CN112185335B - Noise reduction method and device, electronic equipment and storage medium - Google Patents
Noise reduction method and device, electronic equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
- G10K11/1787—General system configurations
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
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Abstract
The disclosure relates to the technical field of signal processing, and in particular relates to a noise reduction method, a device, electronic equipment and a storage medium, which are used for solving the problem of reducing noise in an electric automobile by adopting a mode of reducing the power performance of the electric automobile, and the method comprises the following steps: and acquiring a noise signal in a space to be denoised, acquiring running state information related to the noise signal, determining amplitude of the noise signal based on amplitude reduction proportion output by a trained noise judgment model when the noise signal matched with the service state information is not recorded in a noise signal library, determining amplitude of the noise signal based on the amplitude of the noise signal and the amplitude reduction proportion, and transmitting the noise signal to playing equipment in the space to be denoised for playing. Therefore, on the basis of not affecting the internal performance of the power system, the effective treatment of the noise in the vehicle can be realized, the noise treatment efficiency is ensured, and the treatment cost is reduced.
Description
Technical Field
The disclosure relates to the technical field of signal processing, and in particular relates to a noise reduction method, a device, electronic equipment and a storage medium.
Background
In the development and design process of the automobile product, the problems of noise, vibration and harshness (Noise, vibration, harshness, NVH) are necessarily involved, and for the existing electric automobile and fuel automobile, referring to the total sound level comparison of the electric automobile and the fuel automobile illustrated in fig. 1, as the electric automobile cannot generate engine noise and intake and exhaust noise in the running process, the total sound level of the electric automobile illustrated by the black column is relatively smaller than that of the fuel automobile illustrated by the gray column. High-frequency motor noise still exists, and riding experience of passengers in the automobile is greatly affected.
Under the prior art, in order to reduce the noise in the electric automobile, modes such as rotor inclined stage, controller harmonic injection are generally adopted, the motor rotor structure and motor control are adjusted to optimize, inhibit and modulate the noise, under the condition that the actual automobile operation stage of the electric assembly can generate various different working conditions, the noise and the order have different sound pressure amplitude performances in different frequency bands under the change of torque and rotating speed, so that the electric power assembly is difficult to comprehensively optimize in the prior art, and the existing processing mode can also cause the reduction and loss of other performances such as frequency, heat transfer and the like, thereby increasing the cost.
Disclosure of Invention
The embodiment of the disclosure provides a noise reduction method, a device, electronic equipment and a storage medium, which are used for solving the problem that in the prior art, in-vehicle noise is reduced by adopting a mode of reducing the power performance of an electric automobile.
The specific technical scheme provided by the embodiment of the disclosure is as follows:
in a first aspect, a noise reduction method applied to noise reduction in an automobile is provided, including:
acquiring a noise signal in a space to be denoised, and acquiring operation state information associated with the noise signal, wherein the operation state information comprises operation parameter information of a noise source and user information of a target user in the space to be denoised;
when the noise reduction signals matched with the service state information are not recorded in the noise reduction signal library established, inputting the running state information into a noise judgment model after training to obtain the amplitude reduction ratio of the output of the noise judgment model, wherein the noise judgment model is established by adopting a machine learning technology, the noise reduction processing library comprises noise signals related with the running state information and corresponding noise reduction signals, and the amplitude reduction ratio represents the amplitude reduction ratio when the noise signals are regulated to the noise signals tolerated by the target user;
Determining the amplitude of the noise signal, and determining the amplitude of a noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, wherein the noise reduction signal has the same frequency as the noise signal and opposite phase;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
Optionally, the acquiring the noise signal in the space to be noise reduced and acquiring the running state information associated with the noise signal include:
acquiring a noise signal sent by a noise reduction control device, wherein the noise signal is collected by a sound signal collecting device arranged in a space to be noise reduced and then reported to the noise reduction control device;
the obtaining the operation state information associated with the noise data comprises the following steps:
and acquiring the operation parameter information of the noise source sent by the noise reduction control device, wherein the operation parameter information at least comprises motor rotation speed information reported by a speed sensor received by the noise reduction control device and running state information reported by a positioning component received by the noise reduction control device.
Optionally, the method further comprises:
determining the noise reduction signal matched with the operation parameter information when the noise reduction signal matched with the service state information is recorded in the noise reduction signal library;
And sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
Optionally, before the operating state information is input into the trained noise judgment model, training the noise judgment model is further included:
acquiring sample data, wherein one piece of sample data comprises operation parameter information of a noise source generating a noise signal, user information in a noise space and a amplitude reduction proportion of the noise signal after noise reduction processing;
the following operations are respectively executed for each sample data until the difference value between the amplitude reduction ratio predicted by the noise judgment model and the amplitude reduction ratio in the sample data is continuously smaller than the preset value for times reaching a set threshold value:
inputting operation parameter information and user information in one sample data into the noise judgment model to obtain the predicted amplitude reduction ratio of the noise judgment model, wherein the noise judgment model is built based on a machine learning technology;
model parameters for generating a reduction ratio in the noise decision model are adjusted based on a numerical difference between the predicted reduction ratio and the reduction ratio in the one sample data.
Optionally, after determining the amplitude of the noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, the method further includes:
and storing the noise signals, the service state information and the determined noise reduction signals into the noise reduction signal library.
Optionally, the method further comprises:
when the indication information of the target noise reduction proportion sent by the terminal equipment related to the space to be noise reduced is determined, the amplitude of a noise reduction signal is directly determined based on the amplitude of the noise signal and the target noise reduction proportion, and the noise reduction signal has the same frequency and opposite phase with the noise signal;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
In a second aspect, a noise reduction device is provided, applied to noise reduction in an automobile, including:
the device comprises an acquisition unit, a noise reduction unit and a control unit, wherein the acquisition unit is used for acquiring a noise signal in a space to be reduced and acquiring operation state information related to the noise signal, and the operation state information comprises operation parameter information of a noise source and user information of a target user in the space to be reduced;
the judging unit is used for determining that the noise reduction signal library is created, when the noise reduction signal matched with the service state information is not recorded, inputting the running state information into a trained noise judging model to obtain the amplitude reduction ratio of the output of the noise judging model, wherein the noise judging model is created by adopting a machine learning technology, the noise reduction processing library comprises noise signals related with the running state information and corresponding noise reduction signals, and the amplitude reduction ratio represents the amplitude reduction ratio when the noise signals are regulated to the noise signals tolerated by the target user;
A determining unit, configured to determine an amplitude of the noise signal, and determine an amplitude of a noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, where the noise reduction signal has the same frequency and opposite phase to the noise signal;
and the output unit is used for sending the noise reduction signal to the playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
Optionally, when the acquiring the noise signal in the space to be noise reduced and acquiring the running state information associated with the noise signal, the acquiring unit is configured to:
acquiring a noise signal sent by a noise reduction control device, wherein the noise signal is collected by a sound signal collecting device arranged in a space to be noise reduced and then reported to the noise reduction control device;
the obtaining the operation state information associated with the noise data comprises the following steps:
and acquiring the operation parameter information of the noise source sent by the noise reduction control device, wherein the operation parameter information at least comprises motor rotation speed information reported by a speed sensor received by the noise reduction control device and running state information reported by a positioning component received by the noise reduction control device.
Optionally, the determining unit is further configured to:
determining the noise reduction signal matched with the operation parameter information when the noise reduction signal matched with the service state information is recorded in the noise reduction signal library;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
Optionally, before the running state information is input into the trained noise judgment model, the judgment unit is further configured to train the noise judgment model:
acquiring sample data, wherein one piece of sample data comprises operation parameter information of a noise source generating a noise signal, user information in a noise space and a amplitude reduction proportion of the noise signal after noise reduction processing;
the following operations are respectively executed for each sample data until the difference value between the amplitude reduction ratio predicted by the noise judgment model and the amplitude reduction ratio in the sample data is continuously smaller than the preset value for times reaching a set threshold value:
inputting operation parameter information and user information in one sample data into the noise judgment model to obtain the predicted amplitude reduction ratio of the noise judgment model, wherein the noise judgment model is built based on a machine learning technology;
Model parameters for generating a reduction ratio in the noise decision model are adjusted based on a numerical difference between the predicted reduction ratio and the reduction ratio in the one sample data.
Optionally, after the determining the amplitude of the noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, the determining unit is further configured to:
and storing the noise signals, the service state information and the determined noise reduction signals into the noise reduction signal library.
Optionally, the determining unit is further configured to:
when the indication information of the target noise reduction proportion sent by the terminal equipment related to the space to be noise reduced is determined, the amplitude of a noise reduction signal is directly determined based on the amplitude of the noise signal and the target noise reduction proportion, and the noise reduction signal has the same frequency and opposite phase with the noise signal;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
In a third aspect, an electronic device is provided, including:
a memory for storing executable instructions;
a processor for reading and executing executable instructions stored in memory to implement the method of any one of the preceding claims.
In a fourth aspect, a computer readable storage medium is presented, which when executed by an electronic device, causes the electronic device to perform the method of any of the above.
The beneficial effects of the present disclosure are as follows:
in summary, in the embodiment of the present disclosure, a noise signal in a space to be denoised is obtained, and operation state information associated with the noise signal is obtained, where the operation state information includes operation parameter information of a noise source and user information of a target user in the space to be denoised, then, in a created noise reduction signal library, when a noise reduction signal matched with the service state information is not recorded, the operation state information is input into a trained noise determination model, a noise reduction ratio output by the noise determination model is obtained, the noise determination model is created by using a machine learning technology, the noise reduction processing library includes noise signals and corresponding noise reduction signals each associated with operation state information, the noise reduction ratio represents an amplitude reduction ratio when the noise signal is adjusted to the noise signal tolerated by the target user, then, an amplitude of the noise signal is determined, and based on the amplitude of the noise signal and the noise reduction ratio, the noise reduction signal is identical in frequency and opposite in phase to the noise signal, and then, the noise reduction signal is sent to a playing device in the space to be denoised, so that the corresponding sound wave playing device can output. Therefore, on the basis of not affecting the internal performance of the power system, the effective treatment of the noise in the vehicle can be realized, the noise treatment efficiency is ensured, and the treatment cost is reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present disclosure, and that other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of noise contrast generated by a fuel vehicle and an electric vehicle according to the prior art in an embodiment of the present disclosure;
fig. 2 is a schematic view of an application scenario in an embodiment of the disclosure;
FIG. 3 is a schematic diagram of a noise reduction process according to an embodiment of the disclosure;
FIG. 4a is a schematic diagram of training data in an embodiment of the present disclosure;
FIG. 4b is a diagram of training data after being digitized in an embodiment of the disclosure;
FIG. 5 is a schematic diagram of test code in an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a logic structure of a noise reduction device according to an embodiment of the disclosure;
fig. 7 is a schematic physical structure of a noise reduction device in an embodiment of the disclosure.
Detailed Description
In order to solve the problem that in the prior art, noise in a vehicle is reduced in a manner of sacrificing power performance, the embodiment of the disclosure provides a noise reduction method, which is applied to noise reduction in the vehicle, firstly, noise signals in a space to be reduced are obtained, and operation state information related to the noise signals is obtained, wherein the operation state information comprises operation parameter information of a noise source and user information of a target user in the space to be reduced, then the operation state information is input into a noise judgment model after training when noise signals matched with the service state information are not recorded in a noise signal library established, a noise reduction proportion output by the noise judgment model is obtained, the noise judgment model is created by adopting a machine learning technology, the noise processing library comprises noise reduction signals which are respectively related with the operation state information, the noise reduction proportion is used for representing amplitude reduction proportions when the noise signals are adjusted to the noise signals which are tolerated by the target user, then, the amplitude of the noise signals are determined, the amplitude of the noise signals is determined based on the amplitude of the noise signals and the noise reduction proportion, the amplitude of the noise signals is obtained, the amplitude of the noise signals is equal to the amplitude of the playing signals, the noise signals are transmitted to the playing frequency of the corresponding noise signals, and the noise reduction equipment is opposite to the amplitude of the noise signals, and the noise reduction equipment is output. Therefore, active noise reduction is realized under the condition that the performance of the automobile is not affected, and the noise reduction cost is greatly reduced.
The following description is made for some simple descriptions of application scenarios applicable to the technical solutions of the embodiments of the present disclosure, and it should be noted that the application scenarios described below are only used for illustrating the embodiments of the present disclosure, but not limiting. In specific implementation, the technical scheme provided by the embodiment of the disclosure can be flexibly applied according to actual needs.
The following describes, with reference to fig. 2, an application scenario related to an embodiment of the present disclosure:
the application scene comprises a signal acquisition device, playing equipment for outputting noise reduction sound waves, a noise reduction control device and a cloud server.
The signal acquisition device is deployed on the automobile and can optionally include, but is not limited to, the following acquisition devices: noise signal collection system, rotational speed collection system, acceleration collection system, motor torque detection device, collection system such as collection system of gathering target user's user information in the car, wherein, noise signal collection system specifically can be the microphone for gather noise signal, rotational speed collection system specifically can be speedtransmitter for gather motor rotational speed signal, image collection system can be the camera of settling in the car different positions for shoot the image that can discern target user's age at least on the different positions.
The noise reduction control device consists of a communication signal transmitting chip and a storage chip, is flexible in deployment position, can be selectively deployed on an automobile or in other positions which can be connected with the signal acquisition device, can receive operation parameter information of noise sources reported by the signal acquisition devices and user information of target users in a noise reduction scene related to the embodiment of the disclosure, wherein the operation state information comprises motor rotation speed information, acceleration information, running state information, motor torque information, image information of the target users and the like, reports the obtained operation state information to a cloud server, and sends the noise reduction signal to a playing device for outputting noise reduction sound waves after receiving the noise reduction signal fed back by the cloud server. The communication signal may specifically be a communication signal of a fifth-generation mobile communication technology (5th generation mobile networks,5G) existing at present, or another communication signal capable of implementing communication.
It should be noted that, in some embodiments of the present disclosure, the noise signal collecting device and the playing device that outputs the noise reduction sound wave may be the same device or the same device, for example, both are microphones, and in other embodiments, the noise signal collecting device and the noise signal playing device may be different devices, for example, the noise signal collecting device is a microphone, and the playing device is a speaker, which is not particularly limited in this disclosure.
The cloud server is in wireless connection with the noise reduction control device and is used for obtaining noise reduction signals after calculation and processing based on the received noise signals and the service state information, and establishing a noise reduction signal library according to the processed noise signals associated with the service state information and the noise reduction signals of the processed noise signals. And training to obtain a noise judgment model, wherein the noise judgment model takes the operation parameter information of a noise source and the user information of a target user as model inputs, and outputs a proportion for representing amplitude reduction when the noise signal is regulated to the noise signal tolerated by the target user.
In some scenarios of the disclosed examples, the terminal device may participate in the noise reduction process, and the terminal device may establish a connection with the cloud server to implement the degree of human intervention noise reduction.
In order to further explain the technical solutions provided by the embodiments of the present disclosure, the following details are described with reference to the accompanying drawings and the detailed description. Although the embodiments of the present disclosure provide the method operational steps as shown in the following embodiments or figures, more or fewer operational steps may be included in the method based on routine or non-inventive labor. In steps where there is logically no necessary causal relationship, the order of execution of the steps is not limited to the order of execution provided by embodiments of the present disclosure.
It should be noted that, for the noise reduction problem in the automobile during the running process of the automobile, noise sources in different types of automobiles are different, in the embodiment of the disclosure, when the noise reduction problem of different automobiles is solved, for example, when the noise judgment model is trained, the operation parameter information of the noise source is adaptively increased, but the noise reduction logics of different types of automobiles are the same, the noise reduction problem of each type of automobile will not be separately described, and in the following description, only the noise reduction in the electric automobile will be described as an example.
In the embodiment of the disclosure, for different types of automobiles, a cloud server creates a corresponding noise reduction signal library in advance, where the noise reduction signal library includes noise reduction signals related to running state information, and a model of automobile is as follows: the process of establishing and maintaining the noise reduction signal library by the cloud server is described by taking an X-type automobile as an example.
When the noise reduction mode disclosed by the embodiment of the disclosure is operated in the noise test stage of the X-type automobile, firstly, a large number of users with different sexes are selected to participate in the test in each age group, then, each user is positioned in the X-type automobile, the noise of the automobile under different operation parameters is sensed under the condition that no other noise is generated in the automobile, and noise elimination signals with adjustable amplitude are synchronously applied in the automobile until the user can tolerate the noise in the current automobile, and the current noise reduction information is recorded. After the noise reduction signal is associated with the user information and the operation parameter information, a noise signal, the noise reduction signal determined corresponding to the noise signal and the associated service state information are stored in a noise reduction signal library as one piece of data.
Similarly, according to the noise tolerance of different users under different running parameters of the automobile, the obtained noise reduction signals gradually establish a noise reduction signal library containing a large amount of data, wherein the noise reduction processing library comprises noise signals and corresponding noise reduction signals which are related with running state information.
In the embodiment of the disclosure, when the noise reduction signal library is built, the position of the user in the vehicle can be used as a consideration factor and used as a part of running state information to determine the noise tolerance condition of the same user at different positions in the vehicle under the same running condition. Thus, more influence factors which comprehensively influence the noise tolerance of the user can be integrated.
It should be noted that, in the embodiment of the disclosure, in the running process after the subsequent dose of the automobile, when determining that the user allows to collect the noise signal in the automobile and performs noise reduction processing, and determining that no matched noise reduction signal exists in the noise reduction signal library, the cloud server establishes a new piece of data based on the noise information, the associated running state information and the corresponding determined noise reduction signal after completing the processing of the collected noise signal, and stores the new piece of data into the signal library to be noise reduced.
Therefore, the data stored in the noise reduction signal library maintained by the cloud server are more and more comprehensive, so that in the driving process of a user, the existing noise reduction signals in the noise database are obtained through the cloud server of the Internet of vehicles, the noise reduction processing of the noise in the vehicle is realized, the sound quality is ensured, the riding comfort is improved, meanwhile, the recorded influence parameters can be increased according to the actual operation requirement, the processing time of the noise signals is greatly shortened, and the use experience of the user is improved.
The following describes the noise reduction procedure in the embodiment of the present disclosure in detail with reference to fig. 3:
step 301: acquiring a noise signal in a space to be denoised, and acquiring operation state information associated with the noise signal, wherein the operation state information comprises operation parameter information of a noise source and user information of a target user in the space to be denoised.
The cloud server acquires noise signals sent by the noise reduction control device, wherein the noise signals are collected by the noise signal collection device deployed in the space to be reduced and then reported to the noise reduction control device, and meanwhile, the cloud server acquires operation parameter information of a noise source sent by the noise reduction control device and user information of a target user in the space to be reduced, wherein the operation parameter information at least comprises motor rotation speed information reported by a speed sensor received by the noise reduction control device and running state information reported by a positioning component received by the noise reduction control device, and also can comprise acceleration information collected by an accepted acceleration sensor, motor torque information collected by a torque detection device and the like.
Specifically, the user information of the target user in the space to be noise reduced, which is acquired by the cloud server, includes, but is not limited to, age information or gender information of the target user obtained by acquiring an image of the target user in the space to be noise reduced.
In the embodiment of the present disclosure, the age and the sex of the user are considered as consideration factors in the noise reduction process, and the age and the sex are considered as consideration characteristics in order to better adapt to the needs of the user because the tolerance degree to the noise signals is different for the users with different ages and different sexes.
Step 302: determining whether the noise reduction signal library is recorded with noise reduction signals matched with the service state information, if so, executing step 303, otherwise, executing step 304.
In the embodiment of the disclosure, after acquiring a noise signal and service state information, a cloud server compares the service state information with noise reduction signals associated with the service state information recorded in a noise reduction signal library, so as to determine whether the noise reduction signals associated with the service state information and used for noise reduction are recorded in the noise reduction signal library.
If it is determined that the noise reduction signal library has the noise reduction signal matched with the service state information, the operation of step 303 is directly performed, otherwise, the operation of step 304 is directly performed.
Step 303: and determining a noise reduction signal matched with the operation parameter information, and sending the noise reduction signal to a playing device in the space to be noise reduced, so that the playing device outputs corresponding noise reduction sound waves.
When the cloud server determines that the noise reduction signal library stores the noise reduction signal matched with the currently acquired running state information, the cloud server determines the noise reduction signal matched with the running state information and sends the noise reduction signal to the playing device in the noise reduction space through the noise reduction control device so that the playing device outputs corresponding noise reduction sound waves.
Therefore, the noise reduction signal used for noise reduction can be determined directly by means of the noise reduction signals which are stored in the noise reduction signal library and are associated with the running state information, the processing time of the noise signals is greatly shortened, and the processing efficiency of the noise signals is improved.
Step 304: and inputting the running state information into a trained noise judgment model to obtain the amplitude reduction ratio of the output of the noise judgment model.
And when the noise reduction signal library does not exist, the cloud server inputs the running state information into a trained noise judgment model to obtain the amplitude reduction ratio of the output of the noise judgment model, wherein the noise judgment model is created by adopting a machine learning technology, and the amplitude reduction ratio is used for representing the amplitude reduction ratio when the noise signal is regulated to the noise signal tolerated by the target user.
In the embodiment of the disclosure, the noise determination model may specifically be trained according to a logistic regression algorithm, and the input of the noise determination model includes influence factors that may affect the amplitude of the noise reduction signal, for example, in the operation parameter information of the noise source, the noise determination model may specifically include acceleration information, motor rotation speed information, running state information, motor torque information, and the like, and the included user information may specifically be age, sex, position in the space to be reduced, and the like of the user, so that more consideration factors are synthesized, and the determination of the model can be more effective, and can be more suitable for the noise reduction requirements of different target users.
It should be noted that, in the embodiment of the present disclosure, parameters unrelated to noise reduction processing, such as a brand of a device for collecting noise signals, may exist in the acquired information, so in the embodiment of the present disclosure, when determining the input features of the noise determination model, parameters having a large influence on the amplitude reduction ratio may be selected as the input features, and parameters having a small influence on the amplitude reduction ratio may be screened out.
In the following description, the training process of the noise determination model of the present disclosure will be schematically described taking only the input in the noise determination model as the running state information, the motor rotation speed information, and the sex and age of the user as examples of the amplitude reduction ratio.
S1: sample data is acquired, wherein one piece of sample data comprises operation parameter information of a noise source generating a noise signal, user information in a noise space and a reduction ratio of the noise signal after noise reduction processing.
First, sample data is generated based on data recorded in a noise reduction signal library, and one data in the noise reduction signal library includes a noise signal associated with operation state information and a corresponding noise reduction signal, and the ratio of the amplitude of the noise reduction signal to the amplitude of the noise signal is referred to as a reduction ratio. Specifically, the step of determining the amplitude reduction ratio may be to randomly select several sampling points for the noise signal and the noise reduction signal with opposite phases, calculate a ratio value of the amplitude of the noise reduction signal at each sampling point to the amplitude of the noise signal, and further calculate an average value of each ratio value after the highest ratio value and the lowest ratio value are removed, as the corresponding amplitude reduction ratio.
Further, a sample data set is established, wherein one piece of sample data includes operation parameter information of a noise source generating a noise signal, user information in a noise space, and a reduction ratio of the noise signal after the noise reduction processing.
For example, referring to fig. 4a and 4b, fig. 4a schematically shows a part of a sample data set when a driving state, a motor rotation speed, a user gender, and a user age are taken as inputs of a noise determination model and a reduction ratio is taken as outputs of the noise determination model, and further, the driving state and the user gender in fig. 4a are digitally processed for processing convenience, so as to obtain the illustration in fig. 4b, wherein "man" is marked as 1, "woman" is marked as 2, "uphill" is marked as 1, "downhill" is marked as 2, "brake" is marked as 3, and "acceleration" is marked as 4.
S2, respectively executing the following operations for each sample data until the times of continuously smaller than a preset value reach a set threshold value, wherein the times are equal to the difference value between the amplitude reduction ratio predicted by the noise judgment model and the sample amplitude reduction ratio: inputting operation parameter information and user information in one sample data into the noise judgment model to obtain the predicted amplitude reduction ratio of the noise judgment model, wherein the noise judgment model is built based on a machine learning technology; model parameters for generating a reduction ratio in the noise decision model are adjusted based on a numerical difference between the predicted reduction ratio and the reduction ratio in the one sample data.
Specifically, training a noise judgment model based on each obtained sample data, taking a process of processing one sample data as an example, inputting operation parameter information and user information in one sample data into the noise judgment model to obtain a predicted amplitude reduction ratio of the noise judgment model, comparing the predicted amplitude reduction ratio with the amplitude reduction ratio in the one sample data, and adjusting model parameters for generating the amplitude reduction ratio in the noise judgment model based on a numerical difference between the predicted amplitude reduction ratio and the amplitude reduction ratio in the one sample data.
Further, the number of times continuously smaller than the preset value reaches the set threshold value after determining the difference between the amplitude reduction ratio predicted by the noise judgment model and the amplitude reduction ratio in the sample data.
For example, assuming that the preset value is 0.01 and the threshold value is set to 10, when it is determined that the number of times that the difference between the predicted amplitude reduction ratio output by the noise determination model and the amplitude reduction ratio in the sample data is continuously smaller than 0.01 reaches 10, it is determined that model training is completed. In addition, in a specific test process, the test code shown in fig. 5 can be adopted to realize the training of the noise judgment model.
The training of the noise judgment model is realized by schematically adopting a logistic regression algorithm in the code of fig. 5, firstly, the text in the sample data is subjected to digital processing, then the data which are used as the input and output data of the model in the sample data are determined, and further, the processed sample data are adopted for training, wherein 100 pieces of sample data are taken as examples, the first 90 pieces of sample data are taken as training samples in the code, and the noise judgment model after training is adopted for outputting the prediction results of the last 10 training samples so as to judge the accuracy of the noise judgment model.
It should be noted that in the embodiment of the present disclosure, the noise determination model may be retrained periodically to ensure the effectiveness of the noise determination model.
Further, the cloud server inputs the obtained running state information into a trained noise judgment model to obtain the amplitude reduction ratio of the output of the noise judgment model.
Therefore, by means of the noise judgment model obtained through training, the current amplitude reduction proportion which can meet the needs of the target user is effectively analyzed, the amplitude reduction operation of noise data is more targeted, and the use experience of the user can be improved.
And S305, determining the amplitude of the noise signal, and determining the amplitude of the noise reduction signal based on the amplitude of the noise signal and the amplitude reduction proportion.
Specifically, after the amplitude of the noise signal received by the cloud server, determining the amplitude of the corresponding noise reduction signal according to the obtained amplitude reduction ratio, and generating a noise reduction signal which has the same frequency as the noise signal, has opposite phase and meets the phase requirement by adopting the existing noise reduction principle.
In the process of determining the amplitude of the noise reduction signal according to the amplitude reduction ratio output by the noise judgment model, if the cloud server determines that the indication information of the target noise reduction ratio sent by the terminal equipment associated with the space to be noise reduced is received, the amplitude of the noise reduction signal is determined directly based on the amplitude of the noise signal and the target amplitude reduction ratio.
Therefore, the determination of the amplitude reduction ratio is more flexible, and the cloud server can receive the indication information of the amplitude reduction ratio sent by the user by means of the terminal equipment.
S306: and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
After the cloud server determines the noise reduction signal used for reducing the noise signal, the noise reduction signal is sent to the playing device in the space to be reduced through the noise reduction control device, so that the playing device plays the noise reduction signal used for reducing the noise signal. And simultaneously, the cloud server stores the noise signals, the service state information and the determined noise reduction signals into the noise reduction signal library so as to realize the continuously and perfectly established noise reduction signal library.
Therefore, tolerance differences of noise signals corresponding to users of different age groups can be effectively considered, the noise signals in the vehicle can be actively processed in all driving states comprehensively, real-time automatic noise reduction is achieved through real-time acquisition and processing of the noise signals, and meanwhile development cost is greatly reduced.
In the actual operation, when the collected noise signal includes the human voice and the music voice, the noise signal library and the noise determination model are established in advance to process only the noise generated by the noise source of the automobile, so that the human voice and the music voice existing in the noise signal can be effectively reserved in the actual process, the calculation amount of the actual calculation of the cloud server can be greatly reduced based on the data stored in the noise signal library, and the noise reduction delay time can be reduced.
Based on the same inventive concept, referring to fig. 6, an embodiment of the present disclosure provides a noise reduction device, including: an acquisition unit 601, a determination unit 602, a determination unit 603, and an output unit 604, wherein,
an obtaining unit 601, configured to obtain a noise signal in a space to be denoised, and obtain operation state information associated with the noise signal, where the operation state information includes operation parameter information of a noise source and user information of a target user in the space to be denoised;
A determining unit 602, configured to determine, in a created noise reduction signal library, that noise reduction signals matched with the service state information are not recorded, input the running state information into a trained noise determination model, and obtain a reduction ratio output by the noise determination model, where the noise determination model is created by using a machine learning technology, and the noise reduction processing library includes noise signals associated with the running state information and corresponding noise reduction signals, where the reduction ratio represents a reduction ratio of amplitude of the noise signals when the noise signals are adjusted to noise signals tolerated by the target user;
a determining unit 603, configured to determine an amplitude of the noise signal, and determine an amplitude of a noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, where the noise reduction signal has the same frequency and opposite phase to the noise signal;
and the output unit 604 is configured to send the noise reduction signal to a playback device in the space to be noise reduced, so that the playback device outputs a corresponding noise reduction sound wave.
Optionally, when the acquiring a noise signal in the space to be noise reduced and acquiring running state information associated with the noise signal, the acquiring unit 601 is configured to:
Acquiring a noise signal sent by a noise reduction control device, wherein the noise signal is collected by a sound signal collecting device arranged in a space to be noise reduced and then reported to the noise reduction control device;
the obtaining the operation state information associated with the noise data comprises the following steps:
and acquiring the operation parameter information of the noise source sent by the noise reduction control device, wherein the operation parameter information at least comprises motor rotation speed information reported by a speed sensor received by the noise reduction control device and running state information reported by a positioning component received by the noise reduction control device.
Optionally, the determining unit 602 is further configured to:
determining the noise reduction signal matched with the operation parameter information when the noise reduction signal matched with the service state information is recorded in the noise reduction signal library;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
Optionally, before the operating state information is input into the trained noise decision model, the decision unit 602 is further configured to train the noise decision model:
Acquiring sample data, wherein one piece of sample data comprises operation parameter information of a noise source generating a noise signal, user information in a noise space and a amplitude reduction proportion of the noise signal after noise reduction processing;
the following operations are respectively executed for each sample data until the difference value between the amplitude reduction ratio predicted by the noise judgment model and the amplitude reduction ratio in the sample data is continuously smaller than the preset value for times reaching a set threshold value:
inputting operation parameter information and user information in one sample data into the noise judgment model to obtain the predicted amplitude reduction ratio of the noise judgment model, wherein the noise judgment model is built based on a machine learning technology;
model parameters for generating a reduction ratio in the noise decision model are adjusted based on a numerical difference between the predicted reduction ratio and the reduction ratio in the one sample data.
Optionally, after the determining the amplitude of the noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, the determining unit 603 is further configured to:
and storing the noise signals, the service state information and the determined noise reduction signals into the noise reduction signal library.
Optionally, the determining unit 603 is further configured to:
when the indication information of the target noise reduction proportion sent by the terminal equipment related to the space to be noise reduced is determined, the amplitude of a noise reduction signal is directly determined based on the amplitude of the noise signal and the target noise reduction proportion, and the noise reduction signal has the same frequency and opposite phase with the noise signal;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
Based on the same inventive concept, referring to fig. 7, an embodiment of the present disclosure proposes an electronic device, including a memory 701 and a processor 702, where the processor is configured to read computer instructions stored in the memory and perform the above operations.
Based on the same inventive concept, a computer-readable storage medium is provided in an embodiment of noise reduction in the embodiments of the present disclosure, which when instructions in the storage medium are executed by an electronic device, enables the electronic device to perform the above-described noise reduction method.
In summary, in the embodiment of the present disclosure, a noise signal in a space to be denoised is obtained, and operation state information associated with the noise signal is obtained, where the operation state information includes operation parameter information of a noise source and user information of a target user in the space to be denoised, then, in a created noise reduction signal library, when a noise reduction signal matched with the service state information is not recorded, the operation state information is input into a trained noise determination model, a noise reduction ratio output by the noise determination model is obtained, the noise determination model is created by using a machine learning technology, the noise reduction processing library includes noise signals and corresponding noise reduction signals each associated with operation state information, the noise reduction ratio represents an amplitude reduction ratio when the noise signal is adjusted to the noise signal tolerated by the target user, then, an amplitude of the noise signal is determined, and based on the amplitude of the noise signal and the noise reduction ratio, the noise reduction signal is identical in frequency and opposite in phase to the noise signal, and then, the noise reduction signal is sent to a playing device in the space to be denoised, so that the corresponding sound wave playing device can output. Therefore, on the basis of not affecting the internal performance of the power system, the effective treatment of the noise in the vehicle can be realized, the noise treatment efficiency is ensured, and the treatment cost is reduced.
It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present disclosure have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the disclosure.
It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed embodiments without departing from the spirit and scope of the disclosed embodiments. Thus, given that such modifications and variations of the disclosed embodiments fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to encompass such modifications and variations.
Claims (10)
1. A method of noise reduction applied to the interior of an automobile, comprising:
acquiring a noise signal in a space to be denoised, and acquiring operation state information associated with the noise signal, wherein the operation state information comprises operation parameter information of a noise source and user information of a target user in the space to be denoised;
when the noise reduction signals matched with the running state information are not recorded in the noise reduction signal library established, inputting the running state information into a noise judgment model after training to obtain the amplitude reduction ratio of the output of the noise judgment model, wherein the noise judgment model is established by adopting a machine learning technology, the noise reduction signal library comprises noise signals related with the running state information and corresponding noise reduction signals, and the amplitude reduction ratio represents the amplitude reduction ratio when the noise signals are regulated to the noise signals tolerated by the target user;
Determining the amplitude of the noise signal, and determining the amplitude of a noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, wherein the noise reduction signal has the same frequency as the noise signal and opposite phase;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
2. The method of claim 1, wherein the acquiring the noise signal in the space to be noise reduced and the acquiring the operational state information associated with the noise signal comprise:
acquiring a noise signal sent by a noise reduction control device, wherein the noise signal is collected by a sound signal collecting device arranged in a space to be noise reduced and then reported to the noise reduction control device;
the obtaining the operation state information associated with the noise data comprises the following steps:
and acquiring the operation parameter information of the noise source sent by the noise reduction control device, wherein the operation parameter information at least comprises motor rotation speed information reported by a speed sensor received by the noise reduction control device and running state information reported by a positioning component received by the noise reduction control device.
3. The method of claim 1 or 2, further comprising:
Determining the noise reduction signals matched with the operation parameter information when the noise reduction signals matched with the operation state information are recorded in the noise reduction signal library;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
4. The method of claim 1 or 2, wherein before inputting the operating state information into the trained noise decision model, further comprising training the noise decision model:
acquiring sample data, wherein one piece of sample data comprises operation parameter information of a noise source generating a noise signal, user information in a noise space and a amplitude reduction proportion of the noise signal after noise reduction processing;
the following operations are respectively executed for each sample data until the difference value between the amplitude reduction ratio predicted by the noise judgment model and the amplitude reduction ratio in the sample data is continuously smaller than the preset value for times reaching a set threshold value:
inputting operation parameter information and user information in one sample data into the noise judgment model to obtain the predicted amplitude reduction ratio of the noise judgment model, wherein the noise judgment model is built based on a machine learning technology;
Model parameters for generating a reduction ratio in the noise decision model are adjusted based on a numerical difference between the predicted reduction ratio and the reduction ratio in the one sample data.
5. The method of claim 1, wherein after the determining the amplitude of the noise reduction signal based on the amplitude of the noise signal and the reduction ratio, further comprising:
and storing the noise signals, the running state information and the determined noise reduction signals into the noise reduction signal library.
6. The method as recited in claim 1, further comprising:
when the indication information of the target noise reduction proportion sent by the terminal equipment related to the space to be noise reduced is determined, the amplitude of a noise reduction signal is directly determined based on the amplitude of the noise signal and the target noise reduction proportion, and the noise reduction signal has the same frequency and opposite phase with the noise signal;
and sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
7. A noise reduction device for use in an interior of an automobile, comprising:
the device comprises an acquisition unit, a noise reduction unit and a control unit, wherein the acquisition unit is used for acquiring a noise signal in a space to be reduced and acquiring operation state information related to the noise signal, and the operation state information comprises operation parameter information of a noise source and user information of a target user in the space to be reduced;
The judging unit is used for determining that when the noise reduction signals matched with the running state information are not recorded in the created noise reduction signal library, inputting the running state information into a trained noise judging model to obtain the amplitude reduction ratio of the output of the noise judging model, wherein the noise judging model is created by adopting a machine learning technology, the noise reduction signal library comprises noise signals related with the running state information and corresponding noise reduction signals, and the amplitude reduction ratio represents the amplitude reduction ratio when the noise signals are regulated to the noise signals tolerated by the target user;
a determining unit, configured to determine an amplitude of the noise signal, and determine an amplitude of a noise reduction signal based on the amplitude of the noise signal and the amplitude reduction ratio, where the noise reduction signal has the same frequency and opposite phase to the noise signal;
and the output unit is used for sending the noise reduction signal to the playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
8. The apparatus of claim 7, wherein the determination unit is further to:
determining the noise reduction signals matched with the operation parameter information when the noise reduction signals matched with the operation state information are recorded in the noise reduction signal library;
And sending the noise reduction signal to playing equipment in the space to be noise reduced, so that the playing equipment outputs corresponding noise reduction sound waves.
9. An electronic device, comprising:
a memory for storing executable instructions;
a processor for reading and executing executable instructions stored in a memory to implement the method of any one of claims 1 to 6.
10. A computer readable storage medium, characterized in that instructions in the storage medium, when executed by an electronic device, enable the electronic device to perform the method of any one of claims 1 to 6.
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| CN113450755A (en) * | 2021-04-30 | 2021-09-28 | 青岛海尔科技有限公司 | Method, device, storage medium and electronic device for reducing noise |
| CN117395568B (en) * | 2023-12-12 | 2024-04-02 | 深圳市同创依诺数码科技有限公司 | Noise reduction method, device and equipment for audio playing equipment and storage medium |
| CN117690303B (en) * | 2024-02-04 | 2024-04-26 | 四川三元环境治理股份有限公司 | Noise early warning system, device and early warning method based on traffic data acquisition |
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