Method and device for detecting static acoustic feedback of a behind-the-ear hearing aid, and hearing aid

Abstract

This invention provides a method, device, and hearing aid for detecting and analyzing static acoustic feedback in behind-the-ear (BTE) hearing aids, belonging to the field of hearing aid technology. The method combines silencing testing fixtures and automated testing methods, using microphone arrays in each chamber to capture the sound signals emitted by various parts of the hearing aid during the testing process. Signal processing algorithms are then used to accurately identify the characteristic parameters of the acoustic feedback signal, ensuring the accuracy of the test results and providing a scientific basis for optimizing and adjusting the hearing aid. In particular, the method allows for quantitative analysis of the various factors constituting the acoustic feedback loop, helping to accurately locate and prevent static acoustic feedback, thereby improving user experience and hearing aid performance.

Description

Technical Field

[0001] This invention relates to the field of hearing aid technology, and in particular to a method, apparatus, and hearing aid for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid. Background Technology

[0002] Behind-the-ear (BTE) hearing aids are widely used by people with hearing loss. However, in actual use, improper fitting or unreasonable parameter settings often lead to an undesirable phenomenon called acoustic feedback, commonly known as "whistling." Acoustic feedback not only affects the comfort of wearing the hearing aid but also significantly reduces its effectiveness and may even cause further damage to the user's hearing.

[0003] Feedback is mainly caused by the close proximity of the microphone and speaker (receiver) in a hearing aid system. When the microphone picks up the sound emitted by the speaker and amplifies it again, a positive feedback loop is formed. Once the gain of this loop exceeds a certain threshold, a strong feedback sound will be produced. Feedback is related to a variety of factors, which can be broadly divided into dynamic acoustic feedback and static acoustic feedback. The former includes poor sealing between the shell or earmold and the ear canal during wear, excessively large vent holes, and the earpiece being too close to the microphone during a phone call, causing the output sound leaking from the ear canal to be emitted to the microphone.

[0004] Static acoustic feedback in hearing aids can manifest in various ways, not just as whistling. It typically occurs during normal operation and is caused by internal factors within the hearing aid. This feedback persists even without external dynamic changes such as user movement or environmental shifts, negatively impacting the hearing aid's effectiveness and the user's hearing experience. Therefore, acoustic feedback detection is crucial during hearing aid fitting.

[0005] In existing technologies, the detection and elimination of static acoustic feedback in hearing aids typically rely on the subjective judgment of assembly workers, such as testing by bringing the hearing aid close to the ear or observing its condition. These methods are inefficient and inaccurate, and can only identify simple feedback problems. Furthermore, there is a lack of specialized devices and methods to locate the factors causing static acoustic feedback—that is, feedback generated when the hearing aid is not worn—through improved design and assembly processes. Summary of the Invention

[0006] This invention provides a method, device, and hearing aid for detecting and analyzing static acoustic feedback in behind-the-ear hearing aids, thereby addressing the shortcomings of existing technologies in accurately and efficiently identifying static acoustic feedback during hearing aid testing, and achieving the effect of accurately locating and preventing the occurrence of static acoustic feedback.

[0007] This invention provides a method for detecting and analyzing static acoustic feedback in behind-the-ear hearing aids, comprising: The behind-the-ear hearing aid is positioned within a noise reduction testing fixture; the noise reduction testing fixture includes at least two chambers such that the sound-emitting position of the behind-the-ear hearing aid is located within the chambers; the chambers are provided with flexible sound-insulating material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid. The system determines that the behind-the-ear hearing aid has started working and controls the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; The microphone array installed inside the chamber captures the detection sound signals emitted by various parts of the hearing aid; The detected sound signal is analyzed based on its location to determine the corresponding audio features and obtain the abnormal sound feedback location and abnormal sound feedback signal features of the behind-the-ear hearing aid. The audio features include at least one of the following: sound source location, intensity, frequency, and time variation features.

[0008] According to the present invention, a method for detecting and analyzing static acoustic feedback in a behind-the-ear (BTE) hearing aid includes analyzing the detected sound signal based on its position to determine the audio features corresponding to the detected sound signal, and obtaining the abnormal acoustic feedback position and abnormal acoustic feedback signal features of the BTE hearing aid. Based on the audio features corresponding to the detected sound signal, extract the audio features corresponding to the abnormal sound feedback; Based on the audio features corresponding to the abnormal sound feedback, the sound source location is analyzed to identify the location of the abnormal sound feedback. Based on the audio features corresponding to the abnormal sound feedback and the location of the abnormal sound feedback, the audio features corresponding to each abnormal sound feedback location are identified. Based on the audio features corresponding to each abnormal sound feedback location, the type of abnormal sound feedback corresponding to each abnormal sound feedback location and the abnormal sound feedback signal characteristics of each type of abnormal sound feedback are obtained.

[0009] According to the present invention, a method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid includes obtaining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on the audio features corresponding to each abnormal acoustic feedback location, comprising: Identify the intensity information in the audio features corresponding to each abnormal sound feedback location, and obtain the magnitude of the intensity in the audio features and the intensity features that change with time; perform spectral analysis on the audio features corresponding to each abnormal sound feedback location to identify the proportion of sound in different frequency bands and periodic patterns, and obtain frequency features; perform time-domain analysis on the audio features corresponding to each abnormal sound feedback location to identify the time-domain features corresponding to the time-domain waveform. Based on at least one of the intensity feature, frequency feature, and time domain feature of the audio features corresponding to each abnormal sound feedback location, the type of abnormal sound feedback corresponding to each abnormal sound feedback location is determined; the type of abnormal sound feedback includes at least one of high-frequency howling, resonant noise, periodic vibration noise, and low-frequency leakage sound.

[0010] According to the present invention, a method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid includes determining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on at least one of the intensity feature, frequency feature, and time domain feature of the audio features corresponding to each abnormal acoustic feedback location. Based on the intensity and frequency features of the audio features corresponding to the abnormal sound feedback location, when the intensity of the audio features at the target frequency band corresponding to the abnormal sound feedback location is greater than the first target intensity value, the target intensity growth rate of the audio features at the target frequency band is determined. When the target intensity increase is greater than the velocity threshold, the type of abnormal acoustic feedback at the abnormal acoustic feedback location is determined to be resonance noise.

[0011] According to the present invention, a method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid includes determining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on at least one of the intensity feature, frequency feature, and time domain feature of the audio features corresponding to each abnormal acoustic feedback location. Based on the intensity, frequency, and time-domain characteristics of the audio features corresponding to each abnormal acoustic feedback location, it is determined that there are periodic high-frequency or low-frequency components in the audio features, and the type of abnormal acoustic feedback corresponding to the abnormal acoustic feedback location is determined to be periodic vibration noise.

[0012] According to the present invention, a method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid includes determining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on at least one of the intensity feature, frequency feature, and time domain feature of the audio features corresponding to each abnormal acoustic feedback location. Based on the intensity, frequency and time-domain features of the audio features corresponding to each abnormal sound feedback location, when the intensity in the low-frequency band of the audio features corresponding to the abnormal sound feedback location is greater than the second target intensity value, the coefficient of variation of the corresponding time-domain feature waveform amplitude is determined. If the coefficient of variation is less than a preset coefficient, the type of abnormal acoustic feedback at the location of the abnormal acoustic feedback is determined to be low-frequency leakage sound.

[0013] According to the present invention, a method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid includes, in which the sound source location is analyzed based on the audio features corresponding to the abnormal acoustic feedback, and the abnormal acoustic feedback location is identified, comprising: The phase difference and time difference of the audio features corresponding to the abnormal sound feedback detected by different microphones are obtained, and the signal phase and amplitude are adjusted by beamforming algorithm to enhance the sound signal in the target direction; The location of the abnormal sound feedback is calculated by analyzing the sound signal after amplification of the target direction using a geometric positioning algorithm.

[0014] The present invention also provides a detection and analysis device for static acoustic feedback of behind-the-ear hearing aids, comprising: A configuration module is used to determine that the behind-the-ear hearing aid is configured in a noise reduction testing fixture; the noise reduction testing fixture includes at least two chambers such that the sound-emitting position of the behind-the-ear hearing aid is located in the chamber; the chamber is provided with flexible sound insulation material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid; The startup module is used to determine when the behind-the-ear hearing aid is started and to control the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; The capture module is used to capture the detection sound signals emitted by various parts of the hearing aid through a microphone array set inside the cavity; The processing module is used to analyze the detected sound signal based on the location of the detected sound signal, determine the audio features corresponding to the detected sound signal, and obtain the abnormal sound feedback location and abnormal sound feedback signal features of the behind-the-ear hearing aid; the audio features include at least one of sound source location, intensity, frequency, and time variation features.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the detection and analysis method for static acoustic feedback of the behind-the-ear hearing aid as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the detection and analysis method for static acoustic feedback of a behind-the-ear hearing aid as described in any of the above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the detection and analysis method for static acoustic feedback of a behind-the-ear hearing aid as described above.

[0018] The present invention provides a method, device, and hearing aid for detecting and analyzing static acoustic feedback in behind-the-ear hearing aids. By combining silencing detection fixtures and automated detection methods, and using microphone arrays in each chamber to capture the detection sound signals emitted by various parts of the hearing aid during the detection process, the invention utilizes signal processing algorithms to accurately identify the characteristic parameters of the acoustic feedback signal, ensuring the accuracy of the detection results and providing a scientific basis for the optimization and adjustment of the hearing aid. In particular, it can quantitatively analyze the various factors constituting the acoustic feedback loop, which helps to accurately locate and prevent static acoustic feedback, thereby improving user experience and hearing aid performance. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is one of the flowcharts illustrating the detection and analysis method for static acoustic feedback in behind-the-ear hearing aids provided by this invention. Figure 2 This is the second flowchart of the detection and analysis method for static acoustic feedback of behind-the-ear hearing aids provided by the present invention. Figure 3 This is a schematic diagram of the detection and analysis device for static acoustic feedback of behind-the-ear hearing aids provided by the present invention. Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined Figures 1-4 The present invention describes a method, apparatus, and hearing aid for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid.

[0023] like Figure 1 As shown, the detection and analysis method for static acoustic feedback of behind-the-ear hearing aids in this embodiment of the invention mainly includes steps 110, 120, 130 and 140.

[0024] Step 110: Confirm that the behind-the-ear hearing aid is positioned within the noise reduction testing fixture.

[0025] The noise reduction testing fixture includes at least two chambers so that the sound-emitting positions of the behind-the-ear hearing aid are all located in the chambers; the chambers are equipped with flexible sound insulation material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid.

[0026] Understandably, behind-the-ear hearing aids are hearing aids worn behind or inside the ear that transmit enhanced sound signals into the ear canal through a speaker to help people with hearing loss hear the sounds around them.

[0027] Anechoic testing fixtures are experimental devices used to test and evaluate the sound production and sound signals of devices such as behind-the-ear hearing aids. They provide a controlled environment that reduces the interference of external noise on the test.

[0028] The chamber is a separate space or area in the tooling used to place behind-the-ear hearing aids. Usually, different designs are used to block external noise and ensure the quietness of the testing environment in order to achieve sound insulation.

[0029] Flexible sound insulation materials are used to absorb or reduce the reflection of sound waves. They are typically used for sound insulation or noise reduction to ensure that the environment inside the chamber is suitable for testing.

[0030] During the testing process, the behind-the-ear hearing aid is placed within a specially designed anechoic testing fixture to ensure a noise-free testing environment. The fixture is designed with at least two chambers to position the behind-the-ear hearing aid's sound emission within these chambers, ensuring a controlled sound propagation path. The chambers are also equipped with flexible sound-insulating material to reduce external noise interference and improve testing accuracy. Furthermore, a microphone array is installed within the chambers to capture the sound signals emitted by the behind-the-ear hearing aid during operation.

[0031] In some embodiments, the noise reduction testing fixture may consist of a rigid profile, a vibration isolation material layer, and a microporous sound-absorbing material layer, and may contain two or more chambers, with spaces reserved between the chambers for placing behind-the-ear hearing aids, which are filled with flexible sound-insulating material (sound insulation cotton).

[0032] Microphone arrays can be symmetrically arranged and deployed on the chamber walls, with at least one pair of microphones deployed in each chamber. The microphone arrays are used to capture sound signals emitted from various parts of the hearing aid; analyzing the sound signal intensity collected by the microphone arrays in the same chamber can further pinpoint the location of the sound source.

[0033] The sound signals acquired through the microphone can be amplified by a power (preamplifier). The intensity of directly leaked sound signals differs significantly from that generated by vibration or resonance; using preamplifiers with different amplification factors allows for adaptation to the signals to be detected in different tests. Furthermore, a multi-channel data acquisition module can be used to convert analog signals into digital signals for processor processing.

[0034] Understandably, the anechoic testing fixture, through its effective sound insulation design and material usage, ensures that the testing environment is virtually unaffected by external noise, thereby improving the accuracy and reliability of the test. The microphone array within the chamber can accurately capture sound signals from various parts of the behind-the-ear hearing aid, helping to detect and locate abnormal acoustic feedback.

[0035] Step 120: Confirm that the behind-the-ear hearing aid has started working and control the speaker of the behind-the-ear hearing aid to emit a target detection sound signal.

[0036] Activation refers to turning on the behind-the-ear hearing aid, putting it into normal working mode, and the speaker begins to emit sound signals.

[0037] Target detection sound signals are sound signals of specific frequencies, intensities, and patterns generated to detect the static acoustic feedback characteristics of hearing aids. The purpose of this signal is to test whether the device produces abnormal feedback.

[0038] In a noise-canceling testing fixture, once a behind-the-ear hearing aid is activated, the control system causes its speaker to emit a predetermined target test sound signal. These signals typically have specific frequencies, intensities, and durations to accurately assess the hearing aid's performance. The control system interacts with the hearing aid's communication interface to precisely control the sound signal output, ensuring that the desired test sound signal reaches and propagates under appropriate environmental conditions.

[0039] Understandably, by controlling the hearing aid to emit specific target detection sound signals, the standardization of the test can be ensured, thereby eliminating possible variables in the testing process. This allows for clear identification and localization of the source of acoustic feedback, avoiding interference from other environmental factors on the test results.

[0040] Step 130: Capture the detection sound signals emitted by various parts of the hearing aid through a microphone array set inside the chamber.

[0041] A microphone array is an array system composed of multiple microphones that can simultaneously receive sound signals from different locations and analyze the direction of the sound source using techniques such as time difference and phase difference.

[0042] The sound signal emitted by the speaker of the behind-the-ear hearing aid is transmitted through various components of the hearing aid and then captured by the microphone array.

[0043] In this step, the sound signal emitted by the behind-the-ear hearing aid through its speaker propagates into the cavity during operation and is captured by a microphone array positioned within the cavity. The microphone array consists of multiple microphones precisely positioned within the cavity, capable of collecting sound signals from various parts of the behind-the-ear hearing aid, such as the speaker and housing. These microphones allow for the simultaneous capture of sound signals from multiple locations, providing ample data support for subsequent analysis.

[0044] Understandably, the design of the microphone array allows sound signals to be captured from multiple angles, which improves the comprehensiveness and accuracy of acoustic feedback signal analysis. In addition, the acquisition by multiple microphones can effectively reduce the errors or blind spots that may be generated by a single microphone, thereby improving the reliability of the detection results.

[0045] Step 140: Analyze the detected sound signal based on its location to determine the corresponding audio features and obtain the location and features of the abnormal sound feedback signal of the behind-the-ear hearing aid.

[0046] Audio features include at least one of the following: sound source location, intensity, frequency, and time variation characteristics.

[0047] Audio features refer to the characteristic parameters of a sound signal, including information such as the location of the sound source, intensity, frequency, and time variations, which are used to classify and analyze sound signals.

[0048] Abnormal acoustic feedback refers to acoustic feedback phenomena that do not conform to normal operation during the operation of a hearing aid due to internal malfunctions, design defects, or external environmental factors. It usually manifests as excessive noise, echo, or distortion.

[0049] In step 140, the source and characteristics of the acoustic feedback are first determined by analyzing the sound signal from the microphone array and combining it with location data such as the spatial location of the sound source. Signal processing algorithms can extract features such as the frequency, intensity, and temporal variations of the sound signal. Based on these features, the location of abnormal acoustic feedback in the behind-the-ear hearing aid can be further determined, and the characteristics of the abnormal feedback signal corresponding to that location can be identified. For example, an excessively strong signal at a certain frequency may indicate feedback between the hearing aid speaker and microphone, or the influence of external interference.

[0050] Understandably, detailed analysis of sound signals can accurately identify and locate potential sources of abnormal acoustic feedback in behind-the-ear hearing aids, helping to quickly diagnose problems, optimize hearing aid design and performance, and prevent static acoustic feedback caused by design flaws or manufacturing issues.

[0051] In other words, through precise detection devices and analytical methods, abnormal acoustic feedback that may occur during the operation of behind-the-ear hearing aids can be effectively identified. These steps not only improve the accuracy of hearing aid performance evaluation but also help developers optimize products during the design phase, avoiding potential feedback problems, thereby enhancing user experience and the overall effectiveness of the hearing aid.

[0052] The method for detecting and analyzing static acoustic feedback in behind-the-ear hearing aids provided by embodiments of the present invention combines silencing detection fixtures and automated detection methods. It captures the detection sound signals emitted by various parts of the hearing aid during the detection process using microphone arrays in each chamber. Signal processing algorithms are then used to accurately identify the characteristic parameters of the acoustic feedback signal, ensuring the accuracy of the detection results and providing a scientific basis for the optimization and adjustment of the hearing aid. In particular, it enables quantitative analysis of the various factors constituting the acoustic feedback loop, helping to accurately locate and prevent static acoustic feedback, thereby improving user experience and hearing aid performance.

[0053] In some embodiments, such as Figure 2 As shown, the detected sound signal is analyzed based on its location to determine the corresponding audio features and obtain the abnormal sound feedback location and abnormal sound feedback signal features of the behind-the-ear hearing aid, including steps 210, 220, 230 and 240.

[0054] Step 210: Extract the audio features corresponding to the abnormal sound feedback based on the audio features corresponding to the detected sound signal.

[0055] Step 220: Based on the audio features corresponding to the abnormal sound feedback, perform sound source location analysis to identify the location of the abnormal sound feedback.

[0056] Step 230: Based on the audio features corresponding to the abnormal sound feedback and the location of the abnormal sound feedback, identify the audio features corresponding to each abnormal sound feedback location.

[0057] Step 240: Based on the audio features corresponding to each abnormal sound feedback location, obtain the type of abnormal sound feedback corresponding to each abnormal sound feedback location and the abnormal sound feedback signal features of each type of abnormal sound feedback.

[0058] Understandably, the first step is to extract key audio features from the detected sound signal. These audio features can include the sound's frequency, intensity, time-domain waveform, and waveform amplitude variations.

[0059] In the sound source location analysis stage, audio characteristics can be used to perform sound source localization analysis and determine the location of abnormal acoustic feedback. For example, the location of the sound source can be estimated by analyzing the time difference and phase difference of the sound signals received by multiple microphones. Through this sound source localization analysis, it can be clearly identified which part of the sound feedback is abnormal.

[0060] Based on the audio features corresponding to abnormal sound feedback, sound source location analysis is performed to identify the location of abnormal sound feedback. This includes: obtaining the phase difference and time difference of the audio features corresponding to abnormal sound feedback detected by different microphones; adjusting the signal phase and amplitude through beamforming algorithms to enhance the sound signal in the target direction; and using geometric positioning algorithms to analyze the enhanced sound signal in the target direction to estimate the location of abnormal sound feedback.

[0061] In a multi-microphone system, multiple microphones simultaneously receive sound signals from different directions. These microphones detect sound signals with varying time arrival and waveform phase differences. These differences arise from the physical properties of sound propagation in space and the varying microphone positions. By analyzing these time and phase differences, the direction of the sound source can be deduced.

[0062] Phase difference is the difference in the periodic phase of a sound wave when it is received by different microphones, due to variations in distance. By analyzing the phase difference, the direction of the sound can be determined.

[0063] The time difference is the different amounts of time it takes for sound to travel from its source to different microphones. The time difference can help determine the source of the sound.

[0064] Beamforming is a signal processing technique commonly used in multi-microphone arrays. Its core principle is to adjust the phase and amplitude of the signals received by the microphones, thereby enhancing signals from a specific direction (the target direction) while suppressing noise or interference from other directions.

[0065] Specifically, by controlling the phase of the signal received by each microphone, the phase alignment of the sound signals from the target direction can be ensured, thus enhancing the target sound signal. The amplitude of the signal can also be adjusted to further strengthen the target sound signal and suppress background noise.

[0066] Understandably, this process can focus the microphone array and amplify signals from specific directions, such as the direction of abnormal sound feedback, thereby making signal analysis more accurate.

[0067] Geometric localization algorithms are used to estimate the location of sound sources based on the propagation characteristics of sound signals, such as the time difference or phase difference of sound waves arriving at the microphone. After signal enhancement, geometric localization algorithms are used to analyze these enhanced signals to determine the specific location of abnormal acoustic feedback.

[0068] Geometric localization algorithms rely on time or phase differences measured by multiple microphone arrays, combined with the spatial arrangement of the microphones, to calculate the location of the sound source. For example, the positions of three or more microphones and the time differences of the received signals can help calculate the spatial location of the sound.

[0069] It should be noted that analyzing the enhanced signal using a geometric positioning algorithm can accurately identify the location of abnormal acoustic feedback. This is because the enhanced signal clearly shows the sound signal in the direction of the target, thus helping to deduce the source of the feedback.

[0070] In this embodiment, determining the location of the feedback sound source helps engineers or technicians accurately pinpoint the problematic area within the hearing aid. For example, if the sound signal originates near the speaker in a behind-the-ear hearing aid, it may indicate a design flaw in the speaker.

[0071] In the step of identifying the audio features corresponding to each location of abnormal acoustic feedback, based on the previous analysis, we can further identify the audio features at different locations by combining the location of the acoustic feedback. That is, each location of abnormal acoustic feedback may exhibit different audio feature characteristics. For example, abnormal feedback near a speaker may exhibit specific frequencies and intensities, while feedback near a microphone may have different frequency patterns. This step helps to distinguish different abnormal acoustic feedback generated from different locations.

[0072] It should be noted that by combining the analysis of feedback location and audio characteristics, it is possible to more precisely identify different abnormal feedback generated in different locations. For example, the different positions of the speaker and microphone may result in different feedback signal characteristics (such as frequency and duration). By analyzing the audio characteristics of each location, the source of the problem can be accurately identified, helping engineers to locate and repair it.

[0073] Understandably, based on the location and audio characteristics of each anomalous acoustic feedback, the type of each type of anomalous acoustic feedback can be further analyzed and determined. For example, some feedback may be caused by an acoustic loop between the speaker and the microphone, typically manifesting as high-frequency howling; while other feedback may be caused by excessive signal strength or audio distortion. By identifying these types, the system can classify each anomalous feedback and extract the corresponding signal features.

[0074] When faced with a mixture of multiple abnormal sound types, various methods can be combined, such as frequency domain analysis, time-frequency analysis, signal separation techniques, machine learning models, and noise suppression algorithms, to effectively extract and distinguish different sound types. For each type of abnormal sound feedback, its typical signal characteristics, such as frequency range, duration, and intensity, can be extracted. These characteristics help in further analyzing the root cause of the problem, such as equipment design flaws, environmental factors, or usage issues.

[0075] Based on the audio features corresponding to each abnormal sound feedback location, the type of abnormal sound feedback corresponding to each abnormal sound feedback location is obtained, including the following process.

[0076] It can identify the intensity information in the audio features corresponding to each abnormal sound feedback location, obtain the magnitude of the intensity in the audio features and the intensity features that change with time; perform spectral analysis on the audio features corresponding to each abnormal sound feedback location, identify the proportion of sound in different frequency bands and periodic patterns, and obtain frequency features; perform time-domain analysis on the audio features corresponding to each abnormal sound feedback location, and identify the time-domain features corresponding to the time-domain waveform.

[0077] Based on this, the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location is determined by at least one of the intensity feature, frequency feature, and time domain feature of the audio features corresponding to each abnormal acoustic feedback location; the type of abnormal acoustic feedback includes at least one of high-frequency howling, resonance noise, periodic vibration noise, and low-frequency leakage sound.

[0078] Intensity refers to the volume or loudness of an audio signal, such as decibels. It is an important characteristic of the signal and reflects the strength of the sound.

[0079] The characteristic of intensity changing over time indicates the fluctuation of audio signal intensity over time. For example, the intensity of sound may increase or decrease periodically, or it may change abruptly. Through time series analysis, the patterns of sound change can be obtained.

[0080] Spectrum analysis is an analytical method that transforms audio signals into different frequency components. Typically, Fourier transform (FFT) is used to transform the audio signal from the time domain to the frequency domain, obtaining its distribution across various frequency bands.

[0081] The proportion of sound in different frequency bands means analyzing the distribution of sound intensity within different frequency ranges. For example, the proportion of high-frequency, mid-frequency, and low-frequency components can reveal the type of sound.

[0082] Some sounds exhibit periodic fluctuations over time, such as certain mechanical resonance noises. Spectral analysis can help identify these patterns and distinguish between continuous, stable frequencies and discontinuous, varying frequencies.

[0083] Time-domain analysis directly observes the waveform characteristics of an audio signal on the time axis. Through time-domain analysis, the suddenness of sound and the shape of the waveform, such as sharp fluctuations and smooth changes, can be identified.

[0084] Sharp peaks and troughs may correspond to certain types of noise, such as howling or impact noise, while smooth waveforms may be associated with background noise or low-frequency vibrations.

[0085] Based on the intensity characteristics, frequency characteristics, and time domain characteristics mentioned above, the type of abnormal sound can be comprehensively judged and determined.

[0086] High-frequency whistling is typically a sharp sound in the higher frequency range, which may have a noticeable periodicity and is a common type of system feedback noise. Resonance noise occurs when the natural frequencies of a hearing aid's mechanical or structural system match the frequency of an external excitation, resulting in continuous vibration and noise at specific frequencies. Periodic vibration noise is a sound that repeats with a certain period and may be caused by the periodic mechanical movement of the hearing aid or the operation of the equipment. Low-frequency leakage noise is typically noise in the low-frequency range and may originate from equipment leakage, vibration, or poor contact.

[0087] Intensity characteristics, frequency characteristics, and time-domain characteristics can be used in combination. By analyzing the variation patterns of these characteristics, the type of noise can be determined.

[0088] For example, high-frequency howling typically has high intensity and a frequency concentration within a specific band. Resonant noise may manifest as periodic waveforms with intensity peaks at specific frequency bands. Low-frequency leakage noise may appear as continuous sound waves at lower frequencies. Ultimately, based on these audio characteristics, the type of noise can be determined, providing a basis for further processing or repair.

[0089] In this embodiment, different types of abnormal sounds can be identified and classified through intensity, frequency and time domain analysis. Key features such as intensity changes, frequency components and time domain waveforms are extracted from the audio signal, and then these features are combined to identify the type of abnormal sound.

[0090] It should be noted that the signal spectrum can be observed using Fourier Transform (FFT). Howling signals are typically concentrated in a specific high-frequency range (e.g., 2kHz to 10kHz), exhibiting sharp peaks. Howling signals usually have prominent waveform characteristics, manifesting as periodic high-frequency oscillations or sudden sharp fluctuations.

[0091] Based on the above, a bandpass filter can be used to limit the signal to a specific high-frequency range to enhance the howling signal and filter out other noise. Time and frequency analysis methods such as wavelet transform can be used to capture the time and frequency characteristics of the howling signal, which is especially suitable for identifying short-lived and high-frequency howling.

[0092] Of course, in some embodiments, feedback can also be automatically detected by training machine learning models such as SVM and CNN, which is especially suitable for complex or variable signal environments.

[0093] In some embodiments, the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location is determined based on at least one of the intensity feature, frequency feature, and time domain feature in the audio features corresponding to each abnormal acoustic feedback location. This includes: based on the intensity feature and frequency feature in the audio features corresponding to the abnormal acoustic feedback location, if the intensity of the audio feature at the target frequency band corresponding to the abnormal acoustic feedback location is greater than a first target intensity value, determining the target intensity increase rate of the audio feature at the target frequency band; if the target intensity increase rate is greater than a speed threshold, determining the type of abnormal acoustic feedback corresponding to the abnormal acoustic feedback location as resonance noise.

[0094] The target frequency band refers to a specific frequency range that requires special attention when conducting anomalous acoustic feedback analysis. Different types of noise will exhibit distinct characteristics within a specific frequency band. For example, resonant noise may be concentrated in a particular frequency range.

[0095] An intensity greater than the first target intensity value means that the sound intensity in the target frequency band of interest needs to reach a set threshold before further analysis can be performed. If the audio intensity of the frequency band does not exceed this value, it may not be considered a potential source of anomalous noise.

[0096] The target intensity growth rate refers to the rate at which the audio intensity in a target frequency band changes over time. Specifically, it's the rate of change in the intensity of an audio signal within a specific time period. If the intensity of a certain frequency band increases rapidly in a short period, then this rate of increase is defined as the target intensity growth rate.

[0097] The velocity threshold is a preset value used to determine whether the intensity increase is fast enough. If the audio intensity increase in the target frequency band exceeds this threshold, it means that the audio signal intensity in that frequency band is changing abnormally rapidly, which is usually related to some specific noise phenomenon.

[0098] Resonance noise typically occurs when the natural frequencies of mechanical or electronic systems match the frequency of an external excitation. In this case, certain parts of the hearing aid will vibrate violently, causing a sudden and rapid increase in the sound intensity of certain frequency bands, which may last for a period of time.

[0099] When the increase in target intensity exceeds the velocity threshold, it indicates an abnormally rapid increase in sound intensity within that frequency band over a short period, and this rapid increase is consistent with the characteristics of resonant noise. Therefore, the abnormal acoustic feedback can be identified as resonant noise.

[0100] If the feedback signal appears in a specific frequency band and exhibits a strong resonant response (especially with a significant increase in amplitude at certain frequencies), it may be due to resonance caused by certain components of the equipment (such as speakers, housings, and structural parts) matching the natural frequencies of the sound waves. Resonance can be reduced by changing the structural design, adding damping materials, or adjusting the position of components. In other words, resonance noise exhibits a sharp increase in amplitude at a specific frequency, and in the time domain, it exhibits periodic fluctuations. Especially at the specific moment when vibration or resonance occurs, the waveform may show regular increases or decreases.

[0101] In some embodiments, the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location is determined based on at least one of the intensity features, frequency features, and time-domain features in the audio features corresponding to each abnormal acoustic feedback location, including: determining that there are periodic high-frequency or low-frequency components in the audio features based on the intensity features, frequency features, and time-domain features in the audio features corresponding to each abnormal acoustic feedback location, and determining that the type of abnormal acoustic feedback corresponding to the abnormal acoustic feedback location is periodic vibration noise.

[0102] Periodic components refer to the parts of an audio signal whose frequency and amplitude change regularly over time. In this case, particular attention is paid to the high-frequency or low-frequency components of the audio signal.

[0103] High-frequency components generally refer to sound components with higher frequencies, which usually sound harsh or shrill. Low-frequency components generally refer to sound components with lower frequencies, which usually sound like a deep hum or rumble.

[0104] When an audio signal contains periodic high-frequency or low-frequency components, it means that the sound repeats similar patterns within a specific time interval. This repetitive characteristic is often found in hearing aid devices and may be caused by vibration or periodic responses.

[0105] Understandably, a characteristic of this noise is its periodicity, meaning that the sound repeats itself at regular time intervals. It may contain low-frequency or high-frequency components, depending on the nature and frequency of the vibrations.

[0106] The audio characteristics at the location of abnormal acoustic feedback can be analyzed, especially the periodic components. If periodically changing high-frequency or low-frequency components appear in the audio signal, and these components repeat in time, it is considered periodic noise.

[0107] It is understandable that periodic vibration noise manifests as high-frequency or low-frequency oscillations with obvious periodicity, and periodic frequency components may appear in the spectrum.

[0108] If the noise exhibits periodic fluctuations and originates from the movement or vibration of specific components such as speaker diaphragms or housings, it may be due to loose components, vibration, or poor contact. This issue can be resolved by checking the installation and securing of components and improving component design.

[0109] In some embodiments, the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location is determined based on at least one of the intensity feature, frequency feature, and time domain feature in the audio features corresponding to each abnormal acoustic feedback location. This includes: based on the intensity feature, frequency feature, and time domain feature in the audio features corresponding to each abnormal acoustic feedback location, if the intensity in the low-frequency band of the audio features corresponding to the abnormal acoustic feedback location is greater than the second target intensity value, determining the coefficient of variation of the corresponding time domain feature waveform amplitude; if the coefficient of variation is less than a preset coefficient, determining that the type of abnormal acoustic feedback corresponding to the abnormal acoustic feedback location is low-frequency leakage sound.

[0110] When analyzing audio signals, special attention needs to be paid to low-frequency signals. Low frequencies typically refer to the frequency range below 200 Hz.

[0111] When the intensity of the low-frequency band exceeds a certain preset target intensity value, it is considered that the sound signal at that location contains a strong low-frequency component, indicating that there is a significant low-frequency noise component in the abnormal sound source.

[0112] Waveform amplitude refers to the magnitude (intensity) of an audio signal on the time axis. Changes in waveform amplitude reflect the changing patterns of sound. The coefficient of variation (CV) refers to the relative dispersion of data distribution, usually the ratio of the standard deviation to the mean. A low coefficient of variation means that the waveform amplitude changes are relatively stable, with small amplitude variations, indicating that the signal exhibits relatively uniform and regular changes in the time domain.

[0113] In this scenario, if the coefficient of variation is less than the preset threshold, meaning that the amplitude of the low-frequency signal changes relatively smoothly and consistently without drastic fluctuations or sudden changes, it can be identified as low-frequency leakage sound.

[0114] Low-frequency leakage sound may be caused by a defect or poor seal in equipment, system, or structure, resulting in low-frequency noise leaking into the external environment. This leakage sound often has continuous low-frequency characteristics, and the signal changes relatively smoothly with small fluctuations, which conforms to the characteristics of low coefficient of variation mentioned above.

[0115] If the sound source is located close to the equipment housing or sealed areas, and the signal strength is weak and stable, it is usually due to low-frequency leakage sound caused by poor sealing or sound transmission in the housing material. This problem can be mitigated by improving the sealing design or selecting more sound-absorbing materials.

[0116] In this embodiment, the type of abnormal sound is identified by the intensity, frequency, and time-domain variations of audio characteristics. In particular, when the intensity of the low-frequency band is high and its time-domain waveform changes smoothly (with a small coefficient of variation), the sound can be determined to be a low-frequency leakage sound.

[0117] The following describes the detection and analysis device for static acoustic feedback of behind-the-ear hearing aids provided by the present invention. The detection and analysis device for static acoustic feedback of behind-the-ear hearing aids described below can be referred to in correspondence with the detection and analysis method for static acoustic feedback of behind-the-ear hearing aids described above.

[0118] like Figure 3 As shown, the detection and analysis device for static acoustic feedback of behind-the-ear hearing aids in this embodiment of the invention mainly includes a configuration module 310, a start module 320, a capture module 330, and a processing module 340.

[0119] The configuration module 310 is used to determine that the behind-the-ear hearing aid is configured in the anechoic test fixture; the anechoic test fixture includes at least two chambers so that the sound-emitting position of the behind-the-ear hearing aid is located in the chamber; the chamber is provided with flexible sound insulation material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid. The activation module 320 is used to determine when the behind-the-ear hearing aid is activated and to control the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; The capture module 330 is used to capture the detection sound signals emitted by various parts of the hearing aid through a microphone array set inside the cavity; The processing module 340 is used to analyze the detected sound signal based on the location of the detected sound signal, determine the audio features corresponding to the detected sound signal, and obtain the abnormal sound feedback location and abnormal sound feedback signal features of the behind-the-ear hearing aid; the audio features include at least one of the following: sound source location, intensity, frequency, and time change features.

[0120] The detection and analysis device for static acoustic feedback of behind-the-ear hearing aids provided in this embodiment of the invention combines silencing detection fixtures and automated detection methods. It captures the detection sound signals emitted by various parts of the hearing aid during the detection process using microphone arrays in each chamber. It uses signal processing algorithms to accurately identify the characteristic parameters of the acoustic feedback signal, ensuring the accuracy of the detection results and providing a scientific basis for the optimization and adjustment of the hearing aid. In particular, it can quantitatively analyze the various factors constituting the acoustic feedback loop, which helps to accurately locate and prevent the occurrence of static acoustic feedback, improve user experience and hearing aid performance.

[0121] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communications bus 440. The processor 410 can call logic instructions in the memory 430 to execute a method for detecting and analyzing static acoustic feedback of a behind-the-ear (BTE) hearing aid. This method includes: determining that the BTE hearing aid is positioned within a noise-canceling detection fixture; the noise-canceling detection fixture includes at least two chambers such that the sound-emitting positions of the BTE hearing aid are all located within the chambers; the chambers are provided with flexible sound-insulating material and a microphone array for capturing sound signals emitted from various parts of the BTE hearing aid; determining that the BTE hearing aid has started operating, and controlling the speaker of the BTE hearing aid to emit a target detection sound signal; capturing the detection sound signals emitted from various parts of the hearing aid through the microphone array located within the chambers; analyzing the detection sound signals based on their positions, determining the corresponding audio features, and obtaining the abnormal acoustic feedback position and abnormal acoustic feedback signal characteristics of the BTE hearing aid; the audio features include at least one of sound source position, intensity, frequency, and time variation characteristics.

[0122] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the detection and analysis method for static acoustic feedback of a behind-the-ear hearing aid provided by the above methods. The method includes: determining that the behind-the-ear hearing aid is configured in a noise-canceling detection fixture; the noise-canceling detection fixture includes at least two chambers such that the sound-emitting position of the behind-the-ear hearing aid is located in the chambers; a flexible sound-insulating material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid are disposed inside the chambers; determining that the behind-the-ear hearing aid has started working and controlling the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; capturing the detection sound signals emitted from various parts of the hearing aid through the microphone array disposed inside the chambers; analyzing the detection sound signals based on the position of the detection sound signals, determining the audio features corresponding to the detection sound signals, and obtaining the abnormal acoustic feedback position and abnormal acoustic feedback signal features of the behind-the-ear hearing aid; the audio features include at least one of sound source position, intensity, frequency, and time variation features.

[0124] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for detecting and analyzing static acoustic feedback of a behind-the-ear hearing aid provided by the methods described above. The method includes: determining that the behind-the-ear hearing aid is disposed within a noise-canceling detection fixture; the noise-canceling detection fixture includes at least two chambers such that the sound-emitting positions of the behind-the-ear hearing aid are all located within the chambers; a flexible sound-insulating material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid are disposed within the chambers; determining that the behind-the-ear hearing aid has started operating, and controlling the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; capturing the detection sound signals emitted from various parts of the hearing aid through the microphone array disposed within the chambers; analyzing the detection sound signals based on their positions, determining the audio features corresponding to the detection sound signals, and obtaining the abnormal acoustic feedback position and abnormal acoustic feedback signal features of the behind-the-ear hearing aid; the audio features include at least one of sound source position, intensity, frequency, and time variation features.

[0125] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0126] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid, characterized in that, include: The behind-the-ear hearing aid is positioned within a noise reduction testing fixture; the noise reduction testing fixture includes at least two chambers such that the sound-emitting position of the behind-the-ear hearing aid is located within the chambers; the chambers are provided with flexible sound-insulating material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid. The system determines that the behind-the-ear hearing aid has started working and controls the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; The microphone array installed inside the chamber captures the detection sound signals emitted by various parts of the hearing aid; The detected sound signal is analyzed based on its location to determine the corresponding audio features and obtain the abnormal sound feedback location and abnormal sound feedback signal features of the behind-the-ear hearing aid. The audio features include at least one of the following: sound source location, intensity, frequency, and time variation features.

2. The method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid according to claim 1, characterized in that, The step of analyzing the detected sound signal based on its location to determine the corresponding audio features and obtain the abnormal acoustic feedback location and abnormal acoustic feedback signal features of the behind-the-ear hearing aid includes: Based on the audio features corresponding to the detected sound signal, extract the audio features corresponding to the abnormal sound feedback; Based on the audio features corresponding to the abnormal sound feedback, the sound source location is analyzed to identify the location of the abnormal sound feedback. Based on the audio features corresponding to the abnormal sound feedback and the location of the abnormal sound feedback, the audio features corresponding to each abnormal sound feedback location are identified. Based on the audio features corresponding to each abnormal sound feedback location, the type of abnormal sound feedback corresponding to each abnormal sound feedback location and the abnormal sound feedback signal characteristics of each type of abnormal sound feedback are obtained.

3. The method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid according to claim 2, characterized in that, The method of obtaining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on the audio features corresponding to each abnormal acoustic feedback location includes: Identify the intensity information in the audio features corresponding to each abnormal sound feedback location, and obtain the magnitude of the intensity in the audio features and the intensity features that change with time; perform spectral analysis on the audio features corresponding to each abnormal sound feedback location to identify the proportion of sound in different frequency bands and periodic patterns, and obtain frequency features; perform time-domain analysis on the audio features corresponding to each abnormal sound feedback location to identify the time-domain features corresponding to the time-domain waveform. Based on at least one of the intensity feature, frequency feature, and time domain feature of the audio features corresponding to each abnormal sound feedback location, the type of abnormal sound feedback corresponding to each abnormal sound feedback location is determined; the type of abnormal sound feedback includes at least one of high-frequency howling, resonant noise, periodic vibration noise, and low-frequency leakage sound.

4. The method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid according to claim 3, characterized in that, The method of determining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on at least one of the intensity feature, frequency feature, and time domain feature in the audio features corresponding to each abnormal acoustic feedback location includes: Based on the intensity and frequency features of the audio features corresponding to the abnormal sound feedback location, when the intensity of the audio features at the target frequency band corresponding to the abnormal sound feedback location is greater than the first target intensity value, the target intensity growth rate of the audio features at the target frequency band is determined. When the target intensity increase is greater than the velocity threshold, the type of abnormal acoustic feedback at the abnormal acoustic feedback location is determined to be resonance noise.

5. The method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid according to claim 3, characterized in that, The method of determining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on at least one of the intensity feature, frequency feature, and time domain feature in the audio features corresponding to each abnormal acoustic feedback location includes: Based on the intensity, frequency, and time-domain characteristics of the audio features corresponding to each abnormal acoustic feedback location, it is determined that there are periodic high-frequency or low-frequency components in the audio features, and the type of abnormal acoustic feedback corresponding to the abnormal acoustic feedback location is determined to be periodic vibration noise.

6. The method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid according to claim 3, characterized in that, The method of determining the type of abnormal acoustic feedback corresponding to each abnormal acoustic feedback location based on at least one of the intensity feature, frequency feature, and time domain feature in the audio features corresponding to each abnormal acoustic feedback location includes: Based on the intensity, frequency and time-domain features of the audio features corresponding to each abnormal sound feedback location, when the intensity in the low-frequency band of the audio features corresponding to the abnormal sound feedback location is greater than the second target intensity value, the coefficient of variation of the corresponding time-domain feature waveform amplitude is determined. If the coefficient of variation is less than a preset coefficient, the type of abnormal acoustic feedback at the location of the abnormal acoustic feedback is determined to be low-frequency leakage sound.

7. The method for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid according to claim 2, characterized in that, The step of analyzing the sound source location based on the audio features corresponding to the abnormal sound feedback and identifying the location of the abnormal sound feedback includes: The phase difference and time difference of the audio features corresponding to the abnormal sound feedback detected by different microphones are obtained, and the signal phase and amplitude are adjusted by beamforming algorithm to enhance the sound signal in the target direction; The location of the abnormal sound feedback is calculated by analyzing the sound signal after amplification of the target direction using a geometric positioning algorithm.

8. A device for detecting and analyzing static acoustic feedback in a behind-the-ear hearing aid, characterized in that, include: A configuration module is used to determine that the behind-the-ear hearing aid is configured in a noise reduction testing fixture; the noise reduction testing fixture includes at least two chambers such that the sound-emitting position of the behind-the-ear hearing aid is located in the chamber; the chamber is provided with flexible sound insulation material and a microphone array for capturing sound signals emitted from various parts of the behind-the-ear hearing aid; The startup module is used to determine when the behind-the-ear hearing aid is started and to control the speaker of the behind-the-ear hearing aid to emit a target detection sound signal; The capture module is used to capture the detection sound signals emitted by various parts of the hearing aid through a microphone array set inside the cavity; The processing module is used to analyze the detected sound signal based on the location of the detected sound signal, determine the audio features corresponding to the detected sound signal, and obtain the abnormal sound feedback location and abnormal sound feedback signal features of the behind-the-ear hearing aid; the audio features include at least one of sound source location, intensity, frequency, and time variation features.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the program, it implements the method for detecting and analyzing static acoustic feedback of a behind-the-ear hearing aid as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for detecting and analyzing static acoustic feedback of a behind-the-ear hearing aid as described in any one of claims 1 to 7.

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