A method, system and storage medium for evaluating call noise reduction headphones

Abstract

The present invention discloses a call noise reduction headset evaluation method, system and storage medium, the method comprising: an evaluation server plays a first voice file and a second voice file to N noise reduction headsets installed in a soundproof box at the same time, and then controls a mobile terminal connected to the call noise reduction headset to record the call uplink voice to obtain N third voice files; obtains a noise spectrum curve according to the second voice file, and obtains a call spectrum curve according to the third voice file; subtracts the call spectrum curve from the noise spectrum curve to obtain a call noise reduction curve; scores the call noise reduction curve to obtain a call noise reduction score. Through the embodiments of the present invention, call noise reduction evaluation is performed on multiple evaluated headsets at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headsets are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given to facilitate consumers to choose headphones.

Description

Technical Field

[0001] The present invention relates to the field of earphone testing technology, and in particular to a method, system and storage medium for evaluating call noise reduction earphones. Background Art

[0002] With the continuous development of technology, after TWS headphones gradually solved the basic problem of stable connection, call noise reduction function has also become an important demand. Call noise reduction not only solves the problem of high-quality calls in complex environments, but also serves as the basis for voice interaction. There are many TWS headphones on the market. When consumers choose TWS headphones, they hope that third-party evaluation agencies can evaluate TWS headphones and evaluate the performance of various TWS headphones (such as the call noise reduction performance of TWS headphones) to facilitate consumers to choose suitable TWS headphones. At present, the evaluation of TWS headphones is generally to use evaluation tools or manual methods to evaluate the TWS headphones that need to be evaluated one by one, and then give an evaluation report to the TWS headphones based on the evaluation results one by one.

[0003] The current TWS earphone evaluation has low evaluation efficiency. TWS earphones are evaluated one by one. Since the usage scenario changes with each test, the evaluation results are not accurate enough. There is no comprehensive evaluation of the evaluation results, which makes it difficult for ordinary consumers to quickly and reasonably choose TWS earphones based on the evaluation results. Summary of the invention

[0004] The main purpose of the present invention is to provide a call noise reduction headset evaluation method, system and storage medium, aiming to solve the problems in the prior art of TWS headset evaluation such as low evaluation efficiency, inaccurate evaluation results, and inability of ordinary consumers to quickly and reasonably select TWS headsets based on the evaluation results.

[0005] To achieve the above object, the present invention provides a method for evaluating call noise reduction headphones, the method comprising:

[0006] Install call noise reduction headphones in N (N is greater than or equal to 1) soundproof boxes, wherein a first voice playback device and / or a second voice playback device and / or a first mobile device and / or a second mobile device and / or an artificial head are installed in the soundproof box; the soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process; the first voice playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device, and the second voice playback device is installed on the second mobile device or the inner wall of the soundproof box; the call noise reduction headphones are installed on the ear of the artificial head;

[0007] Setting usage scenarios of the call noise reduction headset and assigning a first weight value to each usage scenario;

[0008] Setting an evaluation method, and assigning a second weight value to each of the evaluation methods; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation;

[0009] When the evaluation method is a noise elimination evaluation, the evaluation server plays the first voice file to N (N is greater than or equal to 1) first voice playback devices through the first broadcast system according to the usage scenario; the evaluation server plays the second voice file to N (N is greater than or equal to 1) second voice playback devices through the second broadcast system according to the usage scenario;

[0010] The evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files;

[0011] Acquire a noise spectrum curve according to the second voice file, and acquire a call spectrum curve according to the third voice file; and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve;

[0012] Scoring the call noise reduction curves corresponding to N (N is greater than or equal to 1) call noise reduction headphones to obtain N (N is greater than or equal to 1) call noise reduction scores;

[0013] When the evaluation method is an objective evaluation of human voice, a MOS test is performed on the N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset by a MOS test device to obtain N (N is greater than or equal to 1) MOS scores;

[0014] When the evaluation method is a subjective evaluation of human voice, N (N is greater than or equal to 1) of the call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores;

[0015] The evaluation server calculates a comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or the MOS scores and / or the manual subjective scores and the first weight value and / or the second weight value corresponding to each score.

[0016] Optionally, the N first voice playback devices are voice playback devices of the same model; the N second voice playback devices are voice playback devices of the same model; and the N mobile terminals are mobile terminals of the same model.

[0017] Optionally, the method further comprises the following steps:

[0018] The N mobile terminals and the evaluation server perform time synchronization.

[0019] Optionally, the method further comprises the following steps:

[0020] In each of the usage scenarios, the call noise reduction headset is evaluated once in the evaluation method of noise cancellation evaluation; and / or,

[0021] In each of the usage scenarios, the call noise reduction headset is evaluated once in the evaluation method of subjective human voice evaluation; and / or,

[0022] The call noise reduction headset is evaluated once in each usage scenario using the objective evaluation of human voice as the evaluation method.

[0023] Optionally, the evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files, including the following steps:

[0024] Obtain the start time and end time of playing the first voice file;

[0025] The recording of the uplink voice of the call of the mobile terminal is started at the starting time, and the recording of the uplink voice of the call of the mobile terminal is stopped at the ending time.

[0026] Optionally, when the evaluation method is a subjective evaluation of human voice, N (N is greater than or equal to 1) call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores, including the following steps:

[0027] The tester wears the call noise reduction headset to perform a call test in each of the usage scenarios, and then the tester scores the call noise reduction of each of the call noise reduction headsets to obtain a first manual subjective score; and / or,

[0028] publishing the first voice file, the second voice file, and the third voice file corresponding to each of the call noise reduction headphones to an Internet platform, allowing Internet users to evaluate the first voice file, the second voice file, and the third voice file and score the call noise reduction, and averaging the call noise reduction scores to obtain a second manual subjective score; and / or,

[0029] An average of the first artificial subjective score and the second artificial subjective score is obtained to obtain a third artificial subjective score.

[0030] Optionally, the method further comprises the following steps:

[0031] The evaluation server controls the first moving device to drive the artificial head to move or remain stationary at a first preset speed according to the usage scenario; and / or

[0032] The evaluation server controls the second mobile device to drive the second voice playback device to move or remain stationary at a second preset speed according to the usage scenario.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also proposes a call noise reduction headset evaluation system, comprising: an evaluation server, a soundproof box, a first voice playback device, a second voice playback device, a call noise reduction headset, a mobile terminal, a first mobile device, a second mobile device, an artificial head, and a MOS testing device;

[0034] The first voice playing device and / or the second voice playing device and / or the first moving device and / or the second moving device and / or the artificial head are installed in the soundproof box;

[0035] The soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process;

[0036] The first speech playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device;

[0037] The second voice playing device is installed on the second mobile device or the inner wall of the soundproof box;

[0038] The call noise reduction headset is installed on the ear of the artificial head;

[0039] The call noise reduction headset is connected to the mobile terminal, and the mobile terminal is used to establish a voice call and record the call uplink voice;

[0040] The MOS testing device is used to perform a MOS test on the call voice of the N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to obtain N (N is greater than or equal to 1) MOS scores;

[0041] The evaluation server is used to set the usage scenario of the call noise reduction headset and assign a first weight value to each usage scenario; it is also used to set the evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective human voice evaluation, and objective human voice evaluation;

[0042] The evaluation server is further configured to play the first voice file to N (N is greater than or equal to 1) first voice playing devices through the first broadcasting system according to the usage scenario when the evaluation method is noise elimination evaluation;

[0043] The evaluation server is further configured to play the second voice file to N (N is greater than or equal to 1) second voice playing devices through the second broadcasting system according to the usage scenario;

[0044] The evaluation server is further used to control N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtain N (N is greater than or equal to 1) third voice files;

[0045] The evaluation server is further configured to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve;

[0046] The evaluation server is further used to record N (N greater than or equal to 1) call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N (N greater than or equal to 1) call noise reduction headphones;

[0047] The evaluation server is further used to record N (N is greater than or equal to 1) MOS scores obtained by the MOS testing device for the MOS test of the call voice of the N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset when the evaluation method is objective human voice evaluation;

[0048] The evaluation server is further used to record N (N is greater than or equal to 1) manual subjective scores obtained by manually performing call noise reduction evaluation of N (N is greater than or equal to 1) call noise reduction headphones in the usage scenario when the evaluation method is subjective voice evaluation;

[0049] The evaluation server is also used to calculate the comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or the MOS scores and / or the manual subjective scores and the first weight value and / or the second weight value corresponding to each score.

[0050] Optionally, the evaluation server includes:

[0051] A parameter setting module, used to set the usage scenario of the call noise reduction headset and assign a first weight value to each usage scenario; and also used to set an evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation;

[0052] A voice playing module, used for playing a first voice file to N (N is greater than or equal to 1) first voice playing devices through a first broadcasting system according to the usage scenario when the evaluation method is a noise elimination evaluation, and playing a second voice file to N (N is greater than or equal to 1) second voice playing devices through a second broadcasting system according to the usage scenario;

[0053] A recording control module, used to control N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtain N (N is greater than or equal to 1) third voice files;

[0054] a curve generating module, configured to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve;

[0055] A noise reduction score recording module, used to record N (N greater than or equal to 1) call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N (N greater than or equal to 1) call noise reduction headphones;

[0056] A MOS score recording module, for recording N (N greater than or equal to 1) MOS scores obtained by a MOS test device for performing a call voice MOS test on the N (N greater than or equal to 1) mobile terminals connected to the call noise reduction headset when the evaluation method is an objective evaluation of human voice;

[0057] A manual scoring recording module, for recording N (N is greater than or equal to 1) manual subjective scores obtained by manual evaluation of call noise reduction of N (N is greater than or equal to 1) call noise reduction headphones in the usage scenario when the evaluation method is subjective evaluation of human voice;

[0058] A scoring calculation module is used to calculate the comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or the MOS scores and / or the manual subjective scores and the first weight value and / or the second weight value corresponding to each score.

[0059] In addition, to achieve the above-mentioned purpose, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the call noise reduction headset evaluation method as described above is implemented.

[0060] It can be seen from the above technical methods that this application has the following advantages:

[0061] Through the embodiment of the present invention, call noise reduction evaluation is performed on multiple evaluated headphones at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headphones are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given, which is convenient for consumers to choose headphones. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of a flow chart of the method for evaluating a call noise reduction headset provided by the present invention.

[0063] Figure 2 A flowchart of call recording provided by the present invention.

[0064] Figure 3 A schematic diagram of a process for manual evaluation of the noise-cancelling headset for calls provided by the present invention.

[0065] Figure 4 It is a structural schematic diagram of an embodiment of the call noise reduction headset evaluation system of the present invention.

[0066] Figure 5 This is one of the overall structural schematic diagrams of the sound insulation box embodiment provided by the present invention.

[0067] Figure 6 This is one of the internal structure schematic diagrams of the sound insulation box embodiment provided by the present invention.

[0068] Figure 7 This is a structural block diagram of a test bench embodiment provided by the present invention.

[0069] Figure 8 This is a structural block diagram of an embodiment of an artificial head provided by the present invention.

[0070] Fig. 9 This is a structural block diagram of an embodiment of the evaluation server provided by the present invention.

[0071] Fig.10 It is a structural diagram of the hardware operating environment involved in the embodiment of the present invention.

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0074] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0075] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0076] It should be understood that this application is applied to the call noise reduction headset evaluation system, please refer to Figure 4 , Figure 4 Schematic diagram of the structure of an embodiment of the call noise reduction headset evaluation system in the embodiment of the present application, as shown in FIG. Figure 4 As shown, Figure 4 The system includes: an evaluation server 100, a soundproof box 30, a first voice playback device 90, a second voice playback device 40, a call noise reduction headset 60, a mobile terminal 20, a first mobile device 70, a second mobile device 80, an artificial head 50, and a MOS test device 10;

[0077] The first voice playing device 90 and / or the second voice playing device 40 and / or the first moving device 70 and / or the second moving device 80 and / or the artificial head 50 are installed in the soundproof box 30; the soundproof box 30 is a semi-anechoic box, which is used to isolate the sound during the evaluation process;

[0078] The first speech playback device 90 is installed at the mouth of the artificial head 50 to simulate human speech; the artificial head 50 is installed on the first moving device 70;

[0079] The second voice playback device 40 is installed on the second mobile device 80 or the inner wall of the soundproof box 30;

[0080] The call noise reduction headset 60 is installed on the ear of the artificial head 50;

[0081] The call noise reduction headset 60 is connected to the mobile terminal 20, and the mobile terminal 20 is used to establish a voice call and record the call uplink voice;

[0082] The MOS test device 10 is used to perform a MOS test on the call voice of N (N is greater than or equal to 1) mobile terminals 20 connected to the call noise reduction headset 60, and obtain N (N is greater than or equal to 1) MOS scores;

[0083] The evaluation server 100 is used to set the usage scenario of the call noise reduction headset 60 and assign a first weight value to each usage scenario; it is also used to set the evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective human voice evaluation, and objective human voice evaluation;

[0084] The evaluation server 100 is further used to play the first voice file to N (N is greater than or equal to 1) first voice playback devices 90 through the first broadcasting system according to the usage scenario when the evaluation method is noise elimination evaluation;

[0085] The evaluation server 100 is further configured to play the second voice file to N (N is greater than or equal to 1) second voice playing devices 40 through the second broadcasting system according to the usage scenario;

[0086] The evaluation server 100 is further used to control N (N is greater than or equal to 1) mobile terminals 20 connected to the call noise reduction headset 60 to record the call uplink voice, and obtain N (N is greater than or equal to 1) third voice files;

[0087] The evaluation server 100 is further used to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and subtract the call spectrum curve from the noise spectrum curve to obtain a call noise reduction curve;

[0088] The evaluation server 100 is also used to record N (N greater than or equal to 1) call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N (N greater than or equal to 1) call noise reduction headphones 60;

[0089] The evaluation server 100 is also used to record N (N is greater than or equal to 1) MOS scores obtained by the MOS test device 10 for the MOS test of the call voice of N (N is greater than or equal to 1) mobile terminals 20 connected to the call noise reduction headset 60 when the evaluation method is objective evaluation of human voice;

[0090] The evaluation server 100 is also used to record N (N is greater than or equal to 1) manual subjective scores of N (N is greater than or equal to 1) call noise reduction headphones 60 obtained by manual call noise reduction evaluation in the use scenario when the evaluation method is subjective voice evaluation;

[0091] The evaluation server 100 is also used to calculate the comprehensive score of the call noise reduction headset 60 based on N (N is greater than or equal to 1) call noise reduction scores and / or MOS scores and / or manual subjective scores and the first weight value and / or second weight value corresponding to each score.

[0092] In one embodiment, Figure 1 As shown, the present invention provides a method for evaluating a call noise reduction headset, the method comprising:

[0093] Step S101, installing call noise reduction headphones in N (N is greater than or equal to 1) soundproof boxes, wherein a first voice playback device and / or a second voice playback device and / or a first mobile device and / or a second mobile device and / or an artificial head are installed in the soundproof box; the soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process; the first voice playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device, and the second voice playback device is installed on the second mobile device or the inner wall of the soundproof box; the call noise reduction headphones are installed on the ear of the artificial head.

[0094] See also Figure 4 , Figure 4 Schematic diagram of the structure of an embodiment of the call noise reduction headset evaluation system in the embodiment of the present application, as shown in FIG. Figure 4 As shown, Figure 4 The system includes: an evaluation server 100, a soundproof box 30, a first voice playback device 90, a second voice playback device 40, a call noise reduction headset 60, a mobile terminal 20, a first mobile device 70, a second mobile device 80, an artificial head 50, and a MOS test device 10;

[0095] like Figure 5 and Figure 6 As shown, the soundproof box 30 is a semi-anechoic chamber, including: a hard box bottom 31, a sound-absorbing side wall 33, a sound-absorbing top cover 34 and universal wheels 32. The hard box bottom 31 is a wooden sound-absorbing board; the sound-absorbing side wall 33 is composed of a wooden sound-absorbing board and a sound-absorbing wedge 36, and the sound-absorbing wedge 36 is installed on one side of the wooden sound-absorbing board; the sound-absorbing top cover 34 is composed of a wooden sound-absorbing board and a sound-absorbing wedge 36, and the sound-absorbing wedge 36 is installed on one side of the wooden sound-absorbing board. Universal wheels 32 are respectively installed on the four feet of the semi-anechoic box 30 to facilitate the movement of the semi-anechoic box. One side of the sound-absorbing top cover 34 is connected to one side of the sound-absorbing side wall 33 by a hinge to facilitate the opening of the sound-absorbing top cover 34.

[0096] like Figure 6As shown, the hard box bottom 31, the sound-absorbing side walls 33 and the sound-absorbing top cover 34 enclose an experimental space 35 for speech recognition testing. Two first moving devices 70 and a second moving device 80 that can move forward and backward, left and right, and up and down (i.e., linear motion of the X, Y, and Z coordinate axes) are installed in the experimental space 35. The first moving device 70 and the second moving device 80 are respectively composed of four linear stepping motors, namely, two linear motors 71 that move forward and backward, a linear motor 72 that moves left and right, and a linear motor 73 that moves up and down. The two linear motors 71 that move forward and backward are fixed on the hard box bottom 31, and then the two ends of the linear motor 72 that moves left and right are respectively fixed on the linear moving parts of the two linear motors 71, and the linear motor 72 is driven to move forward and backward by the linear motor 71 that moves forward and backward. The linear motor 73 that moves up and down is vertically installed on the moving part of the linear motor 72 that moves left and right, and the linear motor 73 is driven to move left and right by the linear motor 72 that moves left and right. The second voice player 40 (such as a multimedia speaker or loudspeaker) and the test bench 74 are mounted on the moving part of the linear motor 73 that moves up and down, and the second voice player 40 and the test bench 74 are driven to move up and down by the linear motor 73 that moves up and down. The second voice player 40 can also be mounted on the inner wall of the semi-muffler 30, such as mounting a second voice player 40 (such as a multimedia speaker or loudspeaker) at each of the four corners.

[0097] A camera can be installed in the semi-anechoic box 30 to shoot the evaluation process; the camera can have a pan-tilt function, and the evaluation process can be viewed by rotating the pan-tilt. The evaluation personnel can observe the evaluation process in real time on the display screen.

[0098] A socket 36 is installed on one side of the sound-absorbing side wall 33, and the socket 36 includes a power socket, a network socket, and an audio and video socket. The power socket is used to electrically connect the first voice playback device 90, the second voice playback device 40, the first mobile device 70, and the second mobile device 80 installed in the test space 35, and is powered by an external power supply (such as a mains); the network socket is used to provide a network connection, such as an IP network connection, a USB connection, a serial port connection, etc., to the first mobile device 70 and the second mobile device 80; the audio and video socket is used to provide an audio and video connection to the first voice playback device 90, the second voice playback device 40, and the camera.

[0099] like Figure 7 As shown, the test bench 74 includes a base 741 and a clamping fixture 742. The clamping fixture 742 is installed on the top of the base 741 for fixing the artificial head 50; the base 741 is installed on the moving part of the linear motor 73 of the first moving device 70 that moves up and down.

[0100] like Figure 8As shown, the first voice playback device 90 is an artificial mouth, which is installed at the mouth position 53 of the artificial head 50 to simulate human speech; the artificial head 50 is installed on the test bench 74 of the first mobile device 70; the call noise reduction headset 60 is installed at the ear position 52 of the artificial head 50; the call noise reduction headset 60 is a TWS headset, and can also be other headsets.

[0101] The call noise reduction headset 60 is connected to the mobile terminal 20, and the mobile terminal 20 is used to establish a voice call and record the call uplink voice.

[0102] The evaluation server 100 is connected to N (N is greater than or equal to 1) soundproof boxes 30. The specific number of soundproof boxes connected is determined according to the actual headphones to be evaluated. If 6 TWS headphones need to be evaluated, the evaluation server 100 is connected to 6 soundproof boxes 30, and a set of TWS headphones is installed in each soundproof box 30 for call noise reduction evaluation.

[0103] In order to ensure that the usage environment of each TWS headset tested is the same, each sound insulation box 30 is required to be the same model of sound insulation box to ensure the same sound insulation effect; each first voice playback device 90 is the same model of artificial mouth to ensure that the effect of the voice played by each artificial mouth is the same; each second voice playback device 40 is the same model of multimedia speaker or speaker; each mobile terminal 20 uses the same model of smartphone (such as Mate40 smartphone) to ensure that the usage scenarios of each TWS headset are consistent.

[0104] After the TWS headset to be evaluated is installed, when the evaluation server 100 starts the evaluation, it is necessary to perform NTP time synchronization on each mobile terminal 20 and the evaluation server 100 to ensure that the time of each mobile terminal 20 and the evaluation server 100 is consistent.

[0105] Step S102: setting usage scenarios of the call noise reduction headset and assigning a first weight value to each usage scenario.

[0106] Set the usage scenarios of the call noise cancelling headset and assign a weight value to each usage scenario. The weight can be set according to the number of times (or percentage) the usage scenario is used. The more the usage scenario is used, the higher the weight value. The usage scenarios of the call noise cancelling headset can be set according to the following scenarios, as shown in the following table:

[0107]

[0108]

[0109] This technical solution does not limit the usage scenarios of the call noise reduction headset and can be set according to actual usage.

[0110] Step S103, setting an evaluation method, and assigning a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation.

[0111] The evaluation methods for call noise-cancelling headphones include: noise cancellation evaluation, subjective voice evaluation, and objective voice evaluation. Under each evaluation method, a test needs to be conducted separately according to the usage scenario of the call noise-cancelling headphones.

[0112] The weight of the test results of each evaluation method in the final test results is different, so a weight value (such as weight ratio) needs to be assigned to each test method. The configured weight values ​​are shown in the following table:

[0113] Test method Weight ratio Spectral noise reduction evaluation 70％ Objective evaluation of human voice 20 Manual subjective evaluation 10％

[0114] This technical solution does not limit the weight of the evaluation method, and the corresponding weight value can be set according to actual usage.

[0115] Step S104, when the evaluation method is noise elimination evaluation, the evaluation server plays the first voice file to N (N is greater than or equal to 1) first voice playback devices through the first broadcast system according to the usage scenario; the evaluation server plays the second voice file to N (N is greater than or equal to 1) second voice playback devices through the second broadcast system according to the usage scenario.

[0116] The evaluation server 100 will play the corresponding call voice file and environmental noise file according to the usage scenario of the user's TWS headset. Each usage scenario can correspond to one call voice file and environmental noise file, or multiple usage scenarios can correspond to one call voice file and environmental noise file. As shown in the following table:

[0117]

[0118]

[0119] The call voice file may be a real voice call recording or an audio generated by a frequency sweep signal; the environmental noise file may be environmental noise recorded in a real environment or white noise.

[0120] In order to simultaneously play the voice in the call voice file to the first voice playback devices 90 (such as artificial mouths) in multiple soundproof boxes 30, the evaluation server 100 needs to use a broadcasting system for voice broadcasting, so that multiple artificial mouths connected to the broadcasting system can play the voice at the same time; in order to simultaneously play the voice in the environmental noise file to the second voice playback devices 40 (such as multimedia speakers) in multiple soundproof boxes 30, it is necessary to use a broadcasting system for voice broadcasting, so that multiple multimedia speakers connected to the broadcasting system can play the voice at the same time.

[0121] The evaluation server 100 controls the first mobile device 70 to drive the artificial head 50 to move or stay still at a first preset speed according to the usage scenario; the evaluation server 100 controls the second mobile device 80 to drive the second voice playback device 40 to move or stay still at a second preset speed according to the usage scenario.

[0122] For example, when the evaluation server 100 plays recording files and environmental noise according to the TWS headset usage scenario, it can control the mobile device in the soundproof box to move the artificial head equipped with the TWS headset to simulate the user using the TWS headset in usage scenarios such as walking, running, and working. It can also control the mobile device equipped with a multimedia speaker to simulate environmental noise, such as the noise generated during vehicle driving. The evaluation server 100 can control the first mobile device 70 or the second mobile device 80 to move in three axes, and the speed of movement can be set according to actual conditions.

[0123] Step S105: The evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files.

[0124] The evaluation server controls the mobile terminal (smartphone) to record the uplink voice of the call. If it is a telephone voice call, the smartphone is controlled to record the uplink voice of the call. The uplink voice of the call can be recorded at the modem layer of the smartphone or in the Android system; if it is not a telephone voice call but a VOIP call, a customized APP is used to record the uplink voice file of VOIP.

[0125] The uplink voice recording is performed on the mobile terminal instead of collecting it through the microphone on the other end (such as the other party's smartphone). This is because during a voice call or a VOIP call of an APP, it will pass through different servers, and different servers will have different call noise reduction functions, resulting in the voice file obtained from the other end cannot truly reflect the call noise reduction performance of the TWS headset.

[0126] Since some TWS headphones do not support TWS headphones for recording, when evaluating TWS headphones, it is impossible to obtain the noise-reduced call voice of all TWS headphones participating in the evaluation through TWS headphones recording. Therefore, only the uplink voice after call noise reduction can be recorded on the mobile terminal (smartphone). These problems can be avoided by recording the uplink voice of the call through the smartphone.

[0127] Choose a mobile phone that supports uplink voice recording. You can also customize the Android system to implement the uplink voice recording function during phone calls. If you use VOIP calls (similar to WeChat voice calls), you can deploy a customized VOIP APP to implement uplink voice recording of VOIP calls.

[0128] To record the uplink voice of a call, see Figure 2 The process shown.

[0129] Step S201: Obtain the start time and end time of playing the first voice file.

[0130] Step S202: Start recording of the uplink voice of the call of the mobile terminal at the start time, and stop recording of the uplink voice of the call of the mobile terminal at the end time.

[0131] The start time and end time of the mobile terminal recording the uplink voice file are set by the evaluation server. The evaluation server sets it according to the start and end time of the voice recording file. As shown in the following table:

[0132]

[0133] When recording the uplink voice of the voice call of the mobile terminal in each usage scenario, classify each recording file according to the usage scenario to facilitate the selection of the corresponding recording file according to the scenario during the subsequent noise reduction comparison. At the same time, each mobile terminal's recording file has a corresponding unique identifier, which is used to identify which mobile terminal's recording file it is. Each mobile terminal is connected to a set of TWS headphones. Through this unique identifier, you can get which TWS headset generated the call recording file after call noise reduction. As shown in the following table:

[0134]

[0135]

[0136] Step S106: Acquire a noise spectrum curve according to the second voice file, and acquire a call spectrum curve according to the third voice file; and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve.

[0137] Perform spectrum analysis on the call voice files, environmental noise files, and call recording files corresponding to each scenario and each TWS headset. If FFT spectrum analysis is used, the spectrum is smoothed to obtain the signal spectrum curve of the headset. Subtract the obtained call recording spectrum curve from the noise spectrum curve to obtain the call noise reduction curve.

[0138] The noise cancellation test is a test of the call noise reduction headset in each usage scenario, so each TWS headset has a corresponding call noise reduction curve in each application scenario. As shown in the following table:

[0139]

[0140] Step S107: Score the call noise reduction curves corresponding to the N (N is greater than or equal to 1) call noise reduction headphones to obtain N (N is greater than or equal to 1) call noise reduction scores.

[0141] The testers sorted out the call noise reduction curves of each TWS headset, and analyzed the call noise reduction curves to obtain the call noise reduction performance scores of each TWS headset in the corresponding usage scenarios. The best noise reduction is given the highest score, and the scores of the poor noise reduction performance are gradually reduced. As shown in the following table:

[0142]

[0143] Step S108: When the evaluation method is objective evaluation of human voice, a MOS test is performed on the N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset through a MOS test device to obtain N (N is greater than or equal to 1) MOS scores.

[0144] In addition to judging through the noise reduction curve, the TWS headset call noise reduction evaluation also needs to be evaluated through an objective human voice evaluation method. The evaluator uses a MOS test device, such as a MOS tester, to perform a MOS test on voice calls initiated through TWS headsets. When conducting a MOS test, it is also necessary to test each TWS headset one by one in the usage scenario. The usage scenario can be simulated by a voice playback device and a mobile device used in the noise cancellation evaluation. This step is not described in detail, and please refer to step S104 for details.

[0145] In the corresponding usage scenario, after the MOS test of the voice call of the TWS headset is carried out, the MOS score is calculated according to the MOS value obtained in the test. The higher the MOS value, the higher the MOS score. The scoring results are shown in the following table:

[0146]

[0147]

[0148] Step S109: When the evaluation method is subjective voice evaluation, N (N is greater than or equal to 1) call noise reduction headphones are manually evaluated for manual calls in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores.

[0149] In addition to judging by the noise reduction curve, the TWS headset call noise reduction evaluation also needs to be manually evaluated. For manual evaluation of the TWS headset call noise reduction experience, see Figure 3 The process shown:

[0150] Step S301: The tester wears the call noise reduction headset to perform a call test in each usage scenario, and then the tester scores the call noise reduction of each call noise reduction headset to obtain a first manual subjective score.

[0151] The testers will wear the corresponding TWS earphones to experience the call noise reduction in the corresponding scenarios, and then score the TWS earphones participating in the evaluation. Each earphone will be tested once in different scenarios, and then the corresponding evaluation score will be given. The evaluation scores are shown in the following table:

[0152]

[0153] Step S302: publish the first voice file, the second voice file, and the third voice file corresponding to each of the call noise reduction headphones to an Internet platform, and have Internet users evaluate the first voice file, the second voice file, and the third voice file and score the call noise reduction, and average the call noise reduction scores to obtain a second manual subjective score.

[0154] The test server publishes the call voice files, environmental noise files, and corresponding call recording files of each TWS headset in each application scenario obtained by the noise cancellation evaluation to the Internet platform. A large number of Internet users manually listen to the corresponding recording files and then rate the call noise reduction of the corresponding TWS headset. For example, the rating of TWS headset A is shown in the following table:

[0155]

[0156] After a large number of Internet users have evaluated the TWS headset, the evaluation server saves the evaluation results of each Internet user. Then the average of all evaluation results is calculated to obtain the Internet evaluation score of TWS headset call noise reduction, as shown in the following table:

[0157]

[0158]

[0159] Step S303: averaging the first artificial subjective score and the second artificial subjective score to obtain a third artificial subjective score.

[0160] The evaluation server averages the manual evaluation call noise reduction score and the Internet evaluation call noise reduction score to obtain the manual evaluation score of the call noise reduction of the TWS headset, as shown in the following table:

[0161]

[0162] Step S110: The evaluation server calculates a comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or the MOS scores and / or the manual subjective scores and the first weight value and / or the second weight value corresponding to each score.

[0163] After the evaluation server completes the evaluation according to the above evaluation methods, it calculates the scores of various evaluation methods according to the weights corresponding to each application scenario and the weights corresponding to the evaluation methods to obtain the comprehensive score of the call noise reduction of the TWS headset. The calculation process is as follows, taking TWS headset A as an example:

[0164] a. The comprehensive score of the spectrum noise reduction evaluation method is calculated by adding the product of each scene score and the scene weight, and then dividing it by the cumulative sum of the scene weights. For example:

[0165] (90*50+80*15+85*10+90*5+75*10+80*10) / (50+15+10+5+10+10)

[0166] =8550 / 100=85.5

[0167] b. The comprehensive score of the objective evaluation method of human voice (MOS score), calculated by adding the product of the score of each scene and the scene weight, and then dividing the sum of the weights of each scene. For example:

[0168] (85*50+80*15+80*10+80*5+90*10+90*10) / (50+15+10+5+10+10)

[0169] =8450 / 100=84.5

[0170] c. The comprehensive score of the subjective vocal evaluation method is calculated by adding the product of the scores of each scene and the scene weight, and then dividing the sum of the weights of each scene. For example,

[0171] (82.5*50+85*15+75*10+82.5*5+85*10+82.5*10) / (50+15+10+5+10+10)

[0172] =8237.5 / 100=82.375

[0173] d. The comprehensive score of TWS headphones is calculated by adding the scores of each evaluation method and multiplying the weight of the evaluation method. For example:

[0174] 85.5*70%+84.5*20%+82.375*10%=84.9875

[0175] The TWS headphones tested are ranked according to their comprehensive scores, as shown in the following table:

[0176] TWS earphones Final Rating Ranking Headphones A 84.9875 2 Headphones B 78.9625 3 Headphones C 85.2 1 Headphones D 76.1 4

[0177] Through the embodiment of the present invention, call noise reduction evaluation is performed on multiple evaluated headphones at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headphones are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given, which is convenient for consumers to choose headphones.

[0178] In addition, the embodiment of the present invention also provides a call noise reduction headset evaluation system, please refer to Figure 4 , Figure 4 Schematic diagram of the structure of an embodiment of the call noise reduction headset evaluation system in the embodiment of the present application, as shown in FIG. Figure 4 As shown, the call noise reduction headset evaluation system 1 includes: an evaluation server 100, a soundproof box 30, a first voice playback device 90, a second voice playback device 40, a call noise reduction headset 60, a mobile terminal 20, a first mobile device 70, a second mobile device 80, an artificial head 50, and a MOS test device 10;

[0179] The first voice playing device 90 and / or the second voice playing device 40 and / or the first moving device 70 and / or the second moving device 80 and / or the artificial head 50 are installed in the soundproof box 30; the soundproof box 30 is a semi-anechoic box, which is used to isolate the sound during the evaluation process;

[0180] The first speech playback device 90 is installed at the mouth of the artificial head 50 to simulate human speech; the artificial head 50 is installed on the first moving device 70;

[0181] The second voice playback device 40 is installed on the second mobile device 80 or the inner wall of the soundproof box 30;

[0182] The call noise reduction headset 60 is installed on the ear of the artificial head 50;

[0183] The call noise reduction headset 60 is connected to the mobile terminal 20, and the mobile terminal 20 is used to establish a voice call and record the call uplink voice;

[0184] The evaluation server 100 is used to set the usage scenario of the call noise reduction headset 60 and assign a first weight value to each usage scenario; it is also used to set the evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective human voice evaluation, and objective human voice evaluation;

[0185] The evaluation server 100 is further used to play the first voice file to N (N is greater than or equal to 1) first voice playback devices 90 through the first broadcasting system according to the usage scenario when the evaluation method is noise elimination evaluation;

[0186] The MOS test device 10 is used to perform a MOS test on the call voice of N (N is greater than or equal to 1) mobile terminals 20 connected to the call noise reduction headset 60, and obtain N (N is greater than or equal to 1) MOS scores;

[0187] The evaluation server 100 is further configured to play the second voice file to N (N is greater than or equal to 1) second voice playing devices 40 through the second broadcasting system according to the usage scenario;

[0188] The evaluation server 100 is further used to control N (N is greater than or equal to 1) mobile terminals 20 connected to the call noise reduction headset 60 to record the call uplink voice, and obtain N (N is greater than or equal to 1) third voice files;

[0189] The evaluation server 100 is further used to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and subtract the call spectrum curve from the noise spectrum curve to obtain a call noise reduction curve;

[0190] The evaluation server 100 is also used to record N (N greater than or equal to 1) call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N (N greater than or equal to 1) call noise reduction headphones 60;

[0191] The evaluation server 100 is also used to record N (N is greater than or equal to 1) MOS scores obtained by the MOS test device 10 for the MOS test of the call voice of N (N is greater than or equal to 1) mobile terminals 20 connected to the call noise reduction headset 60 when the evaluation method is objective evaluation of human voice;

[0192] The evaluation server 100 is also used to record N (N is greater than or equal to 1) manual subjective scores of N (N is greater than or equal to 1) call noise reduction headphones 60 obtained by manual call noise reduction evaluation in the use scenario when the evaluation method is subjective voice evaluation;

[0193] The evaluation server 100 is also used to calculate the comprehensive score of the call noise reduction headset 60 based on N (N is greater than or equal to 1) call noise reduction scores and / or MOS scores and / or manual subjective scores and the first weight value and / or second weight value corresponding to each score.

[0194] like Figure 5 and Figure 6 As shown, the soundproof box 30 is a semi-anechoic chamber, including: a hard box bottom 31, a sound-absorbing side wall 33, a sound-absorbing top cover 34 and universal wheels 32. The hard box bottom 31 is a wooden sound-absorbing board; the sound-absorbing side wall 33 is composed of a wooden sound-absorbing board and a sound-absorbing wedge 36, and the sound-absorbing wedge 36 is installed on one side of the wooden sound-absorbing board; the sound-absorbing top cover 34 is composed of a wooden sound-absorbing board and a sound-absorbing wedge 36, and the sound-absorbing wedge 36 is installed on one side of the wooden sound-absorbing board. Universal wheels 32 are respectively installed on the four feet of the semi-anechoic box 30 to facilitate the movement of the semi-anechoic box. One side of the sound-absorbing top cover 34 is connected to one side of the sound-absorbing side wall 33 by a hinge to facilitate the opening of the sound-absorbing top cover 34.

[0195] like Figure 6 As shown, the hard box bottom 31, the sound-absorbing side walls 33 and the sound-absorbing top cover 34 enclose an experimental space 35 for speech recognition testing. Two first moving devices 70 and a second moving device 80 that can move forward and backward, left and right, and up and down (i.e., linear motion of the X, Y, and Z coordinate axes) are installed in the experimental space 35. The first moving device 70 and the second moving device 80 are respectively composed of four linear stepping motors, namely, two linear motors 71 that move forward and backward, a linear motor 72 that moves left and right, and a linear motor 73 that moves up and down. The two linear motors 71 that move forward and backward are fixed on the hard box bottom 31, and then the two ends of the linear motor 72 that moves left and right are respectively fixed on the linear moving parts of the two linear motors 71, and the linear motor 72 is driven to move forward and backward by the linear motor 71 that moves forward and backward. The linear motor 73 that moves up and down is vertically installed on the moving part of the linear motor 72 that moves left and right, and the linear motor 73 is driven to move left and right by the linear motor 72 that moves left and right. The second voice player 40 (such as a multimedia speaker or loudspeaker) and the test bench 74 are mounted on the moving part of the linear motor 73 that moves up and down, and the second voice player 40 and the test bench 74 are driven to move up and down by the linear motor 73 that moves up and down. The second voice player 40 can also be mounted on the inner wall of the semi-muffler 30, such as mounting a second voice player 40 (such as a multimedia speaker or loudspeaker) at each of the four corners.

[0196] A camera can be installed in the semi-anechoic box 30 to shoot the evaluation process; the camera can have a pan-tilt function, and the evaluation process can be viewed by rotating the pan-tilt. The evaluation personnel can observe the evaluation process in real time on the display screen.

[0197] A socket 36 is installed on one side of the sound-absorbing side wall 33, and the socket 36 includes a power socket, a network socket, and an audio and video socket. The power socket is used to electrically connect the first voice playback device 90, the second voice playback device 40, the first mobile device 70, and the second mobile device 80 installed in the test space 35, and is powered by an external power supply (such as a mains); the network socket is used to provide a network connection, such as an IP network connection, a USB connection, a serial port connection, etc., to the first mobile device 70 and the second mobile device 80; the audio and video socket is used to provide an audio and video connection to the first voice playback device 90, the second voice playback device 40, and the camera.

[0198] like Figure 7 As shown, the test bench 74 includes a base 741 and a clamping fixture 742. The clamping fixture 742 is installed on the top of the base 741 for fixing the artificial head 50; the base 741 is installed on the moving part of the linear motor 73 of the first moving device 70 that moves up and down.

[0199] like Figure 8 As shown, the first voice playback device 90 is an artificial mouth, which is installed at the mouth position 53 of the artificial head 50 to simulate human speech; the artificial head 50 is installed on the test bench 74 of the first mobile device 70; the call noise reduction headset 60 is installed at the ear position 52 of the artificial head 50; the call noise reduction headset 60 is a TWS headset, and can also be other headsets.

[0200] The call noise reduction headset 60 is connected to the mobile terminal 20, and the mobile terminal 20 is used to establish a voice call and record the call uplink voice.

[0201] The evaluation server 100 is connected to N (N is greater than or equal to 1) soundproof boxes 30. The specific number of soundproof boxes connected is determined according to the actual headphones to be evaluated. If 6 TWS headphones need to be evaluated, the evaluation server 100 is connected to 6 soundproof boxes 30, and a set of TWS headphones is installed in each soundproof box 30 for call noise reduction evaluation.

[0202] In order to ensure that the usage environment of each TWS headset tested is the same, each sound insulation box 30 is required to be the same model of sound insulation box to ensure the same sound insulation effect; each first voice playback device 90 is the same model of artificial mouth to ensure that the effect of the voice played by each artificial mouth is the same; each second voice playback device 40 is the same model of multimedia speaker or speaker; each mobile terminal 20 uses the same model of smartphone (such as Mate40 smartphone) to ensure that the usage scenarios of each TWS headset are consistent.

[0203] After the TWS headset to be evaluated is installed, when the evaluation server 100 starts the evaluation, it is necessary to perform NTP time synchronization on each mobile terminal 20 and the evaluation server 100 to ensure that the time of each mobile terminal 20 and the evaluation server 100 is consistent.

[0204] Through the embodiment of the present invention, call noise reduction evaluation is performed on multiple evaluated headphones at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headphones are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given, which is convenient for consumers to choose headphones.

[0205] In addition, the embodiment of the present invention also provides an evaluation server, referring to Fig. 9 , the server 100 comprises:

[0206] The parameter setting module 101 is used to set the usage scenario of the call noise reduction headset and assign a first weight value to each usage scenario; it is also used to set an evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation;

[0207] The voice playing module 102 is used for playing the first voice file to N (N is greater than or equal to 1) first voice playing devices through the first broadcasting system according to the usage scenario when the evaluation method is noise elimination evaluation, and playing the second voice file to N (N is greater than or equal to 1) second voice playing devices through the second broadcasting system according to the usage scenario;

[0208] The recording control module 103 is used to control N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtain N (N is greater than or equal to 1) third voice files;

[0209] The curve generating module 104 is used to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and perform a subtraction between the call spectrum curve and the noise spectrum curve to obtain a call noise reduction curve;

[0210] The noise reduction score recording module 105 is used to record N (N is greater than or equal to 1) call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N (N is greater than or equal to 1) call noise reduction headphones;

[0211] MOS score recording module 106, used for recording N (N greater than or equal to 1) MOS scores obtained by the MOS test device for the MOS test of the call voice of N (N greater than or equal to 1) mobile terminals connected to the call noise reduction headset when the evaluation method is objective evaluation of human voice;

[0212] The manual scoring recording module 107 is used to record N (N is greater than or equal to 1) manual subjective scores of N (N is greater than or equal to 1) noise reduction headphones for manual calls in the usage scenario when the evaluation method is subjective voice evaluation;

[0213] The score calculation module 108 is used to calculate the comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or MOS scores and / or manual subjective scores and the first weight value and / or second weight value corresponding to each score.

[0214] Through the embodiment of the present invention, call noise reduction evaluation is performed on multiple evaluated headphones at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headphones are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given, which is convenient for consumers to choose headphones.

[0215] Reference Fig.10 , Fig.10 It is a schematic diagram of the structure of the hardware operating environment of the test server 100 involved in the embodiment of the present invention.

[0216] like Fig.10 As shown, the hardware operating environment may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as WI-FI, 4G, 5G interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0217] Those skilled in the art will understand that Fig.10 The structure shown in the figure does not constitute a limitation on the test server 100, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0218] like Fig.10 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a call noise reduction headset evaluation program.

[0219] exist Fig.10In the hardware operating environment shown, the network interface 1004 is mainly used for data communication with the external network; the user interface 1003 is mainly used for receiving user input instructions; the hardware operating environment calls the call noise reduction headset evaluation program stored in the memory 1005 through the processor 1001, and performs the following operations:

[0220] Install call noise reduction headphones in N (N is greater than or equal to 1) soundproof boxes, wherein a first voice playback device and / or a second voice playback device and / or a first mobile device and / or a second mobile device and / or an artificial head are installed in the soundproof box; the soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process; the first voice playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device, and the second voice playback device is installed on the second mobile device or the inner wall of the soundproof box; the call noise reduction headphones are installed on the ear of the artificial head;

[0221] Setting usage scenarios of the call noise reduction headset and assigning a first weight value to each usage scenario;

[0222] Setting an evaluation method, and assigning a second weight value to each of the evaluation methods; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation;

[0223] When the evaluation method is a noise elimination evaluation, the evaluation server plays the first voice file to N (N is greater than or equal to 1) first voice playback devices through the first broadcast system according to the usage scenario; the evaluation server plays the second voice file to N (N is greater than or equal to 1) second voice playback devices through the second broadcast system according to the usage scenario;

[0224] The evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files;

[0225] Acquire a noise spectrum curve according to the second voice file, and acquire a call spectrum curve according to the third voice file; and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve;

[0226] Scoring the call noise reduction curves corresponding to N (N is greater than or equal to 1) call noise reduction headphones to obtain N (N is greater than or equal to 1) call noise reduction scores;

[0227] When the evaluation method is an objective evaluation of human voice, a MOS test is performed on the N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset by a MOS test device to obtain N (N is greater than or equal to 1) MOS scores;

[0228] When the evaluation method is a subjective evaluation of human voice, N (N is greater than or equal to 1) of the call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores;

[0229] The evaluation server calculates a comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or the MOS scores and / or the manual subjective scores and the first weight value and / or the second weight value corresponding to each score.

[0230] Optionally, the N first voice playback devices are voice playback devices of the same model; the N second voice playback devices are voice playback devices of the same model; and the N mobile terminals are mobile terminals of the same model.

[0231] Optionally, the method further comprises the following steps:

[0232] The N mobile terminals and the evaluation server perform time synchronization.

[0233] Optionally, the method further comprises the following steps:

[0234] In each of the usage scenarios, the call noise reduction headset is evaluated once in the evaluation method of noise cancellation evaluation; and / or,

[0235] In each of the usage scenarios, the call noise reduction headset is evaluated once in the evaluation method of subjective human voice evaluation; and / or,

[0236] The call noise reduction headset is evaluated once in each usage scenario using the objective evaluation of human voice as the evaluation method.

[0237] Optionally, the evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files, including the following steps:

[0238] Obtain the start time and end time of playing the first voice file;

[0239] The recording of the uplink voice of the call of the mobile terminal is started at the starting time, and the recording of the uplink voice of the call of the mobile terminal is stopped at the ending time.

[0240] Optionally, when the evaluation method is a subjective evaluation of human voice, N (N is greater than or equal to 1) call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores, including the following steps:

[0241] The tester wears the call noise reduction headset to perform a call test in each of the usage scenarios, and then the tester scores the call noise reduction of each of the call noise reduction headsets to obtain a first manual subjective score; and / or,

[0242] publishing the first voice file, the second voice file, and the third voice file corresponding to each of the call noise reduction headphones to an Internet platform, allowing Internet users to evaluate the first voice file, the second voice file, and the third voice file and score the call noise reduction, and averaging the call noise reduction scores to obtain a second manual subjective score; and / or,

[0243] An average of the first artificial subjective score and the second artificial subjective score is obtained to obtain a third artificial subjective score.

[0244] Optionally, the method further comprises the following steps:

[0245] The evaluation server controls the first moving device to drive the artificial head to move or remain stationary at a first preset speed according to the usage scenario; and / or

[0246] The evaluation server controls the second mobile device to drive the second voice playback device to move or remain stationary at a second preset speed according to the usage scenario.

[0247] Through the embodiment of the present invention, call noise reduction evaluation is performed on multiple evaluated headphones at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headphones are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given, which is convenient for consumers to choose headphones.

[0248] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a call noise reduction headset evaluation program is stored. When the call noise reduction headset evaluation program is executed by a processor, the following operations are implemented:

[0249] Install call noise reduction headphones in N (N is greater than or equal to 1) soundproof boxes, wherein a first voice playback device and / or a second voice playback device and / or a first mobile device and / or a second mobile device and / or an artificial head are installed in the soundproof box; the soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process; the first voice playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device, and the second voice playback device is installed on the second mobile device or the inner wall of the soundproof box; the call noise reduction headphones are installed on the ear of the artificial head;

[0250] Setting usage scenarios of the call noise reduction headset and assigning a first weight value to each usage scenario;

[0251] Setting an evaluation method, and assigning a second weight value to each of the evaluation methods; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation;

[0252] When the evaluation method is a noise elimination evaluation, the evaluation server plays the first voice file to N (N is greater than or equal to 1) first voice playback devices through the first broadcast system according to the usage scenario; the evaluation server plays the second voice file to N (N is greater than or equal to 1) second voice playback devices through the second broadcast system according to the usage scenario;

[0253] The evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files;

[0254] Acquire a noise spectrum curve according to the second voice file, and acquire a call spectrum curve according to the third voice file; and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve;

[0255] Scoring the call noise reduction curves corresponding to N (N is greater than or equal to 1) call noise reduction headphones to obtain N (N is greater than or equal to 1) call noise reduction scores;

[0256] When the evaluation method is an objective evaluation of human voice, a MOS test is performed on the N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset by a MOS test device to obtain N (N is greater than or equal to 1) MOS scores;

[0257] When the evaluation method is a subjective evaluation of human voice, N (N is greater than or equal to 1) of the call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores;

[0258] The evaluation server calculates a comprehensive score of the call noise reduction headset based on N (N is greater than or equal to 1) call noise reduction scores and / or the MOS scores and / or the manual subjective scores and the first weight value and / or the second weight value corresponding to each score.

[0259] Optionally, the N first voice playback devices are voice playback devices of the same model; the N second voice playback devices are voice playback devices of the same model; and the N mobile terminals are mobile terminals of the same model.

[0260] Optionally, the method further comprises the following steps:

[0261] The N mobile terminals and the evaluation server perform time synchronization.

[0262] Optionally, the method further comprises the following steps:

[0263] In each of the usage scenarios, the call noise reduction headset is evaluated once in the evaluation method of noise cancellation evaluation; and / or,

[0264] In each of the usage scenarios, the call noise reduction headset is evaluated once in the evaluation method of subjective human voice evaluation; and / or,

[0265] The call noise reduction headset is evaluated once in each usage scenario using the objective evaluation of human voice as the evaluation method.

[0266] Optionally, the evaluation server controls N (N is greater than or equal to 1) mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtains N (N is greater than or equal to 1) third voice files, including the following steps:

[0267] Obtain the start time and end time of playing the first voice file;

[0268] The recording of the uplink voice of the call of the mobile terminal is started at the starting time, and the recording of the uplink voice of the call of the mobile terminal is stopped at the ending time.

[0269] Optionally, when the evaluation method is a subjective evaluation of human voice, N (N is greater than or equal to 1) call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N (N is greater than or equal to 1) manual subjective scores, including the following steps:

[0270] The tester wears the call noise reduction headset to perform a call test in each of the usage scenarios, and then the tester scores the call noise reduction of each of the call noise reduction headsets to obtain a first manual subjective score; and / or,

[0271] publishing the first voice file, the second voice file, and the third voice file corresponding to each of the call noise reduction headphones to an Internet platform, allowing Internet users to evaluate the first voice file, the second voice file, and the third voice file and score the call noise reduction, and averaging the call noise reduction scores to obtain a second manual subjective score; and / or,

[0272] An average of the first artificial subjective score and the second artificial subjective score is obtained to obtain a third artificial subjective score.

[0273] Optionally, the method further comprises the following steps:

[0274] The evaluation server controls the first moving device to drive the artificial head to move or remain stationary at a first preset speed according to the usage scenario; and / or

[0275] The evaluation server controls the second mobile device to drive the second voice playback device to move or remain stationary at a second preset speed according to the usage scenario.

[0276] Through the embodiment of the present invention, call noise reduction evaluation is performed on multiple evaluated headphones at the same time, thereby improving evaluation efficiency; at the same time, since multiple evaluated headphones are evaluated in the same application scenario, the evaluation results are highly accurate; each evaluation result is scored according to the weight, and then a comprehensive score is given, which is convenient for consumers to choose headphones.

[0277] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0278] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0279] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, controller, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0280] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating call noise reduction headphones, characterized in that: The method comprises: Install call noise reduction headphones in N soundproof boxes, where N is greater than or equal to 1, and install a first voice playback device, a second voice playback device, a first mobile device, a second mobile device and an artificial head in the soundproof box; the soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process; the first voice playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device, and the second voice playback device is installed on the second mobile device or the inner wall of the soundproof box; the call noise reduction headphones are installed on the ear of the artificial head; Setting usage scenarios of the call noise reduction headset and assigning a first weight value to each usage scenario; Setting an evaluation method, and assigning a second weight value to each of the evaluation methods; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation; When the evaluation method is a noise elimination evaluation, the evaluation server plays the first voice file to the N first voice playback devices through the first broadcast system according to the usage scenario; the evaluation server plays the second voice file to the N second voice playback devices through the second broadcast system according to the usage scenario; The evaluation server controls N mobile terminals connected to the call noise reduction headset to record the call uplink voice to obtain N third voice files; Acquire a noise spectrum curve according to the second voice file, and acquire a call spectrum curve according to the third voice file; and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve; Scoring the call noise reduction curves corresponding to the N call noise reduction headphones to obtain N call noise reduction scores; When the evaluation method is an objective evaluation of human voice, a MOS test is performed on the N mobile terminals connected to the call noise reduction headset by a MOS test device to obtain N MOS scores; When the evaluation method is a subjective voice evaluation, the N call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N manual subjective scores; The evaluation server calculates a comprehensive score of the call noise reduction headset according to the N call noise reduction scores, the MOS score, the manual subjective score, and the first weight value and the second weight value corresponding to each score; The method further comprises the following steps: Conducting an evaluation of the call noise reduction headset in each usage scenario using the noise cancellation evaluation as the evaluation method; In each of the usage scenarios, the call noise reduction headset is evaluated once using the evaluation method of subjective human voice evaluation; Conducting an evaluation of the call noise reduction headset in each usage scenario using the objective evaluation of human voice as the evaluation method; Wherein, when the evaluation method is a subjective evaluation of human voice, the N call noise reduction headphones are manually evaluated for call noise reduction in the usage scenario to obtain N manual subjective scores, including the following steps: The tester wears the call noise reduction headset to perform a call test in each usage scenario, and then the tester scores the call noise reduction of each call noise reduction headset to obtain a first manual subjective score; Publishing the first voice file, the second voice file, and the third voice file corresponding to each of the call noise reduction headphones to an Internet platform, allowing Internet users to evaluate the first voice file, the second voice file, and the third voice file and score the call noise reduction, and averaging the call noise reduction scores to obtain a second manual subjective score; averaging the first artificial subjective score and the second artificial subjective score to obtain a third artificial subjective score; The method further comprises the following steps: The evaluation server controls the first moving device to drive the artificial head to move or remain stationary at a first preset speed according to the usage scenario; and / or The evaluation server controls the second mobile device to drive the second voice playback device to move or remain stationary at a second preset speed according to the usage scenario.

2. The method according to claim 1, characterized in that: The N first voice playing devices are voice playing devices of the same model; The N second voice playing devices are voice playing devices of the same model; The N mobile terminals are of the same model.

3. The method according to claim 1, characterized in that: The method further comprises the following steps: The N mobile terminals and the evaluation server perform time synchronization.

4. The method according to claim 1, characterized in that: The evaluation server controls N mobile terminals connected to the call noise reduction headset to record call uplink voices to obtain N third voice files, including the following steps: Obtain the start time and end time of playing the first voice file; The recording of the uplink voice of the call of the mobile terminal is started at the starting time, and the recording of the uplink voice of the call of the mobile terminal is stopped at the ending time.

5. A call noise-cancelling headset evaluation system, applied to the call noise-cancelling headset evaluation method according to any one of claims 1 to 4, characterized in that: include: Evaluation server, soundproof box, first voice playback device, second voice playback device, call noise reduction headset, mobile terminal, first mobile device, second mobile device, artificial head, MOS testing device; The first voice playing device, the second voice playing device, the first moving device, the second moving device and the artificial head are installed in the soundproof box; The soundproof box is a semi-anechoic box, which is used to isolate sound during the evaluation process; The first speech playback device is installed at the mouth of the artificial head to simulate human speech; the artificial head is installed on the first mobile device; The second voice playing device is installed on the second mobile device or the inner wall of the soundproof box; The call noise reduction headset is installed on the ear of the artificial head; The call noise reduction headset is connected to the mobile terminal, and the mobile terminal is used to establish a voice call and record the call uplink voice; The MOS testing device is used to perform a MOS test on the call voice of the N mobile terminals connected to the call noise reduction headset to obtain N MOS scores, where N is greater than or equal to 1; The evaluation server is used to set the usage scenario of the call noise reduction headset and assign a first weight value to each usage scenario; it is also used to set the evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective human voice evaluation, and objective human voice evaluation; The evaluation server is further configured to play the first voice file to N first voice playing devices through the first broadcasting system according to the usage scenario when the evaluation method is noise elimination evaluation; The evaluation server is further configured to play the second voice file to N second voice playing devices through the second broadcasting system according to the usage scenario; The evaluation server is further used to control N mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtain N third voice files; The evaluation server is further configured to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve; The evaluation server is further used to record N call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N call noise reduction headphones; The evaluation server is further configured to record N MOS scores obtained by the MOS testing device for performing a call voice MOS test on the N mobile terminals connected to the call noise reduction headset when the evaluation method is an objective human voice evaluation; The evaluation server is further configured to, when the evaluation method is subjective voice evaluation, record N subjective scores of the N call noise reduction headphones obtained by manually performing call noise reduction evaluations of manual calls in the usage scenario; The evaluation server is further used to calculate the comprehensive score of the call noise reduction headset based on the N call noise reduction scores, the MOS score and the manual subjective score and the first weight value and the second weight value corresponding to each score.

6. The system according to claim 5, characterized in that: The evaluation server comprises: A parameter setting module, used to set the usage scenario of the call noise reduction headset and assign a first weight value to each usage scenario; and also used to set an evaluation method and assign a second weight value to each evaluation method; the evaluation method includes at least one of the following: noise elimination evaluation, subjective voice evaluation, and objective voice evaluation; A voice playing module, used for playing a first voice file to N first voice playing devices through a first broadcasting system according to the usage scenario when the evaluation method is a noise elimination evaluation, and playing a second voice file to N second voice playing devices through a second broadcasting system according to the usage scenario; A recording control module, used to control N mobile terminals connected to the call noise reduction headset to record the call uplink voice, and obtain N third voice files; a curve generating module, configured to obtain a noise spectrum curve according to the second voice file, obtain a call spectrum curve according to the third voice file, and obtain a call noise reduction curve by subtracting the call spectrum curve from the noise spectrum curve; A noise reduction score recording module, used to record N call noise reduction scores obtained by the evaluator according to the call noise reduction curves corresponding to the N call noise reduction headphones; A MOS score recording module, for recording N MOS scores obtained by a MOS test device for performing a call voice MOS test on the N mobile terminals connected to the call noise reduction headset when the evaluation method is an objective evaluation of human voice; A manual scoring recording module, for recording N manual subjective scores obtained by manual noise reduction evaluation of manual calls on N call noise reduction headphones in the usage scenario when the evaluation method is subjective human voice evaluation; A score calculation module is used to calculate the comprehensive score of the call noise reduction headset based on the N call noise reduction scores, the MOS score and the manual subjective score and the first weight value and the second weight value corresponding to each score.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating a call noise-cancelling headset as described in any one of claims 1 to 4 is implemented.

Images