CN103581819A - Microphone detection method - Google Patents

Abstract

The invention discloses a microphone detection method. In the method, a microphone to be tested and a reference microphone are used for respectively receiving sound waves from a loudspeaker to respectively generate a first characteristic point distribution graph and a second characteristic point distribution graph. The first feature point distribution graph and the second feature point distribution graph respectively comprise a plurality of feature points corresponding to a frequency quantization numerical value. And judging the quality of the microphone to be tested by comparing the quantity difference of the characteristic points of the first characteristic point distribution graph and the second characteristic point distribution graph in a specific frequency quantization numerical value interval. The microphone detection method can be directly used in an open place for detection, and after the microphone is manufactured on a production line, the microphone can be immediately and quickly detected beside the production line without moving the product to a soundless room, so that the overall efficiency is greatly improved.

Description

Microphone detection method

technical field

The present invention relates to a microphone detection method, and in particular to a microphone detection method capable of accurately judging the quality of the microphone in the presence of background noise. the

Background technique

With the rapid development of audio-visual technology, microphones are currently used in a wide range of applications on the market. For example, camcorders, network cameras, and earphone devices are usually equipped with microphones to perform sound collection. the

In order to maintain the product quality of the microphone, the quality management of the microphone is usually carried out before shipment, and the microphone is usually measured by a detection instrument to obtain detection data and detection waveforms. Afterwards, the measured detection data and detection waveforms are compared with the standard data and standard waveforms stored in the detection instrument in advance. the

However, the factory is an open place, and the microphone is a radio device. Therefore, whether it is the operation of the machinery in the factory or background noise such as loud human voices, it will inevitably be recorded by the microphone. In this way, the test data and test waveforms measured by the microphone in the factory will be the test data and test waveforms including background noise. Therefore, it is not necessary to compare them with standard data and standard waveforms. Reasonable, because the test data and test waveforms do not simply reflect the quality of the microphone itself, but also include noise such as background noise. the

Furthermore, since the standard data and standard waveforms are pre-built into the test instrument, it is of course impossible to know the degree of interference of the current background noise on the test microphone. In this way, it is unreasonable to compare the test waveform and the standard waveform , this does not correctly reflect the sound quality of the microphone, so it is impossible to distinguish the difference between good and bad products. the

Therefore, in order to avoid the above situation, the manufacturer must build an additional anechoic room, which is an independent soundproof test area, and make the microphone collect sound in the anechoic room, and then compare it with a standard waveform to find A poor quality microphone. However, this also requires additional manpower and time costs in the delivery of the microphone, which is not ideal; in addition, the cost of the anechoic room is high, which will inevitably increase the cost significantly. the

In view of this, it is an urgent problem to be solved in the industry to provide a microphone detection method that can accurately detect defective microphones even in an open factory with background noise, thereby improving detection efficiency. the

Contents of the invention

The main purpose of the present invention is to provide a microphone detection method, which utilizes an additional reference microphone with good quality for verification to collect sound simultaneously with the microphone to be tested, so the two microphones respectively measure two waveforms, and then execute the function to convert them into two features Point distribution graph, by comparing the difference between the two feature point distribution graphs, the difference between the feature points in the specific frequency quantization value range, to determine whether the microphone under test is a good product. the

Another object of the present invention is to provide a microphone detection method, comprising the following steps: (a) providing a microphone to be tested, a reference microphone and a processing unit, the microphone to be tested and the reference microphone are connected to the processing unit respectively ; (b) providing a loudspeaker so that the microphone under test and the reference microphone receive the sound waves emitted by the speaker; wherein, the microphone under test receives the sound wave and generates a first digital signal to the processing unit, and the reference The microphone receives the sound wave and generates a second digital signal to the processing unit, wherein the processing unit generates a first feature point distribution pattern according to the first digital signal, and generates a second feature point distribution according to the second digital signal graph, and the first feature point distribution graph and the second feature point distribution graph respectively include a plurality of feature points, and each of the feature points corresponds to a frequency quantization value; and (c) compare the first feature point distribution graph with The quality of the microphone under test is determined by the difference in the number of feature points of the second feature point distribution graph in a specific frequency quantization value interval; wherein, when the difference in the number of feature points is less than a predetermined value, it is determined that the microphone under test is a good product , and when the difference in the number of feature points is greater than a predetermined value, it is determined that the microphone under test is a defective product. the

In a preferred embodiment, wherein the processing unit includes a chip module and an application program module, the step (b) includes the following steps: (b1) making the chip module receive the first digital signal and transmit it to the The application program module is used to generate a first waveform, and perform function conversion on the first waveform to generate the first feature point distribution graph. the

In a preferred embodiment, the following steps are further included after step (b1): (b2) making the chip module receive the second digital signal and send it to the application program module to generate a second waveform, and The second waveform performs function conversion to generate the second feature point distribution graph. the

In a preferred embodiment, the function is transformed into Fourier Transform or Wavelet Transform. the

In a preferred embodiment, the frequency of the sound wave emitted by the speaker is 1 kHz. the

The microphone testing method in this case can be tested directly in an open place, such as a production factory. Therefore, after the production is completed on the production line, there is no need to move the product to the anechoic room, but it can be quickly tested on the side of the production line immediately. In this way, the overall efficiency is greatly improved. the

Description of drawings

FIG. 1 is a schematic block diagram of the microphone detection method of the present invention. the

FIG. 2 is a flow chart of the microphone detection method of the present invention. the

FIG. 3 is a coordinate diagram of the first waveform of the microphone under test in the microphone detection method of the present invention. the

FIG. 4 is a first feature point distribution graph of the microphone detection method of the present invention. the

FIG. 5 is a second waveform coordinate diagram of the reference microphone of the microphone detection method of the present invention. the

FIG. 6 is a second characteristic point distribution graph of the microphone detection method of the present invention. the

Among them, the reference signs are explained as follows:

1: Speaker

21: Microphone under test

210: The first digital signal

22: Reference Microphone

220: Second digital signal

3: Processing unit

36: chip module

37: Application Modules

41: The first waveform

42: Second waveform

51: The first feature point distribution graph

52: Second feature point distribution graph

S1～S3: Steps

P: Feature points on the first feature point distribution graph

P1～P12: The first feature point distribution graphic frequency quantization value is located at the feature point of 0.4～0.6

P': the feature point on the second feature point distribution graph

P’1: The second characteristic point distribution graphic frequency quantization value is located at the characteristic point of 0.4～0.6

Detailed ways

What needs to be explained first is that the microphone detection method disclosed in the present invention is no longer limited by the environment like the traditional one, as it must be carried out in a closed anechoic room environment. In other words, the microphone detection method disclosed in the present invention can In the general open environment with background noise (for example: in the factory where the production is carried out), the quality inspection of the microphone is carried out. the

Please refer to FIG. 1 , which is a schematic block diagram of the microphone detection method of the present invention; FIG. 2 is a flowchart of the microphone detection method of the present invention. Please refer to Figure 1 and Figure 2 together. In step S1 , firstly, a test microphone 21 , a reference microphone 22 and a processing unit 3 are provided. The microphone under test 21 and the reference microphone 22 are connected to the processing unit 3 respectively. Wherein, the microphone to be tested 21 is a new finished product whose quality is to be tested, for example, a microphone that has just been produced on the production line, and the reference microphone 22 is a microphone that has been verified to be of good quality. In this case, the microphone 21 under test and the reference microphone 22 perform the sound collection action in the same environment at the same time, and then compare the sound collection content of the two to determine whether the microphone 21 under test can achieve the same sound collection level as the reference microphone 22 . the

Next, in step S2 , a speaker 1 is provided and the speaker 1 emits sound waves toward the microphone 21 under test and the reference microphone 22 , so that the microphone 21 under test and the reference microphone 22 receive the sound waves. In one embodiment, the sound wave is a sound wave of a fixed frequency, such as a sound wave of 1k frequency, but is not limited to this frequency. the

FIG. 3 is a coordinate diagram of the first waveform of the microphone to be tested in the microphone detection method of the present invention; FIG. 4 is a first characteristic point distribution graph of the microphone detection method of the present invention. Please refer to Figures 1 to 4 in combination. Wherein, the microphone 21 under test generates a first digital signal 210 to the processing unit 3 by receiving the sound wave, and the processing unit 3 generates a first feature point distribution graph 51 according to the first digital signal 210 . Similarly, please refer to FIG. 5 and FIG. 6 , FIG. 5 is a second waveform coordinate diagram of the reference microphone of the microphone detection method of the present invention; FIG. 6 is a second characteristic point distribution graph of the microphone detection method of the present invention. Wherein, the reference microphone 22 receives the sound wave and generates a second digital signal 220 to the processing unit 3 , and the processing unit 3 generates a second feature point distribution graph 52 according to the second digital signal 220 . the

Next, the first feature point distribution graph 51 and the second feature point distribution graph 52 of this case are described in detail. Please refer to FIG. 1 to FIG. 6 together. In detail, the processing unit 3 includes a chip module 36 and an application program module 37 . The chip module 36 receives the first digital signal 210 and transmits it to the application program module 37 to generate a first waveform 41 . As shown in FIG. 3 , the horizontal axis of the first waveform 41 represents time, and the vertical axis represents frequency. Thereafter, the function conversion is performed on the first waveform 41 to generate a plurality of feature points P for identification and comparison, that is, the first feature point distribution graph 51 as shown in FIG. 4 is generated, and the horizontal axis represents each feature point , and the vertical axis represents the frequency quantization value; in other words, each feature point on the first feature point distribution graph 51 corresponds to a frequency quantization value. Similarly, the chip module 36 receives the second digital signal 220 and sends it to the application module 37 to generate a second waveform 42, as shown in FIG. 5, the horizontal axis of the second waveform 42 represents time, and the vertical axis represents frequency; Afterwards, the function conversion is performed on the second waveform 42 to generate a plurality of feature points P' for identification and comparison, that is, the second feature point distribution graph 52 as shown in Figure 6 is generated, and the horizontal axis represents each feature point , and the vertical axis represents the frequency quantization value; that is, each feature point on the second feature point distribution graph 52 corresponds to a frequency quantization value. the

What needs to be explained here is that the method of function conversion can be Fourier Transform, Wavelet Transform or other function conversion that can convert the time domain of the waveform obtained by the microphone into the frequency domain, and so on. , all fall within the scope of the possible application of this case. the

Subsequently, step S3 is executed. In step S3, the difference in the number of feature points in a specific frequency quantization value interval between the first feature point distribution graph 51 and the second feature point distribution graph 52 is compared. Wherein, when the difference in the number of feature points between the two is less than a predetermined value, it is determined that the microphone 21 to be tested is a good product, and when the difference in the number of feature points between the two is greater than a predetermined value, it is determined that the microphone to be tested is 21 is a defective product. the

For example, please refer to FIG. 4 and FIG. 6 together. The first feature point distribution graph 51 shown in FIG. 4 includes fifty feature points P, and each of the fifty feature points P has a corresponding vertical axis Frequency quantization value on . Please refer to FIG. 6 again, the second feature point distribution graph 52 also includes fifty feature points P', and each of the fifty feature points P' also has a frequency quantization value corresponding to the vertical axis. Next, the inspector can designate any specific frequency quantized value interval in the two feature point distribution graphs as the discrimination interval, and then further calculate in this discrimination interval, the number of feature points in the first feature point distribution graph 51 and the second feature point The difference in the number of feature points of the distribution graph 52 . For example, if the specific frequency quantization range specified by the inspector is between 0.4 and 0.6, and the difference between the number of specified feature points is less than or equal to 7, it is a good product, and the difference between the number of feature points is greater than 7, then it is not good. Good product, as shown in Figure 4 and Figure 6, there are twelve feature points P on the first feature point distribution graph 51 in the frequency quantization value interval between 0.4 and 0.6, which are respectively marked as P1 to P12, There is one feature point between 0.4 and 0.6 on the second feature point distribution graph 52, which is marked as P'1, and the difference between the two is 11, and the difference is greater than 7, so here In the example, we judge that the microphone 21 under test is a defective product. Of course, the above-mentioned specific frequency quantization range and the difference of the number of feature points can be transformed, which is just an example for convenience of description, and is not limited. the

To sum up, the microphone detection method disclosed in the present invention uses another reference microphone to collect sound simultaneously with the microphone to be tested, and compares the measured contents of the two, so that the detection results will not be affected by machine operation. Or there is an error due to the interference of background noise such as loud voices. Therefore, the microphone testing method in this case can be tested directly in an open place, such as a production factory. Therefore, after the production is completed on the production line, there is no need to move the product to the anechoic room, but it can be quickly tested on the side of the production line immediately. , thus greatly improving the overall efficiency. the

However, the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the scope of patent protection of the present invention. Therefore, all equivalent changes made by using the description and drawings of the present invention are all included in the scope of the present invention. Within the scope of rights protection, I agree with Chen Ming. the

Claims (5)

1. A microphone detection method, comprising the following steps: (a) providing a microphone to be tested, a reference microphone and a processing unit, the microphone to be tested and the reference microphone are respectively signal-connected to the processing unit; (b) A speaker is provided, so that the microphone under test and the reference microphone receive the sound waves emitted by the speaker; wherein, the microphone under test receives the sound wave and generates a first digital signal to the processing unit, and the reference microphone receiving the sound wave and generating a second digital signal to the processing unit, wherein the processing unit generates a first feature point distribution pattern according to the first digital signal, and generates a second feature point distribution pattern according to the second digital signal , and the first feature point distribution graph and the second feature point distribution graph respectively include a plurality of feature points, and each of the feature points corresponds to a frequency quantization value; and (c) comparing the difference in the number of feature points between the first feature point distribution graph and the second feature point distribution graph in a specific frequency quantization value range to determine the quality of the microphone to be tested; wherein, when the difference in the number of feature points is less than The microphone under test is determined to be a good product when a predetermined value is reached, and the microphone to be tested is determined to be a defective product when the difference in the number of feature points is greater than a predetermined value. 2. The microphone detection method as claimed in claim 1, wherein the processing unit comprises a chip module and an application program module, comprising the following steps in step (b): (b1) Make the chip module receive the first digital signal and send it to the application program module to generate a first waveform, and perform function conversion on the first waveform to generate the first feature point distribution graph. 3. The microphone detection method as claimed in claim 2, further comprising the following steps after step (b1): (b2) Make the chip module receive the second digital signal and send it to the application program module to generate a second waveform, and perform function conversion on the second waveform to generate the second feature point distribution graph. 4. The microphone detection method according to claim 3, wherein the function transform is Fourier transform or wavelet transform. 5. The microphone detection method as claimed in claim 1, wherein the frequency of the sound wave emitted by the speaker is 1 kHz. the

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