Open type wearable acoustic equipment noise reduction method and system

Abstract

The invention provides a noise reduction method and system for open type wearable acoustic equipment, and belongs to the technical field of noise reduction, and the method comprises the steps: responding to an acoustic equipment working in a learning mode, analyzing the obtained first environment noise, and determining whether the first environment noise has periodic noise or not; in response to the presence of periodic noise in the first environment noise, noise reduction is performed on the periodic noise based on the noise characteristics of the periodic noise, and the noise characteristics of the periodic noise and a noise reduction strategy of the periodic noise are stored; and performing noise reduction on non-periodic noise in the first environment noise based on the working scene of the acoustic equipment. According to the open type wearable acoustic equipment noise reduction method and system provided by the invention, the problem that the noise reduction mode of external noise is single in a relatively fixed scene at present can be solved.

Description

Technical Field

[0001] The present application belongs to the field of noise reduction technology, and more specifically, relates to a noise reduction method and system for an open wearable acoustic device. Background Art

[0002] Open-type wearable acoustic devices are often subject to interference from various ambient noises, which not only degrade the user's listening experience but also mask important environmental information, affecting the user's sense of safety.

[0003] Periodic noise is a type of noise with a specific periodic pattern. This type of noise is more common in scenarios such as industrial production and transportation. Its periodicity and intensity changes make it difficult for traditional noise reduction methods to effectively deal with it. The existing noise reduction methods for external noise in relatively fixed scenarios are relatively simple. Summary of the Invention

[0004] The purpose of this application is to provide a noise reduction method and system for an open wearable acoustic device to solve the problem that existing acoustic devices have a single noise reduction method for external noise in fixed scenarios.

[0005] A first aspect of an embodiment of the present application provides a method for noise reduction of an open wearable acoustic device, comprising: In response to the acoustic device operating in the learning mode, analyzing the acquired first environmental noise to determine whether periodic noise exists in the first environmental noise; In response to the presence of periodic noise in the first environmental noise, the periodic noise is reduced based on the noise characteristics of the periodic noise, and the noise characteristics of the periodic noise and the noise reduction strategy for the periodic noise are stored; and, non-periodic noise in the first environmental noise is reduced based on the working scenario of the acoustic device.

[0006] A second aspect of an embodiment of the present application provides an open wearable acoustic device noise reduction system, comprising: a noise analysis module, configured to analyze the acquired first environmental noise in response to the acoustic device operating in the learning mode, and determine whether periodic noise exists in the first environmental noise; A storage noise reduction module is used to, in response to the presence of periodic noise in the first environmental noise, reduce the periodic noise based on the noise characteristics of the periodic noise, and store the noise characteristics of the periodic noise and the noise reduction strategy for the periodic noise; and, based on the working scenario of the acoustic device, reduce the non-periodic noise in the first environmental noise.

[0007] According to a third aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned open wearable acoustic device noise reduction method are implemented.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned open wearable acoustic device noise reduction method are implemented.

[0009] The advantageous effects of the open wearable acoustic device noise reduction method and system provided in the embodiments of the present application are: While reducing periodic noise, the system also reduces non-periodic noise within the primary ambient noise based on the acoustic device's operating scenario. This comprehensive approach to reducing both periodic and non-periodic noise comprehensively addresses all types of noise in the environment, effectively suppressing both periodic and non-periodic noise, providing users with a purer and clearer audio environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a noise reduction method for an open wearable acoustic device provided in one embodiment of the present application; Figure 2 This is a structural block diagram of an open wearable acoustic device noise reduction system provided in one embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 This is a flow chart of a noise reduction method for an open wearable acoustic device provided in one embodiment of the present application, the method comprising: S101: In response to the acoustic device operating in a learning mode, analyzing acquired first environmental noise to determine whether periodic noise exists in the first environmental noise.

[0015] In this embodiment, the acoustic device is a device capable of receiving, processing, and transmitting sound signals, and may be a headset. Learning mode is an operating state of the acoustic device. In learning mode, the device analyzes, learns, and processes ambient noise to obtain relevant noise characteristics and develop noise reduction strategies, providing data and strategy support for subsequent regular operating modes. The first ambient noise is the ambient noise signal acquired by the acoustic device in learning mode.

[0016] In this embodiment, the first environmental noise may be analyzed in the following manner: in response to the acoustic device operating in the learning mode, analyzing the acquired first environmental noise to determine whether periodic noise is present in the first environmental noise includes: In response to the acoustic device operating in a learning mode, performing sound source separation on the first environmental noise to obtain a plurality of third environmental noises; calculating the autocorrelation function of the third environmental noise respectively to obtain multiple groups of noise peaks; In response to the distribution of a group of noise peaks on the time axis meeting the first condition, the third environmental noise corresponding to the group of noise peaks is regarded as periodic noise.

[0017] In this embodiment, sound source separation can separate multiple mixed sound source signals to obtain signals corresponding to each independent sound source. In this scenario, the noise from different sources in the first environmental noise is separated to obtain multiple third environmental noises, so as to more accurately analyze the characteristics of each individual sound source. The principle of sound source separation will not be repeated in this application.

[0018] In this embodiment, the autocorrelation function is a mathematical function used to describe the correlation between signals at different times. For noise signals, by calculating the autocorrelation function, its temporal repeatability and regularity can be analyzed, thereby determining whether it is periodic noise. A noise peak is a relatively large value that appears in the calculation result of the autocorrelation function, reflecting the strong correlation of the noise signal at certain time intervals. The first condition can be that the time intervals are fixed. Whether the distribution of noise peaks on the time axis meets the first condition can be determined based on a trained neural network model, whose training dataset is a dataset consisting of a large number of noise peak sequences and their corresponding results of whether the first condition is met.

[0019] S102: In response to the presence of periodic noise in the first environmental noise, reduce the periodic noise based on the noise characteristics of the periodic noise, and store the noise characteristics of the periodic noise and the noise reduction strategy for the periodic noise; and reduce the non-periodic noise in the first environmental noise based on the working scenario of the acoustic device.

[0020] In this embodiment, noise characteristics refer to some parameters that can describe the characteristics of periodic noise, such as the frequency, intensity, period, waveform, etc. of the noise, which can help acoustic equipment accurately identify and distinguish different periodic noises so that corresponding noise reduction measures can be taken.

[0021] In this embodiment, noise reduction for periodic noise can refer to its period, waveform, and sound intensity. Based on active noise reduction technology, one or more periodic opposite signals are generated to offset external noise. Noise reduction is then performed on the periodic signal in accordance with the generated opposite signals for each subsequent period or periods. After obtaining one or more periodic opposite signals, they can be stored and used as a noise reduction strategy for subsequent occurrences of the periodic noise signal.

[0022] In this embodiment, different types of active noise reduction can be applied to the non-periodic noise within the first ambient noise based on the current environment. For example, in an office setting, non-periodic noise is primarily concentrated in the mid- and low-frequency bands, such as human voices and computer equipment operating sounds. Therefore, the noise reduction intensity corresponding to the mid- and low-frequency noise can be appropriately enhanced. In outdoor street scenes, non-periodic noise is more complex, including high-frequency wind noise and mid- and low-frequency traffic noise. Therefore, the same noise reduction intensity can be applied to noise across the entire frequency band.

[0023] In this embodiment, the working scene of the acoustic device refers to the environment in which the acoustic device itself is located, rather than the scene in which the user is working in the office. The working scene of the acoustic device can be determined based on matching the frequency or other characteristics of the received ambient noise with the pre-stored frequency or other characteristics, such as the working sounds of the aforementioned low- and medium-end computer devices.

[0024] As can be seen above, while periodic noise is being reduced, non-periodic noise within the first ambient noise is also reduced based on the acoustic device's operating scenario. This comprehensive approach to periodic and non-periodic noise effectively addresses all types of noise in the environment, effectively suppressing both periodic and non-periodic noise, providing users with a purer and clearer audio environment.

[0025] In one embodiment of the present application, the method for noise reduction of an open wearable acoustic device further includes: In response to the acoustic device operating in the normal mode, analyzing the acquired second environmental noise to determine whether the target periodic noise appears in the second environmental noise; wherein the target periodic noise is the periodic noise stored when the acoustic device operates in the learning mode; In response to the absence of the target periodic noise in the second ambient noise, reducing the second ambient noise based on a working scenario of the acoustic device; In response to the presence of target periodic noise in the second environmental noise, the target periodic noise is reduced based on a target noise reduction strategy, and the non-periodic noise in the second environmental noise is reduced based on a working scenario of the acoustic device; wherein the target noise reduction strategy is a noise reduction strategy corresponding to the target periodic noise.

[0026] In this embodiment, normal mode refers to the normal operating mode of the acoustic device, as opposed to learning mode, in which the device processes ambient noise based on stored learning records. The second ambient noise is the ambient noise detected by the acoustic device in normal mode. The target periodic noise refers to the periodic noise stored by the acoustic device in learning mode.

[0027] In this embodiment, a method for determining whether the target periodic noise appears in the second environmental noise can be to first perform sound source separation to obtain multiple sound source signals, and match the multiple sound source signals with the stored periodic signals. If the matching degree is greater than a preset threshold, it is considered that the second environmental noise contains periodic noise that has been learned and stored in the learning mode. The calculation of the matching degree can be determined based on cosine similarity or Pearson correlation coefficient.

[0028] If the target periodic noise does not exist in the second ambient noise, that is, the periodic noise previously recorded in learning mode does not appear in the current ambient noise, the acoustic device will perform noise reduction on the entire second ambient noise based on its own working scenario. For example, if the device is in an outdoor scene, it may focus on noise reduction on non-periodic noise such as wind and traffic noise to provide a better listening experience.

[0029] If the target periodic noise is present in the second ambient noise, the device can perform noise reduction processing in two parts. On the one hand, for the target periodic noise, the device will use the target noise reduction strategy to perform noise reduction processing. The target noise reduction strategy is the noise reduction strategy previously developed and stored for the target periodic noise in learning mode. For example, previously in learning mode, specific filter parameters were set to reduce noise for a certain periodic noise, and now the stored parameters will be directly used. On the other hand, for the non-periodic noise part of the second ambient noise other than the target periodic noise, the device will perform noise reduction processing based on its own working scenario. For example, if the device is in an indoor meeting scene, in addition to noise reduction of the identified target periodic noise, it will also perform corresponding noise reduction processing on non-periodic noise such as people's conversations and occasional object collisions to ensure clear and quiet communication in the meeting.

[0030] From the above, it can be concluded that in this application, since the target noise reduction strategy is specially formulated and stored for specific target periodic noise in the learning mode, it can provide more accurate and effective noise reduction effects when processing the same or similar periodic noises. Compared with the general noise reduction method, it can better meet the user's noise reduction needs for specific noises. The noise reduction method of this embodiment can process periodic noise and non-periodic noise at the same time. When there is target periodic noise, a special target noise reduction strategy is used for targeted processing; for non-periodic noise, noise reduction is optimized according to the working scenario. This comprehensive noise reduction method enables the device to fully cope with various complex noise environments, whether it is a single periodic noise interference or a complex environment with a mixture of multiple noises, it can provide stable and efficient noise reduction effects.

[0031] In one embodiment of the present application, storing noise characteristics of periodic noise and a noise reduction strategy for the periodic noise includes: Store the noise intensity of the periodic noise; The actual noise reduction strategy is adjusted based on the noise intensity of the periodic noise to obtain the noise reduction strategy of the periodic noise, and the noise reduction strategy of the periodic noise is stored.

[0032] In this embodiment, the actual noise reduction strategy refers to the noise reduction parameters of the acoustic device for one or more periodic noises. However, in actual application scenarios, the intensity of each noise is different, which will lead to inconsistency in the intensity of the noise reduction parameters. If the actual noise reduction strategy is not adjusted, when the periodic noise appears again, only a cancellation signal can be generated according to a fixed period, but the intensity of the cancellation signal may be different from the periodic noise received this time. Therefore, in this embodiment, it is necessary to adjust the actual noise reduction strategy based on the noise intensity of the periodic noise to obtain the noise reduction strategy for this noise under a preset fixed noise intensity. That is, the main goal of adjusting the actual noise reduction strategy is the noise reduction intensity, and the noise reduction intensity is adjusted to the intensity corresponding to when the external periodic noise is a preset fixed value.

[0033] In one embodiment of the present application, denoising the periodic noise based on the noise characteristics of the periodic noise, and storing the noise characteristics of the periodic noise and the noise reduction strategy of the periodic noise includes: In response to the fact that there are multiple periodic noises, noise reduction is performed on the corresponding periodic noise based on the noise characteristics of each periodic noise; Each periodic noise and the noise reduction strategy corresponding to each periodic noise are stored respectively.

[0034] In one embodiment of the present application, in response to the presence of target periodic noise in the second environmental noise, performing noise reduction processing on the target periodic noise based on a target noise reduction strategy includes: In response to the target periodic noise being one, using a noise reduction strategy for the target periodic noise when the acoustic device operates in a learning mode as a target noise reduction strategy, and performing noise reduction processing on the target periodic noise based on the target noise reduction strategy; In response to there being multiple target periodic noises, when the acoustic device operates in a learning mode, noise reduction strategies for the respective target periodic noises are integrated to obtain a target noise reduction strategy, and noise reduction processing is performed on the multiple target periodic noises based on the target noise reduction strategy.

[0035] In this embodiment, when the acoustic device is in learning mode and detects the presence of multiple periodic noises in the first ambient noise, it applies a corresponding noise reduction strategy based on the noise characteristics (e.g., frequency, amplitude, phase, waveform, etc.) of each periodic noise. For example, an opposite cancellation signal is generated for each ambient noise. The noise reduction strategy corresponding to each periodic noise is then stored in the aforementioned storage method.

[0036] When the acoustic device is in normal mode and there is target periodic noise in the second environmental noise, different processing methods need to be adopted according to the number of target periodic noises. If there is only one target periodic noise, the noise reduction strategy formulated and stored by the acoustic device for the target periodic noise in the learning mode can be used as the current target noise reduction strategy, and noise reduction processing can be performed on the target periodic noise based on this strategy.

[0037] If there are multiple target periodic noises, the noise reduction strategies applied by the acoustic device during learning mode for each target periodic noise must be combined. This combination can be implemented in various ways, such as weighted fusion (weighting is determined based on factors such as noise intensity and frequency), to produce a new target noise reduction strategy. Based on this combined target noise reduction strategy, noise reduction is performed simultaneously on multiple target periodic noises.

[0038] It should be noted that when the stored noise reduction strategy is used as the target noise reduction strategy, the noise reduction strategy corresponding to the stored periodic noise should be adjusted according to the current intensity of the periodic noise and the aforementioned preset fixed value. The same logic as the aforementioned, at this time, its intensity should also be adjusted to the same or similar intensity as the current periodic noise intensity.

[0039] From the above, it can be concluded that the present application can obtain an accurate noise reduction strategy that adapts to different noise intensities by storing the noise intensity of periodic noise and adjusting the actual noise reduction strategy based on the noise intensity. When encountering the same or similar periodic noise in the subsequent normal mode, the corresponding information and strategy can be quickly called to improve the noise reduction efficiency and effect.

[0040] In one embodiment of the present application, when the acoustic device operates in a learning mode, noise reduction strategies for various target periodic noises are integrated to obtain a target noise reduction strategy, including: When the acoustic device is operating in a learning mode, the noise reduction strategies for each target periodic noise are weighted and integrated to obtain a target noise reduction strategy; The weight of the noise reduction strategy for each target periodic noise is determined based on the intensity of each periodic noise.

[0041] In this embodiment, the weighted fusion weight of the noise reduction strategy for the target periodic noise is determined based on the intensity of the periodic noise. Periodic noise with greater intensity generally has a greater impact on the overall noise environment. Therefore, when fusing the strategy, it is necessary to give the corresponding noise reduction strategy a higher weight to ensure that the noise can be more effectively suppressed. On the other hand, the weight of the noise reduction strategy for periodic noise with less intensity is relatively low. For example, if there are two target periodic noises, and the intensity of noise A is significantly higher than that of noise B, then when fusing the strategy, the noise reduction strategy for noise A will have a greater weight. Specifically, the ratio of the intensity of each periodic noise to the total intensity can be used as the weight.

[0042] In one embodiment of the present application, the method for noise reduction of an open wearable acoustic device further includes: In response to the number of target periodic noises being greater than a preset number, adjusting the weights of the noise reduction strategies corresponding to the periodic noises that meet the preset conditions among all target periodic noises to zero, and increasing the weight of the noise reduction strategy corresponding to the first target periodic noise based on the characteristics of the periodic noises that meet the preset conditions; Among them, the first target periodic noise is noise whose similarity with the periodic noise that meets the preset condition is greater than the first similarity and whose intensity is greater than the preset intensity; the preset condition is that among all the target periodic noises, there is noise with a similarity greater than the first similarity and the intensity of the target periodic noise is less than the first intensity.

[0043] In this embodiment, when the acoustic device is in normal mode and the number of detected target periodic noises is greater than a preset number, it indicates that the acoustic device cannot process them all simultaneously or that the computational effort is too large, resulting in a reduction in the noise reduction effect. Therefore, for periodic noises that meet the preset conditions, the weight of the corresponding noise reduction strategy is adjusted to zero. This means that the original noise reduction strategy for these noises will not be used in subsequent noise reduction processing because their intensity is relatively low and similar to other noises. By processing other similar and stronger noises, the noise reduction effect on them can be better covered.

[0044] In this embodiment, based on the characteristics of the periodic noise that meets the preset conditions, the weight of the noise reduction strategy corresponding to the first target periodic noise is increased, specifically, the noise intensity of the periodic noise that meets the preset conditions is merged into the noise intensity with a similarity greater than the first similarity.

[0045] In this embodiment, the first target periodic noise is very similar to the noise determined to meet the preset conditions. The purpose of increasing its weight is to focus on utilizing the noise reduction strategy for the first target periodic noise when processing similar noises because it is relatively more representative (due to reasons such as greater intensity). This allows for more effective noise reduction for this type of similar noise, while simplifying the fusion process of the noise reduction strategies, avoiding the use of different strategies for too many similar noises with lower intensity, and improving the noise reduction efficiency and effectiveness.

[0046] From the above, it can be concluded that the present application performs weighted fusion of the noise reduction strategies of each target periodic noise, and the weights are determined according to the noise intensity, so that the noise reduction strategy can be more in line with the actual noise situation and the noise reduction effect in a complex noise environment is improved. In the present application, when the number of target periodic noises is greater than the preset number, the acoustic equipment may face the problem of being unable to process them simultaneously or the amount of calculation is too large, resulting in reduced noise reduction effect. At this time, the weight of the noise reduction strategy corresponding to the periodic noise that meets the preset conditions is adjusted to zero, avoiding the waste of computing resources on these noises with low intensity and similar to other noises, so that the acoustic equipment can focus more resources on the processing of key noises, ensuring that the overall noise reduction effect is not affected by excessive noise interference.

[0047] Corresponding to the noise reduction method of the open wearable acoustic device in the above embodiment, Figure 2 This is a structural block diagram of an open wearable acoustic device noise reduction system provided in one embodiment of the present application. For ease of illustration, only the parts related to the embodiment of the present application are shown. Figure 2 The open wearable acoustic device noise reduction system 20 includes: a noise analysis module 21 and a storage noise reduction module 22.

[0048] The noise analysis module 21 is configured to analyze the acquired first environmental noise in response to the acoustic device operating in the learning mode, and determine whether periodic noise exists in the first environmental noise; The storage noise reduction module 22 is used to, in response to the presence of periodic noise in the first environmental noise, reduce the periodic noise based on the noise characteristics of the periodic noise, and store the noise characteristics of the periodic noise and the noise reduction strategy for the periodic noise; and reduce the non-periodic noise in the first environmental noise based on the working scenario of the acoustic device.

[0049] In one embodiment of the present application, the open wearable acoustic device noise reduction system 20 further includes: a normal working module, configured to, in response to the acoustic device operating in the normal mode, analyze the second environmental noise acquired by the acoustic device and determine whether a target periodic noise appears in the second environmental noise; wherein the target periodic noise is the periodic noise stored when the acoustic device operates in the learning mode; In response to the absence of the target periodic noise in the second ambient noise, reducing the second ambient noise based on a working scenario of the acoustic device; In response to the presence of target periodic noise in the second environmental noise, the target periodic noise is reduced based on a target noise reduction strategy, and the non-periodic noise in the second environmental noise is reduced based on a working scenario of the acoustic device; wherein the target noise reduction strategy is a noise reduction strategy corresponding to the target periodic noise.

[0050] In one embodiment of the present application, the storage noise reduction module 22 is configured to, in response to the periodic noise being multiple, perform noise reduction on the corresponding periodic noise based on the noise characteristics of each periodic noise; Each periodic noise and the noise reduction strategy corresponding to each periodic noise are stored respectively.

[0051] In one embodiment of the present application, the conventional working module is specifically configured to, in response to the target periodic noise being one, use the noise reduction strategy for the target periodic noise when the acoustic device operates in the learning mode as the target noise reduction strategy, and perform noise reduction processing on the target periodic noise based on the target noise reduction strategy; In response to the fact that there are multiple target periodic noises, the noise reduction strategies for the respective target periodic noises are integrated when the acoustic device operates in a learning mode to obtain a target noise reduction strategy, and noise reduction processing is performed on the multiple target periodic noises based on the target noise reduction strategy.

[0052] In one embodiment of the present application, the conventional working module is further configured to perform weighted fusion of the noise reduction strategies for each target periodic noise when the acoustic device operates in the learning mode to obtain a target noise reduction strategy; The weight of the noise reduction strategy for each target periodic noise is determined based on the intensity of each periodic noise.

[0053] In one embodiment of the present application, the noise reduction module 22 is specifically configured to store the noise intensity of the periodic noise; The actual noise reduction strategy is adjusted based on the noise intensity of the periodic noise to obtain the noise reduction strategy of the periodic noise, and the noise reduction strategy of the periodic noise is stored.

[0054] In one embodiment of the present application, the noise analysis module 21 is specifically configured to perform sound source separation on the first environmental noise in response to the acoustic device operating in the learning mode to obtain a plurality of third environmental noises; calculating the autocorrelation function of the third environmental noise respectively to obtain multiple groups of noise peaks; In response to the distribution of a group of noise peaks on the time axis meeting the first condition, the third environmental noise corresponding to the group of noise peaks is regarded as periodic noise.

[0055] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned system embodiments, such as Figure 2 The functions of the noise analysis module 21 and the storage noise reduction module 22 are shown.

[0056] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0057] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0058] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store device type information.

[0059] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present application can execute the implementation methods described in the first and second embodiments of the open wearable acoustic device noise reduction method provided in the embodiments of the present application, and can also execute the implementation methods of the electronic device described in the embodiments of the present application, which will not be repeated here.

[0060] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0061] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0062] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0063] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0064] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0065] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0066] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0067] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A noise reduction method for an open wearable acoustic device, characterized in that: include: In response to the acoustic device operating in a learning mode, analyzing the acquired first environmental noise to determine whether periodic noise exists in the first environmental noise; In response to the periodic noise existing in the first environmental noise, reducing the periodic noise based on the noise characteristics of the periodic noise, and storing the noise characteristics of the periodic noise and the noise reduction strategy for the periodic noise; And, based on the working scenario of the acoustic device, the non-periodic noise in the first environmental noise is reduced.

2. The open wearable acoustic device noise reduction method according to claim 1, wherein: Also includes: In response to the acoustic device operating in the normal mode, analyzing the acquired second environmental noise to determine whether target periodic noise appears in the second environmental noise; wherein the target periodic noise is the periodic noise stored when the acoustic device operates in the learning mode; In response to the target periodic noise not being present in the second environmental noise, reducing the second environmental noise based on a working scenario of the acoustic device; In response to the presence of the target periodic noise in the second environmental noise, the target periodic noise is subjected to noise reduction processing based on a target noise reduction strategy, and the non-periodic noise in the second environmental noise is subjected to noise reduction processing based on the working scenario of the acoustic device; wherein the target noise reduction strategy is a noise reduction strategy corresponding to the target periodic noise.

3. The method for noise reduction of an open wearable acoustic device according to claim 1, wherein: The performing noise reduction on the periodic noise based on the noise characteristics of the periodic noise, and storing the noise characteristics of the periodic noise and the noise reduction strategy of the periodic noise, includes: In response to the fact that there are multiple periodic noises, noise reduction is performed on the corresponding periodic noise based on the noise characteristics of each periodic noise; The periodic noises and the noise reduction strategies corresponding to the periodic noises are stored respectively.

4. The method for noise reduction of an open wearable acoustic device according to claim 2, wherein: In response to the presence of the target periodic noise in the second environmental noise, performing noise reduction processing on the target periodic noise based on a target noise reduction strategy includes: In response to the target periodic noise being one, using a noise reduction strategy for the target periodic noise when the acoustic device operates in a learning mode as the target noise reduction strategy, and performing noise reduction processing on the target periodic noise based on the target noise reduction strategy; In response to the number of target periodic noises being multiple, the noise reduction strategies for the target periodic noises are integrated when the acoustic device operates in a learning mode to obtain the target noise reduction strategy, and noise reduction processing is performed on the multiple target periodic noises based on the target noise reduction strategy.

5. The method for noise reduction of an open wearable acoustic device according to claim 4, wherein: The step of fusing the noise reduction strategies for each target periodic noise when the acoustic device operates in a learning mode to obtain the target noise reduction strategy includes: performing weighted fusion on the noise reduction strategies of the target periodic noises when the acoustic device operates in a learning mode to obtain the target noise reduction strategy; The weight of the noise reduction strategy for each target periodic noise is determined based on the intensity of each periodic noise.

6. The method for noise reduction of an open wearable acoustic device according to claim 1, wherein: The storing of the noise characteristics of the periodic noise and the noise reduction strategy of the periodic noise includes: storing the noise intensity of the periodic noise; The actual noise reduction strategy is adjusted based on the noise intensity of the periodic noise to obtain the noise reduction strategy of the periodic noise, and the noise reduction strategy of the periodic noise is stored.

7. The open wearable acoustic device noise reduction method according to claim 1, wherein: In response to the acoustic device operating in the learning mode, analyzing the acquired first environmental noise to determine whether periodic noise exists in the first environmental noise includes: In response to the acoustic device operating in a learning mode, performing sound source separation on the first environmental noise to obtain a plurality of third environmental noises; Calculating the autocorrelation function of the third environmental noise respectively to obtain multiple groups of noise peaks; In response to the distribution of a group of noise peaks on the time axis meeting the first condition, the third environmental noise corresponding to the group of noise peaks is regarded as periodic noise.

8. An open wearable acoustic device noise reduction system, characterized in that: include: a noise analysis module, configured to analyze the acquired first environmental noise in response to the acoustic device operating in the learning mode, and determine whether periodic noise exists in the first environmental noise; a storage noise reduction module, configured to, in response to the presence of the periodic noise in the first environmental noise, reduce the periodic noise based on noise characteristics of the periodic noise, and store the noise characteristics of the periodic noise and a noise reduction strategy for the periodic noise; And, based on the working scenario of the acoustic device, the non-periodic noise in the first environmental noise is reduced.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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