CN114845231B - Method and system for testing noise reduction effect of ENC (electronic noise control) through electroacoustic testing equipment - Google Patents
Method and system for testing noise reduction effect of ENC (electronic noise control) through electroacoustic testing equipment Download PDFInfo
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Abstract
The invention provides a method and a system for testing an ENC noise reduction effect through electroacoustic testing equipment. Acquiring all voice databases, extracting single voice, and generating standard artificial mouth data and earphone artificial mouth data; acquiring all environment databases, extracting front-stage noise and rear-stage noise, and fusing the front-stage noise and the rear-stage noise to generate a standard noise signal and an earphone noise signal; acquiring the noise signal of the earphone, and extracting a first bathing fluctuation, a second bathing fluctuation and an environment replacement moment according to the time sequence of the noise; carrying out online recording proportion adjustment and noise prediction at a future moment to generate a first recording proportion and a recording difference value at a next ten moments; acquiring the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value; and performing online evaluation to judge whether to stop the current noise reduction adjustment task. According to the scheme, the on-line intelligent ENC test and noise reduction are realized through learning and comparison of the environment sound and the human voice of the earphones and the microphones.
Description
Technical Field
The invention relates to the technical field of noise reduction testing, in particular to a method and a system for testing an ENC noise reduction effect through electroacoustic testing equipment.
Background
The electroacoustic test equipment is used for testing environmental noise and reducing noise, and plays an important role in improving the performance of the earphone. In the ENC process, a dual microphone array is used to filter and select specific ambient noise, where one microphone is responsible for receiving speech and the other microphone is responsible for capturing external ambient sound.
Before the technology of the invention, the conventional test ENC adopted by the prior art needs the simulation mouth 1 to play a human voice signal, the simulation mouth 2 to play noise, the ENC noise reduction switch of the Bluetooth headset is opened through an SPP instruction, two sound sources are played simultaneously, and the sound is recorded once; closing an ENC noise reduction switch of the Bluetooth headset, simultaneously playing two sound sources, recording sound once again, and simultaneously putting the two recording files into Adobe audio for processing and analysis to obtain a difference value of two curves, namely an ENC noise reduction effect; in addition, when the ENC noise reduction switch of the bluetooth headset cannot be turned on and off, the ENC noise reduction effect cannot be tested, and another method needs to be found to test the ENC noise reduction effect.
Disclosure of Invention
In view of the above problems, the present invention provides a method and a system for testing ENC noise reduction effect through an electroacoustic testing device, which implement on-line intelligent ENC testing and noise reduction by learning and comparing the corresponding earphone and microphone ambient sound with human voice.
According to a first aspect of embodiments of the present invention, there is provided a method of testing the noise reduction effect of an ENC by an electro-acoustic testing device.
In one or more embodiments, preferably, the method for testing the ENC noise reduction effect through the electro-acoustic testing device comprises:
acquiring all voice databases, extracting single voice, and generating standard artificial mouth data and earphone artificial mouth data;
acquiring all environment databases, extracting front-stage noise and rear-stage noise, and fusing the front-stage noise and the rear-stage noise to generate a standard noise signal and an earphone noise signal;
acquiring the noise signal of the earphone, and extracting a first noise fluctuation, a second noise fluctuation and an environment replacement moment according to the time sequence of noise;
carrying out online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference at a next ten moment;
acquiring the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value;
and acquiring the standard artificial mouth data, the earphone artificial mouth data, the first noise fluctuation and the second noise fluctuation, performing online evaluation, and judging whether to stop the current noise reduction adjustment task.
In one or more embodiments, preferably, the acquiring all the voice databases, extracting a single voice and playing, and then generating standard artificial mouth data and earphone artificial mouth data specifically includes:
acquiring all voice databases, and randomly extracting first voice in the voice databases;
playing through a manual mouth according to the first human voice;
the standard microphone and the Bluetooth headset are used for recording at the same time, signals recorded by the standard microphone are stored as standard voice signals, and signals recorded by the Bluetooth headset are stored as headset voice signals.
In one or more embodiments, preferably, the acquiring all the environment databases, extracting the front-stage noise and the back-stage noise, and fusing the front-stage noise and the back-stage noise to generate a standard noise signal and an earphone noise signal, specifically includes:
acquiring all environment databases, and randomly extracting a first noise voice and a second noise voice, wherein the first noise voice is a building noise time sequence, and the second noise voice is a traffic noise time sequence;
acquiring all environment databases, and randomly extracting third noise voices, wherein the third noise voices are life noise time sequences;
fusing the first noise voice and the second noise voice into front-section noise according to time;
taking the third noise voice as a back-end noise;
and forming a group of data by the front section noise and the rear section noise through a standard microphone and a Bluetooth headset, simultaneously recording, storing a signal recorded by the standard microphone as a standard noise signal, and storing a signal recorded by the Bluetooth headset as an earphone noise signal.
In one or more embodiments, preferably, the acquiring the noise signal of the headset, and extracting the first pink noise fluctuation, the second pink noise fluctuation, and the environment replacement time according to a time sequence of noise specifically includes:
setting a preset building sampling coefficient and a preset traffic sampling coefficient;
extracting the earphone noise signals as noise time sequence data, and extracting the traffic noise time sequence, the building noise time sequence and the life noise time sequence corresponding to each time point from the noise time sequence data;
calculating the first pink noise fluctuation by using a first calculation formula;
calculating the second pink noise fluctuation by using a second calculation formula;
calculating the environment replacing time by using a third calculation formula according to the noise time sequence data;
the first calculation formula is:
wherein, J i For the ith said building noise time sequence, P i For the ith said traffic noise time sequence, n 1 For presetting the number of samples of the preceding section, k 1 For presetting a building sampling coefficient, k 2 Presetting a traffic sampling coefficient, S f Is the first pink noise fluctuation;
the second calculation formula is:
wherein l j For the jth said noise timing sequence, n 2 For presetting the number of sampling of the later stage, S l Is the second pink noise fluctuation;
the third calculation formula is:
wherein, T i＝1 For the corresponding time scale, T, of the 1 st building noise time sequence i＝n1 Is n th 1 Corresponding time scale, T, of said building noise time sequence j＝1 For the corresponding time scale, T, of the building noise time sequence of item 1 j＝n2 Is the n-th 2 Corresponding time scale, T, of said building noise time sequence P Time of day is replaced for the environment.
In one or more embodiments, preferably, the performing online recording scale adjustment and noise prediction at a future time according to the front-stage noise and the back-stage noise to generate a first recording scale and a recording difference at a next tenth time includes:
setting the first wave recording proportion and the second wave recording proportion;
calculating a wave recording difference value by using a fourth calculation formula according to the front section noise and the rear section noise;
extracting an optimal value of the first wave recording proportion by using a fifth calculation formula according to the wave recording difference value;
estimating the wave recording difference value at the next tenth moment by using a sixth calculation formula;
the fourth calculation formula is:
wherein CZ is the recording difference value, B 1 Is the first recording scale, B 2 Is the second recording scale, c k And m k The k th measured value and the recording value in the front section noise, c z And m z Sequentially obtaining a kth measured value and the recorded wave value in the back-stage noise;
the fifth calculation formula is:
B 1 ＝argmin[CZ(B 1 )]
wherein, B 1 Is the first recording ratio, argmin [ [ alpha ] ]]To take the minimum value corresponds to B 1 A function of the parameter;
the sixth calculation formula is:
wherein, CZ 10 For said wave recording difference at the next ten times, CZ P (B 1 ) For the previous p historical recording differences, x p Are predicted difference coefficients.
In one or more embodiments, preferably, the acquiring the single voice, the front-stage noise, and the rear-stage noise, and simultaneously playing and recording to form a standard value and an actual measurement value includes:
the front section noise and the rear section noise are played in sequence according to the environment replacement time to serve as first voice;
taking the single voice as a second voice;
the first voice and the second voice are played simultaneously and are recorded simultaneously through a standard microphone and a Bluetooth headset;
and in the recording process, the standard microphone forms the measured value, and the Bluetooth headset forms the standard value.
In one or more embodiments, preferably, the obtaining the standard artificial mouth data, the earphone artificial mouth data, the first pink noise fluctuation, and the second pink noise fluctuation, performing online evaluation, and determining whether to stop the current noise reduction adjustment task specifically includes:
acquiring the standard artificial mouth data and the earphone artificial mouth data, and taking an absolute value of a difference value between the standard artificial mouth data and the earphone artificial mouth data as a sound characteristic fluctuation parameter;
judging whether the wave recording difference value at the next tenth moment is greater than a preset value, if not, sending a noise reduction stopping adjustment command, if so, obtaining the first pink noise fluctuation and the second pink noise fluctuation, and calculating an environmental noise reduction evaluation parameter by using a seventh calculation formula;
acquiring the standard value and the measured value, and calculating a measured mixed difference value;
judging the size relationship between the actually measured mixed difference value and the environmental noise reduction evaluation parameter, and sending out the size relationship;
if the actually measured mixed difference value is lower than the environmental noise reduction evaluation parameter, sending a noise reduction stopping adjustment command;
if the actually measured mixed difference value is not lower than the environmental noise reduction evaluation parameter, continuously acquiring data from a voice database and an environmental database, and sending a continuous noise reduction adjustment command;
the seventh calculation formula is:
K＝BR-S l +S f
wherein K is the environmental noise reduction evaluation parameter, BR is the sound characteristic fluctuation parameter, S f For the first pink noise fluctuation, S l The second pink noise fluctuation is obtained.
According to a second aspect of embodiments of the present invention, there is provided a system for testing the noise reduction effect of an ENC by means of an electro-acoustic testing device.
In one or more embodiments, preferably, the system for testing ENC noise reduction effect by the electro-acoustic testing device comprises:
the first playing module is used for acquiring all voice databases, extracting single voice and playing the voice to generate standard artificial mouth data and earphone artificial mouth data;
the second playing module is used for acquiring all environment databases, extracting front-section noise and rear-section noise, and fusing the front-section noise and the rear-section noise to generate standard noise signals and earphone noise signals;
the noise monitoring module is used for acquiring the noise signal of the earphone and extracting a first noise fluctuation, a second noise fluctuation and an environment replacement moment according to the time sequence of noise;
the prediction evaluation module is used for carrying out online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference value at a next ten moment;
the third playing module is used for acquiring the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value;
and the environment evaluation module is used for acquiring the standard artificial mouth data, the earphone artificial mouth data, the first noise reduction fluctuation and the second noise reduction fluctuation, performing online evaluation, and judging whether to stop the current noise reduction adjustment task.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method according to any one of the first aspect of embodiments of the present invention.
According to a fourth aspect of embodiments of the present invention, there is provided an electronic device, comprising a memory and a processor, the memory being configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any one of the first aspect of embodiments of the present invention.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
in the scheme of the invention, automatic ENC learning under the condition of environment switching is carried out by extracting random noise on line, so that the environment adaptability of noise reduction is improved.
In the scheme of the invention, the command of stopping or continuing to reduce noise is sent out by carrying out online operation on the recording proportion and the recording difference value and combining the standard value and the mixed value.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method of testing the noise reduction effect of an ENC with an electro-acoustic testing apparatus in accordance with one embodiment of the present invention.
Fig. 2 is a flowchart of generating standard artificial mouth data and earphone artificial mouth data after extracting a single human voice and playing the extracted voice, in the method for testing ENC noise reduction effect by using electroacoustic testing equipment according to an embodiment of the present invention.
Fig. 3 is a flowchart of acquiring a total environment database, extracting front-stage noise and back-stage noise, and merging them to generate a standard noise signal and an earphone noise signal in a method for testing ENC noise reduction effect by an electroacoustic testing device according to an embodiment of the present invention.
Fig. 4 is a flowchart of acquiring the noise signal of the earphone and extracting the first pink noise fluctuation, the second pink noise fluctuation and the environment replacement time according to the time sequence of the noise in the method for testing the ENC noise reduction effect by the electroacoustic testing equipment according to the embodiment of the present invention.
Fig. 5 is a flowchart of the method for testing the ENC noise reduction effect through the electroacoustic testing equipment, according to an embodiment of the present invention, performing online recording scale adjustment and noise prediction at a future time according to the front-stage noise and the rear-stage noise to generate a first recording scale and a recording difference at a next ten time.
Fig. 6 is a flowchart of acquiring the single human voice, the front-stage noise and the rear-stage noise, and simultaneously playing and recording the standard value and the measured value in the method for testing the noise reduction effect of the ENC through the electroacoustic testing equipment according to an embodiment of the present invention.
Fig. 7 is a flowchart of acquiring the standard artificial mouth data, the headphone artificial mouth data, the first pink noise fluctuation, and the second pink noise fluctuation, performing online evaluation, and determining whether to stop the current noise reduction adjustment task in the method for testing the ENC noise reduction effect by the electroacoustic testing device according to an embodiment of the present invention.
FIG. 8 is a block diagram of a system for testing the noise reduction effect of an ENC with an electro-acoustic testing device in accordance with one embodiment of the present invention.
Fig. 9 is a block diagram of an electronic device in one embodiment of the invention.
Detailed Description
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit the types of "first" and "second".
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 of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The electroacoustic test equipment is used for testing environmental noise and reducing noise, and plays an important role in improving the performance of the earphone. In the ENC process, a dual microphone array is used to filter and select specific ambient noise, where one microphone is responsible for receiving speech and the other microphone is responsible for capturing external ambient sound.
Before the technology of the invention, the conventional test ENC adopted by the prior art needs the simulation mouth 1 to play a human voice signal, the simulation mouth 2 to play noise, the ENC noise reduction switch of the Bluetooth headset is turned on through an SPP instruction, two sound sources are played simultaneously, and the sound is recorded once; closing an ENC noise reduction switch of the Bluetooth headset, simultaneously playing two sound sources, recording sound once again, and simultaneously putting the two recording files into Adobe audio for processing and analysis to obtain a difference value of two curves, namely an ENC noise reduction effect; in addition, when the ENC noise reduction switch of the bluetooth headset cannot be turned on and off, the ENC noise reduction effect cannot be tested, and another method needs to be found to test the ENC noise reduction effect.
The embodiment of the invention provides a method and a system for testing an ENC noise reduction effect through electroacoustic testing equipment. According to the scheme, the learning and comparison of the corresponding earphone and microphone ambient sound and human voice are carried out, so that the on-line intelligent ENC test and noise reduction are realized.
According to a first aspect of embodiments of the present invention, there is provided a method of testing the noise reduction effect of an ENC by an electro-acoustic testing device.
FIG. 1 is a flow chart of a method of testing the noise reduction effect of an ENC with an electro-acoustic testing apparatus in accordance with one embodiment of the present invention.
In one or more embodiments, preferably, the method for testing the ENC noise reduction effect through the electro-acoustic testing device comprises:
s101, acquiring all voice databases, extracting single voice, and generating standard artificial mouth data and earphone artificial mouth data;
s102, acquiring all environment databases, extracting front-stage noise and rear-stage noise, and fusing to generate standard noise signals and earphone noise signals;
s103, acquiring the noise signal of the earphone, and extracting a first pink noise fluctuation, a second pink noise fluctuation and an environment replacement moment according to the time sequence of noise;
s104, performing online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference at a next ten moments;
s105, obtaining the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value;
s106, acquiring the standard artificial mouth data, the earphone artificial mouth data, the first noise reduction fluctuation and the second noise reduction fluctuation, performing online evaluation, and judging whether to stop the current noise reduction adjustment task.
In the embodiment of the present invention, to implement the above-mentioned process, a part of devices needs to be prepared for testing, and the configuration of the early-stage arrangement includes: the device comprises a first simulation mouth, a test analysis system, a B & K4191 free field microphone, a second simulation mouth, a power amplifier, a sound card, a Bluetooth port and a tested Bluetooth earphone with an ENC noise reduction function; the specific connection relationship is as follows: the first simulation mouth and the second simulation mouth are connected to a sound card through a power amplifier, in addition, a B & K4191 free field microphone is electrically connected with the sound card, the sound card is electrically connected with a computer, a test analysis system controls the first simulation mouth, the second simulation mouth and a Bluetooth headset and obtains a recording file, and the tested Bluetooth headset with the ENC noise reduction function is connected to the computer through a Bluetooth port.
Fig. 2 is a flowchart of generating standard artificial mouth data and earphone artificial mouth data after acquiring a database of all human voices and extracting a single human voice for playing in the method for testing the ENC noise reduction effect by the electroacoustic testing device according to the embodiment of the present invention.
As shown in fig. 2, in one or more embodiments, preferably, the acquiring all the voice databases, extracting a single voice, and playing, generating standard artificial mouth data and earphone artificial mouth data specifically includes:
s201, acquiring all voice databases, and randomly extracting first voice in the voice databases;
s202, playing through a manual mouth according to the first human voice;
s203, simultaneously recording through a standard microphone and a Bluetooth headset, storing a signal recorded by the standard microphone as a standard voice signal, and storing a signal recorded by the Bluetooth headset as an earphone voice signal;
in the embodiment of the invention, in order to effectively process noise information and acquire information in online earphone acquisition, before testing, simultaneous recording of a standard microphone and a human voice signal is firstly carried out, data of the standard microphone is used as accurate data to be implemented, and the human voice signal of the earphone is used as data to be evaluated to be input.
Fig. 3 is a flowchart of acquiring a total environment database, extracting front-stage noise and rear-stage noise, and fusing the front-stage noise and the rear-stage noise to generate a standard noise signal and an earphone noise signal in a method for testing ENC noise reduction effect by an electroacoustic testing device according to an embodiment of the present invention.
As shown in fig. 3, in one or more embodiments, preferably, the acquiring the total environment database, extracting the front-stage noise and the back-stage noise, and fusing them to generate a standard noise signal and an earphone noise signal, specifically includes:
s301, acquiring all environment databases, and randomly extracting a first noise voice and a second noise voice, wherein the first noise voice is a building noise time sequence, and the second noise voice is a traffic noise time sequence;
s302, acquiring all environment databases, and randomly extracting a third noise voice, wherein the third noise voice is a life noise time sequence;
s303, fusing the first noise voice and the second noise voice into front-section noise according to time;
s304, taking the third noise voice as a rear-stage noise;
s305, a group of data consisting of the front section noise and the rear section noise is recorded simultaneously through a standard microphone and a Bluetooth headset, a signal recorded by the standard microphone is stored as a standard noise signal, and a signal recorded by the Bluetooth headset is stored as the headset noise signal.
In the embodiment of the invention, in order to process the noise signal of the standard and rapid earphone, after the random noise signal is generated, the rapid test of the real Bluetooth earphone information is realized through one-time noise fusion.
Fig. 4 is a flowchart of acquiring the noise signal of the earphone and extracting the first pink noise fluctuation, the second pink noise fluctuation and the environment replacement time according to the time sequence of the noise in the method for testing the ENC noise reduction effect by the electroacoustic testing equipment according to the embodiment of the present invention.
As shown in fig. 4, in one or more embodiments, preferably, the acquiring the noise signal of the earphone, and extracting the first pink noise fluctuation, the second pink noise fluctuation, and the environment replacement time according to a time sequence of noise specifically includes:
s401, setting a preset building sampling coefficient and a preset traffic sampling coefficient;
s402, extracting the earphone noise signals as noise time sequence data, and extracting the traffic noise time sequence, the building noise time sequence and the life noise time sequence corresponding to each time point from the noise time sequence data;
s403, calculating the first pink noise fluctuation by using a first calculation formula;
s404, calculating the second pink noise fluctuation by using a second calculation formula;
s405, calculating the environment replacing time by using a third calculation formula according to the noise time sequence data;
the first calculation formula is:
wherein, J i For the ith said building noise time sequence, P i For the ith said traffic noise time sequence, n 1 For presetting the number of samples of the preceding section, k 1 For presetting a building sampling coefficient, k 2 Presetting a traffic sampling coefficient, S f Is the first pink noise fluctuation;
the second calculation formula is:
wherein l j For the jth of said life noise time series, n 2 For presetting the number of sampling of the later stage, S l Is the second pink noise fluctuation;
the third calculation formula is:
wherein, T i＝1 Corresponding time scale, T, for the 1 st building noise time sequence i＝n1 Is the n-th 1 Corresponding time scale, T, of said building noise time sequence j＝1 For the corresponding time scale, T, of the building noise time sequence of item 1 j＝n2 Is n th 2 Corresponding time scale, T, of said building noise time sequence P Time of day is replaced for the environment.
In the embodiment of the invention, before noise evaluation, initially obtained noise original information is firstly obtained, online evaluation is carried out, the evaluation comprises two aspects, namely fluctuation of front-stage noise on the one hand, and fluctuation of rear-stage noise on the other hand, specific environmental switching time points are combined through the fluctuation of the two aspects, the time points serve as a set index for subsequently carrying out environmental noise filtering capacity, the learning result under which environmental noise switching condition is confirmed to be more accurate, and then the proportion is adopted for carrying out noise reduction analysis.
Fig. 5 is a flowchart of the method for testing ENC noise reduction effect by the electroacoustic testing equipment, according to an embodiment of the present invention, performing online recording scale adjustment and noise prediction at a future time according to the front-stage noise and the rear-stage noise, and generating a first recording scale and a recording difference at a next ten time.
As shown in fig. 5, in one or more embodiments, preferably, the performing online recording scale adjustment and noise prediction at a future time according to the front-stage noise and the back-stage noise to generate a first recording scale and a recording difference at a next tenth time includes:
s501, setting the first wave recording proportion and the second wave recording proportion;
s502, calculating a wave recording difference value by using a fourth calculation formula according to the front section noise and the rear section noise;
s503, extracting an optimal value of the first wave recording proportion by using a fifth calculation formula according to the wave recording difference value;
s504, estimating the wave recording difference value at the next tenth moment by using a sixth calculation formula;
the fourth calculation formula is:
wherein CZ is the recording difference value, B 1 Is the first recording scale, B 2 Is the second recording scale, c k And m k The k th measured value and the recording value in the front section noise, c z And m z In turn is a rear sectionThe k-th measured value and the recorded wave value in the noise;
the fifth calculation formula is:
B 1 ＝argmin[CZ(B 1 )]
wherein, B 1 Is the first recording ratio, argmin [ [ alpha ] ]]To take the minimum value corresponds to B 1 A function of the parameter;
the sixth calculation formula is:
wherein, CZ 10 For said wave recording difference at the next ten times, CZ P (B 1 ) For the first p historical recording differences, x p Are predicted difference coefficients.
In the embodiment of the invention, in order to adjust the proportion of the front-stage noise and the rear-stage noise on line and provide the recording difference value in a future period of time, the noise filtering capability in the current period of time and the noise filtering capability in the future period of time are ensured to a certain extent by prediction, and the recording capability of the earphone is effectively limited.
Fig. 6 is a flowchart of acquiring the single human voice, the front-stage noise and the rear-stage noise, and simultaneously playing and recording the standard value and the measured value in the method for testing the noise reduction effect of the ENC through the electroacoustic testing equipment according to an embodiment of the present invention.
As shown in fig. 6, in one or more embodiments, preferably, the acquiring the single human voice, the front-stage noise, and the rear-stage noise, and simultaneously playing and recording to form a standard value and an actual measurement value specifically includes:
s601, playing the front section noise and the rear section noise in sequence according to the environment replacement time as a first voice;
s602, taking the single voice as a second voice;
s603, simultaneously playing the first voice and the second voice, and simultaneously recording through a standard microphone and a Bluetooth headset;
s604, in the recording process, the standard microphone forms the measured value, and the Bluetooth headset forms the standard value.
Because the standard value and the measured value obtained by direct recording contain noise and human voice, the method can effectively evaluate the actual recording effect generated under the condition of mixed environmental noise. .
Fig. 7 is a flowchart of acquiring the standard artificial mouth data, the headphone artificial mouth data, the first pink noise fluctuation, and the second pink noise fluctuation, performing online evaluation, and determining whether to stop the current noise reduction adjustment task in the method for testing the ENC noise reduction effect by the electroacoustic testing device according to an embodiment of the present invention.
As shown in fig. 7, in one or more embodiments, preferably, the acquiring the standard artificial mouth data, the headphone artificial mouth data, the first pink noise fluctuation, and the second pink noise fluctuation, performing online evaluation, and determining whether to stop the current noise reduction adjustment task specifically includes:
s701, acquiring the standard artificial mouth data and the earphone artificial mouth data, and taking an absolute value of a difference value between the standard artificial mouth data and the earphone artificial mouth data as a sound characteristic fluctuation parameter;
s702, judging whether the wave recording difference value at the next tenth moment is larger than a preset value or not, if not, sending a noise reduction stopping adjustment command, if so, obtaining the first pink noise fluctuation and the second pink noise fluctuation, and calculating an environmental noise reduction evaluation parameter by using a seventh calculation formula;
s703, acquiring the standard value and the measured value, and calculating a measured mixed difference value;
s704, judging the size relationship between the actually measured mixed difference value and the environmental noise reduction evaluation parameter, and sending out the size relationship;
s705, if the actually measured mixed difference value is lower than the environmental noise reduction evaluation parameter, sending a noise reduction stopping adjustment command;
s706, if the actually measured mixed difference value is not lower than the environmental noise reduction evaluation parameter, continuously acquiring data from a voice database and an environmental database, and sending a continuous noise reduction adjustment command;
the seventh calculation formula is:
K＝BR-S l +S f
wherein K is the environmental noise reduction evaluation parameter, BR is the sound characteristic fluctuation parameter, S f For the first pink noise fluctuation, S l The second pink noise fluctuation is obtained.
In the embodiment of the invention, after the final test is finished, the current noise reduction level is automatically evaluated according to the environment noise reduction level and the sound characteristic fluctuation condition, and when the noise reduction energy is insufficient, the environment noise reduction evaluation parameter combining the combination of the human voice data and the environment noise reduction data is utilized to carry out online control to determine whether to continue the noise adjustment task.
According to a second aspect of embodiments of the present invention, there is provided a system for testing the noise reduction effect of an ENC by means of an electro-acoustic testing device.
FIG. 8 is a block diagram of a system for testing the noise reduction effect of an ENC with an electro-acoustic testing device in accordance with one embodiment of the present invention.
In one or more embodiments, preferably, the system for testing ENC noise reduction effect by the electro-acoustic testing device comprises:
the first playing module 801 is configured to acquire all voice databases, extract a single voice, and generate standard artificial mouth data and earphone artificial mouth data;
the second playing module 802 is configured to obtain all environment databases, extract front-stage noise and back-stage noise, and merge the front-stage noise and the back-stage noise to generate a standard noise signal and an earphone noise signal;
the noise monitoring module 803 is configured to acquire the noise signal of the earphone, and extract a first noise fluctuation, a second noise fluctuation, and an environment replacement time according to a time sequence of noise;
the prediction evaluation module 804 is used for performing online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference value at a next ten moment;
a third playing module 805, configured to obtain the single voice, the front-segment noise, and the rear-segment noise, and simultaneously play and record to form a standard value and an actual measurement value;
and the environment evaluation module 806 is configured to obtain the standard artificial mouth data, the earphone artificial mouth data, the first noise reduction fluctuation, and the second noise reduction fluctuation, perform online evaluation, and determine whether to stop the current noise reduction adjustment task.
In the embodiment of the invention, the design of the comprehensive method is carried out in a modularized way, and an automatic setting way is adopted for all modules in the design, so that the difference caused by the difference of configuration environments every time can be avoided, and therefore, the environment noise reduction can be effectively carried out under different scenes, and the noise reduction test under the environment that the Bluetooth cannot be connected can also be carried out.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method according to any one of the first aspect of embodiments of the present invention.
According to a fourth aspect of the embodiments of the present invention, there is provided an electronic apparatus. Fig. 9 is a block diagram of an electronic device in one embodiment of the invention. The electronic device shown in fig. 9 is a general device for testing ENC noise reduction effect through an electroacoustic testing device. The electronic device can be a smart phone, a tablet computer and the like. As shown, the electronic device 900 includes a processor 901 and memory 902. The processor 901 is electrically connected to the memory 902. The processor 901 is a control center of the terminal 900, connects various parts of the entire terminal using various interfaces and lines, and performs various functions of the terminal and processes data by running or calling a computer program stored in the memory 902 and calling data stored in the memory 902, thereby performing overall monitoring of the terminal.
In this embodiment, the processor 901 in the electronic device 900 loads instructions corresponding to one or more processes of the computer program into the memory 902 according to the following steps, and the processor 901 runs the computer program stored in the memory 902, so as to implement various functions: acquiring all voice databases, extracting single voice, and generating standard artificial mouth data and earphone artificial mouth data; acquiring a whole environment database, extracting front-stage noise and rear-stage noise, and fusing the front-stage noise and the rear-stage noise to generate a standard noise signal and an earphone noise signal; acquiring the noise signal of the earphone, and extracting a first noise fluctuation, a second noise fluctuation and an environment replacement moment according to the time sequence of noise; carrying out online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference at a next ten moment; acquiring the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value; and acquiring the standard artificial mouth data, the earphone artificial mouth data, the first noise fluctuation and the second noise fluctuation, performing online evaluation, and judging whether to stop the current noise reduction adjustment task.
Memory 902 may be used to store computer programs and data. Memory 902 stores computer programs comprising instructions executable in the processor. The computer program may constitute various functional modules. The processor 901 executes various functional applications and data processing by calling a computer program stored in the memory 902.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
in the scheme of the invention, automatic ENC learning is carried out under the condition of environment switching by extracting random noise on line, and the environment adaptability of noise reduction is improved.
In the scheme of the invention, the command of stopping or continuing to reduce noise is sent out by carrying out online operation on the recording proportion and the recording difference value and combining the standard value and the mixed value.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of testing the noise reduction effect of ENC by an electro-acoustic testing device, the method comprising:
acquiring all voice databases, extracting single voice, and generating standard artificial mouth data and earphone artificial mouth data;
acquiring all environment databases, extracting front-stage noise and rear-stage noise, and fusing the front-stage noise and the rear-stage noise to generate a standard noise signal and an earphone noise signal;
acquiring the noise signal of the earphone, and extracting a first noise fluctuation, a second noise fluctuation and an environment replacement moment according to the time sequence of noise;
carrying out online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference at a next ten moment;
acquiring the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value;
and acquiring the standard artificial mouth data, the earphone artificial mouth data, the first noise fluctuation and the second noise fluctuation, performing online evaluation, and judging whether to stop the current noise reduction adjustment task.
2. The method for testing the noise reduction effect of the ENC through the electroacoustic testing equipment as claimed in claim 1, wherein the obtaining of the entire human voice database, the extracting of the single human voice playing, the generating of the standard artificial mouth data and the earphone artificial mouth data specifically comprises:
acquiring all voice databases, and randomly extracting first voice in the voice databases;
playing through a manual mouth according to the first human voice;
the signals recorded by the standard microphone are stored as standard voice signals, and the signals recorded by the Bluetooth headset are stored as headset voice signals.
3. The method for testing the noise reduction effect of the ENC through the electroacoustic testing device as claimed in claim 2, wherein the obtaining of the entire environmental database, the extracting of the front stage noise and the back stage noise, and the merging thereof into the standard noise signal and the earphone noise signal comprises:
acquiring all environment databases, and randomly extracting a first noise voice and a second noise voice, wherein the first noise voice is a building noise time sequence, and the second noise voice is a traffic noise time sequence;
acquiring all environment databases, and randomly extracting third noise voices, wherein the third noise voices are life noise time sequences;
fusing the first noise voice and the second noise voice into front-section noise according to time;
taking the third noise voice as a back-end noise;
and forming a group of data by the front section noise and the rear section noise through a standard microphone and a Bluetooth headset, simultaneously recording, storing a signal recorded by the standard microphone as a standard noise signal, and storing a signal recorded by the Bluetooth headset as the headset noise signal.
4. The method for testing the noise reduction effect of the ENC through the electro-acoustic testing equipment as claimed in claim 3, wherein the obtaining the noise signal of the earphone and extracting the first pink noise fluctuation, the second pink noise fluctuation and the environment replacement time according to the time sequence of the noise specifically comprises:
setting a preset building sampling coefficient and a preset traffic sampling coefficient;
extracting the earphone noise signals as noise time sequence data, and extracting the traffic noise time sequence, the building noise time sequence and the life noise time sequence corresponding to each time point from the noise time sequence data;
calculating the first pink noise fluctuation by using a first calculation formula;
calculating the second pink noise fluctuation by using a second calculation formula;
calculating the environment replacing time by using a third calculation formula according to the noise time sequence data;
the first calculation formula is:
wherein, J i For the ith said building noise time sequence, P i For the ith said traffic noise time sequence, n 1 To preset the number of front section samples, k 1 For presetting a building sampling coefficient, k 2 Presetting a traffic sampling coefficient, S f Is the first pink noise fluctuation;
the second calculation formula is:
wherein l j For the jth said noise timing sequence, n 2 For presetting the number of sampling of the later stage, S l Is the second pink noise fluctuation;
the third calculation formula is:
wherein, T i＝1 Corresponding time scale, T, for the 1 st building noise time sequence i＝n1 Is the n-th 1 Corresponding time scale, T, of said building noise time sequence j＝1 For the corresponding time scale, T, of the 1 st said building noise time sequence j＝n2 Is n th 2 Corresponding time scale, T, of said building noise time sequence P The time of day is replaced for the environment.
5. The method for testing the noise reduction effect of the ENC through the electro-acoustic testing equipment, as set forth in claim 1, wherein the step of performing on-line recording scale adjustment and noise prediction at a future time according to the front section noise and the rear section noise to generate a first recording scale and a recording difference at a next ten time comprises:
setting the first wave recording proportion and the second wave recording proportion;
calculating a wave recording difference value by using a fourth calculation formula according to the front section noise and the rear section noise;
extracting an optimal value of the first wave recording proportion by using a fifth calculation formula according to the wave recording difference value;
estimating the wave recording difference value at the next tenth moment by using a sixth calculation formula;
the fourth calculation formula is:
wherein CZ is the recording difference value, B 1 Is the first recording scale, B 2 Is the second recording scale, c k And m k The k th measured value and the wave recording value in the front section noise are sequentially z And m z Sequentially obtaining a kth measured value and the recorded wave value in the back-stage noise;
the fifth calculation formula is:
B 1 ＝argmin[CZ(B 1 )]
wherein, B 1 Is the first recording ratio, argmin [ [ alpha ] ]]To take the minimum value corresponds to B 1 A function of the parameter;
the sixth calculation formula is:
wherein, CZ 10 For said wave recording difference at the next ten times, CZ P (B 1 ) For the previous p historical recording differences, x p Are predicted difference coefficients.
6. The method as claimed in claim 1, wherein the step of obtaining the single human voice, the front stage noise and the rear stage noise, and simultaneously playing and recording the obtained standard value and measured value comprises:
the front section noise and the rear section noise are played in sequence according to the environment replacement time to serve as first voice;
taking the single human voice as a second voice;
the first voice and the second voice are played simultaneously and are recorded simultaneously through a standard microphone and a Bluetooth headset;
and in the recording process, the standard microphone forms the measured value, and the Bluetooth headset forms the standard value.
7. The method for testing the ENC noise reduction effect through the electro-acoustic testing equipment according to claim 1, wherein the obtaining the standard artificial mouth data, the earphone artificial mouth data, the first pink noise fluctuation and the second pink noise fluctuation, performing online evaluation, and determining whether to stop the current noise reduction adjustment task specifically comprises:
acquiring the standard artificial mouth data and the earphone artificial mouth data, and taking an absolute value of a difference value between the standard artificial mouth data and the earphone artificial mouth data as a sound characteristic fluctuation parameter;
the environment evaluation module judges whether the wave recording difference value at the next tenth moment is greater than a preset value or not, if not, a noise reduction stopping adjustment command is sent out, if so, the first pink noise fluctuation and the second pink noise fluctuation are obtained, and an environment noise reduction evaluation parameter is calculated by using a seventh calculation formula;
the environment evaluation module acquires the standard value and the measured value and calculates a measured mixed difference value;
the environment evaluation module judges the size relationship between the actually measured mixed difference value and the environment noise reduction evaluation parameter and sends out the size relationship;
judging that the actually measured mixed difference value is lower than the environment noise reduction evaluation parameter, and then the environment evaluation module sends a noise reduction stopping adjustment command;
if the actually measured mixed difference value is not lower than the environmental noise reduction evaluation parameter, the environmental evaluation module continues to acquire data from a voice database and an environmental database and sends a continuous noise reduction adjustment command;
the seventh calculation formula is:
K＝BR-S l +S f
wherein K is the environmental noise reduction evaluation parameter, BR is the sound characteristic fluctuation parameter, S f For the first pink noise fluctuation, S l The second pink noise fluctuation is obtained.
8. A system for testing the noise reduction effects of ENC with an electro-acoustic testing device, the system comprising:
the first playing module is used for acquiring all voice databases, extracting single voice and playing the voice to generate standard artificial mouth data and earphone artificial mouth data;
the second playing module is used for acquiring all the environment databases, extracting front-stage noise and rear-stage noise, and fusing the front-stage noise and the rear-stage noise to generate a standard noise signal and an earphone noise signal;
the noise monitoring module is used for acquiring the earphone noise signal and extracting a first noise fluctuation, a second noise fluctuation and an environment replacing moment according to the time sequence of noise;
the prediction evaluation module is used for carrying out online recording proportion adjustment and noise prediction at a future moment according to the front-stage noise and the rear-stage noise to generate a first recording proportion and a recording difference value at a next ten moment;
the third playing module is used for acquiring the single voice, the front section noise and the rear section noise, and simultaneously playing and recording to form a standard value and an actual measurement value;
and the environment evaluation module is used for acquiring the standard artificial mouth data, the earphone artificial mouth data, the first noise reduction fluctuation and the second noise reduction fluctuation, performing online evaluation, and judging whether to stop the current noise reduction adjustment task.
9. A computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-7.
10. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program noise reduction test instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any one of claims 1-7.
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