Hearing threshold test compensation method, apparatus, device, and storage medium

Abstract

The application discloses a hearing threshold test compensation method and device, equipment and a storage medium, relates to the technical field of hearing aid devices, receives a user's identification feedback signal, the identification feedback signal is generated by the user based on the test tone signal and the masking tone signal feedback; analyze the identification feedback signal, determine the relative intensity of the masking tone signal relative to the test tone signal; determine the target mapping relationship of the pure tone threshold and the relative intensity, generate a hearing compensation strategy based on the target mapping relationship and the environmental scene representation information. The application determines the relative intensity by analyzing the identification feedback signal, determines the target mapping relationship of the pure tone threshold and the relative intensity, and then generates the hearing compensation strategy, thereby improving the effectiveness of the hearing threshold test and the accuracy of the hearing compensation.

Description

Technical Field

[0001] This application relates to the field of hearing aids technology, and in particular to hearing threshold testing compensation methods, devices, equipment and storage media. Background Technology

[0002] Hearing threshold testing is a fundamental step in audiological diagnosis and fitting of hearing aids, and the accuracy of the test results directly affects the effectiveness of subsequent hearing compensation. Currently, the most widely used hearing threshold testing and compensation method in clinical practice and routine fitting is the pure-tone audiometry test. This method involves playing a single-frequency pure-tone signal into the subject's ear in a soundproof environment. By progressively adjusting the intensity of the pure-tone signal, the minimum intensity value at which the subject can just hear the sound at that frequency is determined; this is the pure-tone threshold. Based on the measured pure-tone thresholds for each frequency, audiologists or fitting systems generate corresponding hearing compensation plans to configure the gain parameters of hearing aids and other hearing-assistive devices.

[0003] However, the aforementioned pure-tone hearing threshold testing method has significant drawbacks in practical applications: because the test is conducted in a quiet environment, the measured pure-tone thresholds fail to reflect the test subject's auditory perception ability in a real sound field. When the test subject is in a real-life environment with background noise, their auditory system is affected by the masking effect of noise, resulting in a significant deviation between the actual identifiable sound threshold and the pure-tone threshold measured in a quiet environment. This leads to a mismatch between the hearing compensation strategy generated based on the pure-tone threshold and the auditory needs in real-world scenarios, affecting the effectiveness of hearing aids in actual use.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a hearing threshold testing compensation method, device, equipment and storage medium, which aims to solve the technical problem of insufficient accuracy of hearing compensation.

[0006] To achieve the above objectives, this application proposes a hearing threshold testing compensation method, the method comprising: Receive the identification feedback signal sent by the user, which is generated by the user based on the test tone signal and the masking tone signal; Analyze the identification feedback signal to determine the relative distinguishing strength of the masking sound signal relative to the test sound signal; A target mapping relationship between the pure tone threshold and the relative distinguishing intensity is determined, and a hearing compensation strategy is generated based on the target mapping relationship and environmental scene representation information.

[0007] In one embodiment, before the step of receiving the identification feedback signal sent by the user, wherein the identification feedback signal is generated by the user providing feedback based on the test tone signal and the masking tone signal, the method further includes: Determine the target frequency to be tested, and generate a test tone signal corresponding to the target frequency; Based on the target frequency, a corresponding masking sound signal is generated, and the audio playback module is controlled to play the test sound signal and the masking sound signal.

[0008] In one embodiment, the step of generating a corresponding masking tone signal based on the target frequency includes: Calculate the critical bandwidth based on the target frequency; Based on the critical bandwidth, a first center frequency is calculated that is offset by a first preset amount towards the low-frequency side with the target frequency as a reference, and a first noise signal is generated based on the first center frequency. Based on the critical bandwidth, a second center frequency is calculated that is offset by a second preset amount towards the high-frequency side with the target frequency as a reference, and a second noise signal is generated based on the second center frequency; The first noise signal and the second noise signal are superimposed and combined to generate a masking sound signal.

[0009] In one embodiment, the step of analyzing the identification feedback signal to determine the relative distinguishing strength of the masking sound signal relative to the test sound signal includes: The user's recognition status of the test tone signal is determined based on the recognition feedback signal, and the recognition status includes a recognizable state and / or an unrecognizable state. When the identification state is unidentifiable, the intensity of the masking sound signal is reduced and played until a critical intensity point is determined from the masking sound signal to distinguish the test sound signal from the test sound signal. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity. When the identification state is identifiable, the intensity of the masking sound signal is increased and played until it is determined that the user can distinguish the test sound signal from the masking sound signal at a critical intensity point. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity.

[0010] In one embodiment, the step of receiving the identification feedback signal sent by the user further includes: The reaction delay time is determined based on the identified feedback signal; The reaction delay time is compared with a preset effective time window to obtain the reaction delay result; The credibility analysis of the response delay result is performed based on the preset confidence rating information. When the credibility analysis fails, the corresponding identification feedback signal is filtered out.

[0011] In one embodiment, the step of determining the target mapping relationship between the pure tone threshold and the relative distinguishing intensity includes: Determine the pure tone threshold at the target frequency; Establish a data pair between the pure tone threshold and the relative discrimination intensity at the target frequency, and calculate the masking margin between the relative discrimination intensity and the pure tone threshold; The target mapping relationship is determined based on the associated data pairs and the masking margin.

[0012] In one embodiment, the step of generating a hearing compensation strategy based on the target mapping relationship and environmental scene representation information includes: Analyze the environmental noise feature parameters in the environmental scene representation information to determine the sound environment scene corresponding to the environmental scene representation information; When the sound environment scene is a noisy scene, the environmental noise feature parameters are matched with the preset noisy scene judgment conditions, and the corresponding relative discrimination intensity is extracted based on the target mapping relationship to obtain the target compensation threshold; When the sound environment scene is a quiet scene, the target compensation threshold is obtained by extracting the corresponding pure tone threshold based on the target mapping relationship; The gain compensation amount for each frequency band is calculated based on the target compensation threshold, and a hearing compensation strategy is generated based on the gain compensation amount.

[0013] Furthermore, to achieve the above objectives, this application also proposes a hearing threshold testing compensation device, which includes: The signal receiving module is used to receive the identification feedback signal sent by the user, which is generated by the user based on the test tone signal and the masking tone signal. The analysis and determination module is used to analyze the identification feedback signal and determine the relative differentiation strength of the masking sound signal relative to the test sound signal; The strategy generation module is used to determine the target mapping relationship between the pure tone threshold and the relative distinguishing intensity, and to generate a hearing compensation strategy based on the target mapping relationship and environmental scene representation information.

[0014] In addition, to achieve the above objectives, this application also proposes a hearing threshold testing compensation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the hearing threshold testing compensation method as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the hearing threshold test compensation method described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the hearing threshold test compensation method described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: This application proposes a hearing threshold testing compensation method, apparatus, device, and storage medium. It receives a user-sent identification feedback signal, generated by the user's feedback based on a test tone signal and a masking tone signal. The method analyzes the identification feedback signal to determine the relative distinguishing strength of the masking tone signal relative to the test tone signal. It then determines a target mapping relationship between a pure tone threshold and the relative distinguishing strength, and generates a hearing compensation strategy based on the target mapping relationship and environmental scene representation information. This application improves the effectiveness of hearing threshold testing and the accuracy of hearing compensation by analyzing the identification feedback signal to determine the relative distinguishing strength, establishing the target mapping relationship between the pure tone threshold and the relative distinguishing strength, and then generating a hearing compensation strategy. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the hearing threshold testing compensation method of this application. Figure 2 A simplified flowchart illustrating the hearing threshold testing compensation method provided in this application embodiment; Figure 3 This is a schematic diagram of the module structure of the hearing threshold testing compensation device according to an embodiment of this application; Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the hearing threshold testing compensation method in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The main solution of this application embodiment is: receiving a recognition feedback signal sent by a user, the recognition feedback signal being generated by the user based on a test tone signal and a masking tone signal; analyzing the recognition feedback signal to determine the relative discrimination intensity of the masking tone signal relative to the test tone signal; determining a target mapping relationship between a pure tone threshold and the relative discrimination intensity; and generating a hearing compensation strategy based on the target mapping relationship and environmental scene representation information.

[0025] In this embodiment, for ease of description, the hearing threshold testing compensation device will be used as the execution subject for the following description.

[0026] Hearing threshold testing is a fundamental step in audiological diagnosis and fitting of hearing aids, and the accuracy of the test results directly affects the effectiveness of subsequent hearing compensation. Currently, the most widely used hearing threshold testing and compensation method in clinical practice and routine fitting is the pure-tone audiometry test. This method involves playing a single-frequency pure-tone signal into the subject's ear in a soundproof environment. By progressively adjusting the intensity of the pure-tone signal, the minimum intensity value at which the subject can just hear the sound at that frequency is determined; this is the pure-tone threshold. Based on the measured pure-tone thresholds for each frequency, audiologists or fitting systems generate corresponding hearing compensation plans to configure the gain parameters of hearing aids and other hearing-assistive devices.

[0027] However, the aforementioned pure-tone hearing threshold testing method has significant drawbacks in practical applications: because the test is conducted in a quiet environment, the measured pure-tone thresholds fail to reflect the test subject's auditory perception ability in a real sound field. When the test subject is in a real-life environment with background noise, their auditory system is affected by the masking effect of noise, resulting in a significant deviation between the actual identifiable sound threshold and the pure-tone threshold measured in a quiet environment. This leads to a mismatch between the hearing compensation strategy generated based on the pure-tone threshold and the auditory needs in real-world scenarios, affecting the effectiveness of hearing aids in actual use.

[0028] This application provides a solution that determines the relative discrimination intensity by analyzing and identifying feedback signals, establishes a target mapping relationship between pure tone thresholds and relative discrimination intensity, and then generates a hearing compensation strategy, thereby improving the effectiveness of hearing threshold testing and the accuracy of hearing compensation.

[0029] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a hearing threshold testing compensation device. The following description uses a hearing threshold testing compensation device as an example to illustrate this embodiment and the subsequent embodiments.

[0030] Based on this, the embodiments of this application provide a hearing threshold testing compensation method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the hearing threshold testing compensation method of this application.

[0031] In this embodiment, the hearing threshold test compensation method includes steps S11 to S13: Step S11: Receive the identification feedback signal sent by the user. The identification feedback signal is generated by the user based on the test sound signal and the masking sound signal.

[0032] It should be noted that the hearing threshold test compensation method is applied to the hearing threshold test compensation system, which includes a feedback receiving module and an audio playback module. The feedback receiving module is a hardware functional module in the hearing threshold test compensation system used to receive external feedback commands from the user, realizing the acquisition and transmission of user feedback signals. The identification feedback signal is a feedback electrical signal sent by the user through a specified interaction method after perceiving the test sound signal and the masking sound signal, transmitting the user's identification result of the test sound signal to the system.

[0033] Additionally, it should be noted that the test sound signal is a single-frequency pure tone electroacoustic signal used to detect the user's hearing threshold, serving as the reference signal for hearing threshold testing; the masking sound signal is a noise electroacoustic signal used to simulate the masking effect of a real sound field, creating a near-realistic auditory environment for the test sound signal.

[0034] Understandably, the purpose of this step is to obtain the user's perceptual feedback results on the test sound signal and the masking sound signal. The system uses a dedicated module to standardize the reception of user feedback signals, avoid external signal interference, and provide raw feedback data for subsequent analysis of the user's auditory recognition ability. By accurately receiving the user's recognition feedback signals, the system can directly obtain the user's auditory perception results, laying a real user feedback foundation for subsequent threshold analysis.

[0035] Specifically, the hearing threshold testing compensation system first establishes a communication connection between the feedback receiving module and the user interactive terminal. The interactive terminal can be a button panel, a touch screen, or a voice recognizer. When using a button panel, the user sends different recognition feedback signals by pressing different buttons, achieving rapid triggering of the feedback signal. When using a touch screen, the user sends recognition feedback signals by clicking touch icons, improving the intuitiveness of the interaction. When using a voice recognizer, the user sends recognition feedback signals through voice commands, adapting to test users with mobility impairments. After the system detects the signal trigger of the interactive terminal, the feedback receiving module performs analog-to-digital conversion on the trigger signal, converting the analog signal into a digital electrical signal that the system can recognize, thus completing the reception of the recognition feedback signal. The combination of different interactive terminals can adapt to test users of different ages and physical conditions, improving the system's universality.

[0036] For example, the user presses a physical button connected to the feedback receiving module. Pressing the "Confirm" button indicates that the test tone signal can be identified, and pressing the "Cancel" button indicates that the test tone signal cannot be identified. After receiving the button's press signal, the feedback receiving module converts it into a binary digital signal and transmits it to the system's core processing unit to complete the reception of this identification feedback signal.

[0037] Step S12: Analyze the identification feedback signal to determine the relative differentiation strength of the masking sound signal relative to the test sound signal.

[0038] It should be noted that relative distinguishing strength is the intensity difference between the masking tone signal and the test tone signal. It is a parameter that characterizes the critical intensity relationship between the masking tone signal and the test tone signal under the interference of the masking tone signal, and reflects the user's actual auditory discrimination ability when there is masking interference.

[0039] Understandably, the purpose of this step is to extract effective auditory recognition feature parameters from the user's recognition feedback signal. Through the system's analysis and calculation of the recognition feedback signal, the qualitative feedback result is transformed into quantitative intensity parameters, establishing an intensity correlation between the masking sound signal and the test sound signal. This provides quantitative data support for the subsequent construction of the mapping relationship. By transforming the recognition feedback signal into relative distinguishing intensity, the system can achieve quantitative analysis of the user's actual auditory ability, avoiding the ambiguity of qualitative feedback.

[0040] Specifically, the hearing threshold test compensation system first analyzes the received identification feedback signal to determine the identification result corresponding to the signal. Then, it retrieves the intensity data of the currently playing masking sound signal and the test sound signal pre-stored in the system and calculates the intensity difference between the two through subtraction. If the identification feedback signal represents a valid identification result, the intensity difference is directly determined as the relative discrimination intensity. If the identification feedback signal represents an invalid identification result, the system first adjusts the intensity of the masking sound signal and re-acquires the identification feedback signal until a valid identification result is obtained before calculating the intensity difference. This implementation method can ensure the validity of the relative discrimination intensity and avoid parameter errors caused by invalid feedback.

[0041] For example, the system parses the identification feedback signal to indicate that the user can identify the test tone signal, and then retrieves the intensity of the current masking tone signal as 30dB (Decibel) and the intensity of the test tone signal as 60dB. By calculating 60dB-30dB=30dB, 30dB is determined as the relative distinguishing intensity of the masking tone signal relative to the test tone signal in this test.

[0042] Step S13: Determine the target mapping relationship between the pure tone threshold and the relative distinguishing intensity, and generate a hearing compensation strategy based on the target mapping relationship and environmental scene representation information.

[0043] It should be noted that the pure tone threshold is the minimum intensity value at which a user can perceive pure tone signals of different frequencies in a quiet environment without any sound interference; the target mapping relationship is the correlation between the pure tone threshold and the relative distinguishing intensity, providing a basis for parameter matching for hearing compensation in different scenarios.

[0044] Additionally, it should be noted that environmental scene representation information is digital information that characterizes the features of the external auditory environment, including the frequency, intensity, and distribution of environmental noise, providing the system with a basis for scene determination; hearing compensation strategy is a personalized audio gain adjustment scheme formulated by the system for users, including audio gain parameters and adjustment rules for different frequency bands, guiding hearing aids to achieve personalized processing of audio signals and adapt to the user's actual hearing needs.

[0045] Understandably, the purpose of this step is to establish the correlation between basic auditory parameters and actual auditory parameters under masking, and to generate personalized hearing compensation schemes in combination with actual scenarios. By establishing the correspondence between pure tone thresholds and relative distinguishing intensities through mathematical modeling, and then calling the corresponding parameters according to the characteristics of the environmental scene, a compensation strategy adapted to the scene is formulated, realizing a closed loop from auditory threshold testing to personalized compensation strategy generation. This allows the hearing compensation scheme to fit the user's auditory needs in different actual scenarios. By combining target mapping relationships and environmental scene representation information, the hearing compensation strategy can break free from the limitation of a single basic threshold and achieve scenario-based accurate compensation.

[0046] Specifically, the hearing threshold testing compensation system first retrieves the pre-stored pure tone thresholds for each frequency of the user, and then correlates the pure tone thresholds with the relative discrimination intensity to establish a target mapping relationship between the two. Next, it collects scene information of the external environment, converts it into environmental scene representation information, analyzes the scene features of this information, and retrieves the pure tone thresholds or relative discrimination intensity at the corresponding frequencies based on the target mapping relationship. Combining the hardware parameters of the hearing aid device, it calculates the audio gain parameters of each frequency band through the gain calculation formula, and finally integrates all parameters to generate a standardized hearing compensation strategy. Among them, regression analysis is suitable for parameter modeling of linear correlation, while fitting modeling is suitable for parameter modeling of nonlinear correlation. The selection of different modeling methods can improve the accuracy of the target mapping relationship.

[0047] For example, the system retrieves the pure tone threshold of 25dB at the user's 1kHz (Kilohertz) frequency, and the relative discrimination intensity at this frequency is 30dB. The target mapping relationship between the two is established by linear fitting as y=1.2x (x is the pure tone threshold, and y is the relative discrimination intensity). The system collects an external environment of a noisy street scene. The corresponding environmental scene representation information indicates that the environmental noise is mainly concentrated in the 800Hz-1.2kHz frequency band. Based on the target mapping relationship, the system retrieves the relative discrimination intensity of this frequency band as the compensation benchmark and calculates the audio gain of this frequency band as 35dB. The gain of other frequency bands is calculated based on the pure tone threshold. Finally, the gain parameters of each frequency band are integrated to generate a hearing compensation strategy suitable for noisy street scenes.

[0048] This embodiment, through the above-described scheme, receives the user's identification feedback signal based on the test tone signal and the masking tone signal via a feedback receiving module. The qualitative feedback result is converted into a quantitative relative discrimination intensity. A target mapping relationship between the pure tone threshold and the relative discrimination intensity is then established. Combined with environmental scene representation information, a hearing compensation strategy is generated. This solves the technical problems of traditional pure tone testing, which does not consider the masking effect of the real sound field and results in large deviations from actual hearing. It allows hearing threshold testing to closely match the real auditory environment. Simultaneously, the hearing compensation strategy generated based on scene characteristics can accurately adapt to the user's actual auditory needs in different environments, improving the scene adaptability and practical effectiveness of hearing compensation. This upgrades the fitting of hearing aids from basic threshold fitting to precise fitting that matches the actual sound field.

[0049] Based on the above implementation scheme, in one feasible implementation, before the step of receiving the identification feedback signal sent by the user, wherein the identification feedback signal is generated by the user based on the test tone signal and the masking tone signal, steps S21-S22 are further included: Step S21: Determine the target frequency to be tested and generate a test tone signal corresponding to the target frequency.

[0050] It should be noted that the target frequency is the pure tone signal frequency selected by the system for detecting the user's hearing threshold. It serves as the frequency benchmark for hearing threshold testing, and its selection range covers the audible frequency band of the human ear, enabling targeted testing of the user's hearing ability at different frequencies. The test tone signal generation is the process by which the system converts digital frequency parameters into electroacoustic signals through the signal generation module. The generated test tone signal is a single-frequency pure tone signal, providing a standardized benchmark test signal for hearing testing.

[0051] Understandably, this step involves selecting a specific frequency benchmark for hearing threshold testing and generating a corresponding standardized test sound signal. The target frequency is selected based on the characteristics of the human ear's hearing frequency band, and a pure tone electroacoustic signal of the corresponding frequency is generated through digital signal generation technology. This ensures the frequency accuracy and standardization of the test sound signal, laying the foundation for the subsequent synchronous playback of the test sound signal and the masking sound signal. By determining the target frequency and generating the corresponding test sound signal, it is possible to test the user's hearing ability at different frequencies one by one, thereby improving the comprehensiveness of hearing threshold testing.

[0052] Specifically, the hearing threshold testing compensation system first presets a set of frequencies to be tested based on the audible frequency range of the human ear (20Hz-20kHz) and commonly used frequencies in clinical hearing tests. The system selects a frequency from the set sequentially or randomly as the target frequency to be tested, or it can select a target frequency specifically based on the user's previous hearing screening results. Then, through the system's signal generation module, a corresponding single-frequency pure tone electrical signal is generated based on the selected target frequency using direct digital frequency synthesis technology. This electrical signal is used as the test tone signal, and the amplitude of the test tone signal is calibrated to ensure the stability of the signal strength. Targeted selection of the target frequency can improve testing efficiency, sequential selection can ensure the comprehensiveness of the test, and combinations of different selection methods can take into account both the comprehensiveness and efficiency of the test.

[0053] For example, the system presets the commonly used clinical test frequency set as 250Hz, 500Hz, 1kHz, 2kHz, 4kHz and 8kHz. The system selects 1kHz as the target frequency to be tested in sequence, generates a 1kHz single-frequency pure tone electrical signal through direct digital frequency synthesis technology, and determines it as the test tone signal for this test after amplitude calibration.

[0054] Step S22: Generate a corresponding masking sound signal based on the target frequency, and control the audio playback module to play the test sound signal and the masking sound signal.

[0055] It should be noted that the audio playback module is a hardware functional module in the hearing threshold test compensation system used to convert electroacoustic signals into audible sound signals. It includes a digital-to-analog converter and a sound generation unit to realize the audio output of test sound signals and masking sound signals. Masking sound signal generation is the process by which the system generates corresponding noise electroacoustic signals based on the characteristics of the target frequency. The generated masking sound signal is a noise signal in a frequency band adjacent to the target frequency, simulating the auditory masking effect on the target frequency in a real sound field. Playback refers to the audio playback module simultaneously converting the test sound signal and the masking sound signal into audio signals for synchronous output, ensuring that the playback time domains of the two signals completely overlap, creating a masking auditory environment that is close to reality.

[0056] Understandably, the purpose of this step is to generate a masking tone signal that matches the target frequency, and to achieve synchronous playback of the test tone signal and the masking tone signal through the audio playback module. The principle is to generate masking tone signals in adjacent frequency bands based on the frequency band characteristics of the target frequency. The digital-to-analog conversion and sound generation unit of the audio playback module achieve synchronous audio output of the two signals, creating a realistic auditory testing environment with a masking effect for the user, so that the user's perceptual feedback is close to the actual hearing. By synchronously playing the matched test tone signal and the masking tone signal, the subsequent identification feedback signal can more realistically reflect the user's hearing ability in the actual sound field.

[0057] Specifically, the hearing threshold test compensation system determines the adjacent frequency band range based on the selected target frequency, generates a broadband noise electrical signal within that frequency band range using noise signal generation technology, and uses it as the corresponding masking sound signal. The generated test sound signal and masking sound signal are then transmitted to the audio playback module. The system sends a synchronous playback command to the audio playback module, which first performs digital-to-analog conversion on the two electroacoustic signals, converting the digital electrical signal into an analog audio signal. Then, the two analog audio signals are simultaneously output through the sound unit to achieve synchronous playback. At the same time, the system can adjust the initial intensity ratio of the two signals according to the test requirements to adapt to different masking test needs.

[0058] For example, the system selects a target frequency of 1kHz and determines its adjacent frequency band as 800Hz-1.2kHz. It generates a broadband noise electrical signal in this frequency band as a masking sound signal. The 1kHz test sound signal and the 800Hz-1.2kHz masking sound signal are transmitted to the audio playback module. After sending a synchronous playback command, the audio playback module converts the two electrical signals into audio signals and plays them to the user simultaneously through professional hearing test headphones, thereby achieving synchronous playback of the test sound signal and the masking sound signal.

[0059] This embodiment, through the above-described scheme, generates a standardized test sound signal by selecting a specific target frequency, and then generates a matching masking sound signal based on the target frequency. The audio playback module enables the synchronous playback of the two, allowing the user's hearing test to be conducted in a masked environment that closely resembles the real sound field. This solves the problem of test result distortion caused by the lack of specificity of the signal and the asynchronous playback in traditional tests. At the same time, it makes the subsequently acquired identification feedback signal and relative differentiation intensity more closely resemble the user's auditory perception of specific frequencies in the actual sound field, further improving the accuracy of hearing threshold testing and the scenario adaptability of hearing compensation strategies, and realizing targeted testing and compensation of the user's hearing ability at different frequencies.

[0060] Based on the above implementation scheme, in one feasible implementation, the step of generating the corresponding masking tone signal based on the target frequency includes S31~S34: Step S31: Calculate the critical bandwidth based on the target frequency.

[0061] It should be noted that the critical bandwidth refers to the frequency resolution bandwidth of the human ear for different frequencies of sound. It is a parameter characterizing the frequency domain resolution capability of human hearing. Different target frequencies correspond to different critical bandwidths, providing a basis for human hearing characteristics to determine the frequency range of masking sound signals. The technology used in the calculation is a critical bandwidth calculation method based on the human hearing model. This technology combines the physiological hearing characteristics of the human ear to achieve accurate calculation of the critical bandwidth at different frequencies.

[0062] Furthermore, the critical band is a core concept in auditory psychology, referring to the specific frequency range on the basilar membrane of the human ear that can integrate and process sound energy. When multiple sound components fall within the same critical band, they are perceived as a single entity by the auditory system, producing a masking effect; if they exceed this range, they are treated as independent signals. The critical band width is a parameter characterizing the range of this frequency region, reflecting the frequency resolution capability of the human ear at different frequencies. The higher the frequency, the wider the critical band width, and the lower the frequency resolution of the human ear.

[0063] In one embodiment of this application, the Moore-Glasberg model is used as the calculation model for the critical frequency band of human hearing, and the calculation formula is as follows: ERB(f) = 24.7 × (4.37f + 1) Where f is the target frequency in kHz (kilohertz); ERB(f) is the critical bandwidth.

[0064] Understandably, the purpose of this step is to obtain frequency domain bandwidth parameters that conform to the characteristics of human hearing based on the target frequency. The principle is based on the critical band effect of human hearing, and obtains the critical band width corresponding to the target frequency through a special calculation method. This provides a scientific basis for subsequently determining the center frequency and frequency band range of the masking sound signal, making the generation of the masking sound signal more in line with the actual auditory masking characteristics of the human ear. By calculating the critical band width of the target frequency, the frequency band range of the masking sound signal can be avoided from being set too large or too small, ensuring the authenticity of the masking effect.

[0065] Specifically, the core processing unit of the hearing threshold test compensation system has a pre-stored human hearing critical frequency band calculation model. The system substitutes the selected target frequency into the calculation model and performs calculations using the mathematical formulas in the model to obtain the critical frequency band width value corresponding to the target frequency. At the same time, the system verifies the accuracy of the calculation results. If the result exceeds the preset reasonable range, it is recalculated and parameter errors are checked. This implementation method can ensure the accuracy and reasonableness of the critical frequency band width calculation results.

[0066] For example, the system selects a target frequency of 1kHz, substitutes it into a pre-stored human hearing critical frequency band calculation model, and calculates that the critical frequency band width corresponding to 1kHz is 132Hz. After accuracy verification, the calculation result is confirmed to be valid.

[0067] Step S32: Calculate the first center frequency based on the critical bandwidth, with the target frequency as a reference, and offset by a first preset offset to the low-frequency side; generate a first noise signal based on the first center frequency.

[0068] It should be noted that the first preset offset is a frequency value preset by the system and offset towards the low-frequency side based on the target frequency. This value is based on the critical bandwidth setting to determine the center frequency of the low-frequency side masking noise signal. The first center frequency is the frequency reference of the low-frequency side noise signal in the masking sound signal. It is calculated from the target frequency, critical bandwidth and the first preset offset, and provides a frequency reference for the generation of the low-frequency side noise signal. The first noise signal is a broadband noise electroacoustic signal generated based on the first center frequency. It belongs to the low-frequency component of the masking sound signal and simulates the auditory masking effect of the low-frequency side of the target frequency.

[0069] Understandably, the purpose of this step is to generate the low-frequency noise component of the masking sound signal. The principle is to determine the center frequency of the low-frequency side based on the critical bandwidth of the target frequency, and then generate a broadband noise signal corresponding to the center frequency through noise signal generation technology. This provides the low-frequency masking component for the masking sound signal, making the frequency band distribution of the masking sound signal more closely match the masking characteristics of the human ear to the target frequency. By generating the first noise signal, the simulation of the low-frequency masking effect of the target frequency can be achieved, improving the realism of the masking sound signal.

[0070] Specifically, the hearing threshold test compensation system first determines the value of the first preset offset based on the critical bandwidth, subtracts the first preset offset from the target frequency, and calculates the first center frequency. Then, the first center frequency is input to the system's noise signal generation module, which uses broadband noise signal generation technology to generate a broadband noise electrical signal with the first center frequency as the reference and a preset bandwidth. This electrical signal is identified as the first noise signal. At the same time, the frequency and intensity of the first noise signal are calibrated to ensure the stability of the signal. The value of the first preset offset can be dynamically adjusted according to the critical bandwidth to improve the matching degree of the center frequency.

[0071] For example, the target frequency is 1kHz, the critical bandwidth is 132Hz, the system sets the first preset offset to 132 / 2=66Hz, and calculates the first center frequency to be 1kHz-66Hz=934Hz. Based on 934Hz, a broadband noise signal with a bandwidth of 66Hz is generated and determined as the first noise signal.

[0072] Step S33: Calculate the second center frequency based on the critical bandwidth, with the target frequency as a reference, and offset by a second preset amount towards the high-frequency side; generate a second noise signal based on the second center frequency.

[0073] It should be noted that the second preset offset is a frequency value preset by the system and offset towards the higher frequency side based on the target frequency. Based on the critical bandwidth setting, the center frequency of the high-frequency side masking noise signal is determined. The second center frequency is the frequency reference of the high-frequency side noise signal in the masking sound signal. It is calculated from the target frequency, critical bandwidth and the second preset offset, and provides a frequency reference for the generation of the high-frequency side noise signal. The second noise signal is a broadband noise electroacoustic signal generated based on the second center frequency. It belongs to the high-frequency component of the masking sound signal and simulates the auditory masking effect of the high-frequency side of the target frequency. The frequency calculation adopts the basic frequency addition and subtraction operation technology, and the noise signal generation adopts broadband noise signal generation technology.

[0074] Understandably, the purpose of this step is to generate the high-frequency noise component of the masking sound signal. The principle is to determine the center frequency of the high-frequency side based on the critical bandwidth of the target frequency, and then generate a broadband noise signal corresponding to the center frequency through noise signal generation technology. This provides the masking component of the masking sound signal on the high-frequency side, making the frequency band distribution of the masking sound signal more closely match the masking characteristics of the human ear to the target frequency. By generating a second noise signal, the high-frequency masking effect of the target frequency can be simulated, making the masking effect of the masking sound signal more comprehensive.

[0075] Specifically, the hearing threshold test compensation system first determines the value of the second preset offset based on the critical bandwidth, adds the second preset offset to the target frequency, and calculates the second center frequency. Then, the second center frequency is input to the system's noise signal generation module, which uses broadband noise signal generation technology to generate a broadband noise electrical signal with the second center frequency as the reference and a preset bandwidth. This electrical signal is identified as the second noise signal. At the same time, the frequency and intensity of the second noise signal are calibrated to ensure the stability of the signal. The second preset offset can be the same as or different from the first preset offset to adapt to different masking test requirements.

[0076] For example, the target frequency is 1kHz, the critical bandwidth is 132Hz, the system sets the second preset offset to 66Hz, and calculates the second center frequency as 1kHz + 66Hz = 1066Hz. Based on 1066Hz, a broadband noise signal with a bandwidth of 66Hz is generated and determined as the second noise signal.

[0077] Step S34: The first noise signal and the second noise signal are superimposed and synthesized to generate a masking sound signal.

[0078] It should be noted that signal superposition synthesis refers to the process by which a system fuses two independent electroacoustic signals using signal superposition technology to generate a new composite electroacoustic signal. The technology used is time-domain superposition synthesis of electroacoustic signals. This technology can ensure that the two signals retain their respective frequency and intensity characteristics after superposition, and fuse the noise signals on the low-frequency side and the high-frequency side into a complete masking sound signal. The masking sound signal is a composite broadband noise electroacoustic signal synthesized by superposition of the first noise signal and the second noise signal. Its frequency band covers the critical frequency range of the target frequency, creating a masking environment that conforms to the hearing characteristics of the human ear for the test sound signal at the target frequency.

[0079] Understandably, the purpose of this step is to fuse the noise signals from the low-frequency and high-frequency sides into a complete masking sound signal that matches the target frequency. By using electroacoustic signal superposition technology, the first and second noise signals are fused in the time domain, retaining their respective signal characteristics to form a composite noise signal covering the critical frequency band of the target frequency. This generates a masking sound signal that matches the masking characteristics of human hearing, providing a standardized masking signal for subsequent synchronized playback. By superimposing and synthesizing the masking sound signal, the frequency range of the masking sound signal can be accurately matched to the critical frequency band of the target frequency, ensuring that the masking effect is consistent with actual human hearing.

[0080] Specifically, the hearing threshold test compensation system transmits the generated first noise signal and second noise signal to the signal synthesis module. The signal synthesis module uses time-domain superposition synthesis technology to superimpose and fuse the two signals in the time domain to generate a new composite electroacoustic signal. The system performs signal feature detection on the superimposed composite signal to detect whether its frequency range and intensity distribution meet the preset requirements. If they meet the requirements, it is identified as a masking sound signal. If they do not meet the requirements, the parameters of the two noise signals are readjusted and superimposed again. This implementation method can ensure that the signal characteristics of the masking sound signal meet the test requirements.

[0081] For example, the system transmits a first noise signal of 934Hz and a second noise signal of 1066Hz to the signal synthesis module, and fuses the two through time-domain superposition synthesis technology to generate a composite broadband noise signal in the 800Hz-1.2kHz frequency band. After signal feature detection, the signal is determined to be the masking tone signal corresponding to the 1kHz target frequency.

[0082] This embodiment, through the above-described scheme, calculates the critical bandwidth of the target frequency, determines the center frequencies of the low-frequency and high-frequency sides, and generates corresponding noise signals. The two noise signals are then superimposed to synthesize a masking sound signal. This allows the masking sound signal to be generated based on the critical bandwidth characteristics of human hearing, solving the problems of traditional masking sound signal generation lacking scientific auditory basis and having a mismatch between the frequency range and the actual masking effect. The generated masking sound signal accurately simulates the actual auditory masking effect of the human ear on the target frequency, making subsequent hearing tests more consistent with the physiological auditory characteristics of the human ear. This further improves the accuracy of hearing threshold test results. Simultaneously, the hearing compensation strategy generated based on these test results is more consistent with the user's actual auditory perception, enhancing the actual effect of hearing compensation.

[0083] Based on the above implementation scheme, in one feasible implementation, the step of analyzing the identification feedback signal and determining the relative differentiation intensity of the masking sound signal relative to the test sound signal includes S41~S43: Step S41: Determine the user's recognition status of the test tone signal based on the recognition feedback signal. The recognition status includes a recognizable status and / or an unrecognizable status.

[0084] It should be noted that the identification state is the result of the user's perception of the test tone signal, as determined by the system based on the identification feedback signal. It is divided into an identifiable state and an unidentifiable state. An identifiable state indicates that the user can perceive the test tone signal under the interference of the masking tone signal, while an unidentifiable state indicates that the user cannot perceive the test tone signal under the interference of the masking tone signal. These two are qualitative parameters that characterize the user's auditory identification result and provide the system with the trigger basis for adjusting the masking tone signal intensity. The technology used for state determination is electrical signal feature analysis technology. This technology achieves accurate determination of the identification state by analyzing the digital features of the identification feedback signal.

[0085] Understandably, the purpose of this step is to transform the received identification feedback signal into a qualitative identification state that the system can recognize. By analyzing the digital characteristics of the identification feedback signal through electrical signal feature analysis technology, and based on the preset correspondence between signal features and identification states, the user's identification state is determined. This provides a clear trigger condition for subsequent adjustment of the masking sound signal intensity, allowing the system to adaptively adjust the masking sound signal intensity according to the user's identification state. By determining the identification state, the system can quickly respond to the user's auditory feedback, realizing dynamic interaction in the testing process.

[0086] Specifically, the core processing unit of the hearing threshold test compensation system pre-stores the correspondence between the features of the identification feedback signal and the identification state. The system performs digital feature analysis on the received identification feedback signal, extracting features such as the signal amplitude, frequency, and encoding. The extracted features are matched with the pre-stored correspondence. If the match is a valid feature, the identification state is determined to be identifiable; if the match is an invalid feature, the identification state is determined to be unidentifiable. At the same time, the system verifies the matching results to avoid errors in state determination caused by feature analysis errors. This implementation method can ensure the accuracy of identification state determination.

[0087] Step S42: When the identification state is unidentifiable, reduce the intensity of the masking sound signal and play it until it is determined that the user can distinguish the test sound signal from the masking sound signal at a critical intensity point. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity.

[0088] It should be noted that the masking sound signal intensity adjustment is a process in which the system adjusts the sound pressure intensity of the masking sound signal through signal amplitude adjustment technology. The intensity reduction adopts a constant step amplitude reduction technology to reduce the masking effect of the masking sound signal, allowing the user to gradually identify the test sound signal. The critical intensity point refers to the intensity value of the masking sound signal when the user changes from an indistinguishable test sound signal to a identifiable test sound signal. It is a parameter characterizing the user's auditory critical identification ability under masking, providing a precise masking sound signal intensity benchmark for calculating the relative discrimination intensity. The critical masking sound signal intensity is the masking sound signal intensity value corresponding to the critical intensity point, and the test sound signal intensity is the benchmark intensity value that remains constant during the test. The difference between the two is calculated using a basic numerical subtraction technique to obtain a quantitative relative discrimination intensity.

[0089] Understandably, the purpose of this step is to find the user's auditory critical discrimination point by dynamically adjusting the intensity of the masking tone signal when the user cannot identify the test tone signal, and then determine the relative discrimination intensity. By gradually reducing the intensity of the masking tone signal to reduce the masking effect, the user's auditory discrimination state is reversed. After finding the critical intensity point, the intensity difference is calculated to ensure that the relative discrimination intensity can accurately reflect the user's auditory critical discrimination ability under the masking effect, avoiding parameter errors caused by directly using the initial intensity calculation. By dynamically adjusting and determining the critical intensity point, the value of the relative discrimination intensity can be made to better match the user's actual hearing ability.

[0090] Specifically, after determining that the identification state is unidentifiable, the hearing threshold test compensation system reduces the intensity of the masking sound signal by a preset step size using signal amplitude adjustment technology, generating a reduced masking sound signal while maintaining a constant intensity of the test sound signal. The system then controls the audio playback module to replay the reduced masking sound signal and the original test sound signal, and receives the user's identification feedback signal again to determine the identification state. If the identification state is still unidentifiable, the process of reducing intensity, replaying, and determining the state is repeated until the identification state flips to an identifiable state. At this point, the intensity of the current masking sound signal is determined as the critical masking sound signal intensity corresponding to the critical intensity point. The intensity of the test sound signal is retrieved, and the difference between the two is calculated through subtraction. This difference is determined as the relative distinguishing intensity. The preset step size can be adjusted according to test requirements; a smaller step size improves the accuracy of the critical intensity point, while a larger step size improves test efficiency.

[0091] For example, the system determines that the identification state is unidentifiable. The initial masking tone signal strength is 40dB, and the test tone signal strength is 60dB. The system reduces the masking tone signal strength to 35dB in 5dB increments. After replaying, the user still cannot identify it. The system continues to reduce the strength to 30dB, at which point the user's identification state flips to an identifiable state. 30dB is determined as the critical masking tone signal strength. The system calculates 60dB-30dB=30dB and determines 30dB as the relative distinguishing strength.

[0092] Step S43: When the identification state is a recognizable state, increase the intensity of the masking sound signal and play it until it is determined that the user can distinguish the test sound signal from the masking sound signal at a critical intensity point. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity.

[0093] It should be noted that the masking tone signal intensity enhancement adopts a constant step amplitude increment technique to increase the masking effect of the masking tone signal, causing the user's auditory recognition state to change from recognizable to unrecognizable, thereby finding the critical intensity point. The critical intensity point refers to the last recognizable intensity point before the user changes from a recognizable test tone signal to an unrecognizable test tone signal, and is the core parameter characterizing the user's auditory critical recognition ability under masking. The difference between the critical masking tone signal intensity and the test tone signal intensity is calculated using a basic numerical subtraction technique to obtain a quantitative relative distinguishing intensity.

[0094] Understandably, the purpose of this step is to find the user's auditory critical recognition point by dynamically adjusting the intensity of the masking sound signal when the user can recognize the test sound signal, and then determine the relative discrimination intensity. By gradually increasing the intensity of the masking sound signal, the masking effect is increased, causing the user's auditory recognition state to reverse. After finding the critical intensity point, the intensity difference is calculated to ensure that the relative discrimination intensity can accurately reflect the user's auditory critical recognition ability under the masking effect, and to make the value of the relative discrimination intensity more in line with the user's actual auditory limit.

[0095] Specifically, after determining that the identification state is identifiable, the hearing threshold test compensation system increases the intensity of the masking sound signal by a preset step size using signal amplitude adjustment technology to generate an enhanced masking sound signal. While maintaining a constant test sound signal intensity, the system controls the audio playback module to replay the enhanced masking sound signal and the original test sound signal, and then receives the user's identification feedback signal again to determine the identification state. If the identification state is still identifiable, the process of increasing intensity, replaying, and determining the state is repeated until the identification state flips to an indistinguishable state. At this point, the intensity of the previously played masking sound signal is determined as the critical masking sound signal intensity corresponding to the critical intensity point. The test sound signal intensity is retrieved, and the difference between the two is calculated through subtraction. This difference is determined as the relative distinguishing intensity. This implementation method can determine the critical intensity point from the perspective of auditory limits, improving the accuracy of the relative distinguishing intensity.

[0096] For example, the system determines that the identification state is identifiable. The initial masking tone signal strength is 20dB, and the test tone signal strength is 60dB. The system increases the strength in increments of 5dB to 25dB, and the user can still identify it. The system continues to increase the strength to 30dB, and the user can still identify it. The system increases the strength to 35dB, and the user can no longer identify it. The previous 30dB is determined as the critical masking tone signal strength. The system calculates 60dB-30dB=30dB, and determines 30dB as the relative distinguishing strength.

[0097] This embodiment, through the above-described scheme, determines the user's recognition state based on the recognition feedback signal. When recognition is impossible, the masking sound signal intensity is gradually reduced; when recognition is possible, the masking sound signal intensity is gradually increased. This dynamically finds the user's auditory critical recognition point. Based on the critical intensity point, the relative discrimination intensity is calculated. This solves the problem of traditional methods directly using the initial intensity to calculate the relative discrimination intensity, and the parameter cannot reflect the user's auditory critical recognition ability. The relative discrimination intensity can accurately characterize the user's auditory limit recognition ability under the masking effect, improving the accuracy and effectiveness of the parameter. At the same time, the target mapping relationship established based on this accurate parameter is more in line with the user's actual auditory characteristics, thereby making the generated hearing compensation strategy more accurately adapt to the user's actual auditory needs and improving the accuracy of hearing compensation.

[0098] Based on the above implementation scheme, in one feasible implementation, after the step of receiving the identification feedback signal sent by the user, the method further includes steps S51 to S53: Step S51: Determine the reaction delay time based on the identification feedback signal.

[0099] It should be noted that the reaction delay time refers to the time interval from the start of the audio playback module completing the playback of the test audio signal and the masking audio signal to the time interval from the start of the feedback receiving module receiving the user's identification feedback signal. It is a parameter characterizing the user's auditory feedback response speed and provides a time characteristic basis for judging the validity of the test data. The technology used to determine the time is the system timestamp recording and difference calculation technology. This technology obtains the reaction delay time by recording the system timestamps of two time nodes and calculating the difference between the timestamps, which can achieve accurate calculation of the time interval.

[0100] Understandably, the purpose of this step is to obtain the user's response time to the auditory test signal. By recording the two time points of signal playback completion and feedback signal reception through system timestamps, the time difference between the two time points is calculated to obtain the reaction delay time. This provides a quantitative time parameter for the subsequent reliability analysis of the test data, allowing the system to judge the validity of the test data from the perspective of response speed. By determining the reaction delay time, invalid test data caused by slow user response can be filtered out, thereby improving the overall validity of the test data.

[0101] Specifically, the hearing threshold test compensation system records the system timestamp of the time point when the audio playback module completes the playback of the test sound signal and the masking sound signal as the start timestamp; when the feedback receiving module successfully receives the user's identification feedback signal, it records the system timestamp of the time point as the end timestamp; the system calculates the time difference between the end timestamp and the start timestamp, and determines the user's reaction delay time by this difference. At the same time, the system calibrates the recording accuracy of the timestamp to ensure the accuracy of the time difference calculation. The timestamp recording accuracy can be set to the millisecond level to improve the accuracy of the reaction delay time calculation.

[0102] For example, the system records the start timestamp of the audio playback completion as 1690000000000ms and the end timestamp of receiving the identification feedback signal as 1690000003000ms, and calculates the response delay time as 3000ms, or 3 seconds.

[0103] Step S52: Compare the reaction delay time with a preset effective time window to obtain the reaction delay result.

[0104] It should be noted that the preset effective time window is a range of reaction delay time preset by the system based on industry standards for clinical hearing tests and general patterns of user auditory feedback. This range serves as a benchmark for judging whether the user's reaction delay time is reasonable, providing a quantitative basis for determining the reaction delay result. The reaction delay result is a qualitative judgment obtained by the system by comparing the reaction delay time with the preset effective time window, categorized as either within the effective range or outside the effective range, providing a qualitative basis for subsequent reliability analysis. The time comparison technique used is a numerical comparison technique, which obtains the comparison result by directly comparing the magnitudes of two time values.

[0105] Understandably, the purpose of this step is to compare the user's response delay time with the effective benchmark range to obtain a qualitative delay result. By comparing the numerical magnitude comparison technique with the upper and lower limits of the preset effective time window, it is determined whether the response delay time is within the effective range, providing a direct basis for subsequent credibility analysis. This allows the system to quickly filter out obviously invalid feedback data. By comparing and obtaining the response delay result, the initial validity screening of the test data can be achieved, improving the efficiency of subsequent credibility analysis.

[0106] Specifically, the hearing threshold test compensation system has pre-stored upper and lower limits for a preset effective time window. The system compares the calculated reaction delay time with these upper and lower limits. If the reaction delay time is greater than the lower limit but less than the upper limit, the reaction delay result is determined to be "within the preset effective time window"; if the reaction delay time is less than the lower limit or greater than the upper limit, the reaction delay result is determined to be "outside the preset effective time window". At the same time, the system stores the comparison results and related time data to provide data support for subsequent reliability analysis. The preset effective time window can be dynamically adjusted according to the age and physical condition of the test user to improve the universality of the judgment.

[0107] For example, the system presets an effective time window of 1 second to 5 seconds, and the user's reaction delay time is 3 seconds. After comparison, the reaction delay result is determined to be "within the preset effective time window"; if the user's reaction delay time is 7 seconds, it is determined to be "out of the preset effective time window".

[0108] Step S53: Perform a credibility analysis on the response delay result based on the preset confidence rating information. If the credibility analysis fails, filter the corresponding identification feedback signal.

[0109] It should be noted that the preset confidence rating information is the system's pre-defined correspondence between response delay results and confidence levels. Confidence levels are divided into different grades, representing the credibility of the test data, and serve as the core rating basis for credibility analysis in this step. Credibility analysis is the process by which the system comprehensively judges the credibility of the test data by combining the response delay results and confidence rating information, and the core technology used is multi-dimensional data comprehensive judgment. Filtering refers to the system marking identification feedback signals that fail the credibility analysis as invalid data and not transmitting them to the subsequent relative discrimination strength analysis step. In this step, it is used to remove invalid feedback data to ensure the validity of subsequent analysis data.

[0110] Understandably, the purpose of this step is to conduct a comprehensive credibility analysis on the test data based on the identification feedback signal, eliminate invalid data, and match the current reaction delay result with the preset confidence rating information through multi-dimensional data comprehensive judgment technology to obtain the corresponding confidence level. If the level is lower than the preset rating pass threshold, the credibility analysis is judged to have failed, and the corresponding identification feedback signal is filtered out; if it reaches or exceeds the threshold, the analysis is judged to have passed, and the data is transmitted to the subsequent steps. This ensures that the test data used for subsequent analysis are all valid data that can truly reflect the user's hearing ability, avoids the impact of invalid feedback data caused by slow user reaction, misoperation, lack of concentration, etc. on the test results, and improves the overall validity and accuracy of the test data.

[0111] Specifically, the hearing threshold test compensation system pre-stores preset confidence rating information, including confidence levels and rating pass thresholds corresponding to different reaction delay results. The system matches the current reaction delay result with the confidence rating information to obtain the corresponding confidence level. If the confidence level is higher than or equal to the preset rating pass threshold, the confidence analysis is deemed successful, and the current identification feedback signal and related test data are transmitted to the subsequent relative discrimination strength analysis step. If the confidence level is lower than the preset rating pass threshold, the confidence analysis is deemed unsuccessful, and the system immediately marks the identification feedback signal as invalid data and filters it, not using it for subsequent parameter calculations. The preset confidence rating information can be dynamically updated based on clinical test data to improve the accuracy of the confidence analysis.

[0112] For example, the system's preset confidence rating information is as follows: "within the effective time window" corresponds to a confidence level of A, "exceeding the effective time window by 1-2 seconds" corresponds to a confidence level of B, and "exceeding the effective time window by more than 2 seconds" corresponds to a confidence level of C. The passing threshold for the rating is B. If the user's reaction delay result is "exceeding the effective time window by 1 second", it matches a confidence level of B, reaches the passing threshold, the confidence analysis passes, and the data is transmitted to subsequent steps. If the user's reaction delay result is "exceeding the effective time window by 3 seconds", it matches a confidence level of C, does not reach the passing threshold, the confidence analysis fails, and the system filters the corresponding identification feedback signal.

[0113] This embodiment, through the above-described scheme, obtains the user's feedback response time, compares it with a preset effective time window, and then performs a comprehensive credibility analysis based on confidence rating information. It filters out identification feedback signals that fail the credibility analysis, solving the problem of traditional methods directly using all user feedback data without considering feedback validity, leading to distorted test parameters. This method ensures that the identification feedback signals used for subsequent analysis are all valid data that truly reflect the user's actual hearing ability, making the obtained relative discrimination strength parameters more accurate and valuable. The target mapping relationship established based on these valid parameters can truly fit the user's auditory characteristics, and the final generated hearing compensation strategy can accurately match the user's actual auditory needs in different environments, significantly improving the reliability of hearing threshold testing and the actual adaptability of hearing compensation.

[0114] Based on the above implementation scheme, in one feasible implementation, the step of determining the target mapping relationship between the pure tone threshold and the relative distinguishing intensity includes S61~S63: Step S61: Determine the pure tone threshold at the target frequency.

[0115] It should be noted that the pure tone threshold at the target frequency refers to the minimum intensity value of the pure tone signal at that target frequency that a user can perceive in a quiet environment without any sound interference. It is a core parameter characterizing a user's basic hearing ability at a specific frequency and provides basic auditory data for establishing a target mapping relationship. The technique used to determine the threshold is clinical pure tone audiometry data retrieval technology. This technology obtains the pure tone threshold of the corresponding target frequency by retrieving the user's pre-stored clinical pure tone audiometry test results in the system, which can ensure the accuracy and authority of the parameters.

[0116] Understandably, the purpose of this step is to obtain the user's basic hearing threshold parameters at the target frequency. By retrieving the user's pre-stored clinical pure-tone audiometry test data in the system, the pure-tone thresholds of the corresponding target frequency are extracted, providing basic data for establishing the parameter mapping relationship at the target frequency. This allows the target mapping relationship to accurately correspond to the user's hearing characteristics at a specific frequency. By determining the pure-tone thresholds at the target frequency, targeted modeling of a specific frequency can be achieved, improving the frequency specificity and accuracy of the target mapping relationship.

[0117] Specifically, the hearing threshold testing compensation system pre-stores complete clinical pure-tone audiometry data for users, including pure-tone thresholds corresponding to different frequencies. After determining the target frequency to be tested, the system uses data retrieval technology to search for the pure-tone threshold corresponding to the target frequency in the test data and determines the retrieved value as the pure-tone threshold at the target frequency. If the system does not have relevant test data for the user, the system first triggers the pure-tone audiometry test at the target frequency, obtains the test results, and then determines the threshold. This implementation method can ensure the integrity and accuracy of the threshold parameters.

[0118] For example, the system selects a target frequency of 1kHz, retrieves the pure tone threshold of 1kHz from the user's clinical pure tone hearing threshold test data using data retrieval technology, and determines this 25dB as the pure tone threshold at the target frequency.

[0119] Step S62: Establish a data pair between the pure tone threshold and the relative discrimination intensity at the target frequency, and calculate the masking margin between the relative discrimination intensity and the pure tone threshold.

[0120] It should be noted that the associated data pair is a binary data set formed by combining the pure tone threshold and relative discrimination intensity at the target frequency according to the frequency correspondence. It represents the correspondence between two auditory parameters at a specific frequency and provides the basic data unit for establishing the target mapping relationship. The masking margin refers to the numerical difference between the relative discrimination intensity and the pure tone threshold. It is a parameter that represents the degree of influence of the masking effect on the user's auditory ability and provides a quantitative basis for establishing the target mapping relationship. The difference calculation uses the basic numerical subtraction technique, which can achieve accurate difference calculation between the two parameters.

[0121] Understandably, the purpose of this step is to correlate the two core auditory parameters at the target frequency and calculate the masking margin, which characterizes the degree of influence of the masking effect. By combining the data to form correlated data pairs, the difference between the two parameters is calculated through subtraction to obtain the masking margin. This provides richer quantitative data for establishing the target mapping relationship, allowing the target mapping relationship to simultaneously reflect the influence of basic auditory ability and the masking effect. By establishing correlated data pairs and calculating the masking margin, the establishment of the target mapping relationship can be based on more comprehensive auditory parameters, improving the scientific nature and accuracy of the mapping relationship.

[0122] Specifically, the hearing threshold test compensation system combines the pure tone threshold at the determined target frequency with the relative discrimination intensity at that frequency to form a correlated data pair in the form of (pure tone threshold, relative discrimination intensity). At the same time, it calculates the numerical difference between the relative discrimination intensity and the pure tone threshold through subtraction, and determines this difference as the masking margin. The correlated data pair and the masking margin are stored together to provide data support for the subsequent establishment of target mapping relationship. The data storage adopts a frequency classification storage method to improve the efficiency of subsequent data retrieval.

[0123] For example, the pure tone threshold at the target frequency of 1kHz is 25dB and the relative discrimination intensity is 30dB. The system combines the two into an associated data pair of (25dB, 30dB). By calculating 30dB-25dB=5dB, 5dB is determined as the masking margin at this frequency.

[0124] Step S63: Determine the target mapping relationship based on the associated data pair and the masking margin.

[0125] It should be noted that the target mapping relationship is a dynamic correspondence between the pure tone threshold and the relative discrimination intensity established by the system through mathematical modeling based on associated data pairs and masking margins. In this step, it serves as the core parameter matching basis for the subsequent generation of hearing compensation strategies in combination with the scene. The technique used to determine the relationship is mathematical fitting modeling technology, including linear fitting, nonlinear fitting, etc. The appropriate modeling method can be selected according to the data characteristics to ensure the accuracy of the mapping relationship.

[0126] Understandably, the purpose of this step is to establish a precise correlation between the pure tone threshold and the relative distinguishing intensity at the target frequency. The principle is to use the correlated data pairs and the masking margin as modeling samples, and to construct a quantitative correspondence model between the two parameters through mathematical fitting techniques. This allows the system to derive the actual auditory parameters under the masking effect based on the basic auditory threshold, providing a basis for parameter conversion for subsequent scenario-based hearing compensation. By establishing a target mapping relationship based on the correlated data pairs and the masking margin, the mapping relationship can simultaneously reflect the influence of basic auditory ability and the masking effect, improving the accuracy of parameter matching.

[0127] Specifically, the hearing threshold test compensation system inputs the associated data pairs and masking margin at the target frequency into the modeling module. Based on the distribution characteristics of the data, it selects an appropriate mathematical fitting method. If the data is linearly distributed, linear fitting is used; if it is nonlinearly distributed, nonlinear fitting is used. Through fitting operations, a mathematical model is constructed with the pure tone threshold as the independent variable, the relative discrimination intensity as the dependent variable, and the masking margin as the correction coefficient. This mathematical model is determined as the target mapping relationship at the target frequency. At the same time, the accuracy of the model is verified to ensure that the model's fitting degree meets the preset standard.

[0128] For example, the system inputs the associated data pair of (25dB, 30dB) and the masking margin of 5dB into the modeling module. After analyzing the data, it finds that the data is linearly distributed. A mathematical model of y=x+5 (where x is the pure tone threshold and y is the relative discrimination intensity) is constructed using the linear fitting method. After accuracy verification, the model is determined to be the target mapping relationship at a frequency of 1kHz.

[0129] This embodiment, through the above-described scheme, first determines the pure tone threshold at the target frequency, then establishes a correlation data pair between it and the relative distinguishing intensity and calculates the masking margin. Finally, based on these data, it determines the target mapping relationship. This solves the problem that traditional mapping relationship establishment simply merges two thresholds without considering the quantification effect of masking. It allows the establishment of the target mapping relationship to be based on precise quantification data of a specific frequency, while incorporating the degree of influence of masking effect on hearing ability. This enables the established target mapping relationship to more accurately reflect the correlation between the basic hearing threshold and the hearing parameters under masking at a specific frequency. The hearing compensation strategy subsequently generated based on this mapping relationship will also be more in line with the actual hearing needs of users at different frequencies, further improving the frequency targeting and accuracy of hearing compensation.

[0130] Based on the above implementation scheme, in one feasible implementation, the step of generating a hearing compensation strategy based on the target mapping relationship and environmental scene representation information includes S71~S74: Step S71: Analyze the environmental noise feature parameters in the environmental scene representation information to determine the sound environment scene corresponding to the environmental scene representation information.

[0131] It should be noted that the environmental scene representation information is a comprehensive digital information including parameters such as environmental noise frequency, intensity, duration, and distribution characteristics. This information is primarily generated through environmental acoustic sensing technology and serves as the raw data for scene determination in this step. Environmental noise characteristic parameters are core features extracted from the environmental scene representation information (such as the dominant noise frequency band, average noise intensity, and noise fluctuation coefficient), and are used as the basis for classifying sound environment scenes in this step. Sound environment scenes are scene types categorized based on noise characteristic parameters, mainly divided into noisy scenes (such as streets and shopping malls) and quiet scenes (such as homes and studies), and are used as the basis for selecting subsequent compensation thresholds in this step. The core technology used in this step is feature parameter extraction and scene classification matching technology, enabling accurate determination of scene type from raw environmental information.

[0132] Understandably, the purpose of this step is to transform digitized environmental scene information into scene types that can be used to generate compensation strategies. The principle is that the system extracts core feature parameters such as the main noise frequency band and average intensity from the environmental scene representation information through feature extraction algorithms, and then matches these parameters with a preset noisy / quiet scene judgment feature library. Based on the matching results, the current sound environment scene is determined, allowing the subsequent compensation threshold to be selected according to the acoustic features of the actual environment. This solves the problem of traditional compensation strategies lacking scene adaptation and being one-size-fits-all, and provides a core judgment basis for the generation of scene-based compensation strategies.

[0133] Specifically, the hearing threshold test compensation system first calls the feature extraction module to extract three core feature parameters from the environmental scene representation information: the main noise frequency band (noise frequency band accounting for ≥50%), the average noise intensity (the average noise intensity per unit time), and the noise fluctuation coefficient (the quantitative value of intensity fluctuation). Then, the extracted parameters are compared with the preset scene feature library: if the main noise frequency band covers the core hearing frequency band of the human ear (250Hz-8kHz), the average intensity is ≥40dB, and the fluctuation coefficient is ≥0.3, it is judged as a noisy scene; if the main noise frequency band is scattered, the average intensity is ≤20dB, and the fluctuation coefficient is ≤0.1, it is judged as a quiet scene; if the parameters are between the two, the system can further subdivide the scene (such as a slightly noisy scene) or default to judging it as a noisy scene; different judgment rules are adapted to different environmental features to ensure the accuracy of scene judgment.

[0134] For example, the environmental noise feature parameters extracted by the system are: main noise frequency band 800Hz-1.2kHz (core auditory frequency band), average intensity 55dB, and fluctuation coefficient 0.4. If they match the preset noisy scene feature library successfully, the current sound environment scene is determined to be a noisy scene; if the extracted parameters are: no obvious concentration of main noise frequency band, average intensity 15dB, and fluctuation coefficient 0.05, then it is determined to be a quiet scene.

[0135] Step S72: When the sound environment scene is a noisy scene, the environmental noise feature parameters are matched with the preset noisy scene judgment conditions, and the corresponding relative discrimination intensity is extracted based on the target mapping relationship to obtain the target compensation threshold.

[0136] It should be noted that the preset noisy scene judgment conditions are noise feature parameter thresholds set for different types of noisy scenes (such as traffic noise, shopping mall noise), which serve as the basis for subdividing noisy scene types in this step; the target compensation threshold is the core compensation benchmark parameter adapted to the current scene, which is the relative distinguishing intensity extracted based on the target mapping relationship in noisy scenes, and serves as the core benchmark for subsequent gain compensation calculation in this step; the core technology used in this step is scene parameter matching and mapping relationship retrieval technology, which realizes accurate extraction from scene features to compensation threshold.

[0137] Understandably, this step is to determine a compensation threshold that fits the masking effect in noisy scenes. The principle is that the system first matches the environmental noise characteristic parameters with the preset noisy scene judgment conditions to determine the specific noisy scene type. Then, based on the frequency characteristics corresponding to the type, it retrieves the relative differentiation intensity at the corresponding frequency from the target mapping relationship and uses it as the target compensation threshold. This allows the compensation threshold in noisy scenes to reflect the impact of the masking effect on the user's hearing ability, solving the problem that traditional compensation strategies only use pure tone thresholds and are insufficient in noisy environments, thus improving the actual effectiveness of compensation in noisy scenes.

[0138] Specifically, the hearing threshold test compensation system matches the extracted environmental noise feature parameters with preset sub-scene judgment conditions such as traffic noise (main frequency band 500Hz-2kHz, average intensity 50-70dB) and shopping mall noise (main frequency band 1kHz-4kHz, average intensity 40-60dB) to determine the specific noise scene type. Then, it extracts the main noise frequency band of the scene and retrieves the relative distinguishing intensity corresponding to the core frequency of the main frequency band from the target mapping relationship. This relative distinguishing intensity is used as the target compensation threshold. If the main frequency band covers multiple frequencies, the average of the relative distinguishing intensities of each frequency is taken as the final target compensation threshold. Sub-scene matching can improve the accuracy of threshold extraction, and mean processing can adapt to the compensation needs of multi-frequency noise.

[0139] For example, if the current scenario is determined to be a noisy traffic scene, with the main noise frequency band being 500Hz-2kHz and the core frequency being 1kHz, the system retrieves the relative discrimination intensity R=30dB corresponding to 1kHz from the target mapping relationship (R=T+5, T=25dB), and determines 30dB as the target compensation threshold for the noisy scene. If the main frequency band is 500Hz-2kHz and the core frequencies are 500Hz (R=28dB), 1kHz (R=30dB), and 2kHz (R=32dB), then the average value of 30dB is taken as the target compensation threshold.

[0140] Step S73: When the sound environment scene is a quiet scene, the target compensation threshold is obtained by extracting the corresponding pure tone threshold based on the target mapping relationship.

[0141] It should be noted that in quiet scenes, the target compensation threshold is a pure tone threshold extracted based on the target mapping relationship. Since there is no obvious masking effect in quiet scenes, the pure tone threshold can accurately reflect the user's basic auditory needs. The core technology used in this step is the reverse retrieval technology of the mapping relationship, which extracts the corresponding pure tone threshold from the target mapping relationship based on the frequency characteristics, ensuring that the threshold is adapted to the acoustic characteristics of the quiet scene.

[0142] Understandably, this step is to determine a compensation threshold that matches the user's basic hearing ability for quiet scenes. The principle is that the system retrieves the pure tone threshold at the corresponding frequency from the target mapping relationship based on the acoustic characteristics of the quiet scene (without obvious masking noise) and uses it as the target compensation threshold. This ensures that the compensation threshold in quiet scenes matches the user's basic hearing ability, avoiding overcompensation caused by using parameters under the masking effect. This solves the problem of overcompensation and affecting auditory comfort in quiet environments caused by traditional compensation strategies.

[0143] Specifically, the hearing threshold testing compensation system first determines the core auditory frequency in a quiet setting (usually the core frequency of daily communication, such as 1kHz-2kHz); then, through a reverse retrieval technique based on mapping relationships, it extracts the pure tone threshold corresponding to the core frequency from the target mapping relationship; if multiple core frequencies are covered, the average value of the pure tone thresholds of each frequency is taken as the target compensation threshold; the selection of core frequencies is in line with daily hearing needs, and the averaging process ensures the balance of compensation.

[0144] For example, in a quiet scene, the core frequency is 1kHz. The system retrieves the pure tone threshold T=25dB corresponding to 1kHz from the target mapping relationship (R=T+5) and determines 25dB as the target compensation threshold in the quiet scene. If the core frequency is 1kHz (T=25dB) or 2kHz (T=28dB), the average value of 26.5dB is taken as the target compensation threshold.

[0145] Step S74: Calculate the gain compensation amount for each frequency band based on the target compensation threshold, and generate a hearing compensation strategy based on the gain compensation amount.

[0146] It should be noted that the gain compensation amount is the incremental adjustment of audio intensity for each auditory frequency band. It is calculated using the gain calculation formula (gain compensation amount = target compensation threshold - current ambient sound intensity + individual correction value), and serves as the core parameter of the hearing compensation strategy in this step. The hearing compensation strategy is a standardized scheme that includes gain compensation amounts for each frequency band, adjustment rules, and effective scenarios. It is constructed using parameter integration and rule generation technology and can be directly sent to hearing aids for execution. The individual correction value is a personalized correction parameter set based on the user's hearing history and device adaptation characteristics, which improves the individual adaptability of the compensation amount in this step.

[0147] Understandably, the purpose of this step is to transform the target compensation threshold into an executable hearing compensation strategy. The principle is that the system substitutes the target compensation threshold into the gain calculation formula, combines the current ambient sound intensity and individual correction values ​​to calculate the gain compensation amount for each frequency band, and then integrates the compensation amount for each frequency band with the scene activation rules and device execution rules to generate a standardized hearing compensation strategy. This transforms the abstract compensation threshold into a specific solution that can be executed by hearing aids, realizing a closed loop from parameter calculation to strategy implementation, and solving the problems of traditional compensation strategies lacking standardized execution solutions and having poor adaptability.

[0148] Specifically, the hearing threshold test compensation system first acquires the sound intensity data of each frequency band in the current environment and retrieves the user's individual correction value (such as a 5dB correction value set based on the type of hearing loss). Then, it performs calculations on each core auditory frequency band: gain compensation amount = target compensation threshold - ambient sound intensity of the current frequency band + individual correction value. After the calculation is completed, the system verifies the rationality of the compensation amount (such as limiting the maximum compensation amount to ≤40dB) and removes outliers. Finally, it integrates the verified gain compensation amount of each frequency band with the scene activation rules (such as noisy scenes only being effective in the 800Hz-1.2kHz frequency band) and device execution rules (such as gain adjustment step size of 1dB) to generate a standardized hearing compensation strategy that includes frequency band, compensation amount, effective scene, and execution rules, which can be directly distributed to hearing aids, cochlear implants, and other hearing assistive devices.

[0149] For example, in a noisy scene, the target compensation threshold is 30dB, the current ambient sound intensity in the 1kHz band is 20dB, and the individual correction value is 5dB. The gain compensation amount in the 1kHz band is calculated to be 30-20+5=15dB. After system verification, it is confirmed that 15dB≤40dB. The information such as the 1kHz band compensation amount of 15dB, the effective scene being noisy traffic, and the execution step size of 1dB are integrated to generate a complete hearing compensation strategy.

[0150] This embodiment, through the above-described scheme, determines the sound environment scene by parsing the environmental scene representation information. For noisy scenes, it extracts the relative distinguishing intensity as the target compensation threshold, and for quiet scenes, it extracts the pure tone threshold as the target compensation threshold. Then, based on the target compensation threshold, it calculates the gain compensation amount for each frequency band and generates a standardized strategy. This solves the problems of traditional compensation strategies lacking scene distinction and compensation thresholds not matching the actual environment. This method allows the compensation threshold to accurately adapt to the acoustic characteristics of different scenes. In noisy scenes, it matches the auditory needs under the masking effect, and in quiet scenes, it matches basic hearing abilities. The final hearing compensation strategy has scene adaptability and individual targeting, which can significantly improve the user's auditory experience in different environments and achieve an upgrade from general compensation to scene-specific precise compensation.

[0151] For example, to help understand the implementation process of the hearing threshold testing compensation method obtained in this embodiment combined with the first embodiment described above, please refer to... Figure 2 , Figure 2 A simplified flowchart of a hearing threshold testing compensation method is provided, specifically: First, the feedback receiving module of the hearing threshold test compensation system receives the identification feedback signal sent by the user. This feedback signal is the user's auditory feedback result after perceiving the test tone signal and the masking tone signal, and serves as the raw user data for subsequent analysis. After receiving the identification feedback signal, it undergoes professional analysis and interpretation. Through signal feature extraction, intensity data retrieval, and numerical calculation, the relative distinguishing strength of the masking tone signal relative to the test tone signal is determined. This transforms the user's qualitative auditory feedback into quantitative auditory ability parameters, providing a core quantitative basis for subsequent modeling. Finally, the user's pure tone threshold is retrieved, and a target mapping relationship is established between it and the determined relative distinguishing strength through mathematical modeling. Then, combined with the collected environmental scene representation information characterizing the external auditory environment, the gain compensation amount for each frequency band is calculated based on this target mapping relationship. Ultimately, a personalized hearing compensation strategy adapted to the current environment is generated, realizing the core closed loop from hearing threshold testing to scenario-based hearing compensation.

[0152] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the hearing threshold testing compensation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0153] This application also provides a hearing threshold testing compensation device; please refer to... Figure 3 The hearing threshold testing compensation device includes: The signal receiving module 301 is used to receive the identification feedback signal sent by the user, the identification feedback signal being generated by the user based on the test tone signal and the masking tone signal; The analysis and determination module 302 is used to analyze the identification feedback signal and determine the relative differentiation strength of the masking sound signal relative to the test sound signal; The strategy generation module 303 is used to determine the target mapping relationship between the pure tone threshold and the relative distinguishing intensity, and to generate a hearing compensation strategy based on the target mapping relationship and environmental scene representation information.

[0154] The hearing threshold testing compensation device provided in this application, employing the hearing threshold testing compensation method in the above embodiments, can solve the technical problem of insufficient accuracy in hearing compensation. Compared with the prior art, the beneficial effects of the hearing threshold testing compensation device provided in this application are the same as those of the hearing threshold testing compensation method provided in the above embodiments, and other technical features in the hearing threshold testing compensation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0155] This application provides a hearing threshold testing compensation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the hearing threshold testing compensation method in the first embodiment described above.

[0156] The following is for reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing a hearing threshold testing compensation device according to embodiments of this application. The hearing threshold testing compensation device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The hearing threshold testing compensation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0157] like Figure 4As shown, the hearing threshold testing compensation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the hearing threshold testing compensation device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the hearing threshold testing compensation device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows hearing threshold testing compensation devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0158] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0159] The hearing threshold testing compensation device provided in this application, employing the hearing threshold testing compensation method in the above embodiments, can solve the technical problem of insufficient accuracy in hearing compensation. Compared with the prior art, the beneficial effects of the hearing threshold testing compensation device provided in this application are the same as those of the hearing threshold testing compensation method provided in the above embodiments, and other technical features in this hearing threshold testing compensation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0160] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0161] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0162] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the hearing threshold test compensation method in the above embodiments.

[0163] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0164] The aforementioned computer-readable storage medium may be included in the hearing threshold testing compensation device; or it may exist independently and not assembled into the hearing threshold testing compensation device.

[0165] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the hearing threshold testing compensation device, cause the hearing threshold testing compensation device to: receive a recognition feedback signal sent by a user, the recognition feedback signal being generated by the user based on a test tone signal and a masking tone signal; analyze the recognition feedback signal to determine the relative distinguishing intensity of the masking tone signal relative to the test tone signal; determine a target mapping relationship between a pure tone threshold and the relative distinguishing intensity; and generate a hearing compensation strategy based on the target mapping relationship and environmental scene representation information.

[0166] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0167] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0168] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0169] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described hearing threshold test compensation method, thereby solving the technical problem of insufficient accuracy in hearing compensation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the hearing threshold test compensation method provided in the above embodiments, and will not be repeated here.

[0170] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the hearing threshold test compensation method described above.

[0171] The computer program product provided in this application can solve the technical problem of insufficient accuracy in hearing compensation. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the hearing threshold test compensation method provided in the above embodiments, and will not be repeated here.

[0172] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A hearing threshold testing compensation method, characterized in that, The hearing threshold test compensation method includes: Receive the identification feedback signal sent by the user, which is generated by the user based on the test tone signal and the masking tone signal; Analyze the identification feedback signal to determine the relative distinguishing strength of the masking sound signal relative to the test sound signal; Determine the target mapping relationship between the pure tone threshold and the relative distinguishing intensity, and generate a hearing compensation strategy based on the target mapping relationship and environmental scene representation information; The step of receiving the identification feedback signal sent by the user, wherein the identification feedback signal is generated by the user based on the test tone signal and the masking tone signal, further includes the following: Determine the target frequency to be tested, and generate a test tone signal corresponding to the target frequency; Based on the target frequency, a corresponding masking sound signal is generated, and the audio playback module is controlled to play the test sound signal and the masking sound signal. The step of generating the corresponding masking sound signal based on the target frequency includes: Calculate the critical bandwidth based on the target frequency; Based on the critical bandwidth, a first center frequency is calculated that is offset by a first preset amount towards the low-frequency side with the target frequency as a reference, and a first noise signal is generated based on the first center frequency. Based on the critical bandwidth, a second center frequency is calculated that is offset by a second preset amount towards the high-frequency side with the target frequency as a reference, and a second noise signal is generated based on the second center frequency; The first noise signal and the second noise signal are superimposed and combined to generate a masking sound signal; The step of analyzing the identification feedback signal to determine the relative distinguishing strength of the masking sound signal relative to the test sound signal includes: The user's recognition status of the test tone signal is determined based on the recognition feedback signal, and the recognition status includes a recognizable state and / or an unrecognizable state. When the identification state is unidentifiable, the intensity of the masking sound signal is reduced and played until a critical intensity point is determined from the masking sound signal to distinguish the test sound signal from the test sound signal. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity. When the identification state is a recognizable state, the intensity of the masking sound signal is increased and played until it is determined that the user can distinguish the test sound signal from the masking sound signal at a critical intensity point. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity. The step of generating a hearing compensation strategy based on the target mapping relationship and environmental scene representation information includes: Analyze the environmental noise feature parameters in the environmental scene representation information to determine the sound environment scene corresponding to the environmental scene representation information; When the sound environment scene is a noisy scene, the environmental noise feature parameters are matched with the preset noisy scene judgment conditions, and the corresponding relative discrimination intensity is extracted based on the target mapping relationship to obtain the target compensation threshold; When the sound environment scene is a quiet scene, the target compensation threshold is obtained by extracting the corresponding pure tone threshold based on the target mapping relationship; The gain compensation amount for each frequency band is calculated based on the target compensation threshold, and a hearing compensation strategy is generated based on the gain compensation amount.

2. The hearing threshold testing compensation method as described in claim 1, characterized in that, The step of receiving the identification feedback signal sent by the user further includes: The reaction delay time is determined based on the identified feedback signal; The reaction delay time is compared with a preset effective time window to obtain the reaction delay result; The credibility analysis of the response delay result is performed based on the preset confidence rating information. When the credibility analysis fails, the corresponding identification feedback signal is filtered out.

3. The hearing threshold testing compensation method as described in claim 1, characterized in that, The step of determining the target mapping relationship between the pure tone threshold and the relative distinguishing intensity includes: Determine the pure tone threshold at the target frequency; Establish a data pair between the pure tone threshold and the relative discrimination intensity at the target frequency, and calculate the masking margin between the relative discrimination intensity and the pure tone threshold; The target mapping relationship is determined based on the associated data pairs and the masking margin.

4. A hearing threshold testing compensation device, characterized in that, The hearing threshold testing compensation device includes: The signal receiving module is used to receive the identification feedback signal sent by the user, which is generated by the user based on the test tone signal and the masking tone signal. The analysis and determination module is used to analyze the identification feedback signal and determine the relative differentiation strength of the masking sound signal relative to the test sound signal; The strategy generation module is used to determine the target mapping relationship between the pure tone threshold and the relative distinguishing intensity, and to generate a hearing compensation strategy based on the target mapping relationship and environmental scene representation information. The hearing threshold test compensation device further includes a signal generation module, which is used to determine the target frequency to be tested and generate a test tone signal corresponding to the target frequency. Based on the target frequency, a corresponding masking sound signal is generated, and the audio playback module is controlled to play the test sound signal and the masking sound signal. The step of generating the corresponding masking sound signal based on the target frequency includes: Calculate the critical bandwidth based on the target frequency; Based on the critical bandwidth, a first center frequency is calculated that is offset by a first preset amount towards the low-frequency side with the target frequency as a reference, and a first noise signal is generated based on the first center frequency. Based on the critical bandwidth, a second center frequency is calculated that is offset by a second preset amount towards the high-frequency side with the target frequency as a reference, and a second noise signal is generated based on the second center frequency; The first noise signal and the second noise signal are superimposed and combined to generate a masking sound signal; The step of analyzing the identification feedback signal to determine the relative distinguishing strength of the masking sound signal relative to the test sound signal includes: The user's recognition status of the test tone signal is determined based on the recognition feedback signal, and the recognition status includes a recognizable state and / or an unrecognizable state. When the identification state is unidentifiable, the intensity of the masking sound signal is reduced and played until a critical intensity point is determined from the masking sound signal to distinguish the test sound signal from the test sound signal. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity. When the identification state is a recognizable state, the intensity of the masking sound signal is increased and played until it is determined that the user can distinguish the test sound signal from the masking sound signal at a critical intensity point. The difference between the critical masking sound signal intensity and the test sound signal intensity corresponding to the critical intensity point is determined as the relative distinguishing intensity. The hearing compensation strategy generated based on the target mapping relationship and environmental scene representation information includes: Analyze the environmental noise feature parameters in the environmental scene representation information to determine the sound environment scene corresponding to the environmental scene representation information; When the sound environment scene is a noisy scene, the environmental noise feature parameters are matched with the preset noisy scene judgment conditions, and the corresponding relative discrimination intensity is extracted based on the target mapping relationship to obtain the target compensation threshold; When the sound environment scene is a quiet scene, the target compensation threshold is obtained by extracting the corresponding pure tone threshold based on the target mapping relationship; The gain compensation amount for each frequency band is calculated based on the target compensation threshold, and a hearing compensation strategy is generated based on the gain compensation amount.

5. A hearing threshold testing compensation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the hearing threshold testing compensation method as described in any one of claims 1 to 3.

6. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the hearing threshold test compensation method as described in any one of claims 1 to 3.

Images