An audio processing method, apparatus, electronic device, and storage medium

Abstract

Embodiments of the present disclosure relate to an audio processing method and device, electronic equipment and a storage medium. In at least one embodiment of the present disclosure, when a user triggers a preset instruction to start learning, first, environmental audio data is acquired, and then whether it is suitable for learning is determined according to the environmental audio data and a noise interval. If the environmental audio data is not in the noise interval, it is not suitable for learning, and then a prompt information is output and / or the environmental noise is adjusted, so that the environmental audio data after the environmental noise is adjusted is in the noise interval. In this way, the influence of noise on the learning efficiency of the user can be reduced, and the learning efficiency of the user can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to an audio processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of the Internet and smart terminals, online education and offline education have also developed. People use smart terminals (such as smartphones, laptops, tablets, personal computers, etc.) to learn online or offline, meeting the learning needs of different groups. For example, students can use their time outside of school to study, and office workers can use their time after get off work to study.

[0003] However, since the off-campus environment is not specifically designed for learning, it may contain various noises, such as music in public places, car sounds, and conversations among passersby. These noises can affect learning efficiency. Therefore, there is an urgent need to provide an audio processing solution to reduce the impact of noise on users' learning efficiency. Summary of the Invention

[0004] To address at least one problem existing in the prior art, at least one embodiment of this disclosure provides an audio processing method, apparatus, electronic device, and storage medium.

[0005] In a first aspect, embodiments of this disclosure provide an audio processing method, including:

[0006] It acquires ambient audio data in response to preset commands;

[0007] Determine whether the acquired environmental audio data is within a noisy range;

[0008] If the acquired ambient audio data is not within the noise range, a prompt message is output and / or the ambient noise is adjusted so that the adjusted ambient audio data is within the noise range.

[0009] In some embodiments, the method further includes:

[0010] After adjusting the ambient noise level, the ambient audio data falls within the noise range, and simultaneously, efficiency correlation data and adjusted ambient audio data are acquired.

[0011] Based on the acquired efficiency correlation data and the acquired environmental audio data after adjusting for environmental noise, the noise range is optimized.

[0012] In some embodiments, the method further includes:

[0013] If the acquired environmental audio data is within the noise range, then efficiency-related data is acquired simultaneously.

[0014] Based on the acquired efficiency correlation data and the acquired environmental audio data, the noise range is optimized.

[0015] In some embodiments, the noise range is determined as follows:

[0016] It responds to preset commands to acquire efficiency-related data and environmental audio data;

[0017] Based on the efficiency correlation data, efficiency parameters for different time periods are determined;

[0018] Based on the efficiency parameters and the environmental audio data within the different time periods, a noise interval is determined, wherein the efficiency parameter corresponding to the noise interval is greater than or equal to a preset efficiency parameter threshold.

[0019] In some embodiments, determining the noise range based on the efficiency parameters within the different time periods and the environmental audio data includes:

[0020] The data is divided into multiple groups for analysis. Each group of data includes efficiency parameters and environmental audio data acquired within the same time period.

[0021] Based on the environmental audio data in each set of analysis data and the efficiency parameters corresponding to each set of analysis data, a noise range is determined, wherein the efficiency parameter corresponding to the noise range is greater than or equal to a preset efficiency parameter threshold.

[0022] In some embodiments, determining the efficiency parameters for different time periods based on the efficiency correlation data includes:

[0023] Based on the efficiency correlation data, the values ​​of multiple learning evaluation indicators are determined for different time periods.

[0024] Based on the values ​​of multiple learning evaluation indicators and the preset efficiency parameter weights of each learning evaluation indicator in different time periods, the efficiency parameters in different time periods are determined.

[0025] In some embodiments, the plurality of learning evaluation metrics values ​​include at least one of the following:

[0026] Accuracy on the questions and focus.

[0027] Secondly, embodiments of this disclosure also provide an audio processing apparatus, comprising:

[0028] The acquisition unit is used to acquire ambient audio data in response to preset commands;

[0029] The judgment unit is used to determine whether the acquired environmental audio data is in a noisy range;

[0030] An adjustment unit is configured to output a prompt message and / or adjust the ambient noise if the acquired ambient audio data is not within the noise range, so that the adjusted ambient audio data is within the noise range.

[0031] Thirdly, embodiments of this disclosure also provide an electronic device, including: a processor and a memory; the processor executes the steps of the audio processing method as described in any embodiment of the first aspect by calling a program or instructions stored in the memory.

[0032] Fourthly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a program or instructions that cause a computer to perform the steps of the audio processing method as described in any embodiment of the first aspect.

[0033] As can be seen, in at least one embodiment of this disclosure, when a user triggers a preset instruction to start learning, environmental audio data is first acquired, and then the suitability for learning is determined based on the environmental audio data and the noise range. If the environmental audio data is not in the noise range, it indicates that learning is not suitable, and prompt information is output and / or the environmental noise is adjusted so that the environmental audio data after the adjustment is in the noise range. In this way, the impact of noise on the user's learning efficiency can be reduced, and the user's learning efficiency can be improved.

[0034] In at least one embodiment of this disclosure, by acquiring environmental audio data while acquiring efficiency-related data, multiple sets of analysis data can be divided to analyze the correspondence between noise and efficiency parameters. For each set of analysis data, the set of analysis data targets the efficiency parameters and environmental audio data acquired within the same time period. Different sets of analysis data target different time periods. Furthermore, based on the environmental audio data in each set of analysis data and the corresponding efficiency parameters, a noise range can be determined. The efficiency parameter corresponding to this noise range is greater than or equal to a preset efficiency parameter threshold. That is, the correspondence between noise and efficiency parameters is obtained, so that the environmental audio data after adjusting the environmental noise is within this noise range, thereby improving the user's learning efficiency and reducing the impact of noise on the user's learning efficiency. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings.

[0036] Figure 1 This is an exemplary application scenario diagram provided by an embodiment of this disclosure;

[0037] Figure 2 This is an exemplary flowchart of an audio processing method provided in an embodiment of this disclosure;

[0038] Figure 3 This is an exemplary block diagram of an audio processing apparatus provided in an embodiment of this disclosure;

[0039] Figure 4 This is an exemplary flowchart of another audio processing method provided in this disclosure embodiment;

[0040] Figure 5 This is an exemplary flowchart of another audio processing method provided in this disclosure embodiment;

[0041] Figure 6 This is an exemplary block diagram of another audio processing apparatus provided in the embodiments of this disclosure;

[0042] Figure 7 This is an exemplary block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0043] To better understand the above-described objectives, features, and advantages of this disclosure, the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It is to be understood that the described embodiments are only some, not all, of the embodiments of this disclosure. The specific embodiments described herein are merely for explaining this disclosure and are not intended to limit it. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure are within the scope of protection of this disclosure.

[0044] It should be noted that in this article, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0045] Because users encounter various noises during the learning process, these noises can affect their learning efficiency. To reduce the impact of noise on user learning efficiency, embodiments of this disclosure provide an audio processing method, apparatus, electronic device, or storage medium.

[0046] In at least one embodiment of this disclosure, when a user triggers a preset instruction to start learning, ambient audio data is first acquired, and then the suitability for learning is determined based on the ambient audio data and the noise range. If the ambient audio data is not within the noise range, it indicates that learning is not suitable, and prompt information is output and / or the ambient noise is adjusted so that the ambient audio data after the adjustment is within the noise range. In this way, the impact of noise on the user's learning efficiency can be reduced, and the user's learning efficiency can be improved.

[0047] In at least one embodiment of this disclosure, by acquiring environmental audio data while acquiring efficiency-related data, multiple sets of analysis data can be divided to analyze the correspondence between noise and efficiency parameters. For each set of analysis data, the set of analysis data targets the efficiency parameters and environmental audio data acquired within the same time period. Different sets of analysis data target different time periods. Furthermore, based on the environmental audio data in each set of analysis data and the corresponding efficiency parameters, a noise range can be determined. The efficiency parameter corresponding to this noise range is greater than or equal to a preset efficiency parameter threshold. That is, the correspondence between noise and efficiency parameters is obtained, so that the environmental audio data after adjusting the environmental noise is within this noise range, thereby improving the user's learning efficiency and reducing the impact of noise on the user's learning efficiency.

[0048] Figure 1 This is an exemplary application scenario diagram provided for an embodiment of this disclosure. For example... Figure 1 As shown, user 11 learns through learning device 12. User 11 can be a student, company employee, or someone from any industry. In some embodiments, learning device 12 can be any electronic device, and learning device 12 can acquire the user's attention span. In some embodiments, learning device 12 is configured to provide learning materials to user 11; for example, learning device 12 is any electronic device with learning software installed. Learning device 12 is also configured to acquire user 11's efficiency-related data. In some embodiments, learning device 12 can be a portable mobile device such as a smartphone, laptop, tablet, or smart sports equipment, or a fixed device such as a desktop computer, smart TV, or server.

[0049] The learning environment of user 11 can be varied. For example, when the learning device 12 is a fixed device, such as a desktop computer, the learning environment of user 11 is the space where the desktop computer is located, such as a classroom or a study.

[0050] The learning environment in which user 11 is located contains various types of noise, which can affect user 11's learning efficiency. To reduce the impact of noise on user learning efficiency, the learning device 12, in addition to providing learning materials to user 11, is configured to acquire ambient noise. For example, the learning device 12 is equipped with a noise acquisition device (e.g., a microphone) to acquire ambient audio data. After acquiring the ambient audio data, the learning device 12 can perform analysis and other processing on the acquired ambient audio data. In some embodiments, the learning device 12 is configured to acquire ambient audio data simultaneously with user 11's efficiency-related data. In this way, the learning device 12 can combine user 11's efficiency-related data to analyze and process the acquired ambient audio data, correlate the ambient audio data with user 11's efficiency-related data, and provide a basis for reducing the impact of noise on user learning efficiency.

[0051] Figure 2 This is an exemplary flowchart illustrating an audio processing method provided in an embodiment of this disclosure. The method is executed by a learning device, which can be implemented as... Figure 1 The learning device 12 shown, or a portion thereof, is described. In this embodiment, the learning device is configured to acquire environmental audio data while acquiring efficiency-related data, so that the learning device can process the acquired environmental audio data in conjunction with the efficiency-related data.

[0052] like Figure 2 As shown, in step 201, efficiency-related data and environmental audio data are acquired in response to a preset instruction. The efficiency-related data can be of various types, including but not limited to at least one of the following: learning duration, number of questions answered, and number of correct answers. Learning duration is recorded starting upon receiving the preset instruction.

[0053] In some embodiments, the acquisition of efficiency-related data and the acquisition of ambient audio data can begin simultaneously. For example, after receiving a preset instruction, the learning device responds by acquiring ambient audio data and simultaneously acquiring efficiency-related data. The preset instruction, for example, is a learning instruction generated by the user triggering a learning control provided by the learning device. The learning control can be a virtual control or a physical button.

[0054] In some embodiments, efficiency-related data can be acquired first, and then ambient audio data can be acquired when certain conditions are met. For example, after receiving a preset instruction, the learning device acquires efficiency-related data in response to the preset instruction, but does not immediately acquire ambient audio data. Instead, it acquires ambient audio data in response to a recording instruction. The recording instruction is generated by the user triggering a recording control provided by the learning device; the recording control can be a virtual control or a physical button. As another example, after receiving a preset instruction, the learning device acquires efficiency-related data in response to the preset instruction, but does not immediately acquire ambient audio data. Instead, it waits for a preset duration before starting to acquire ambient audio data. That is, the acquisition of ambient audio data is delayed. This is to avoid situations where no efficiency-related data is generated or only a small amount is generated within the preset duration after the user begins learning, making the analysis of ambient audio data meaningless.

[0055] In some embodiments, environmental audio data can be acquired first, and efficiency-related data can be acquired only when certain conditions are met. For example, if a user wants to know the noise level of the current environment, they can first trigger the recording control provided by the learning device. The learning device will then receive the recording instruction and acquire the environmental audio data in response. After the user triggers the recording control and learns the noise level of the current environment through the learning device, they can then trigger the learning control provided by the learning device. The learning device will then receive a preset instruction and acquire the efficiency-related data in response.

[0056] It should be noted that in all the above embodiments, there is a certain time period during which the learning device acquires environmental audio data while acquiring efficiency-related data.

[0057] In step 202, efficiency parameters for different time periods are determined based on efficiency correlation data.

[0058] In step 203, based on the efficiency parameters and environmental audio data in different time periods, a noise interval is determined, and the efficiency parameter corresponding to the noise interval is greater than or equal to a preset efficiency parameter threshold.

[0059] In some embodiments, the learning device can divide the data into multiple sets of analysis data, each set of analysis data including efficiency parameters within the same time period and environmental audio data acquired within the same time period; and then determine the noise range based on the environmental audio data in each set of analysis data and the efficiency parameters corresponding to each set of analysis data.

[0060] After determining the efficiency parameters for different time periods, the learning device can divide the data into multiple sets for analysis to analyze the relationship between noise and efficiency parameters. For each set of analysis data, the data refers to the environmental audio data and efficiency parameters acquired within the same time period. Different sets of analysis data refer to different time periods.

[0061] In some embodiments, the learning device can determine the noise mean of each set of analysis data based on the environmental audio data in each set of analysis data. In this way, each set of analysis data corresponds to two index values: noise mean and efficiency parameter.

[0062] For example, N sets of analysis data are divided. Each set of analysis data focuses on the environmental audio data and efficiency parameters acquired within a single day. The efficiency parameters for that day are calculated based on the efficiency correlation data acquired within that day. Thus, based on the environmental audio data from these N sets of analysis data, the average noise level corresponding to these N sets of analysis data can be determined, which is equivalent to obtaining the average noise level for each of the N days. The efficiency parameters corresponding to these N sets of analysis data are also the efficiency parameters for each of the N days. Therefore, based on the average noise level and efficiency parameters for each of the N days, a noise range can be determined, and the efficiency parameter corresponding to the noise range is greater than or equal to a preset efficiency parameter threshold.

[0063] In some embodiments, since the efficiency parameter is calculated based on efficiency-related data, which is data generated during the user's learning process, that is, different users have different efficiency parameters under the same environmental audio data, and thus different noise ranges are obtained. Therefore, after determining the noise range, the noise range can be associated with the user, so that the noise range associated with the user can be quickly determined based on the association between the noise range and the user.

[0064] As can be seen, by acquiring environmental audio data while obtaining efficiency-related data in the embodiment, multiple sets of analysis data can be divided to analyze the correspondence between noise and efficiency parameters. For each set of analysis data, the analysis data targets the efficiency parameters and environmental audio data acquired within the same time period. Different sets of analysis data target different time periods. Furthermore, based on the environmental audio data and the corresponding efficiency parameters in each set of analysis data, a noise range can be determined. The efficiency parameter corresponding to this noise range is greater than or equal to a preset efficiency parameter threshold. That is, the correspondence between noise and efficiency parameters is obtained, so that the environmental audio data after adjusting the environmental noise is within this noise range, thereby improving the user's efficiency parameters and reducing the impact of noise on the user's learning efficiency. When adjusting ambient noise, if the acquired ambient audio data is below the lower limit of the noise range, the learning device can generate white noise to dynamically supplement the ambient noise, so that the adjusted ambient audio data is within the noise range. If the acquired ambient audio data is above the upper limit of the noise range, the learning device can prompt the user to try to reduce the ambient audio data, such as reminding the user to study in a quieter place or take measures to reduce external sounds, such as closing doors and windows, or using other methods to reduce ambient noise, such as the learning device emitting a sound with the opposite phase to the noise through a speaker for noise cancellation.

[0065] In some embodiments, the learning device can determine the values ​​of multiple learning evaluation indicators in different time periods based on efficiency correlation data, and then determine the efficiency parameters in different time periods based on the values ​​of multiple learning evaluation indicators in different time periods and the preset efficiency parameter weights of each learning evaluation indicator.

[0066] The learning evaluation indicators include, but are not limited to, at least one of the following: question accuracy and focus, where focus includes, but is not limited to, learning focus and online class focus. Question accuracy is the user's correct answer rate, which can be calculated based on the number of questions answered and the number of correct answers. Learning focus is the user's level of concentration during learning, which can be determined based on learning duration and collected user images. It should be noted that there are mature methods for determining learning focus in the teaching field, and this embodiment can use existing methods, which will not be elaborated further. Online class focus is the user's focus during online classes, which can be determined based on the online class duration and collected user images. The calculation process for online class focus is similar to that of learning focus, and existing focus calculation methods can be used, which will not be elaborated further. The user images are collected by the learning device.

[0067] For example, N sets of analysis data were divided. One set of analysis data focuses on the environmental audio data and efficiency parameters acquired within a day. The efficiency parameters within a day are calculated based on the efficiency correlation data acquired within that day.

[0068] Based on the efficiency correlation data obtained each day over these N days, we can determine the corresponding values ​​of multiple learning evaluation indicators for each day over these N days. These multiple learning evaluation indicator values ​​are denoted as question accuracy a, learning focus b, and online class focus c. The preset efficiency parameter weights for each learning evaluation indicator are denoted as: question accuracy weight Va, learning focus weight Vb, and online class focus weight Vc.

[0069] Based on the daily learning evaluation index values ​​and the preset efficiency parameter weights for each of the N days, the efficiency parameters for each day can be determined. The efficiency parameters are calculated using the following formula:

[0070] e = Va*a + Vb*b + Vc*c

[0071] Where e is the efficiency parameter; the higher the efficiency parameter, the larger the value of e. The above formula can be used to obtain the efficiency parameter for each day of N days.

[0072] Furthermore, based on the environmental audio data in these N sets of analysis data, the noise mean corresponding to these N sets of analysis data can be determined, that is, the noise mean for each day of the N days can be obtained. The efficiency parameters and noise mean for each day of the N days are shown in Table 1 below.

[0073] Table 1. Efficiency parameters and mean noise for each day of N days.

[0074] time Day 1 the next day Day 3 …… Day N Efficiency parameters 0.82 0.91 0.87 …… 0.94 Mean noise level (dB) 70.3 50.6 40.1 …… 54.2

[0075] Based on Table 1, the noise range can be determined. For example, if the preset efficiency parameter threshold is 0.9, then the noise range is 50.6dB to 54.2dB, and the efficiency parameter corresponding to this noise range is greater than 0.9.

[0076] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art will understand that the embodiments of this disclosure are not limited to the described order of actions, because according to the embodiments of this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art will understand that the embodiments described in the specification are all optional embodiments.

[0077] Figure 3 This is an exemplary block diagram of an audio processing apparatus 30 provided in an embodiment of the present disclosure. The audio processing apparatus 30 can be implemented as... Figure 1 Learning device 12 or a part of learning device 12. For example... Figure 3As shown, the audio processing device 30 can be divided into multiple units, including but not limited to: an acquisition unit 31, a first determination unit 32 and a second determination unit 33, and other units that can be used for audio processing, such as a storage unit for storing data involved in the audio processing.

[0078] Acquisition unit 31 is used to acquire efficiency-related data and environmental audio data in response to preset instructions.

[0079] The first determining unit 32 is used to determine efficiency parameters for different time periods based on efficiency correlation data.

[0080] The second determining unit 33 is used to determine the noise range based on the efficiency parameters and environmental audio data in different time periods, wherein the efficiency parameter corresponding to the noise range is greater than or equal to a preset efficiency parameter threshold.

[0081] In some embodiments, the first determining unit 32 is specifically used to: determine multiple learning evaluation indicator values ​​within different time periods based on efficiency-related data; and determine efficiency parameters within different time periods based on the multiple learning evaluation indicator values ​​within different time periods and the preset efficiency parameter weights of each learning evaluation indicator. The multiple learning evaluation indicator values ​​include, but are not limited to, at least one of the following: question accuracy and focus. Focus includes, but is not limited to, learning focus and online class focus.

[0082] It should be noted that the specific details of the 30 units of the audio processing device can be found in [reference needed]. Figure 2 The audio processing methods and their related embodiments shown are not described in detail here to avoid repetition.

[0083] In some embodiments, the division of units in the audio processing device 30 is only a logical functional division, and other division methods may be used in actual implementation. For example, at least two units in the audio processing device 30 may be implemented as one unit; the units in the audio processing device 30 may also be divided into multiple sub-units. It is understood that each unit or sub-unit can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application.

[0084] Figure 4 This is an exemplary flowchart of another audio processing method provided in this disclosure. The execution subject of this method is a learning device, which can be implemented as follows: Figure 1 The learning device 12 shown or a part of the learning device 12.

[0085] like Figure 4As shown, in step 401, ambient audio data is acquired in response to a preset command.

[0086] In this embodiment, the learning device can be pre-based on Figure 2 The audio processing method and its related embodiments shown obtain noise intervals. Furthermore, the association between users and noise intervals can be obtained. Thus, when a user uses the learning device, the device can acquire user information (e.g., username) and query the username from the association between the user and the noise interval to obtain the corresponding noise interval. Therefore, the learning device can process the acquired environmental audio data based on noise intervals, reducing the impact of noise on the user's learning efficiency.

[0087] In step 402, it is determined whether the acquired ambient audio data is in a noisy range.

[0088] In this embodiment, after obtaining a noise range in advance and acquiring environmental audio data, the learning device can determine whether the acquired environmental audio data is within the pre-obtained noise range. Furthermore, the learning device acquires environmental audio data in real time, thus determining in real time whether the acquired environmental audio data is within the pre-obtained noise range.

[0089] In step 403, if the acquired ambient audio data is not in the noise range, a prompt message is output and / or the ambient noise is adjusted so that the ambient audio data after the adjustment is in the noise range.

[0090] In this embodiment, if the acquired ambient audio data is not in the noise range, it indicates that the user's efficiency parameter is low in the current environment (i.e., below the preset efficiency parameter threshold). Therefore, by outputting prompt information and / or adjusting the ambient noise itself, the ambient audio data after the adjustment is placed in the noise range, which can improve the user's efficiency parameter (i.e., greater than or equal to the preset efficiency parameter threshold).

[0091] In some embodiments, when adjusting ambient noise, if the acquired ambient audio data is lower than the lower limit of the noise range, the learning device can generate white noise to dynamically supplement the ambient noise so that the adjusted ambient audio data is within the noise range; if the acquired ambient audio data is higher than the upper limit of the noise range, the learning device can prompt the user to try to reduce the ambient audio data, such as reminding the user to study in a quieter place or take measures to reduce external sounds, such as closing doors and windows, or using other methods to reduce ambient noise, such as the learning device emitting a sound with the opposite phase to the noise through a speaker for noise cancellation processing.

[0092] In some embodiments, if the acquired ambient audio data is within a noise range, it indicates that the user's efficiency parameter in the current environment is high (i.e., greater than or equal to a preset efficiency parameter threshold), and therefore the ambient noise is not adjusted. Furthermore, the learning device acquires ambient audio data in real time, thus determining in real time whether the acquired ambient audio data is within a noise range. If the acquired ambient audio data is not within a noise range at a certain moment, the ambient noise is adjusted so that the adjusted ambient audio data falls within the noise range.

[0093] Figure 5 This is an exemplary flowchart of another audio processing method provided in this disclosure. The execution subject of this method is a learning device, which can be implemented as... Figure 1 The learning device 12 shown or a part of the learning device 12.

[0094] like Figure 5 As shown, in step 501, ambient audio data is acquired in response to a preset command.

[0095] In step 502, it is determined whether the acquired ambient audio data is in a noisy range.

[0096] In step 503, if the acquired ambient audio data is not in the noise range, a prompt message is output and / or the ambient noise is adjusted so that the ambient audio data after the adjustment is in the noise range.

[0097] Steps 501 to 503 are respectively with Figure 4 Steps 401 to 403 shown are the same and will not be repeated.

[0098] In step 504, after the environmental audio data after adjusting the ambient noise is in the noise range, efficiency correlation data and environmental audio data after adjusting the ambient noise are acquired simultaneously.

[0099] In this embodiment, considering that there may be a shortage of sample data during the determination of the noise range, that is, at least one of the acquired efficiency correlation data and the acquired environmental audio data may be insufficient, which may lead to inaccurate determination of the noise range, after the environmental audio data after adjusting the environmental noise is within the noise range, the efficiency correlation data and the environmental audio data after adjusting the environmental noise are acquired simultaneously as sample data for optimizing the noise range.

[0100] In step 505, the noise range is optimized based on the acquired efficiency correlation data and the acquired ambient audio data after adjusting for ambient noise.

[0101] In this embodiment, based on the acquired efficiency correlation data and the acquired environmental audio data after adjusting for environmental noise, the following method is adopted: Figure 2The audio processing method and its related embodiments shown optimize the noise range, and the optimized noise range is more accurate than the original noise range.

[0102] In step 503', if the acquired ambient audio data is in the noise range, then efficiency correlation data is acquired simultaneously.

[0103] In this embodiment, if the acquired ambient audio data is in the noise range, efficiency correlation data is acquired simultaneously. The acquired ambient audio data and the acquired efficiency correlation data are used as sample data for optimizing the noise range.

[0104] In step 505, the noise range is optimized based on the acquired efficiency correlation data and the acquired ambient audio data.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art will understand that the embodiments of this disclosure are not limited to the described order of actions, because according to the embodiments of this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art will understand that the embodiments described in the specification are all optional embodiments.

[0106] Figure 6 This is an exemplary block diagram of an audio processing apparatus 60 provided in an embodiment of the present disclosure. The audio processing apparatus 60 can be implemented as... Figure 1 Learning device 12 or a part of learning device 12. For example... Figure 6 As shown, the audio processing device 60 can be divided into multiple units, including but not limited to: an acquisition unit 61, a judgment unit 62, and an adjustment unit 63, as well as other units that can be used for audio processing, such as a storage unit for storing data involved in the audio processing process.

[0107] Acquisition unit 61 is used to acquire ambient audio data in response to preset instructions.

[0108] Judgment unit 62 is used to determine whether the acquired environmental audio data is within a noise range, where the noise range is based on... Figure 2 The audio processing method shown and its related embodiments yield the noise range.

[0109] The adjustment unit 63 is used to output a prompt message and / or adjust the ambient noise if the acquired ambient audio data is not in the noise range, so that the ambient audio data after the adjustment is in the noise range.

[0110] In some embodiments, the acquisition unit 61 is further configured to, after the adjustment unit 63 has adjusted the ambient audio data to fall within a noise range, simultaneously acquire efficiency correlation data and the adjusted ambient audio data. The audio processing apparatus 60 may further include... Figure 6 An optimization unit (not shown) is used to optimize based on the efficiency correlation data acquired by the acquisition unit 61 and the acquired adjusted environmental audio data. Figure 2 The audio processing method shown and its related embodiments optimize the noise range.

[0111] In some embodiments, the acquisition unit 61 is further configured to acquire efficiency-related data simultaneously with the acquisition of the ambient audio data if the acquired ambient audio data is in a noise range. The audio processing apparatus 60 may also include... Figure 6 An optimization unit (not shown) is used to optimize based on the efficiency correlation data acquired by the acquisition unit 61 and the acquired environmental audio data. Figure 2 The audio processing method shown and its related embodiments optimize the noise range.

[0112] In some embodiments, the division of units in the audio processing device 60 is only a logical functional division, and other division methods may be used in actual implementation. For example, at least two units in the audio processing device 60 may be implemented as one unit; the units in the audio processing device 60 may also be divided into multiple sub-units. It is understood that each unit or sub-unit can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application.

[0113] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device can realize... Figure 1 The learning device 12 or a part of the learning device 12.

[0114] like Figure 7 As shown, the electronic device includes at least one processor 71, at least one memory 72, and at least one communication interface 73. The various components of the electronic device are coupled together via a bus system 74. The communication interface 73 is used for information transmission with external devices. Understandably, the bus system 74 is used to implement communication between these components. In addition to a data bus, the bus system 74 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 7 The general labeled all buses as Bus System 74.

[0115] It is understood that the memory 72 in this embodiment may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0116] In some implementations, memory 72 stores elements such as executable units or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0117] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic tasks and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application tasks. The program implementing the audio processing method provided in this disclosure can be included in the application programs.

[0118] In this embodiment of the disclosure, the processor 71 executes the steps of the various embodiments of the audio processing method provided in this disclosure by calling the program or instructions stored in the memory 72, specifically, the program or instructions stored in the application program.

[0119] The audio processing method provided in this disclosure can be applied to or implemented by processor 71. Processor 71 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the hardware of processor 71 or by instructions in software form. The processor 71 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor.

[0120] The steps of the audio processing method provided in this disclosure can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software units in the decoding processor. The software units can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 72, and processor 71 reads information from memory 72 and combines it with hardware to complete the steps of the method.

[0121] This disclosure also proposes a non-transitory computer-readable storage medium that stores a program or instructions that cause a computer to perform steps as described in the various embodiments of the audio processing method. To avoid repetition, these steps will not be repeated here.

[0122] This disclosure also proposes a computer program product, wherein the computer program product includes a computer program stored in a non-transitory computer-readable storage medium, and at least one processor of the computer reads from the storage medium and executes the computer program, causing the computer to perform the steps of the various embodiments of the audio processing method, which will not be repeated here to avoid repetition.

[0123] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0124] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of this disclosure and form different embodiments.

[0125] Those skilled in the art will understand that the descriptions of the various embodiments have different focuses, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An audio processing method, characterized in that, The method includes: It acquires ambient audio data in response to preset commands; Determine whether the acquired environmental audio data is within a noise range; the efficiency parameter corresponding to the noise range is greater than or equal to a preset efficiency parameter threshold. If the acquired ambient audio data is not within the noise range, a prompt message is output and / or the ambient noise is adjusted so that the ambient audio data after the adjustment is within the noise range; The noise range is determined in the following way: It responds to preset commands to acquire efficiency-related data and environmental audio data; Based on the efficiency correlation data, efficiency parameters for different time periods are determined; Based on the efficiency parameters and environmental audio data within the different time periods, noise ranges are determined.

2. The method according to claim 1, characterized in that, The method further includes: After adjusting the ambient noise level, the ambient audio data falls within the noise range, and simultaneously, efficiency correlation data and adjusted ambient audio data are acquired. Based on the acquired efficiency correlation data and the acquired environmental audio data after adjusting for environmental noise, the noise range is optimized.

3. The method according to claim 2, characterized in that, The method further includes: If the acquired environmental audio data is within the noise range, then efficiency-related data is acquired simultaneously. Based on the acquired efficiency correlation data and the acquired environmental audio data, the noise range is optimized.

4. The method according to claim 1, characterized in that, The process of determining the noise range based on the efficiency parameters and environmental audio data within different time periods includes: The data is divided into multiple groups for analysis. Each group of data includes efficiency parameters and environmental audio data acquired within the same time period. Based on the environmental audio data in each set of analysis data and the efficiency parameters corresponding to each set of analysis data, the noise range is determined.

5. The method according to claim 1, characterized in that, The determination of efficiency parameters within different time periods based on the efficiency correlation data includes: Based on the efficiency correlation data, the values ​​of multiple learning evaluation indicators are determined for different time periods. Based on the values ​​of multiple learning evaluation indicators and the preset efficiency parameter weights of each learning evaluation indicator in different time periods, the efficiency parameters in different time periods are determined.

6. The method according to claim 5, characterized in that, The multiple learning evaluation metrics include at least one of the following: Accuracy on the questions and focus.

7. An audio processing device, characterized in that, The device includes: The acquisition unit is used to acquire ambient audio data in response to preset commands; The judgment unit is used to determine whether the acquired environmental audio data is in a noise range; the efficiency parameter corresponding to the noise range is greater than or equal to a preset efficiency parameter threshold. An adjustment unit is configured to output a prompt message and / or adjust the ambient noise if the acquired ambient audio data is not within the noise range, so that the adjusted ambient audio data is within the noise range. The noise range is determined in the following way: It responds to preset commands to acquire efficiency-related data and environmental audio data; Based on the efficiency correlation data, efficiency parameters for different time periods are determined; Based on the efficiency parameters and environmental audio data within the different time periods, noise ranges are determined.

8. An electronic device, characterized in that, include: Processor and memory; The processor executes the steps of the method as described in any one of claims 1 to 6 by invoking programs or instructions stored in the memory.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores a program or instructions that cause a computer to perform the steps of the method as described in any one of claims 1 to 6.

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