset controllers; wherein M, N and F are all greater than 1 and are integers, and F is greater than or equal to N. The active noise reduction processing system comprises: A signal fusion processing module is configured to acquire M initial reference channel signals based on each of the reference microphones, fuse M initial reference channel signals to obtain a single target reference channel signal, and process the target reference channel signal by different preset controllers to obtain N target output signals after noise reduction processing, and output by N loudspeakers respectively. The preset controller is a fixed controller.
8. The active noise reduction processing system in the smart home appliance of claim 7, wherein, The intelligent household appliance comprises the active noise reduction system of claim 7 or 8.
9. A smart home appliance, characterized by, The intelligent household appliance comprises an extractor hood.
10. The smart home appliance of claim 9, wherein,