Noise reduction treatment method for range hood and range hood

Abstract

The invention relates to a noise reduction treatment method for a range hood and the range hood. And a position tracking device, a physical reference sensor, a physical error microphone and a loudspeaker are physically arranged at the smoke exhaust ventilator. The noise reduction processing method comprises the following steps: acquiring ear positions of a person in front of the range hood by using the position tracking device; matching a pre-trained virtual error microphone according to the ear position; noise reduction processing is carried out by using an adaptive active noise reduction algorithm, a noise related signal collected by a physical reference sensor is used as a reference signal x (n), an adaptive filter coefficient W (n) is obtained based on a first secondary path estimation matrix # imgabs0 #, a second secondary path estimation matrix # imgabs1 # and a transfer function estimation matrix # imgabs2 #, and a noise reduction sound wave control signal is generated; and the loudspeaker is controlled to play the noise reduction sound waves to realize active noise reduction. The range hood (100) is configured and subjected to noise reduction treatment according to the noise reduction treatment method.

Description

Technical Field

[0001] The present invention relates to the field of kitchen appliances and particularly to a noise reduction method for a range hood and the range hood. Background Art

[0002] Range hoods are a type of kitchen appliance that purifies the kitchen environment and have become an essential kitchen appliance for modern families. At present, with the continuous improvement of people's quality of life, the demand for quiet range hoods is also increasing, and range hoods with noise reduction functions have appeared.

[0003] In the current noise reduction schemes for range hoods, most of them adopt passive noise reduction methods, such as reducing the aerodynamic noise, motor fan order noise and cabinet structure noise of the range hood by improving the fan structure, adding sound-absorbing cotton and adding silencer air ducts. However, the passive noise reduction method can show a good noise reduction effect for mid-high frequency noise, but the noise of the range hood is mainly concentrated in the mid-low frequency, and the noise of the range hood cannot be effectively controlled by the passive noise reduction method alone.

[0004] There are also active noise reduction (ANC) methods in other fields. For example, Chinese patent document CN115206279A discloses a method for actively controlling low-frequency noise in a car. For another example, Korean patent document KR20050047374A discloses a noise control method based on virtual microphone and neural network learning. However, the above-mentioned active noise reduction methods also have certain defects. Specifically, the in-car noise control method disclosed in Chinese patent document CN115206279A does not take into account the influence of the distance between the error microphone and the ear of the person in the car on the noise reduction effect; although Korean patent document KR20050047374A takes into account the setting of the virtual microphone, it still cannot achieve effective noise reduction at the ear when the person moves. Summary of the invention

[0005] Therefore, an object of the present invention is to provide an active noise reduction solution for a range hood, which can effectively reduce noise at the ear position of the person in front, especially when a person moves in front of the range hood.

[0006] According to one aspect of the present invention, the object is achieved by a noise reduction processing method for a range hood, wherein a position tracking device, a physical reference sensor, a physical error microphone and a speaker are physically provided at the range hood, wherein the noise reduction processing method comprises:

[0007] Using a position tracking device to obtain the ear position of the person in front of the range hood;

[0008] Matching pre-trained virtual error microphones based on ear position;

[0009] The adaptive active noise reduction algorithm is used for noise reduction processing, in which the noise-related signal collected by the physical reference sensor is used as the reference signal x(b), and the first secondary path estimation matrix is ​​used as the reference signal x(b). Second secondary path estimation matrix And the transfer function estimation matrix Obtain the adaptive filter coefficient W(b) and generate a noise reduction wave control signal to control the loudspeaker to play the noise reduction wave to achieve active noise reduction, wherein the first secondary path estimation matrix is the secondary path estimation matrix from the loudspeaker to the physical error microphone, and the second secondary path estimation matrix is the secondary path estimation matrix from the loudspeaker to the virtual error microphone, and the transfer function estimation matrix is the transfer function estimate matrix from the physical error microphone to the virtual error microphone.

[0010] In some preferred embodiments, the noise-related signal includes a vibration acceleration signal and / or a noise sound pressure signal.

[0011] In some preferred implementations, obtaining the adaptive filter coefficient W(b) includes:

[0012] will match the error signal at the location of the virtual error microphone Input the adaptive filter to update the adaptive filter coefficient W(n), thereby realizing the real-time noise reduction function, where:

[0013]

[0014] in, is the residual noise estimate signal of the physical error microphone and is configured as

[0015]

[0016] Among them, e p W(n) is the error signal collected by the physical error microphone, and y(n) is the noise reduction control signal generated based on the reference signal x(n) according to the adaptive filter coefficient W(n).

[0017] In some preferred embodiments, obtaining the adaptive filter coefficient W(n) includes: converting the second secondary path estimation matrix The filtered reference signal x′(n) is input into the adaptive filter to update the adaptive filter coefficient W(n), thereby realizing the real-time noise reduction function, where:

[0018] In some preferred embodiments, the adaptive filter performs adaptive operation according to a least mean square algorithm or an x-filtered least mean square algorithm.

[0019] In some preferred embodiments, the noise reduction processing method further includes: pre-training offline to obtain a first secondary path estimation matrix Pre-train offline to obtain the first secondary path estimation matrix The method comprises: using a white noise signal as an input signal to drive a loudspeaker to emit white noise, using a physical error microphone as an error microphone to collect the white noise to obtain a desired signal, comparing an algorithm output signal of an adaptive filter based on a least mean square algorithm with the desired signal to obtain an error signal, inputting the input signal and the error signal into an adaptive filter, and when the adaptive process of the adaptive filter converges, obtaining a first secondary path estimation matrix that completes the training

[0020] Here, advantageously, the range hood is provided with at least one loudspeaker and at least two physical error microphones, that is, the range hood is provided with M loudspeakers and N physical error microphones, wherein M≥1, N≥2, wherein the first secondary path estimation matrix The first secondary path estimation vectors from M loudspeakers to N physical error microphones obtained by pre-training offline

[0021] In some preferred embodiments, the noise reduction processing method further includes: pre-training offline to obtain multiple second secondary path estimation matrices In noise reduction, the corresponding second secondary path estimation matrix is ​​selected based on the virtual error microphone matched according to the ear position. The second secondary path estimation matrix corresponding to a virtual error microphone is pre-trained offline in this way A physical microphone is placed at the virtual error microphone or at its alternative position; a white noise signal is used as an input signal to drive a loudspeaker to emit white noise, and the physical microphone collects the white noise to obtain a desired signal, and the algorithm output signal of the adaptive filter based on the least mean square algorithm is compared with the desired signal to obtain an error signal, and the input signal and the error signal are input into the adaptive filter. When the adaptive process of the adaptive filter converges, a second secondary path estimation matrix corresponding to a virtual error microphone is obtained. Remove the physical microphone.

[0022] In some preferred embodiments, the noise reduction processing method includes: pre-training multiple transfer function estimations offline When noise reduction is performed, the corresponding transfer function estimation matrix is ​​selected based on the virtual error microphone matched according to the ear position. The transfer function estimation matrix corresponding to a virtual error microphone is trained offline in advance A first physical microphone is placed at the virtual error microphone or at its alternative position; a white noise signal is used as an input signal to drive the speaker to emit white noise, and the input signal is estimated through a transfer function matrix Then, an output signal is generated, and the physical error microphone used as the second physical microphone and the first physical microphone respectively collect white noise to obtain a desired signal, and the output signal and the respectively collected desired signal are operated to obtain an error signal, and the input signal and the error signal are input into an adaptive filter based on the least mean square algorithm. When the adaptive process of the adaptive filter converges, a transfer function estimation matrix corresponding to a virtual error microphone that has completed training is obtained. Remove the first physical microphone.

[0023] Advantageously, the range hood is provided with at least two physical error microphones, wherein the transfer function estimation matrix corresponding to one virtual error microphone is is a transfer function estimation vector from at least two physical error microphones to the one virtual error microphone obtained by offline training in advance

[0024] According to another aspect of the present invention, the object is achieved by a range hood, wherein a position tracking device, a physical reference sensor, a physical error microphone and a speaker, and a controller are physically provided at the range hood, wherein the controller performs noise reduction processing according to the noise reduction processing method described in the aforementioned embodiment.

[0025] In some preferred embodiments, the operating position of the person in front of the range hood is determined based on the lateral size of the range hood or the number of lateral stove eyes of the stove top to which the range hood is adapted, and the position and number of physical error microphones and speakers are configured so that each operating position is configured with at least two physical error microphones and at least one speaker.

[0026] In some preferred embodiments, the physical reference sensor includes a vibration acceleration sensor and / or a noise sound pressure sensor.

[0027] In some preferred embodiments, the position tracking device is a camera device.

[0028] By means of the noise reduction processing method and the range hood for a range hood according to the embodiment of the present invention, an active control algorithm based on a virtual error microphone is used, combined with ear position tracking in the form of video, to achieve effective control of the low- and medium-frequency noise of people in front of the range hood, such as cooks, when they are moving. In particular, real-time tracking and real-time control can be achieved, which solves the limitation that traditional active noise reduction only controls fixed points. In addition, according to the embodiment of the present invention, noise reduction in a larger spatial range can be achieved, for example, more secondary paths and related transfer functions of the possible positions of cooks can be trained in advance, so that the range of noise control will be larger. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The features, advantages and technical effects of the exemplary embodiments of this patent will be described below with reference to the accompanying drawings.

[0030] Figure 1 is a schematic diagram of the system layout of a range hood in an installation environment according to an embodiment;

[0031] Figure 2 is a control flow chart of a noise reduction processing method for a range hood according to an embodiment;

[0032] Figure 3 is a structural diagram of a noise reduction algorithm according to one embodiment;

[0033] Figure 4 is a training first secondary path estimation matrix according to one embodiment Schematic diagram of the algorithm structure;

[0034] Figure 5 is a training second secondary path estimation matrix according to one embodiment Schematic diagram of the algorithm structure;

[0035] Figure 6 is the training transfer function estimation matrix according to one embodiment Schematic diagram of the algorithm structure. DETAILED DESCRIPTION

[0036] Figure 1 FIG. 1 shows a schematic diagram of the system layout of a range hood in an installation environment according to an embodiment. Specifically, Figure 1 The upper part of the diagram schematically shows a range hood 100 according to an embodiment. Figure 1 The lower half of the diagram schematically shows the cooktop 200 to which the range hood is adapted.

[0037] The operating position of the person in front of the range hood is determined according to the lateral size of the range hood 100 or the lateral number of stove holes of the stove 200 to which the range hood is adapted. Here, the determination of the number of operating positions does not take into account the longitudinal size of the range hood or the longitudinal number of stoves. Here, the term "lateral" refers to the lateral size of the person in front of the range hood, such as the cook, when facing the range hood 100 and the stove 200, that is, based on Figure 1 The term "longitudinal" refers to the direction in which a person in front of the range hood, such as a cook, approaches or moves away from the range hood 100 and the stove 200 when facing the range hood 100 and the stove 200. Those skilled in the art can understand that the operating position here is a large three-dimensional space area that can cover the physical characteristics of the person in front of the range hood, especially the cook, as well as the operation, especially the cooking habits, etc. Figure 1 It can be seen that in this embodiment, based on the lateral size of the range hood 100 or the two lateral cooker eyes of the stove 200, namely the first cooker eye 21 and the second cooker eye 22, there are two lateral operating positions, namely the left operating position and the right operating position.

[0038] The range hood 100 is physically provided with a position tracking device 6 , physical reference sensors 5 , 7 , physical error microphones 1 , 2 , 3 , 4 and speakers 8 , 9 .

[0039] The position tracking device 6 is a camera device in this embodiment. The position tracking device 6 is used to detect the number of people in front of the range hood, such as the number of people who cook, the occupied operating positions and / or people, especially the ear positions of moving people, etc. The position tracking device 6 is preferably arranged at the control panel 10 of the range hood 100 facing the front person, especially at the horizontal middle of the control panel 10, so as to be able to detect a larger range of three-dimensional space area.

[0040] In this embodiment, the physical reference sensor includes a vibration acceleration sensor 7 and a noise sound pressure sensor 5. Here, the vibration acceleration sensor 7 is preferably installed at the flue box of the range hood 100 to obtain the vibration acceleration signal of the range hood 100. The noise sound pressure sensor 5 is preferably installed inside the box of the range hood 100 to serve as a physical reference microphone of the noise reduction system to obtain the noise sound pressure signal of the range hood 100. In some other embodiments, the physical reference sensor only includes a vibration acceleration sensor, or the physical reference sensor only includes a noise sound pressure sensor. In addition, in some other embodiments, the physical reference sensor may include more than one vibration acceleration sensor, and / or the physical reference sensor may include more than one noise sound pressure sensor.

[0041] Here, the position and number of the physical error microphones are preferably configured so that each operating position is configured with at least two physical error microphones. In this embodiment, the left operating position is configured with two physical error microphones, namely the first error microphone 1 and the second error microphone 2. Correspondingly, the right operating position is also configured with two physical error microphones, namely the third error microphone 3 and the fourth error microphone 4. Here, the four error microphones 1-4 are arranged at the control panel 10 of the range hood 100, wherein the first error microphone 1 and the second error microphone 2 are correspondingly arranged above the first stove 21, and the third error microphone 3 and the fourth error microphone 4 are correspondingly arranged above the second stove 22. The physical error microphones are used here to monitor the noise distribution of the range hood in real time.

[0042] Here, it is preferred to configure the position and number of the speakers so that each operating position is equipped with at least one speaker. In this embodiment, the left operating position is equipped with a speaker, namely, the first speaker 8. Correspondingly, the right operating position is also equipped with a speaker, namely, the second speaker 9. Here, the first speaker 8 and the second speaker 9 are respectively installed on both sides of the flue of the range hood 100, preferably facing the left operating position and the right operating position respectively, for emitting reverse sound waves with opposite phases to the range hood noise based on the noise reduction processing method to be described in detail later.

[0043] In addition, the range hood 100 further includes a controller, which is used to implement a noise reduction processing method for the range hood 100 in combination with the aforementioned position tracking device 6, physical reference sensors 5, 7, physical error microphones 1, 2, 3, 4 and speakers 8, 9. It can be understood by those skilled in the art that, in order to implement the noise reduction processing method, the range hood 100 further includes other necessary components such as a power amplifier, which will not be described in detail here.

[0044] The following describes a noise reduction method for a range hood. The noise reduction method includes:

[0045] Using the position tracking device to obtain the ear position of the person in front of the range hood, a virtual error microphone is virtually set according to the ear position;

[0046] The adaptive active noise reduction algorithm is used to perform noise reduction processing, wherein the noise-related signal collected by the physical reference sensor is used as the reference signal x(n), and the first secondary path estimation matrix is ​​used as the reference signal x(n). Second secondary path estimation matrix And the transfer function estimation matrix The adaptive filter coefficient W(n) is obtained and a noise reduction wave control signal is generated, and the noise reduction wave is played by the speaker to realize active noise reduction, wherein the first secondary path estimation matrix is the secondary path estimation matrix from the loudspeaker to the physical error microphone, and the second secondary path estimation matrix is the secondary path estimation matrix from the loudspeaker to the virtual error microphone, and the transfer function estimation matrix is the transfer function estimate matrix from the physical error microphone to the virtual error microphone.

[0047] Specifically, reference is made to Figure 2 The control flow chart of the noise reduction processing method for a range hood according to one embodiment is shown. On the one hand, the ear position of the person in front of the range hood is obtained in real time by using a camera device, and a virtual error microphone is matched according to the ear position. Here, the camera device obtains information about the operating position occupied by the person in front of the range hood and about the ear position of the person, especially the position of each person's ears, and this information is transmitted to the controller.

[0048] On the other hand, continue to refer to Figure 2 , the physical reference sensor collects the noise-related signal and inputs the noise-related signal into the controller as the reference signal of the noise reduction algorithm. In this embodiment, the physical reference sensor includes a vibration acceleration sensor and a noise sound pressure sensor. In this case, the noise-related signal includes a vibration acceleration signal and a noise sound pressure signal. In some possible embodiments, the noise-related signal includes more than one vibration acceleration signal and / or more than one noise sound pressure signal. Here, at least two noise-related signals of the same type or different types are preferably input into the controller separately, and then coupled based on the noise reduction algorithm.

[0049] The following is passed by Figure 3 The noise reduction algorithm structure diagram according to one embodiment illustrates a noise reduction algorithm based on ear position information and a reference signal. In this embodiment, an adaptive active noise reduction algorithm is used to perform noise reduction processing.

[0050] See also Figure 3 , the adaptive active noise reduction algorithm structure includes: a primary path transfer function matrix P(z) from the noise source to the physical error microphone, a secondary path transfer function matrix S(z) from the speaker to the physical error microphone, an adaptive filter, and an adaptive filter coefficient W(n) output by the adaptive filter. In this embodiment, the adaptive filter performs adaptive operation according to a least mean square (LMS) algorithm or a filtered x-LMS algorithm.

[0051] According to this embodiment, see Figure 3 , the adaptive active noise reduction algorithm structure also includes: the first secondary path estimation matrix from the speaker to the physical error microphone The second secondary path estimation matrix from the loudspeaker to the virtual error microphone and the transfer function estimate matrix from the physical error microphone to the virtual error microphone Here, depending on the ear position or more precisely the position of the virtual error microphone, the matrix θ is estimated from the pre-trained and stored second secondary path. And the transfer function estimation matrix The second secondary path estimation matrix adapted to the virtual error microphone is selected And the transfer function estimation matrix Due to the binaural position of each person in front and the number of people in front, there are multiple virtual error microphones. Here, it is preferred to integrate the first secondary path estimation matrix through a noise reduction algorithm. A second secondary path estimation matrix corresponding to multiple virtual error microphone positions And the transfer function estimation matrix control link.

[0052] Here, the reference signal x(n) is transferred through the main path transfer function matrix P(z) to obtain the expected noise signal d at the physical error microphone. p (b) The reference signal x(n) is combined with the adaptive filter coefficient W(n) to obtain the noise reduction control signal y(n) of the speaker. The noise reduction control signal y(n) controls the actual sound pressure signal y at the physical error microphone output of the speaker. p (n). At the physical error microphone, the expected noise signal d p (n) and the sound pressure signal y p (n) superposition to obtain the error signal e p (n). The error signal e at the physical error microphone location p (n) is acquired by a physical error microphone.

[0053] The noise reduction control signal y(n) is estimated via the first secondary path matrix Get the speaker sound pressure estimation signal at the physical error microphone position Right now At the physical error microphone, the error signal e p (n) Superimpose the estimated loudspeaker sound pressure signal The inverted signal of the physical error microphone is used to estimate the residual noise. That is to say

[0054]

[0055] Residual noise estimation signal at the physical error microphone The transfer function estimates the matrix Get the expected noise estimate signal at the virtual error microphone That is to say The noise reduction control signal y(n) is estimated via the second secondary path matrix Get the estimated signal of the loudspeaker sound pressure at the virtual error microphone position Right now The expected noise estimate signal at the virtual error microphone and the estimated loudspeaker sound pressure signal at the virtual error microphone position The error signal of the virtual error microphone position is obtained by superposition Right now

[0056]

[0057] The reference signal x(n) is estimated by the second secondary path matrix After filtering, the filtered reference signal x′(n) is obtained, that is, The filtered reference signal x′(n) and the error signal of the virtual error microphone position are The adaptive filter is input to update the adaptive filter coefficient W(n), thereby realizing the real-time noise reduction function. Here, the sound wave distribution at the physical error microphone position is used to estimate the sound pressure distribution at the virtual error microphone position, which can directly realize the noise control of the speaker at the ear position.

[0058] The first secondary path estimation matrix is ​​described below Second secondary path estimation matrix And the transfer function estimation matrix Pre-training.

[0059] Figure 4 The training of the first secondary path estimation matrix according to one embodiment is shown In this embodiment, the first secondary path estimation matrix is ​​pre-trained offline. A white noise signal is used as the input signal x(n) to drive the speaker to emit white noise. A physical error microphone is used as the physical microphone to collect white noise to obtain the expected signal d(n). The algorithm output signal y(n) of the adaptive filter based on the least mean square algorithm is compared with the expected signal d(n) to obtain the error signal e(n). The input signal x(n) and the error signal e(n) are input into the adaptive filter. When the adaptive process of the adaptive filter converges, the first secondary path estimation matrix of the completed training is obtained. In this embodiment, Figure 1The configuration shown in the figure is used as an example to illustrate that the range hood 100 is provided with two speakers and four physical error microphones. It is preferred to pre-train the first secondary path estimation matrix from the two speakers to each physical error microphone offline, that is, the first secondary path estimation matrix from the two speakers to the first physical error microphone The first secondary path estimation matrix from the two loudspeakers to the second physical error microphone The first secondary path estimation matrix from the two loudspeakers to the third physical error microphone and a secondary path matrix from the two loudspeakers to the fourth physical error microphone In other embodiments, the secondary path estimation from each loudspeaker to each physical error microphone can be pre-trained offline, for example, eight secondary first-order path estimation vectors can be trained in the case of two loudspeakers and four physical error microphones.

[0060] Figure 5 shows a training second secondary path estimation matrix according to one embodiment In this embodiment, the second secondary path estimation matrix corresponding to a virtual error microphone is obtained by pre-offline training. First, a physical microphone is placed at a possible position of a virtual error microphone; then, a white noise signal is used as an input signal x(n) to drive a loudspeaker to emit white noise, and the physical microphone collects the white noise to obtain an expected signal d(n). The algorithm output signal y(n) of an adaptive filter based on a least mean square algorithm is compared with the expected signal d(n) to obtain an error signal e(n). The input signal x(n) and the error signal e(n) are input into the adaptive filter. When the adaptive process of the adaptive filter converges, a second secondary path estimation matrix corresponding to a virtual error microphone is obtained. Finally, remove the physical microphone.

[0061] Figure 6 shows a training transfer function estimation matrix according to one embodiment In this embodiment, the transfer function estimation matrix corresponding to a virtual error microphone is obtained by pre-training offline. First, a first physical microphone is placed at a possible position of a virtual error microphone; then, a white noise signal is used as an input signal x(n) to drive a loudspeaker to emit white noise, and the physical error microphone used as the second physical microphone and the first physical microphone respectively collect the white noise to obtain a desired signal d(n), and the algorithm output signal y(n) of an adaptive filter based on a least mean square algorithm is calculated with the respectively collected desired signal d(n) to obtain an error signal e(n), and the input signal x(n) and the error signal e(n) are input into the adaptive filter. When the adaptive process of the adaptive filter converges, a transfer function estimation matrix corresponding to a virtual error microphone that has completed training is obtained. Finally, the first physical microphone is removed. Figure 1 The configuration shown in the figure is used as an example for explanation. Four physical error microphones are provided in the range hood 100. Here, it is preferred to pre-train the transfer function estimation matrix from each physical error microphone to the virtual error microphone offline.

[0062] Especially in the second secondary path estimation matrix And the transfer function estimation matrix In the training, a large number of three-dimensional spatial positions can be selected from the possible operating positions as virtual error microphone positions for training, thereby introducing factors such as the larger range of movement positions and heights of people in front of the range hood into the noise reduction algorithm. Therefore, the noise reduction of the range hood can cover a wider range and the noise reduction control is more precise.

[0063] Here, the active control algorithm based on the virtual error microphone, combined with the ear position tracking in particular in the form of video recording, realizes the effective control of the low- and medium-frequency noise of the person in front of the range hood, such as the cook, when moving, and in particular can track and control in real time, solving the limitation of the traditional active noise reduction that only controls fixed points. When the person in front of the range hood stands in front of the left stove (first stove 21) to cook, the noise reduction algorithm focuses on the positions of the two virtual error microphones on the left or the binaural positions of the cook to reduce noise; similarly, when the person in front of the range hood stands in front of the right stove to cook, the noise reduction algorithm focuses on the positions of the two virtual error microphones on the right or the binaural positions of the cook to reduce noise; when there are cooks at both operating positions, the noise reduction algorithm simultaneously reduces noise at the four virtual error microphone positions, which can maximize the noise reduction effect and efficiency and reduce the amount of calculation of the controller.

[0064] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation methods of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the claims of the present invention. In the description of the present invention, it should be noted that the terms "first", "second" and other ordinal numbers are used only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] Reference numerals list

[0066] 100 Range Hood

[0067] 10 Control Panel

[0068] 1 First error microphone

[0069] 2 Second error microphone

[0070] 3 Third error microphone

[0071] 4 Fourth error microphone

[0072] 5 Reference Microphone

[0073] 6 Camera Device

[0074] 7 Vibration Acceleration Sensor

[0075] 8. First speaker

[0076] 9 Second speaker

[0077] 200 Cooking Range

[0078] 21 First Stove

[0079] 22 Second stove

Claims

1. A noise reduction method for a range hood (100), in, The range hood is physically provided with a position tracking device, a physical reference sensor, a physical error microphone and a speaker, wherein: The noise reduction processing method comprises: Using the position tracking device to obtain the ear position of the person in front of the range hood; Matching a pre-trained virtual error microphone according to the ear position; The noise reduction process is performed using an adaptive active noise reduction algorithm, wherein the noise-related signal collected by the physical reference sensor is used as the reference signal x(n), and based on the first secondary path estimation matrix Second secondary path estimation matrix And the transfer function estimation matrix Acquire the adaptive filter coefficient W(n) and generate a noise reduction wave control signal to control the speaker to play the noise reduction wave to achieve active noise reduction, Among them, the first secondary path estimation matrix is the secondary path estimation matrix from the loudspeaker to the physical error microphone, and the second secondary path estimation matrix is the secondary path estimation matrix from the loudspeaker to the matched virtual error microphone, and the transfer function estimation matrix is a transfer function estimation matrix from the physical error microphone to the matched virtual error microphone.

2. The noise reduction method according to claim 1, in, The noise-related signal includes a vibration acceleration signal and / or a noise sound pressure signal.

3. The noise reduction method according to claim 1, in, Obtaining the adaptive filter coefficients W(n) includes: The error signal at the location of the matched virtual error microphone An adaptive filter is input to update the adaptive filter coefficient W(n), thereby realizing a real-time noise reduction function, wherein: in, is the residual noise estimate signal of the physical error microphone and is configured as Among them, e p W(n) is the error signal collected by the physical error microphone, and y(n) is the noise reduction control signal generated according to the adaptive filter coefficient W(n) based on the reference signal x(n).

4. The noise reduction method according to claim 1, in, Obtaining the adaptive filter coefficients W(n) includes: The second secondary path estimation matrix The filtered reference signal x′(n) is input into the adaptive filter to update the adaptive filter coefficient W(n), thereby realizing the real-time noise reduction function. in, 5. The noise reduction method according to claim 1, in, The adaptive filter performs adaptive operation according to the least mean square algorithm or the x-filtered least mean square algorithm.

6. The noise reduction method according to claim 1, in, The noise reduction processing method further includes: pre-offline training to obtain the first secondary path estimation matrix in, A white noise signal is used as an input signal to drive a loudspeaker to emit white noise, the physical error microphone is used as an error microphone to collect the white noise to obtain a desired signal, an algorithm output signal of an adaptive filter based on a least mean square algorithm is compared with the desired signal to obtain an error signal, the input signal and the error signal are input into the adaptive filter, and when the adaptive process of the adaptive filter converges, the first secondary path estimation matrix that has completed the training is obtained.

7. The noise reduction method according to claim 6, in, The range hood is provided with M loudspeakers and N physical error microphones, wherein M≥1, N≥2, wherein, The first secondary path estimation matrix The first secondary path estimation vectors from M loudspeakers to N physical error microphones obtained by pre-training offline 8. The noise reduction method according to claim 1, in, The noise reduction processing method further includes: pre-training offline to obtain a plurality of second secondary path estimation matrices In noise reduction, the corresponding second secondary path estimation matrix is ​​selected based on the virtual error microphone matched according to the ear position. Among them, the second secondary path estimation matrix corresponding to a virtual error microphone is obtained by pre-offline training Place a physical microphone at the virtual error microphone. A white noise signal is used as an input signal to drive a loudspeaker to emit white noise, the physical microphone collects the white noise to obtain a desired signal, an algorithm output signal of an adaptive filter based on a least mean square algorithm is compared with the desired signal to obtain an error signal, the input signal and the error signal are input into the adaptive filter, and when the adaptive process of the adaptive filter converges, a second secondary path estimation matrix corresponding to a virtual error microphone that has completed training is obtained. Remove the physical microphone.

9. The noise reduction method according to claim 1, in, The noise reduction processing method comprises: pre-training a plurality of transfer function estimation matrices offline respectively. When noise reduction is performed, the corresponding transfer function estimation matrix is ​​selected based on the virtual error microphone matched according to the ear position. Among them, the transfer function estimation matrix corresponding to a virtual error microphone is obtained by pre-offline training A first physical microphone is placed at the virtual error microphone, A white noise signal is used as an input signal to drive a loudspeaker to emit white noise, the physical error microphone used as a second physical microphone and the first physical microphone respectively collect the white noise to obtain a desired signal, an algorithm output signal of an adaptive filter based on a least mean square algorithm is operated with the respectively collected desired signals to obtain an error signal, the input signal and the error signal are input into the adaptive filter, and when the adaptive process of the adaptive filter converges, a transfer function estimation matrix corresponding to a virtual error microphone that has completed training is obtained Remove the first physical microphone.

10. The noise reduction method according to claim 9, in, The range hood is provided with at least two physical error microphones, wherein: The transfer function estimation matrix corresponding to a virtual error microphone is Estimate vector φ(t) for the transfer function of the at least two physical error microphones to the one virtual error microphone 11. Range hood (100), It is characterized in that The range hood is physically provided with a position tracking device (6), a physical reference sensor, a physical error microphone (1-4), a loudspeaker (8, 9) and a controller. Wherein, the controller performs noise reduction processing according to the noise reduction processing method according to any one of claims 1 to 10.

12. The range hood (100) according to claim 11, in, The operating position of the person in front of the range hood is determined according to the lateral size of the range hood (100) or the number of lateral stove holes of the stove (200) to which the range hood is adapted, and the positions and numbers of the physical error microphones (1-4) and the loudspeakers (8, 9) are configured so that each operating position is configured with at least two physical error microphones and at least one loudspeaker.

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