CN112492453A - Automatic detection method for audio interface - Google Patents
Automatic detection method for audio interface Download PDFInfo
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- CN112492453A CN112492453A CN201910862834.6A CN201910862834A CN112492453A CN 112492453 A CN112492453 A CN 112492453A CN 201910862834 A CN201910862834 A CN 201910862834A CN 112492453 A CN112492453 A CN 112492453A
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04R3/00—Circuits for transducers, loudspeakers or microphones
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- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
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- H04R2420/05—Detection of connection of loudspeakers or headphones to amplifiers
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Abstract
The invention relates to the technical field of audio interface detection, in particular to an automatic detection method for an audio interface, which comprises the following steps: s1, connecting the computer audio output end and the microphone input end directly through an audio line to form a loop, S2, generating a 10K Hz sine wave audio by the computer, sampling according to the default sampling frequency of the audio controller to generate an audio file, S3, and playing the audio file generated by S2.
Description
Technical Field
The invention relates to the technical field of audio interface detection, in particular to an automatic detection method for an audio interface.
Background
When a traditional computer tests an audio interface, a tester needs to play audio files of different sound channels, and then the audio files are actually judged in an ear listening mode, so that the method can bring several problems:
1) different testers have different hearing frequency ranges, intensity sensitivities and the like, so that the difference of test results is easily caused;
2) the test efficiency is low, and the test requirement of a factory during mass production cannot be met;
3) the quality card can not be closed by one hundred percent through manual operation;
meanwhile, because various computer audio codec (codec) chips are different, the test programs used by the decoder source factories cannot be unified, which is easy to cause trouble to the testers.
In summary, the present invention provides an automatic detection method for an audio interface to solve the existing problems.
Disclosure of Invention
The present invention is directed to an automatic detection method for an audio interface, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
an automated detection method for an audio interface, comprising the steps of:
s1, directly connecting the audio output end of the computer and the input end of the microphone into a loop through an audio line;
s2, the computer generates a 10K Hz sine wave audio frequency, and the sampling is carried out according to the default sampling frequency of the audio controller to generate an audio file;
s3, playing the audio file generated in S2;
s4, recording the audio by using a microphone and generating a wav file;
s5, filtering the valid data of the recorded wav file by the computer, and removing the invalid data;
s6, carrying out spectrum analysis on the effective audio data through fast Fourier transform to obtain the corresponding relation between frequency and amplitude;
and S7, quickly and accurately calibrating whether the audio interface of the computer is normal or not according to indexes such as peak frequency, energy density and the like.
Preferably, the S4, WAV is in a sound file format and supports msabcdm or CCITT or other compression algorithms, supports audio numbers, sampling frequency and sound channel, and has a sampling frequency of 44.1K, 16-bit quantization number, and the WAV opening tool is a WINDOWS media player.
Preferably, the default sampling frequency of the audio controller is 48000Hz at S2.
Preferably, in the step S6, fast fourier transform is performed, the sequence x (N) is finite in length, N is from 0 to N-1, then the frequency ω is sampled at equal intervals within the range of 0 to 2 π, the number of sampling points is N, and the sampling interval is 2 π/N. The frequency value corresponding to the kth sampling point is 2 pi k/N. The available discrete fourier transform and its inverse are defined as:
if a finite sequence is considered to be a period of a periodic sequence, the discrete Fourier transform is a Fourier series. The discrete fourier transform is also periodic, with a period of N.
The relationship between digital frequency and analog frequency is:
ω 2 π f/fs, i.e.
The analog frequency corresponding to the kth frequency point is:
in spectral analysis using the fast fourier transform, two important parameters are determined: sampling frequency and frequency domain sampling point number, wherein the sampling frequency can be determined according to the Nyquist sampling theorem, and the sampling point number can be determined according to the sequence length or the frequency resolution Deltaf:
The steps of analyzing the spectrum of a continuous signal using a fast fourier transform can be summarized as follows:
(1) determining a proper sampling frequency fs according to the highest frequency of the signal and the requirement of a sampling theorem;
(2) determining the number N of frequency domain sampling points according to the requirement of the frequency spectrum resolution, and determining the frequency resolution according to the actual requirement;
(3) performing fast Fourier transform of N points, expressing the ordinate by power according to a Pasteval relation, and converting the abscissa into analog frequency Hz according to a formula (1-5);
(4) and (4) carrying out analysis according to the obtained result.
Compared with the prior art, the invention has the beneficial effects that:
in the invention, the audio output of the computer and the input of the microphone are directly connected into a loop by adopting an audio line, an audio file with fixed frequency is generated and played, then audio is input from the microphone by utilizing the loop and is subjected to spectrum analysis, and finally whether the audio interface of the computer is normal or not is quickly and accurately calibrated according to indexes such as peak frequency, energy density and the like, so that full-automatic audio detection is realized, the intervention of a tester is not needed, the test error is reduced, the test efficiency is greatly improved, and meanwhile, the detection method is suitable for any hardware system with the audio interfaces for output and input simultaneously.
Drawings
FIG. 1 is a schematic diagram of a detection process structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution:
an automated detection method for an audio interface, comprising the steps of:
s1, directly connecting the audio output end of the computer and the input end of the microphone into a loop through an audio line;
s2, the computer generates a 10K Hz sine wave audio frequency, and the sampling is carried out according to the default sampling frequency of the audio controller to generate an audio file;
s3, playing the audio file generated in S2;
s4, recording the audio by using a microphone and generating a wav file;
s5, filtering the valid data of the recorded wav file by the computer, and removing the invalid data;
s6, carrying out spectrum analysis on the effective audio data through fast Fourier transform to obtain the corresponding relation between frequency and amplitude;
and S7, quickly and accurately calibrating whether the audio interface of the computer is normal or not according to indexes such as peak frequency, energy density and the like.
The S4 is a WAV with sound file format, supporting msatpcm or CCITT or other compression algorithms, supporting audio numbers, sampling frequency and sound channel, and having a sampling frequency of 44.1K, 16-bit quantization number, and the WAV opening tool is a WINDOWS media player.
And S2, the default sampling frequency of the audio controller is 48000 Hz.
And S6, performing fast Fourier transform, wherein the sequence x (N) is finite in length, N is from 0 to N-1, sampling is performed on the frequency omega within the range of 0 to 2 pi at equal intervals, the number of sampling points is N, and the sampling interval is 2 pi/N. The frequency value corresponding to the kth sampling point is 2 pi k/N. The available discrete fourier transform and its inverse are defined as:
if a finite sequence is considered to be a period of a periodic sequence, the discrete Fourier transform is a Fourier series. The discrete fourier transform is also periodic, with a period of N.
The relationship between digital frequency and analog frequency is:
ω 2 π f/fs, i.e.
The analog frequency corresponding to the kth frequency point is:
in spectral analysis using the fast fourier transform, two important parameters are determined: sampling frequency and frequency domain sampling point number, wherein the sampling frequency can be determined according to the Nyquist sampling theorem, and the sampling point number can be determined according to the sequence length or the frequency resolution Deltaf:
The steps of analyzing the spectrum of a continuous signal using a fast fourier transform can be summarized as follows:
(1) determining a proper sampling frequency fs according to the highest frequency of the signal and the requirement of a sampling theorem;
(2) determining the number N of frequency domain sampling points according to the requirement of the frequency spectrum resolution, and determining the frequency resolution according to the actual requirement;
(3) performing fast Fourier transform of N points, expressing the ordinate by power according to a Pasteval relation, and converting the abscissa into analog frequency Hz according to a formula (1-5);
(4) and (4) carrying out analysis according to the obtained result.
The specific implementation case is as follows:
step 1, directly connecting an audio output end of a computer and an input end of a microphone into a loop through an audio line;
step 2, the computer generates a 10K Hz sine wave audio frequency, and the sampling is carried out according to the default sampling frequency of the audio controller of 48000Hz to generate an audio file;
step 3, playing the audio file generated in the step 2;
step 4, recording the audio by using a microphone and generating a wav file;
step 5, the computer filters the effective data of the recorded wav file and eliminates the ineffective data;
step 6, carrying out spectrum analysis on the effective audio data through fast Fourier transform to obtain a corresponding relation between frequency and amplitude;
and 7, quickly and accurately calibrating whether the audio interface of the computer is normal or not according to indexes such as peak frequency, energy density and the like:
I. for dual channel testing, the absolute error of the peak frequency of each channel from the source frequency (10K Hz) must be less than one frequency interval (sampling frequency/number of samples), and the energy density near the peak frequency (plus or minus 5%) must be greater than 99%.
In single-channel testing, the absolute error of the peak frequency of a testing channel and the source frequency (10K Hz) must be less than one frequency interval (sampling frequency/sampling number), and the energy density near the peak frequency (plus or minus 5%) is more than 99%; other channels should satisfy the feature of silence: the maximum amplitude should be less than 1% of the maximum amplitude of the test channel, and at the same time, the peak frequency should be near the source frequency, i.e. less than one frequency interval (sampling frequency/sampling number).
In the process, an audio output and input loop is built, a sound source with obvious characteristics is played and recorded, then the recorded data is filtered and subjected to spectrum analysis, and compared with the characteristics of the sound source, and whether the hardware audio interface is abnormal or not is finally judged, so that full-automatic audio detection is realized, the intervention of testers is not needed, the test error is reduced, the test efficiency is greatly improved, and meanwhile, the detection method is suitable for any hardware system with the output and input audio interfaces.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. An automated detection method for an audio interface, comprising the steps of:
s1, directly connecting the audio output end of the computer and the input end of the microphone into a loop through an audio line;
s2, the computer generates a 10K Hz sine wave audio frequency, and the sampling is carried out according to the default sampling frequency of the audio controller to generate an audio file;
s3, playing the audio file generated in S2;
s4, recording the audio by using a microphone and generating a wav file;
s5, filtering the valid data of the recorded wav file by the computer, and removing the invalid data;
s6, carrying out spectrum analysis on the effective audio data through fast Fourier transform to obtain the corresponding relation between frequency and amplitude;
and S7, quickly and accurately calibrating whether the audio interface of the computer is normal or not according to indexes such as peak frequency, energy density and the like.
2. An automated detection method for an audio interface according to claim 1, characterized in that: the S4 is a WAV with sound file format, supporting msatpcm or CCITT or other compression algorithms, supporting audio numbers, sampling frequency and sound channel, and having a sampling frequency of 44.1K, 16-bit quantization number, and the WAV opening tool is a WINDOWS media player.
3. An automated detection method for an audio interface according to claim 1, characterized in that: and S2, the default sampling frequency of the audio controller is 48000 Hz.
4. An automated detection method for an audio interface according to claim 1, characterized in that: and S6, performing fast Fourier transform, wherein the sequence x (N) is finite in length, N is from 0 to N-1, sampling is performed on the frequency omega within the range of 0 to 2 pi at equal intervals, the number of sampling points is N, and the sampling interval is 2 pi/N. The frequency value corresponding to the kth sampling point is 2 pi k/N. The available discrete fourier transform and its inverse are defined as:
if a finite sequence is considered to be a period of a periodic sequence, the discrete Fourier transform is a Fourier series. The discrete fourier transform is also periodic, with a period of N.
The relationship between digital frequency and analog frequency is:
the analog frequency corresponding to the kth frequency point is:
in spectral analysis using the fast fourier transform, two important parameters are determined: sampling frequency and number of sampling points in frequency domain, wherein the sampling frequency can be determined according to Nyquist sampling theorem, and the number of sampling points can be determined according to sequence length or frequency resolution delta f
The steps of analyzing the spectrum of a continuous signal using a fast fourier transform can be summarized as follows:
(1) determining a proper sampling frequency fs according to the highest frequency of the signal and the requirement of a sampling theorem;
(2) determining the number N of frequency domain sampling points according to the requirement of the frequency spectrum resolution, and determining the frequency resolution according to the actual requirement;
(3) performing fast Fourier transform of N points, expressing the ordinate by power according to a Pasteval relation, and converting the abscissa into analog frequency Hz according to a formula (1-5);
(4) and (4) carrying out analysis according to the obtained result.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113347548A (en) * | 2021-05-31 | 2021-09-03 | 深圳市视美泰技术股份有限公司 | Audio system detection method and device, electronic product and storage medium |
| CN113596695A (en) * | 2021-06-30 | 2021-11-02 | 惠州高盛达科技有限公司 | Automatic detection method and system for double MEMS microphones based on FFT algorithm |
| CN117198373A (en) * | 2023-09-26 | 2023-12-08 | 深圳市爱普泰科电子有限公司 | Method for evaluating and calculating automatic performance of audio IC playing and recording channel |
| CN117311300A (en) * | 2023-11-29 | 2023-12-29 | 西安热工研究院有限公司 | Method and device for dynamically adjusting sampling frequency of distributed control system and electronic equipment |
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Application publication date: 20210312 |