CN114866943A

CN114866943A - Microphone fault detection method, device, equipment, storage medium and program product

Info

Publication number: CN114866943A
Application number: CN202210478654.XA
Authority: CN
Inventors: 崔洋洋; 余俊澎; 王星宇
Original assignee: Youmi Technology Shenzhen Co ltd
Current assignee: Youmi Technology Shenzhen Co ltd
Priority date: 2022-05-05
Filing date: 2022-05-05
Publication date: 2022-08-05

Abstract

The application relates to a microphone fault detection method, a microphone fault detection device, microphone fault detection equipment, a microphone storage medium and a microphone program product, and belongs to the technical field of fault detection. The method comprises the following steps: acquiring a plurality of original audio data received by a target microphone; respectively extracting the characteristics of the plurality of original audio data to obtain a target audio data characteristic set consisting of a plurality of audio data characteristics; and inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result of the target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not. By adopting the method, whether the microphone is abnormal or not can be detected in real time.

Description

Microphone fault detection method, device, equipment, storage medium and program product

Technical Field

The present application relates to the field of fault detection technologies, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for detecting a microphone fault.

Background

With the continuous development of computer technology, network technology and communication technology, multimedia data such as images, videos and audios become important information media in the field of information processing, wherein audio information plays an important role. At present, a microphone for realizing a voice interaction function is configured on each mobile terminal, wherein the microphone has a function of converting a sound signal into an electric signal to realize conversation, recording and the like of the mobile terminal. If the microphone fails, the call or recording function of the mobile terminal cannot be realized. Therefore, how to detect whether the microphone is abnormal in real time becomes a problem which needs to be solved urgently.

Disclosure of Invention

In view of the above, it is necessary to provide a microphone failure detection method, apparatus, device, storage medium and program product for solving the above technical problems.

In a first aspect, the present application provides a microphone fault detection method, including: acquiring a plurality of original audio data received by a target microphone; respectively extracting the characteristics of the plurality of original audio data to obtain a target audio data characteristic set consisting of a plurality of audio data characteristics; and inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result of the target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not.

In one embodiment, the performing feature extraction on the plurality of original audio data to obtain a target audio data feature set composed of a plurality of audio data features includes: preprocessing the plurality of original audio data to obtain a plurality of target audio data; and respectively carrying out feature extraction on the plurality of target audio data to obtain the target audio data feature set.

In one embodiment, the pre-processing the plurality of original audio data to obtain a plurality of target audio data includes: carrying out data cleaning processing operation on the plurality of original audio data to obtain a plurality of candidate audio data; and performing data dimension reduction operation on the candidate audio data to obtain a plurality of target audio data.

In one embodiment, the method further comprises: and if the fault detection result of the target microphone is an abnormal result, sending the fault detection result to the monitoring terminal.

In one embodiment, the method further comprises: and storing a fault detection result representing the abnormity of the target microphone and the model of the target microphone as a group of data.

In one embodiment, the training process of the fault detection model is as follows: acquiring a training sample set, wherein the training sample set comprises a plurality of sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used for indicating whether the sample microphone is abnormal or not; and training the initial fault detection model by using the training sample set until a preset loss function is converged to obtain the fault detection model.

In a second aspect, the application further provides a microphone fault detection device. The device comprises: the first acquisition module is used for acquiring a plurality of original audio data received by a target microphone; the second acquisition module is used for respectively extracting the characteristics of the plurality of original audio data to obtain a target audio data characteristic set consisting of a plurality of audio data characteristics; and the detection module is used for inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result of the target microphone output by the fault detection model, and the fault detection result is used for representing whether the target microphone is abnormal or not.

In one embodiment, the second obtaining module is specifically configured to: preprocessing the plurality of original audio data to obtain a plurality of target audio data; and respectively carrying out feature extraction on the plurality of target audio data to obtain the target audio data feature set.

In one embodiment, the second obtaining module is specifically configured to: carrying out data cleaning processing operation on the plurality of original audio data to obtain a plurality of candidate audio data; and performing data dimension reduction operation on the candidate audio data to obtain a plurality of target audio data.

In one embodiment, the microphone failure detection apparatus further includes: and the sending module is used for sending the fault detection result to the monitoring terminal if the fault detection result of the target microphone is an abnormal result.

In one embodiment, the microphone failure detection apparatus further includes: and the storage module is used for storing a fault detection result representing the abnormity of the target microphone and the model of the target microphone as a group of data.

In one embodiment, the microphone failure detection apparatus further includes a model training module configured to: acquiring a training sample set, wherein the training sample set comprises a plurality of sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used for indicating whether the sample microphone is abnormal or not; and training the initial fault detection model by using the training sample set until a preset loss function is converged to obtain the fault detection model.

In a third aspect, the present application further provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method according to any one of the first aspect when executing the computer program.

In a fourth aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any of the first aspects described above.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, performs the steps of the method of any of the first aspects described above.

The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:

in the embodiment of the application, firstly, a plurality of original audio data received by a target microphone are obtained; secondly, respectively carrying out feature extraction on a plurality of original audio data to obtain a target audio data feature set consisting of a plurality of audio data features; and finally, inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result of a target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not, so that the method and the device can detect whether the microphone is abnormal or not in real time.

Drawings

FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present application;

fig. 2 is a flowchart of a microphone fault detection method according to an embodiment of the present disclosure;

FIG. 3 is a flowchart of a technical process for obtaining a target audio data feature set according to an embodiment of the present application;

FIG. 4 is a flowchart of a training process of a fault detection model according to an embodiment of the present disclosure;

fig. 5 is a flowchart of a microphone failure detection method according to an embodiment of the present disclosure;

fig. 6 is a block diagram of a first microphone failure detection apparatus provided in the present application;

fig. 7 is a block diagram of a second microphone failure detection apparatus provided in the present application;

fig. 8 is an internal structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

With the continuous development of computer technology, network technology and communication technology, multimedia data such as images, videos and audios become important information media forms in the field of information processing, wherein audio information plays an important role. At present, a microphone for realizing a voice interaction function is configured on each mobile terminal, wherein the microphone has a function of converting a sound signal into an electric signal to realize conversation, recording and the like of the mobile terminal. If the microphone fails, the call or recording function of the mobile terminal cannot be realized. Therefore, how to detect whether the microphone is abnormal in real time becomes a problem which needs to be solved urgently.

In view of this, embodiments of the present application provide a microphone fault detection method, apparatus, device, storage medium, and program product, by which whether a microphone is abnormal can be detected, and a fault detection result indicating that the microphone is abnormal can be sent to a remote terminal device, so as to warn a manager to timely maintain the microphone that has failed.

Please refer to fig. 1, which illustrates a schematic diagram of an implementation environment related to a microphone failure detection method according to an embodiment of the present application. As shown in fig. 1, an execution main body of the microphone failure detection method provided in the embodiment of the present application may be one computer device, or may be a computer device cluster composed of multiple computer devices. Different computer devices can communicate with each other in a wired or wireless manner, and the wireless manner can be realized through WIFI, an operator network, NFC (near field communication) or other technologies.

Referring to fig. 2, a flowchart of a microphone failure detection method provided by an embodiment of the present application is shown, where the microphone failure detection method may be applied to the computer device in fig. 1. As shown in fig. 2, the microphone failure detection method may include the steps of:

step 201, the computer device obtains a plurality of original audio data received by a target microphone.

Optionally, the target microphone may be provided with a communication component, the target microphone may send the original audio data to the computer device through the communication component, or the target microphone may be provided in the target terminal device, and the target microphone may send the original audio data to the computer device through the target terminal device.

Step 202, the computer device performs feature extraction on the plurality of original audio data respectively to obtain a target audio data feature set composed of a plurality of audio data features.

Optionally, feature extraction may be performed on the multiple pieces of original audio data by using a convolutional neural network, or feature extraction may be performed on the multiple pieces of original audio data by using other manners, and the manner of feature extraction is not limited in the embodiment of the present application.

And 203, inputting the target audio data feature set into a pre-trained fault detection model by the computer equipment to obtain a fault detection result of the target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not.

Optionally, the fault detection model may be optimized according to the target audio data feature set, where the step of optimizing the fault detection model may include: firstly, inputting a target audio data feature set into a fault detection model by using an automatic parameter searching algorithm for parameter optimization processing to obtain optimized parameters; and secondly, replacing parameters in the fault detection model according to the optimized parameters to obtain the optimized fault detection model. In the embodiment of the application, the fault detection model is optimized, so that the accuracy of the fault detection result output by the fault detection model can be further ensured.

Because the original audio data may include data with missing values, after the original audio data is obtained, the original audio data may be preprocessed to screen out the original audio data with missing values, so as to obtain a target audio data feature set. Referring to fig. 3, a technical process for obtaining a target audio data feature set according to an embodiment of the present application is shown, and as shown in fig. 3, the technical process may include the following steps:

step 301, the computer device performs a preprocessing operation on the plurality of original audio data to obtain a plurality of target audio data.

Optionally, the step of performing a preprocessing operation on the plurality of original audio data to obtain a plurality of target audio data may include: firstly, carrying out data cleaning processing operation on a plurality of original audio data to obtain a plurality of candidate audio data; and secondly, performing data dimension reduction operation on the candidate audio data to obtain a plurality of target audio data.

The data cleaning processing operation refers to a procedure for finding and correcting errors in data, and comprises the steps of checking data consistency, processing data containing missing values and the like; in the field of machine learning and statistics, data dimension reduction operation refers to a process of reducing the number of random variables under certain limited conditions to obtain a group of irrelevant main variables, and dimension reduction of data can save storage space on one hand and eliminate noise in candidate audio data on the other hand to improve performance of a machine learning algorithm.

Optionally, a Local Linear Embedding (LLE) method may be used to perform data dimension reduction on the multiple candidate audio data to obtain multiple target audio data. The manifold is equivalent to an unclosed curved surface of a high-dimensional space, and the manifold learning dimensionality reduction is to be a low-dimensional space on the premise of ensuring that a local structure in the high-dimensional space is not damaged, namely, the LLE algorithm is reduced to be the low-dimensional space on the premise of ensuring that the local structure in the candidate audio data is not damaged.

In the embodiment of the application, firstly, data cleaning processing operation is carried out on a plurality of original audio data to obtain a plurality of candidate audio data, and the original audio data with poor quality and missing values can be screened out through the data cleaning processing operation, so that a plurality of candidate audio data with better quality can be obtained; secondly, data dimensionality reduction operation is carried out on the candidate audio data to obtain a plurality of target audio data, so that the main features of the target audio data can be extracted subsequently, the workload of subsequent feature extraction is reduced, and the speed of feature extraction is improved.

Step 302, the computer device performs feature extraction on the multiple target audio data respectively to obtain a target audio data feature set.

Optionally, feature extraction may be performed on the multiple target audio data through a convolutional neural network, or feature extraction may be performed on the multiple target audio data through other manners, and the manner of feature extraction is not limited in the embodiment of the present application.

In an optional embodiment of the present application, if the fault detection result of the target microphone is an abnormal result, the fault detection result may be sent to the monitoring terminal, where the monitoring terminal is configured to display the fault detection result, or send an alarm prompt tone according to the fault detection result, so that a manager in the monitoring terminal can check the fault detection result, and then the manager can timely maintain the target microphone according to the fault detection result, thereby ensuring that the target microphone can be normally used.

In another alternative embodiment of the present application, the fault detection result indicating the abnormality of the target microphone and the model of the target microphone may be stored as a set of data for subsequent investigation and evidence collection by a manager and subsequent finding of the cause of damage.

Referring to fig. 4, a training process of a fault detection model provided by an embodiment of the present application is shown, and as shown in fig. 4, the training process of the fault detection model may include the following steps:

step 401, a computer device obtains a training sample set, where the training sample set includes a plurality of sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used to indicate whether the sample microphone is abnormal.

Optionally, the audio data of the multiple samples may be preprocessed to obtain audio data of multiple target samples, feature extraction is performed on the audio data of the multiple target samples to obtain an audio data feature set of the target samples, and the audio data feature set of the target samples and the labels corresponding to the sample microphones are used as a training sample set.

Step 402, the computer device trains the initial fault detection model by using the training sample set until the preset loss function is converged, so as to obtain the fault detection model.

Optionally, after the fault detection model is obtained, the fault detection model may be verified according to the audio data feature verification set, and if the verification passes, it is indicated that the fault detection model has been trained; if the verification fails, the method returns to step 401 to continue training the fault detection model, so that it can be ensured that the finally obtained fault detection model is a trained model.

Referring to fig. 5, a flowchart of a microphone failure detection method provided by an embodiment of the present application is shown, where the microphone failure detection method may be applied to the computer device in fig. 1. As shown in fig. 5, the microphone failure detection method may include the steps of:

step 501, the computer device obtains a plurality of original audio data received by a target microphone.

Step 502, the computer device performs data cleaning processing operation on the plurality of original audio data to obtain a plurality of candidate audio data.

Step 503, the computer device performs data dimension reduction operation on the plurality of candidate audio data to obtain a plurality of target audio data.

Step 504, the computer device performs feature extraction on the plurality of target audio data respectively to obtain a target audio data feature set.

And 505, inputting the target audio data feature set into a pre-trained fault detection model by the computer equipment to obtain a fault detection result of the target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not.

In the embodiment of the application, the data cleaning processing operation is carried out on the obtained multiple original audio data, so that the original audio data with poor quality and missing values can be screened out; the data dimensionality reduction operation is carried out on the candidate audio data, so that the main features of the target audio data can be conveniently and respectively extracted, and the feature extraction speed is improved; and inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result, thereby realizing the fault detection of the microphone.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Referring to fig. 6, a block diagram of a first microphone failure detection apparatus 600 provided in the present application is shown, wherein the first microphone failure detection apparatus 600 can be configured in the computer device. As shown in fig. 6, the first microphone failure detection apparatus 600 may include a first obtaining module 601, a second obtaining module 602, and a detection module 603.

The first obtaining module 601 is configured to obtain a plurality of original audio data received by a target microphone; a second obtaining module 602, configured to perform feature extraction on the multiple pieces of original audio data, respectively, to obtain a target audio data feature set composed of multiple audio data features; the detection module 603 is configured to input the target audio data feature set into a pre-trained fault detection model, so as to obtain a fault detection result of the target microphone output by the fault detection model, where the fault detection result is used to represent whether the target microphone is abnormal.

In one embodiment, the second obtaining module 602 is specifically configured to: preprocessing a plurality of original audio data to obtain a plurality of target audio data; and respectively carrying out feature extraction on the plurality of target audio data to obtain a target audio data feature set.

In one embodiment, the second obtaining module 602 is specifically configured to: carrying out data cleaning processing operation on a plurality of original audio data to obtain a plurality of candidate audio data; and performing data dimension reduction operation on the candidate audio data to obtain a plurality of target audio data.

Referring to fig. 7, a block diagram of a second microphone fault detection apparatus 700 provided by the present application is shown, as shown in fig. 7, the second microphone fault detection apparatus 700 includes a transmitting module 604, a storage module 605 and a model training module 606 in addition to the modules of the first microphone fault detection apparatus 600.

The sending module 604 is configured to send the fault detection result to the monitoring terminal if the fault detection result of the target microphone is an abnormal result; a storage module 605, configured to store a fault detection result indicating that the target microphone is abnormal and a model of the target microphone as a set of data; the model training module 606 is configured to obtain a training sample set, where the training sample set includes multiple sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used to indicate whether the sample microphone is abnormal, and train an initial fault detection model by using the training sample set until a preset loss function converges to obtain the fault detection model.

The microphone fault detection device provided by the embodiment of the application can realize the method embodiment, the realization principle and the technical effect are similar, and the details are not repeated.

The modules in the microphone failure detection device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a microphone failure detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment of the present application, there is provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program: acquiring a plurality of original audio data received by a target microphone; respectively extracting the characteristics of a plurality of original audio data to obtain a target audio data characteristic set consisting of a plurality of audio data characteristics; and inputting the target audio data characteristic set into a pre-trained fault detection model to obtain a fault detection result of a target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not.

In one embodiment of the application, the processor when executing the computer program further performs the steps of: preprocessing a plurality of original audio data to obtain a plurality of target audio data; and respectively carrying out feature extraction on the plurality of target audio data to obtain a target audio data feature set.

In one embodiment of the application, the processor when executing the computer program further performs the steps of: carrying out data cleaning processing operation on a plurality of original audio data to obtain a plurality of candidate audio data; and performing data dimension reduction operation on the candidate audio data to obtain a plurality of target audio data.

In one embodiment of the application, the processor when executing the computer program further performs the steps of: and if the fault detection result of the target microphone is an abnormal result, sending the fault detection result to the monitoring terminal.

In one embodiment of the application, the processor when executing the computer program further performs the steps of: and storing the fault detection result representing the abnormity of the target microphone and the model of the target microphone as a group of data.

In one embodiment of the application, the processor when executing the computer program further performs the steps of: acquiring a training sample set, wherein the training sample set comprises a plurality of sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used for indicating whether the sample microphone is abnormal or not; and training the initial fault detection model by using the training sample set until the preset loss function is converged to obtain the fault detection model.

The implementation principle and technical effect of the computer device provided by the embodiment of the present application are similar to those of the method embodiment described above, and are not described herein again.

In an embodiment of the application, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of: acquiring a plurality of original audio data received by a target microphone; respectively extracting the characteristics of a plurality of original audio data to obtain a target audio data characteristic set consisting of a plurality of audio data characteristics; and inputting the target audio data characteristic set into a pre-trained fault detection model to obtain a fault detection result of a target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not.

In one embodiment of the application, the computer program when executed by the processor further performs the steps of: preprocessing a plurality of original audio data to obtain a plurality of target audio data; and respectively carrying out feature extraction on the plurality of target audio data to obtain a target audio data feature set.

In one embodiment of the application, the computer program when executed by the processor further performs the steps of: carrying out data cleaning processing operation on a plurality of original audio data to obtain a plurality of candidate audio data; and performing data dimension reduction operation on the candidate audio data to obtain a plurality of target audio data.

In one embodiment of the application, the computer program when executed by the processor further performs the steps of: and if the fault detection result of the target microphone is an abnormal result, sending the fault detection result to the monitoring terminal.

In one embodiment of the application, the computer program when executed by the processor further performs the steps of: and storing the fault detection result representing the abnormity of the target microphone and the model of the target microphone as a group of data.

In one embodiment of the application, the computer program when executed by the processor further performs the steps of: acquiring a training sample set, wherein the training sample set comprises a plurality of sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used for indicating whether the sample microphone is abnormal or not; and training the initial fault detection model by using the training sample set until the preset loss function is converged to obtain the fault detection model.

The implementation principle and technical effect of the computer-readable storage medium provided by this embodiment are similar to those of the above-described method embodiment, and are not described herein again.

The computer program product provided in this embodiment has similar implementation principles and technical effects to those of the method embodiments described above, and is not described herein again.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A microphone fault detection method, the method comprising:

acquiring a plurality of original audio data received by a target microphone;

respectively carrying out feature extraction on the plurality of original audio data to obtain a target audio data feature set consisting of a plurality of audio data features;

and inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result of the target microphone output by the fault detection model, wherein the fault detection result is used for representing whether the target microphone is abnormal or not.

2. The method of claim 1, wherein the performing feature extraction on the plurality of original audio data to obtain a target audio data feature set composed of a plurality of audio data features comprises:

preprocessing the plurality of original audio data to obtain a plurality of target audio data;

and respectively carrying out feature extraction on the plurality of target audio data to obtain the target audio data feature set.

3. The method of claim 2, wherein the pre-processing the plurality of raw audio data to obtain a plurality of target audio data comprises:

performing data cleaning processing operation on the plurality of original audio data to obtain a plurality of candidate audio data;

and performing data dimension reduction operation on the candidate audio data to obtain the target audio data.

4. The method of claim 1, further comprising:

and if the fault detection result of the target microphone is an abnormal result, sending the fault detection result to a monitoring terminal.

5. The method of claim 1, further comprising:

and storing a fault detection result representing the abnormity of the target microphone and the model of the target microphone as a group of data.

6. The method of claim 1, wherein the training process of the fault detection model is:

acquiring a training sample set, wherein the training sample set comprises a plurality of sample audio data received by a sample microphone and a label corresponding to the sample microphone, and the label is used for indicating whether the sample microphone is abnormal or not;

and training an initial fault detection model by using the training sample set until a preset loss function is converged to obtain the fault detection model.

7. An apparatus for microphone fault detection, the apparatus comprising:

the first acquisition module is used for acquiring a plurality of original audio data received by a target microphone;

the second acquisition module is used for respectively extracting the characteristics of the plurality of original audio data to obtain a target audio data characteristic set consisting of a plurality of audio data characteristics;

and the detection module is used for inputting the target audio data feature set into a pre-trained fault detection model to obtain a fault detection result of the target microphone output by the fault detection model, and the fault detection result is used for representing whether the target microphone is abnormal or not.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.