Calibration method, system and equipment for intelligent household electrical appliance and active noise reduction system of intelligent household electrical appliance
By calibrating the secondary and feedback paths of acoustic devices in smart home appliances, the problem of poor noise reduction effect caused by differences in acoustic devices is solved, achieving low-cost and efficient acoustic device adaptation and calibration, and improving the applicability and accuracy of the noise reduction system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-03-13
AI Technical Summary
In the existing technology, the active noise cancellation system of smart home appliances is difficult to adapt through a single controller parameter due to the differences in acoustic components, resulting in poor noise cancellation effect or howling and abnormal noise, and the time cost of calibrating each device is too high.
By selecting reference acoustic devices, obtaining their secondary and feedback path parameters, calculating target adjustment parameters, and performing calibration to adapt to different acoustic devices, including speakers and microphones, the calibration is then embedded into the control board for precise calibration.
It enables precise calibration of a variety of acoustic devices at low cost, improves noise reduction performance, is applicable to different models of microphones and speakers, and reduces production and installation costs.
Smart Images
Figure CN121662014A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of smart home appliances, and in particular to a calibration method, system, and device for a smart home appliance and its active noise cancellation system. Background Technology
[0002] As people's living standards improve and technologies such as the internet, big data, artificial intelligence, and voice interaction become more widespread, traditional lifestyles are gradually changing, and the use of home appliances is increasingly moving towards intelligentization. While bringing more convenience to users, the functions of various home appliances are also becoming more diversified.
[0003] Currently, when using active noise reduction technology to control noise in range hoods, in order to ensure the control effect, it is usually necessary to ensure that the control model parameters and acoustic devices are matched, that is, to match different control model parameters for different devices.
[0004] For headphone products, due to the large shipment volume, the control model is relatively simple (few control channels, usually single channel, i.e., single reference microphone + single speaker). Acoustic parameters can be collected from a portion of the headphones and the model parameters can be calibrated. Then, a neural network can be trained to match appropriate control model parameters based on the acoustic parameters of the headphones.
[0005] For home appliances, especially kitchen appliances, due to their large size, complex control models (usually employing multi-channel control), and relatively low shipment volumes, it is difficult to use the same parameter calibration methods as for headphones. Specifically, compared to headphone products, active noise cancellation in home appliances presents the following unique technical challenges:
[0006] 1) Multi-channel control requirements (typically 4-18 channels);
[0007] 2) The device layout is complex (it may employ an asymmetric distribution);
[0008] 3) The user's installation location is uncertain.
[0009] Furthermore, since the acoustic components of each device are different, each device must be calibrated in a quiet environment to achieve the best noise reduction effect. However, the time cost of calibrating each machine is too high. If each machine is not calibrated and the same controller parameters are used directly, the noise reduction effect cannot be guaranteed, and even howling and abnormal noise may occur due to the mismatch between the device and controller parameters. Summary of the Invention
[0010] The technical problem to be solved by this disclosure is to overcome the shortcomings of the existing technology in which the use of a single controller parameter cannot be adapted to different acoustic devices, and to provide a calibration method, system and device for smart home appliances and their active noise cancellation systems.
[0011] This disclosure solves the above-mentioned technical problems through the following technical solution:
[0012] According to a first aspect of this disclosure, a calibration method for an active noise cancellation system is provided, the calibration method comprising:
[0013] Several reference acoustic devices are selected; wherein, the reference acoustic devices include a reference loudspeaker, a reference microphone, and an error microphone;
[0014] Obtain reference calibration parameters for each of the reference acoustic devices, the reference calibration parameters including a standard secondary path and a standard feedback path; the standard secondary path is the path between the reference loudspeaker and the error microphone; the standard feedback path is the path between the reference loudspeaker and the reference microphone;
[0015] Obtain the secondary path and feedback path to be calibrated for each device to be calibrated; wherein, the device to be calibrated includes a speaker to be calibrated, a microphone to be calibrated, and an error microphone; the secondary path to be calibrated is the path between the speaker to be calibrated and the error microphone; the feedback path to be calibrated is the path between the speaker to be calibrated and the microphone to be calibrated;
[0016] Based on the standard secondary path, the standard feedback path, the secondary path to be calibrated, and the feedback path to be calibrated, the target adjustment parameters are obtained, and the calibration of the device to be calibrated is completed based on the target adjustment parameters.
[0017] Optionally, the step of obtaining target adjustment parameters based on the standard secondary path, the standard feedback path, the secondary path to be calibrated, and the feedback path to be calibrated, and completing the calibration of the device to be calibrated based on the target adjustment parameters includes:
[0018] Based on the standard secondary path and the secondary path to be calibrated, the first compensation parameter of the speaker to be calibrated is determined;
[0019] The speaker to be calibrated is calibrated based on the first compensation parameter, and the calibration feedback path of the device to be calibrated after the speaker to be calibrated is completed is obtained.
[0020] Based on the standard feedback path and the feedback path to be calibrated, the second compensation parameter of the microphone to be calibrated is determined;
[0021] The microphone to be calibrated is calibrated based on the second compensation parameter.
[0022] Optionally, the step of selecting a plurality of reference acoustic devices includes:
[0023] Acquire a number of initial acoustic devices, wherein any of the initial acoustic devices includes a number of initial loudspeakers and an initial microphone;
[0024] Acquire acoustic characteristic information corresponding to the initial acoustic device, wherein the acoustic characteristic information is characterized as the frequency response information of the initial loudspeaker at a preset frequency point, and / or, the acoustic information is characterized as the sensitivity information of the initial microphone at the preset frequency point;
[0025] If the frequency response information meets a first threshold and / or the sensitivity information meets a second threshold, then the corresponding initial acoustic device is selected as the reference acoustic device.
[0026] Optionally, prior to the step of obtaining the reference calibration parameters for each of the reference acoustic devices, the calibration method further includes:
[0027] The error microphone is compensated.
[0028] Optionally, after the step of obtaining the adjustment parameter corresponding to the target to be calibrated, the calibration method further includes:
[0029] The target adjustment parameters are stored in the controller;
[0030] And / or,
[0031] The calibration method further includes:
[0032] The standard feedback path is updated based on white noise.
[0033] According to a second aspect of this disclosure, a calibration system for an active noise cancellation system is provided, the calibration system comprising:
[0034] An acoustic device screening module is used to screen out a number of reference acoustic devices; wherein, the reference acoustic devices include a reference loudspeaker, a reference microphone, and an error microphone;
[0035] A standard parameter acquisition module is used to acquire reference calibration parameters for each of the reference acoustic devices. The reference calibration parameters include a standard secondary path and a standard feedback path. The standard secondary path is the path between the reference loudspeaker and the error microphone. The standard feedback path is the path between the reference loudspeaker and the reference microphone.
[0036] The calibration information acquisition module is used to acquire the calibration secondary path and calibration feedback path of each device to be calibrated; wherein, the device to be calibrated includes a speaker to be calibrated, a microphone to be calibrated, and an error microphone; the calibration secondary path is the path between the speaker to be calibrated and the error microphone; the calibration feedback path is the path between the speaker to be calibrated and the microphone to be calibrated.
[0037] The calibration processing module is used to obtain adjustment parameters based on the standard secondary path, the standard feedback path, the secondary path to be calibrated, and the feedback path to be calibrated, and to complete the calibration of the device to be calibrated based on the target adjustment parameters.
[0038] Optionally, the calibration processing module is used for:
[0039] Based on the standard secondary path and the secondary path to be calibrated, the first compensation parameter of the speaker to be calibrated is determined;
[0040] The speaker to be calibrated is calibrated based on the first compensation parameter, and the calibration feedback path of the device to be calibrated after the speaker to be calibrated is completed is obtained.
[0041] Based on the standard feedback path and the feedback path to be calibrated, the second compensation parameter of the microphone to be calibrated is determined;
[0042] The microphone to be calibrated is calibrated based on the second compensation parameter.
[0043] Optionally, the acoustic device screening module is used for:
[0044] Acquire a number of initial acoustic devices, wherein any of the initial acoustic devices includes a number of initial loudspeakers and an initial microphone;
[0045] Acquire acoustic characteristic information corresponding to the initial acoustic device, wherein the acoustic characteristic information is characterized as the frequency response information of the initial loudspeaker at a preset frequency point, and / or, the acoustic information is characterized as the sensitivity information of the initial microphone at the preset frequency point;
[0046] If the frequency response information meets a first threshold and / or the sensitivity information meets a second threshold, then the corresponding initial acoustic device is selected as the reference acoustic device.
[0047] Optionally, the calibration system further includes an error microphone compensation module, which is used to compensate the error microphone before acquiring the reference calibration parameters for each of the reference acoustic devices.
[0048] Optionally, the calibration system further includes an adjustment module, which is used to store the target adjustment parameters into the controller after obtaining the target adjustment parameters corresponding to the target to be calibrated;
[0049] And / or,
[0050] The calibration module is also used to update the standard feedback path based on white noise.
[0051] According to a third aspect of this disclosure, a smart home appliance is provided, the smart home appliance including a calibration system for the active noise cancellation system described in the second aspect of this disclosure.
[0052] According to a fourth aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the calibration method of the active noise cancellation system described in the first aspect of this disclosure.
[0053] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the calibration method for the active noise cancellation system described in the first aspect of this disclosure.
[0054] According to a sixth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the calibration method for the active noise cancellation system described in the first aspect of this disclosure.
[0055] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.
[0056] The positive and progressive effects of this disclosure are as follows:
[0057] In the calibration method for active noise cancellation systems improved by this disclosure, by comparing the secondary paths and feedback paths of the standard acoustic device and the acoustic device to be calibrated, the deviation of the acoustic device to be calibrated can be accurately identified, and the corresponding target adjustment parameters can be obtained to achieve the calibration of the device to be calibrated. Furthermore, the calibration method provided by this disclosure is applicable to a variety of acoustic devices. Whether it is a microphone, speaker or other acoustic device of different models, they can all be adjusted through the same calibration process, which further improves the applicability.
[0058] The calibration method for the active noise cancellation system provided in this disclosure also solves the problem that the previous single controller parameters could not be adapted to different acoustic devices by first estimating the acoustic characteristics of the device, then calculating the compensation parameters, then embedding them into the control board, and finally testing and evaluating the compensation effect. This achieves a better noise reduction effect at a lower cost. Attached Figure Description
[0059] Figure 1 This is a flowchart illustrating the calibration method for the active noise cancellation system provided in Example 1;
[0060] Figure 2 This is a schematic diagram of the process for obtaining the target adjustment parameters provided in Example 1;
[0061] Figure 3 This is a schematic diagram of the calibration system for the active noise reduction system provided in Example 2;
[0062] Figure 4 This is a schematic diagram of the electronic device provided in Example 4. Detailed Implementation
[0063] 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.
[0064] 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.
[0065] Currently, there are four calibration schemes for calibrating multi-channel active noise cancellation devices for large-volume home appliances (such as kitchen appliances). The first scheme is to calibrate the parameters of each machine before it leaves the factory, that is, to generate a suitable model for each machine.
[0066] The second approach involves generating multiple control models based on different device parameters, simulating actual operating conditions to conduct noise tests, evaluating the noise reduction effect, and selecting a suitable control model.
[0067] The third approach is to calibrate each component before assembly and then compensate in the control board program based on the calibration results.
[0068] Option 4: Referring to the headphone approach, train a neural network using a large amount of data, and then match control model parameters for each machine.
[0069] One drawback of Option 1 is that calibrating parameters for each larger or installation-required device (such as a split-type range hood) becomes significantly more complex. Calibration is typically time-consuming, taking several minutes even without considering installation and disassembly, which greatly impacts production line efficiency. Furthermore, calibration requires a quiet environment, which places demands on the production line. Common methods involve transporting the equipment to a quiet room far from the production line or performing calibration in a semi-sterilized chamber, further increasing time consumption and reducing production efficiency.
[0070] The disadvantage of Option 2 is that it involves testing the actual effects of multiple control model parameters sequentially under simulated operating conditions, which is not only time-consuming, but also results in a low degree of matching of the final selected model.
[0071] The disadvantages of Option 3 are: all acoustic components need to be calibrated in advance, which is relatively time-consuming, and the speakers, control boards and microphones must be treated as a single material, which is not conducive to production efficiency.
[0072] The drawbacks of Option 4: Building a controller database and training a neural network using a large number of samples requires a sufficient amount of data. This is generally feasible for mature products like noise-canceling headphones, but for large-sized home appliances where active noise cancellation is not yet widely adopted, the limited shipment volume makes it impossible to collect enough data samples. Even if enough devices are available for controller calibration, it is extremely time-consuming. Even if sufficient data can be collected for neural network training, the calibrated controller can only be used for the same model, resulting in high amortized costs due to the low shipment volume.
[0073] In view of this, this disclosure provides a calibration method, system, and device for smart home appliances and their active noise reduction systems. By first estimating the acoustic characteristics of the device, then calculating the compensation parameters, then embedding them into the control board, and finally testing and evaluating the compensation effect when evaluating the noise reduction effect, this improved scheme solves the problem that single controller parameters cannot be adapted to different acoustic devices, and achieves a better noise reduction effect at a lower cost.
[0074] Example 1
[0075] like Figure 1 As shown, this embodiment provides a calibration method for an active noise cancellation system, the calibration method including:
[0076] S11: Select several reference acoustic devices; among which, the reference acoustic devices include reference loudspeakers, reference microphones, and error microphones;
[0077] S12: Obtain the reference calibration parameters for each reference acoustic device. The reference calibration parameters include the standard secondary path and the standard feedback path. The standard secondary path is the path from the reference loudspeaker to the error microphone. The standard feedback path is the path from the reference loudspeaker to the reference microphone.
[0078] S13: Obtain the secondary path and feedback path to be calibrated for each device to be calibrated; wherein, the devices to be calibrated include the speaker to be calibrated, the microphone to be calibrated, and the error microphone; the secondary path to be calibrated is the path between the speaker to be calibrated and the error microphone; the feedback path to be calibrated is the path between the speaker to be calibrated and the microphone to be calibrated;
[0079] S14: Based on the standard secondary path, standard feedback path, secondary path to be calibrated, and feedback path to be calibrated, obtain the target adjustment parameters, and complete the calibration of the device to be calibrated based on the target adjustment parameters.
[0080] By comparing the secondary paths and feedback paths of the standard acoustic device and the acoustic device to be calibrated, the deviation of the acoustic device to be calibrated can be accurately identified, and the corresponding target adjustment parameters can be obtained to achieve the calibration of the device to be calibrated. Furthermore, the calibration method provided in this disclosure is applicable to a variety of acoustic devices. Whether it is a microphone, speaker or other acoustic device of different models, they can all be adjusted through the same calibration process, which further improves the applicability.
[0081] like Figure 2 As shown, in this embodiment, the steps of obtaining target adjustment parameters based on the standard secondary path, standard feedback path, secondary path to be calibrated, and feedback path to be calibrated, and completing the calibration of the device to be calibrated based on the target adjustment parameters include:
[0082] S21: Determine the first compensation parameter of the speaker to be calibrated based on the standard secondary path and the secondary path to be calibrated;
[0083] S22: Calibrate the loudspeaker to be calibrated based on the first compensation parameter, and obtain the calibration feedback path of the device to be calibrated after the loudspeaker to be calibrated is completed;
[0084] S23: Determine the second compensation parameter of the microphone to be calibrated based on the standard feedback path and the feedback path to be calibrated;
[0085] S24: Calibrate the microphone to be calibrated based on the second compensation parameter.
[0086] The calibration method provided in this disclosure is applicable to various acoustic devices, including but not limited to speakers and microphones, and is particularly suitable for scenarios requiring high-precision audio processing and noise reduction, such as active noise-canceling headphones and car audio systems.
[0087] In this embodiment, a set of standard acoustic devices with known characteristics are selected, including standard loudspeakers and standard microphones. The secondary path and feedback path characteristics of these standard devices should be stable and known. By performing detailed acoustic characteristic measurements on the standard acoustic devices, characteristic data of the standard secondary path and standard feedback path are obtained. These data are then used as a calibration benchmark, improving the accuracy of the calibration.
[0088] By comparing the secondary paths and feedback paths of standard acoustic devices and acoustic devices to be calibrated, adjustment parameters are obtained, and the calibration of the acoustic devices to be calibrated is completed accordingly. This significantly improves calibration accuracy, adapts to different acoustic devices, enhances noise reduction, reduces calibration costs, and improves system reliability.
[0089] Due to the characteristic deviations of the acoustic devices controlling the source, it is generally required that the sensitivity deviation of microphone devices be less than ±1.5 dBA (decibels-weighted A, a commonly used unit of sound pressure level used to express the loudness of sound), and the frequency response curve deviation of loud speaker devices be less than ±1 dBA. Since the amplitude-frequency characteristics and phase-frequency characteristics of the devices are coupled, excessive deviations in amplitude-frequency characteristics (i.e., sensitivity and frequency response) will lead to large differences in the phase-frequency characteristics of the devices. These differences in phase-frequency characteristics make it difficult to compensate for them through algorithms. For example, phase compensation usually introduces delay, destroys causality, and thus leads to a decrease in noise reduction.
[0090] Therefore, in this embodiment, several reference acoustic devices are selected to address the problem of characteristic deviations in the control source acoustic devices.
[0091] The steps for selecting several reference acoustic devices include:
[0092] Acquire several initial acoustic devices, including several initial loudspeakers and initial microphones;
[0093] Acquire the acoustic characteristic information of the corresponding initial acoustic device. The acoustic characteristic information is characterized as the frequency response information of the initial loudspeaker at a preset frequency point, and / or, the acoustic information is characterized as the sensitivity information of the initial microphone at a preset frequency point.
[0094] If the frequency response information meets the first threshold and / or the sensitivity information meets the second threshold, then the corresponding initial acoustic device is selected as the reference acoustic device.
[0095] The acoustic characteristics of multiple acoustic devices are measured, the results are analyzed, and a group of acoustic devices whose frequency response characteristics are closest to the average value (since the frequency response curve is a curve, device manufacturers usually use the frequency response at 1kHz as a benchmark, or multiple frequency points can be selected for weighted averaging) is selected (microphones and speakers need to be selected separately). We call this a quasi-standard device (meaning it is close to the standard device) and use it to replace the standard device (the standard device refers to a group of acoustic devices whose frequency response characteristics are consistent with the average value of multiple devices, including speakers and microphones).
[0096] In practice, by obtaining the standard parameters of the quasi-standard device and the actual parameters of the device to be calibrated, the acoustic compensation parameters of the device to be calibrated are calculated based on the compensation algorithm, and the device to be calibrated is calibrated according to the acoustic compensation parameters.
[0097] The steps of calculating the acoustic compensation parameters of each quasi-standard device and writing the compensation parameters into the control board include:
[0098] For example, if the average sensitivity of all microphones in the sample at 1kHz is -25.0dB, and a selected group of microphones has sensitivities of -26.0dB, -25.5dB, -24.5dB, and -24.0dB respectively, then an algorithm is needed to compensate their sensitivities to -25.0dB. Specifically, according to the definition of decibels... We can conclude that an increase of 0.5dB in power corresponds to a power amplification factor of 1.059. Since power is the square of the signal amplitude, the corresponding signal amplitude amplification factor is 1.029. Assuming the original signals from the four microphones are... , , , The microphone signals after compensation and correction are as follows: , , , This is because a microphone with low sensitivity captures a smaller amplitude sound signal compared to a standard sensitivity device. Therefore, the signal amplitude needs to be increased to simulate the signal collected when a standard sensitivity microphone device is placed in the same position.
[0099] If the average frequency response of all speakers in the sample at 1kHz is 80.0dB, and the selected group of speakers has frequency responses of 79.0dB, 79.5dB, 80.5dB, and 81.0dB at 1kHz respectively, and assuming the original control signals for the four speakers are respectively... , , , The compensated speaker signals are as follows: , , , This is because loudspeakers with a larger frequency response have a stronger sound-producing ability, so it is necessary to reduce the amplitude of their control signal to simulate the sound production when a loudspeaker device with a standard frequency response is placed in the same position.
[0100] By determining the quasi-standard device (also known as the reference acoustic device or the semi-standard acoustic device), it is possible to eliminate the need to calibrate each machine, thus solving the previous problem that the parameters of a single controller could not be adapted to different acoustic devices, and achieving a good noise reduction effect at a lower cost.
[0101] In this embodiment, before the step of obtaining the reference calibration parameters for each reference acoustic device, the calibration method further includes: compensating for the error microphone.
[0102] In one specific implementation, if different error microphones are used when testing other devices subsequently, the gain of the error microphones should also be compensated at the algorithm level. The sensitivity of the compensated error microphone does not need to be consistent with the sensitivity of the compensated reference microphone; it is sufficient that the sensitivity within the same group of error microphones remains consistent. For example, different error microphones can be mounted on the tooling of an artificial mouth, ensuring that the positions are identical. Then, a 1kHz single frequency is played using the artificial mouth, and the software gain is adjusted to ensure that the decibel values calculated from the input signals of different error microphones are the same, thus eliminating the sensitivity differences between different error microphones within the same group.
[0103] By compensating for the error microphone, the noise reduction effect on acoustic devices is further improved.
[0104] In this embodiment, after obtaining the corresponding target adjustment parameters to be calibrated, the calibration method further includes storing the target adjustment parameters in the controller.
[0105] In one specific implementation, the target adjustment parameters are written into the control board, thereby verifying the noise reduction effect of the set of target adjustment parameters through the control board.
[0106] In this embodiment, the calibration method further includes:
[0107] The standard feedback path is updated based on white noise.
[0108] In one specific implementation, for example at the user's time, it is necessary to re-identify the feedback path (which is necessary for some devices that need to be installed, such as range hoods), and then adjust the standard feedback path in the controller parameters to achieve the optimal control effect.
[0109] The specific adjustment steps are as follows: use white noise to identify the system and obtain the feedback path parameters; adjust the parameters in the controller, specifically by replacing the standard feedback path in the controller with the feedback path parameters.
[0110] The following examples illustrate the implementation principle of the calibration method for the active noise reduction system disclosed in this paper:
[0111] 1. Controlling the characteristic deviation of source acoustic devices typically requires microphone device sensitivity deviation to be less than ±1.5dBA and speaker device frequency response curve deviation to be less than ±1dBA. This is because the amplitude-frequency characteristics and phase-frequency characteristics of the devices are coupled. Excessive deviation in amplitude-frequency characteristics (i.e., sensitivity and frequency response) will lead to large differences in the phase-frequency characteristics of the devices, and it is difficult to compensate for the differences in the phase-frequency characteristics of the devices through algorithms (compensating for phase usually introduces delay, destroys causality, and thus leads to a decrease in noise reduction).
[0112] 2. Measure the acoustic characteristics of multiple sets of acoustic devices, analyze the results, and select a set of acoustic devices (microphones and speakers need to be selected separately) whose frequency response characteristics are closest to the average value (since the frequency response curve is a curve, device manufacturers usually use the frequency response at 1kHz as the benchmark, or multiple frequency points can be selected for weighted averaging). We call this a quasi-standard device (meaning it is close to the standard device) to replace the standard device (the standard device refers to a set of acoustic devices whose frequency response characteristics are consistent with the average value of multiple sets of devices, including speakers and microphones).
[0113] 3. Based on the test results in the previous step, calculate the acoustic compensation parameters of each quasi-standard device and write the compensation parameters into the control board. For details on how to calculate the acoustic compensation parameters of each quasi-standard device, please refer to the previous steps on calculating the acoustic compensation parameters of each quasi-standard device and writing the compensation parameters into the control board. These steps will not be repeated here.
[0114] 4. Install the quasi-standard device onto the equipment, connect it to the control board in step 3, collect microphone / speaker data (all of which have been compensated), and calculate the control model parameters corresponding to the standard device. Note that at this point, the secondary path (the time-domain FIR filter corresponding to the z-domain transfer function of the speaker -> error microphone) and the feedback path (the time-domain FIR filter corresponding to the z-domain transfer function of the speaker -> reference microphone) corresponding to the standard speaker device are obtained.
[0115] Note that if different error microphones are used when testing other devices in the future, the gain of the error microphones should also be compensated at the algorithm level. The sensitivity of the compensated error microphone does not need to be the same as the sensitivity of the compensated reference microphone; the sensitivity of the error microphones within the same group should be consistent.
[0116] 5. Write the control model parameters into the control board and verify the noise reduction effect of this set of control model parameters;
[0117] 6. When testing other devices with non-standard acoustic components (hereinafter referred to as Device A), first perform system identification, calculate the secondary path, and compare the secondary path with the secondary path corresponding to the quasi-standard device (a quasi-standard device whose frequency response characteristics are close to those of the standard device, and apply compensation gain to simulate the behavior of the standard device) to calculate the compensation gain parameters of each loudspeaker.
[0118] 7. Based on the compensated gain speaker signal and the reference microphone signal, calculate the feedback path and compare it with the feedback path in the control model parameters of the previous 4 to calculate the compensated gain parameters of each reference microphone.
[0119] 8. Write the compensation gain parameters corresponding to the reference microphone and speaker into the control board of device A, and test the noise reduction effect; at the user's time, re-identify the feedback path (which is necessary for some devices that require installation, such as range hoods), and adjust the feedback path in the controller parameters to achieve the optimal control effect.
[0120] By first estimating the acoustic characteristics of the device, then calculating the compensation parameters, then embedding them into the control board, and finally testing and evaluating the compensation effect, the improved scheme solves the problem that a single controller parameter cannot be adapted to different acoustic devices, and achieves a good noise reduction effect at a lower cost.
[0121] Example 2
[0122] like Figure 3 As shown, this embodiment provides a calibration system for an active noise cancellation system. The calibration system includes:
[0123] The acoustic device screening module 100 is used to screen out a number of reference acoustic devices; wherein, the reference acoustic devices include a reference loudspeaker, a reference microphone, and an error microphone;
[0124] The standard parameter acquisition module 200 is used to acquire the reference calibration parameters for each reference acoustic device. The reference calibration parameters include the standard secondary path and the standard feedback path. The standard secondary path is the path between the reference loudspeaker and the error microphone. The standard feedback path is the path between the reference loudspeaker and the reference microphone.
[0125] The calibration information acquisition module 300 is used to acquire the calibration secondary path and calibration feedback path of each device to be calibrated; wherein, the device to be calibrated includes a speaker to be calibrated, a microphone to be calibrated, and an error microphone; the calibration secondary path is the path between the speaker to be calibrated and the error microphone; the calibration feedback path is the path between the speaker to be calibrated and the microphone to be calibrated;
[0126] The calibration processing module 400 is used to obtain target adjustment parameters based on the standard secondary path, standard feedback path, secondary path to be calibrated, and feedback path to be calibrated, and to complete the calibration of the device to be calibrated based on the target adjustment parameters.
[0127] In this embodiment, the calibration processing module 400 is used for:
[0128] Based on the standard secondary path and the secondary path to be calibrated, the first compensation parameter of the speaker to be calibrated is determined.
[0129] The speaker to be calibrated is calibrated based on the first compensation parameter, and the calibration feedback path of the device to be calibrated after the speaker to be calibrated is completed is obtained.
[0130] Based on the standard feedback path and the feedback path to be calibrated, the second compensation parameter of the microphone to be calibrated is determined.
[0131] The microphone to be calibrated is calibrated based on the second compensation parameter.
[0132] In this embodiment, the acoustic device screening module 100 is used for:
[0133] Acquire several initial acoustic devices, including several initial loudspeakers and initial microphones;
[0134] Acquire the acoustic characteristic information of the corresponding initial acoustic device. The acoustic characteristic information is characterized as the frequency response information of the initial loudspeaker at a preset frequency point, and / or, the acoustic information is characterized as the sensitivity information of the initial microphone at a preset frequency point.
[0135] If the frequency response information meets the first threshold and / or the sensitivity information meets the second threshold, then the corresponding initial acoustic device is selected as the reference acoustic device.
[0136] In this embodiment, the calibration system also includes an error microphone compensation module 500, which is used to compensate for the error microphone before obtaining the reference calibration parameters of each reference acoustic device.
[0137] In this embodiment, the calibration system also includes an adjustment module 600, which is used to store the target adjustment parameters into the controller after obtaining the corresponding target adjustment parameters to be calibrated.
[0138] The calibration module 600 is also used to update the standard feedback path based on white noise.
[0139] 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, wherein the units described as separate components may or may not be physically separate, and 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.
[0140] In the calibration system of the active noise cancellation system improved by this disclosure, by comparing the secondary paths and feedback paths of the standard acoustic device and the acoustic device to be calibrated, the deviation of the acoustic device to be calibrated can be accurately identified, and the corresponding target adjustment parameters can be obtained to achieve the calibration of the device to be calibrated. Furthermore, the calibration method provided by this disclosure is applicable to a variety of acoustic devices. Whether it is a microphone, speaker or other acoustic device of different models, they can all be adjusted through the same calibration process, which further improves the applicability.
[0141] The calibration system for the active noise cancellation system provided in this disclosure also solves the problem that the previous single controller parameters could not be adapted to different acoustic devices by first estimating the acoustic characteristics of the device, then calculating the compensation parameters, then embedding them into the control board, and finally testing and evaluating the compensation effect. This achieves a better noise reduction effect at a lower cost.
[0142] Example 3
[0143] This embodiment provides a smart home appliance, which includes, but is not limited to, active noise-canceling headphones, car audio systems, etc.
[0144] The smart home appliance disclosed herein can accurately identify the deviation of the acoustic device to be calibrated by comparing the secondary paths and feedback paths of the standard acoustic device and the acoustic device to be calibrated, and obtain the corresponding target adjustment parameters to achieve calibration of the device to be calibrated. Furthermore, the calibration method disclosed herein is applicable to a variety of acoustic devices. Whether it is a microphone, speaker or other acoustic device of different models, they can all be adjusted through the same calibration process, further improving applicability.
[0145] The smart home appliance disclosed herein also solves the problem that the previous single controller parameters could not be adapted to different acoustic devices by first estimating the acoustic characteristics of the device, then calculating the compensation parameters, then embedding them into the control board, and finally testing and evaluating the compensation effect. This achieves a better noise reduction effect at a lower cost.
[0146] 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.
[0147] 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.
[0148] Example 4
[0149] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the methods described in the above embodiments. Figure 4 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0150] like Figure 4 As shown, the electronic device 30 can be represented in the form of a general computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).
[0151] Bus 33 includes a data bus, an address bus, and a control bus.
[0152] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.
[0153] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.
[0154] The processor 31 performs various functional applications and data processing, such as the methods described in the above embodiments of this disclosure, by running computer programs stored in the memory 32.
[0155] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 4 As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.
[0156] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.
[0157] Example 5
[0158] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the calibration method for the active noise cancellation system provided in any of the above embodiments.
[0159] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.
[0160] Example 6
[0161] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the calibration method for the active noise cancellation system described in any of the preceding embodiments.
[0162] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.
[0163] 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. A calibration method for an active noise cancellation system, characterized in that, The calibration method includes: Several reference acoustic devices are selected; wherein, the reference acoustic devices include a reference loudspeaker, a reference microphone, and an error microphone; Obtain reference calibration parameters for each of the reference acoustic devices, the reference calibration parameters including a standard secondary path and a standard feedback path; the standard secondary path is the path between the reference loudspeaker and the error microphone; the standard feedback path is the path between the reference loudspeaker and the reference microphone; Obtain the secondary path and feedback path to be calibrated for each device to be calibrated; wherein, the device to be calibrated includes a speaker to be calibrated, a microphone to be calibrated, and an error microphone; the secondary path to be calibrated is the path between the speaker to be calibrated and the error microphone; the feedback path to be calibrated is the path between the speaker to be calibrated and the microphone to be calibrated; Based on the standard secondary path, the standard feedback path, the secondary path to be calibrated, and the feedback path to be calibrated, the target adjustment parameters are obtained, and the calibration of the device to be calibrated is completed based on the target adjustment parameters.
2. The calibration method for the active noise cancellation system according to claim 1, characterized in that, The step of obtaining target adjustment parameters based on the standard secondary path, the standard feedback path, the secondary path to be calibrated, and the feedback path to be calibrated, and completing the calibration of the device to be calibrated based on the target adjustment parameters includes: Based on the standard secondary path and the secondary path to be calibrated, the first compensation parameter of the speaker to be calibrated is determined; The speaker to be calibrated is calibrated based on the first compensation parameter, and the calibration feedback path of the device to be calibrated after the speaker to be calibrated is completed is obtained. Based on the standard feedback path and the feedback path to be calibrated, the second compensation parameter of the microphone to be calibrated is determined; The microphone to be calibrated is calibrated based on the second compensation parameter.
3. The calibration method for the active noise cancellation system according to claim 1, characterized in that, The step of selecting a number of reference acoustic devices includes: Acquire a number of initial acoustic devices, wherein any of the initial acoustic devices includes a number of initial loudspeakers and an initial microphone; Acquire acoustic characteristic information corresponding to the initial acoustic device, wherein the acoustic characteristic information is characterized as the frequency response information of the initial loudspeaker at a preset frequency point, and / or, the acoustic information is characterized as the sensitivity information of the initial microphone at the preset frequency point; If the frequency response information meets a first threshold and / or the sensitivity information meets a second threshold, then the corresponding initial acoustic device is selected as the reference acoustic device.
4. The calibration method for the active noise cancellation system according to claim 1, characterized in that, Prior to the step of obtaining the reference calibration parameters for each of the reference acoustic devices, the calibration method further includes: The error microphone is compensated.
5. The calibration method for the active noise cancellation system according to any one of claims 1-4, characterized in that, After the step of obtaining the adjustment parameters corresponding to the target to be calibrated, the calibration method further includes: The target adjustment parameters are stored in the controller; And / or, The calibration method further includes: The standard feedback path is updated based on white noise.
6. A calibration system for an active noise reduction system, characterized in that, The calibration system includes: An acoustic device screening module is used to screen out a number of reference acoustic devices; wherein, the reference acoustic devices include a reference loudspeaker, a reference microphone, and an error microphone; A standard parameter acquisition module is used to acquire reference calibration parameters for each of the reference acoustic devices. The reference calibration parameters include a standard secondary path and a standard feedback path. The standard secondary path is the path between the reference loudspeaker and the error microphone. The standard feedback path is the path between the reference loudspeaker and the reference microphone. The calibration information acquisition module is used to acquire the calibration secondary path and calibration feedback path of each device to be calibrated; wherein, the device to be calibrated includes a speaker to be calibrated, a microphone to be calibrated, and an error microphone; the calibration secondary path is the path between the speaker to be calibrated and the error microphone; the calibration feedback path is the path between the speaker to be calibrated and the microphone to be calibrated. The calibration processing module is used to obtain target adjustment parameters based on the standard secondary path, the standard feedback path, the secondary path to be calibrated, and the feedback path to be calibrated, and to complete the calibration of the device to be calibrated based on the target adjustment parameters.
7. A smart home appliance, characterized in that, The smart home appliance includes the calibration system for the active noise cancellation system as described in claim 6.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the calibration method of the active noise cancellation system according to any one of claims 1-5.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the calibration method of the active noise cancellation system according to any one of claims 1-5.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the calibration method for the active noise cancellation system as described in any one of claims 1-5.