Hearing testing method and device of hearing aid, hearing aid and storage medium

Abstract

The invention relates to the technical field of medical instruments, and discloses a hearing-aid audiometry method and device, a hearing aid and a storage medium, and the hearing-aid audiometry method provided by the invention comprises the steps: obtaining the environment sound of the current environment; determining a signal-to-noise ratio corresponding to the environment sound; and according to the signal-to-noise ratio, adjusting audiometry configuration of the hearing aid, and controlling the hearing aid to perform audiometry according to the audiometry configuration. The hearing testing configuration of the hearing aid can be automatically adjusted according to the real-time sound field environment and the noise condition, hearing testing of a hearing aid user under different noise conditions is achieved, the accuracy and reliability of the testing result are improved so as to adapt to the actual environment where the user is located, personalized hearing support is provided for the user, and the user experience is improved. Therefore, a clearer and more accurate sound enhancement effect is provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to an audiometry method and device for a hearing aid, a hearing aid, and a storage medium. Background Art

[0002] Currently, hearing aid users are often disturbed by ambient noise during hearing tests, which negatively impacts the accuracy of test results. Although current technology tends to perform hearing tests in relatively quiet environments, this approach does not always adapt to the ever-changing noise conditions in daily life. Summary of the Invention

[0003] In view of this, the present invention provides an audiometry method and apparatus for a hearing aid, a hearing aid, and a storage medium to solve the problem of low accuracy and reliability of a hearing aid during audiometry due to interference from ambient noise.

[0004] In a first aspect, the present invention provides an audiometry method for a hearing aid, the method comprising:

[0005] Get the ambient sound of the current environment;

[0006] Determining a signal-to-noise ratio corresponding to the ambient sound;

[0007] According to the signal-to-noise ratio, an audiometry configuration of the hearing aid is adjusted, and the hearing aid is controlled to perform audiometry according to the audiometry configuration.

[0008] In an optional implementation, before adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio, the method further includes:

[0009] Obtaining a sound spectrum corresponding to the ambient sound;

[0010] Acquiring environmental noise energy at a key frequency in the sound spectrum, where the key frequency is a predetermined hearing aid audiometry frequency;

[0011] The adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio includes:

[0012] The audiometric configuration of the hearing aid is adjusted according to the ambient noise energy and the signal-to-noise ratio.

[0013] In an optional implementation, obtaining the ambient noise energy of the key frequency in the sound spectrum includes:

[0014] Obtaining an average power value of the power spectrum at each time point and a power value corresponding to the key frequency, wherein the power spectrum at each time point is the power spectrum of each short-time frame of the ambient sound segmentation;

[0015] Setting a noise discrimination threshold based on the average power value;

[0016] Comparing the power value corresponding to each of the key frequencies with the noise discrimination threshold one by one;

[0017] identifying the key frequency having a power value exceeding the noise discrimination threshold as a noise frequency;

[0018] The environmental noise energy is determined based on the power value of the noise frequency.

[0019] In an optional implementation, adjusting the audiometric configuration of the hearing aid according to the ambient noise energy and the signal-to-noise ratio includes:

[0020] Obtaining a magnitude relationship between the ambient noise energy and a preset noise energy threshold;

[0021] If it is determined that the current environment is suitable for audiometry based on the magnitude relationship, the noise reduction depth in the audiometry configuration is adjusted according to the signal-to-noise ratio.

[0022] In an optional implementation, adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio includes:

[0023] Obtaining a preset signal-to-noise ratio threshold corresponding to the signal-to-noise ratio;

[0024] If the signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold, reducing the noise reduction depth in the audiometry configuration;

[0025] If the signal-to-noise ratio is lower than the preset signal-to-noise ratio threshold, the noise reduction depth in the audiometry configuration is increased.

[0026] In an optional implementation, after obtaining the ambient noise energy of the key frequency in the sound spectrum, the method further includes:

[0027] Calculating the masking threshold of the target signal of the ambient sound in each frequency band according to the frequency and intensity corresponding to the ambient noise energy; the target signal is the audio signal used for audiometry;

[0028] The target signal of the ambient sound is compared with a corresponding masking threshold, and the audibility of the target signal of the ambient sound is modified based on the comparison result.

[0029] In an optional implementation, obtaining a sound spectrum corresponding to the ambient sound includes:

[0030] Using a neural network model to obtain the sound field environment corresponding to the ambient sound;

[0031] A sound spectrum corresponding to the sound field environment is obtained as a sound spectrum corresponding to the ambient sound.

[0032] In an optional embodiment, the using a neural network model to obtain the sound field environment corresponding to the ambient sound includes:

[0033] Extracting Mel-frequency cepstral coefficients of the ambient sound;

[0034] Based on the Mel-frequency cepstral coefficients, generating a corresponding audio feature vector;

[0035] The neural network model is used to classify and evaluate the audio feature vector to obtain the corresponding sound field environment.

[0036] In an optional implementation, extracting the Mel-frequency cepstral coefficients of the ambient sound includes:

[0037] Segmenting the ambient sound into multiple short time frames;

[0038] Applying a preset window function to each of the short time frames;

[0039] Performing a fast Fourier transform on each of the short-time frames to which the preset window function is applied, and calculating a power spectrum of each of the short-time frames;

[0040] Passing the power spectrum of each of the short-time frames through a set of Mel filter banks to generate a plurality of filter outputs;

[0041] Take the logarithm of the energy output by any filter and perform discrete cosine transform on the energy value after taking the logarithm to generate the cepstral coefficient;

[0042] The first N cepstral coefficients are selected as the Mel-frequency cepstral coefficients of the ambient sound.

[0043] In an optional embodiment, the audiometry method for a hearing aid further includes:

[0044] If there are multiple mixed sounds in the sound field environment, the direction of the test sound source is determined based on the directionality estimation algorithm, and the signal reception strength in the corresponding direction is controlled and enhanced.

[0045] In a second aspect, the present invention provides an audiometric device for a hearing aid, the device comprising:

[0046] Ambient sound acquisition module, used to obtain the ambient sound of the current environment;

[0047] a signal-to-noise ratio determination module, configured to determine a signal-to-noise ratio corresponding to the ambient sound;

[0048] The audiometry module is configured to adjust the audiometry configuration of the hearing aid according to the signal-to-noise ratio, and control the hearing aid to perform audiometry according to the audiometry configuration.

[0049] In a third aspect, the present invention provides a hearing aid comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby perform the audiometry method of the hearing aid according to the first aspect or any corresponding embodiment thereof.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to cause a computer to execute the hearing aid audiometry method of the first aspect or any corresponding embodiment thereof.

[0051] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, wherein the computer instructions are used to cause a computer to execute the audiometry method for a hearing aid according to the first aspect or any corresponding embodiment thereof.

[0052] The hearing aid audiometry method, device, hearing aid, and storage medium provided in this embodiment obtain the ambient sound of the user's current environment, then determine the signal-to-noise ratio corresponding to the ambient sound, and finally adjust the hearing aid's audiometry configuration according to the signal-to-noise ratio, and control the hearing aid to perform audiometry according to the adjusted audiometry configuration. That is, the hearing aid can automatically adjust its audiometry configuration according to the real-time sound field environment and noise conditions, thereby realizing hearing testing of hearing aid users under different noise conditions, improving the accuracy and reliability of test results, adapting to the user's actual environment, and providing users with personalized hearing support, thereby providing users with clearer and more accurate sound enhancement effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 is a flow chart of an audiometry method for a hearing aid according to an embodiment of the present invention;

[0055] Figure 2 is a structural block diagram of an audiometric device for a hearing aid according to an embodiment of the present invention;

[0056] Figure 3 Schematic diagram of the hardware structure of a hearing aid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0058] According to an embodiment of the present invention, an embodiment of an audiometry method for a hearing aid is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of executable computer instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in an order different from that shown.

[0059] In this embodiment, a hearing aid audiometry method is provided, which can be used for audiometry equipment in a hearing aid, specifically various terminals, servers, etc. Figure 1 : is a flow chart of an audiometry method for a hearing aid according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0060] Step S101: Acquire the ambient sound of the current environment. The current environment is the required test environment.

[0061] Specifically, the ambient sound of the current environment can be collected by the hearing aid. The hearing aid has a built-in microphone for capturing sound signals in the surrounding environment. This is the first step for the hearing aid to realize its core function (amplification and compensation for hearing loss). The working principle of the hearing aid is: the microphone of the hearing aid converts the external sound (sound wave) into an electrical signal; then the collected electrical signal is analyzed and processed by the digital signal processor (DSP), and the processing process includes: noise suppression, frequency compensation, gain adjustment, etc.; finally, the processed signal is amplified and converted into sound, and transmitted to the user's ear canal through the receiver. Ambient sound can include various background noises, such as traffic noise on the street, the conversation of people indoors, the operation sound of electronic equipment in the office, etc.

[0062] In some optional specific implementations, before step S102, that is, before adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio, the method further includes:

[0063] Step 1: Obtain a sound spectrum corresponding to the ambient sound, wherein the sound spectrum may include sound energy distribution in various frequency bands from low frequency to high frequency.

[0064] In some optional specific implementations, step 1, i.e., obtaining a sound spectrum corresponding to the ambient sound, includes:

[0065] Step 1 (a): using a neural network model to obtain the sound field environment corresponding to the ambient sound.

[0066] In some optional specific implementations, step 1 (a), i.e., using a neural network model to obtain a sound field environment corresponding to the ambient sound, includes:

[0067] Step 1 (a1) extracts the Mel-frequency cepstral coefficients of the ambient sound. When extracting the Mel-frequency cepstral coefficients of the ambient sound, the ambient sound can be preprocessed. Preprocessing can include, for example, noise reduction processing of the ambient sound to reduce the impact of ambient noise on feature extraction. In addition, pre-emphasis can also be included: by applying a high-pass filter to enhance the high-frequency portion, which can increase the energy of the high-frequency component and reduce the impact of high-frequency loss in signal transmission.

[0068] Specifically, step 1 (a1), i.e., extracting the Mel-frequency cepstral coefficients of the ambient sound, includes:

[0069] Step 1 (a11) is to divide the ambient sound into a plurality of short-time frames.

[0070] Short-time framing refers to the process of segmenting a continuous signal into a series of short time windows, where the signal within each window can be approximately considered stationary. For speech signals, a typical short-time frame length is 20-40 milliseconds, which roughly corresponds to the duration of a syllable or formant cycle in human speech. Furthermore, the short-time frame length is set to 20-40 milliseconds to ensure that sufficient audio features are captured during analysis while avoiding the delay caused by excessively long frames. The frame shift when segmenting short-time frames can be around 10 milliseconds, with overlap between adjacent short-time frames.

[0071] Step 1 (a12) applies a preset window function to each of the short time frames. The selection of the window function is crucial for reducing spectrum leakage. Commonly used window functions include Hamming window, Hanning window, and Blackman window.

[0072] Step 1 (a13) performs a fast Fourier transform on each of the short-time frames using a preset window function, and calculates the power spectrum of each short-time frame. The fast Fourier transform (FFT) is a key step in converting a time-domain signal into a frequency-domain signal, providing frequency component information for each short-time frame. In other words, the short-time Fourier transform (STFT) converts the time-domain signal into a frequency-domain representation, obtaining the spectrum information of the short-time frame, facilitating analysis of the frequency composition of the ambient sound signal.

[0073] Step 1 (a14) passes the power spectrum of each short-time frame through a set of Mel filter groups to generate multiple filter outputs. The Mel filter group can be, for example, a triangular filter group distributed according to the Mel scale. These filter outputs reflect the human ear's perception characteristics of sounds of different frequencies. That is, through the processing of the Mel filter group, a spectrum representation that is closer to the human ear's perception can be obtained, because the Mel scale is designed based on the nonlinear characteristics of the human ear's perception of sounds of different frequencies. In an embodiment of the present invention, in view of the different sensitivities of the human ear to sounds of different frequencies, the linear frequency scale is converted to a nonlinear Mel scale. This process is simulated by a Mel filter group composed of a series of triangular filters. Each filter covers a frequency range, the filters in the low-frequency area are denser, and the filters in the high-frequency area are sparser. The sum of the energy output of each filter is calculated to obtain a set of energy values.

[0074] In step 1 (a15), the logarithm of the energy output by any filter is taken and the discrete cosine transform (DCT) is performed on the logarithmic energy value to generate cepstral coefficients. The cepstral coefficients obtained by the discrete cosine transform (DCT) can further compress the audio feature data while retaining information useful for classification evaluation. Here, the logarithm of the filter bank energy obtained in the previous step is taken to compress the dynamic range, because the human ear has a nonlinear perception of changes in sound intensity.

[0075] In step 1 (a16), the first N cepstral coefficients are selected as the Mel-frequency cepstral coefficients of the ambient sound. Selecting the first N cepstral coefficients as feature vectors can effectively reduce the amount of computation while maintaining sufficient classification accuracy. N can be adjusted based on actual conditions to accommodate different computing resources and accuracy requirements. For example, on a resource-constrained device, a smaller N value can be selected to reduce the computational burden; whereas, in situations where higher accuracy is required, the N value can be increased to improve classification accuracy.

[0076] In this embodiment of the present invention, a discrete cosine transform (DCT) is applied to the logarithmic filter bank energy to obtain MFCC coefficients. The DCT converts data from the spatial domain to the frequency domain, similar to the Fourier transform, but uses only cosine functions as basis functions. Typically, only the first N (e.g., 13) DCT coefficients are retained as the final MFCC feature vector, as these low-order coefficients contain the majority of the information.

[0077] Step 1 (a2) generates a corresponding audio feature vector based on the Mel-frequency cepstral coefficients. Mel-frequency cepstral coefficients are a feature parameter widely used in speech recognition and audio signal processing. The Mel-frequency cepstral coefficients can effectively capture the characteristics of the ambient sound, thereby improving the accuracy of the neural network in classifying the sound field environment. Mel-frequency cepstral coefficients (MFCCs) are a form of audio feature vector.

[0078] Step 1 (a3) ​​uses the neural network model to classify and evaluate the audio feature vectors to obtain the corresponding sound field environment. Specifically, the audio feature vectors can be input into a pre-trained neural network model for classification and evaluation. By learning a large number of audio feature vectors under different sound field environments, the neural network model can accurately identify the sound field environment corresponding to the current ambient sound, thereby providing accurate environmental information for the audiometric configuration of the hearing aid. The sound field environment can include indoor and outdoor environments, such as conference rooms, study rooms, libraries, tea rooms, restaurants, downtown areas, parks, and streets.

[0079] In an embodiment of the present invention, a neural network model is used to analyze and obtain the sound field environment corresponding to the ambient sound. Specifically, the neural network calculates the probability value of each scene based on the audio feature vector corresponding to the ambient sound, selects the scene with the largest probability value as output, and thus determines the sound field environment in which the user is currently located. For example, on a noisy street, the hearing aid will collect continuous traffic noise, and then the neural network will calculate the probability value of each scene based on the sound characteristics of the traffic noise (i.e., the audio feature vector), such as the probability of the street is 80%, the probability of the park is 30%, the probability of the restaurant is 63%, and the probability of the library is 14%, and finally determines that the user is in a street environment. In an embodiment of the present invention, the sound field environment can be accurately classified, and the necessary environmental information can be provided for the audiometric configuration of the hearing aid, thereby optimizing the user's auditory experience.

[0080] In this embodiment of the present invention, first determining the sound field environment and then obtaining the sound spectrum corresponding to that sound field environment can improve the accuracy of subsequent ambient sound analysis and, by extension, the accuracy of audiometric configuration adjustments. This is because the characteristics of the sound field environment directly influence phenomena such as the propagation, reflection, and absorption of sound signals, thereby affecting the final received sound spectrum. Directly obtaining the sound spectrum corresponding to the ambient sound without considering the sound field environment may lead to misjudgment of sound characteristics or inaccurate analysis results.

[0081] Step 1 (b): obtaining a sound spectrum corresponding to the sound field environment as a sound spectrum corresponding to the ambient sound.

[0082] The sound spectrum can be obtained by performing frequency domain analysis on the sound signal, specifically converting the time domain signal into a frequency domain signal. The following are the detailed steps and methods for obtaining the sound spectrum:

[0083] 1. Digitize the sound signal (such as the ambient sound mentioned above); the digitized signal is a discrete time series x[n], which represents the amplitude of the sound at each sampling point.

[0084] 2. For non-stationary signals (such as speech), they need to be divided into short-time frames.

[0085] 3. After framing, each frame is multiplied by a window function (such as a Hanning window or rectangular window) to reduce spectral leakage. The window function smoothes the signal boundaries and avoids artifacts caused by sudden changes.

[0086] 4. Apply a discrete Fourier transform (DFT) or a fast Fourier transform (FFT) to the windowed signal.

[0087] 5. Calculate the spectrum amplitude. To better visualize the spectrum, the amplitude can be converted to logarithmic form.

[0088] Step 2: Obtain the ambient noise energy at key frequencies in the sound spectrum. These key frequencies are predetermined hearing aid test frequencies. Key frequencies include 250, 500, 1000, 2000, 4000, and 6000, all of which are hearing aid test frequencies. Key frequencies are particularly important in hearing aid use because they are often associated with key components in speech signals.

[0089] In some optional specific implementations, step 2, i.e., obtaining the ambient noise energy of the key frequency in the sound spectrum, includes:

[0090] Step 2 (a) obtains the average power value of the power spectrum at each time point and the power value corresponding to the key frequency. The power spectrum at each time point is the power spectrum of each short-time frame of the ambient sound segmentation. For the power spectrum of each short-time frame of the ambient sound segmentation, please refer to the short-time frame power spectrum involved in the process of extracting the Mel-frequency cepstral coefficients of the ambient sound above. The average power value of the power spectrum is obtained by adding up the power values ​​of each frequency point and dividing it by the number of frequency points. By calculating the average power value, an indicator reflecting the overall ambient noise level can be obtained.

[0091] The "time point" here actually refers to the time position of a short-time frame. The ambient sound is segmented into multiple short-time frames, each representing the audio signal within a specific time period. By applying a window function and performing a fast Fourier transform (FFT) on each short-time frame, we can obtain the power spectrum of that short-time frame.

[0092] Therefore, a "time point" can be understood as the time marker of the center or starting position of a short time frame. For example, if the short time frame length is 25 milliseconds and the frame shift is 10 milliseconds, the first short time frame covers the time range [0, 25] milliseconds, and its corresponding time point can be 12.5 milliseconds (the center position). The second short time frame covers the time range [10, 35] milliseconds, and its corresponding time point is 22.5 milliseconds, and so on.

[0093] The sound spectrum generally refers to the result obtained after a Fourier transform of a signal, showing the amplitude distribution of the signal at each frequency. The power spectrum, on the other hand, further indicates the power distribution of the signal at different frequencies, that is, the signal power per unit frequency band. In other words, the power spectrum retains the amplitude information in the spectrum but loses the phase information.

[0094] From a mathematical perspective, the power spectrum can be viewed as the squared amplitude of each frequency component in the sound spectrum. For discrete signals, the power spectrum can be obtained by taking the modulus of the complex number result after the Fast Fourier Transform (FFT) and squaring it.

[0095] Power typically refers to the amount of energy transmitted per unit time. In signal processing, it can be understood as the average energy consumed per second by a signal at a specific frequency or across an entire frequency band. In contrast, energy refers to the total energy accumulated over a period of time.

[0096] Step 2 (b): Based on the average power value, a noise discrimination threshold is set. The noise discrimination threshold may be, for example, a preset multiple of the average power value.

[0097] Step 2 (c) compares the power value corresponding to each of the key frequencies with the noise discrimination threshold to determine the ambient noise energy.

[0098] Key frequencies are the frequency points at which hearing aids are measured. These frequencies are particularly important in hearing aid use because they often correlate with key components in speech signals. Determining the ambient noise energy at these key frequencies helps distinguish which frequency components are most affected by noise in the current sound field. By setting a noise discrimination threshold, frequencies with power exceeding the threshold can be identified as noise frequencies, providing a basis for subsequent signal-to-noise ratio calculations.

[0099] Specifically, the ambient noise energy is determined by comparing the power value of each key frequency with the noise discrimination threshold, then identifying the key frequency whose power value exceeds the noise discrimination threshold as a noise frequency; and finally determining the ambient noise energy based on the power value of the noise frequency. For example, the ambient noise energy is determined as the average of the power values ​​of multiple noise frequencies.

[0100] In this embodiment of the present invention, starting from key frequency points, noise frequency points are screened out using a noise discrimination threshold, and the ambient noise energy is finally derived based on the energy values ​​of these noise frequency points. This method ensures an accurate assessment of the noise impact in the sound field environment.

[0101] Step S102: determining a signal-to-noise ratio corresponding to the ambient sound.

[0102] Specifically, the signal-to-noise ratio (SNR) is the ratio of ambient noise energy to signal energy, reflecting the clarity of the signal at a specific frequency. Calculating the SNR is crucial to hearing aid performance because it directly affects whether the user can clearly hear the desired sound. After determining the SNR, the hearing aid's audiometric configuration is adjusted based on the SNR to optimize the user's auditory experience. For example, if the SNR is low, the hearing aid may increase the gain at specific frequencies to improve the user's perception of sounds at those frequencies.

[0103] The calculation of the signal-to-noise ratio (SNR) is based on the relationship between ambient noise energy and signal energy. The SNR calculation process involves multiple steps, including ambient sound acquisition, sound field environment analysis, determination of ambient noise energy at key frequencies, and finally calculation of the SNR based on this data.

[0104] Ambient sound collection and sound field environment analysis:

[0105] First, in step S100, the hearing aid uses its built-in microphone to capture the ambient sound of the user's environment. This ambient sound may include various background noises, such as street traffic noise and conversations indoors. Next, in step S200, a neural network analyzes this ambient sound to determine the corresponding sound field environment. The sound spectrum corresponding to this sound field environment is then analyzed to determine the ambient noise energy at key frequencies within the sound spectrum.

[0106] Ambient noise energy at key frequencies is determined by:

[0107] The sound spectrum covers the distribution of sound energy across all frequency bands, from low to high. By analyzing this sound spectrum data, we can identify which frequency bands have high sound energy and, in turn, determine key frequencies. By analyzing the ambient sound spectrum, we can calculate the noise energy value at each key frequency point. For example, for the 250Hz frequency point, we can use a specific algorithm to calculate the noise energy value at that frequency point, and then repeat this process for all key frequency points.

[0108] Calculation of signal-to-noise ratio:

[0109] Once the ambient noise energy at the critical frequency point is determined, the signal-to-noise ratio can be calculated. The signal-to-noise ratio is defined as the ratio of the signal power to the noise power, usually expressed in decibels (dB). The calculation formula is:

[0110]

[0111] Here, Ps represents the average power of the signal, and Pn represents the average power of the noise. In this scenario, signal power Ps can be considered the energy of the sound (such as speech) that the user expects to hear, while noise power Pn is the ambient noise energy at the key frequency point calculated in the above steps.

[0112] Step S103: Adjust the audiometric configuration of the hearing aid based on the signal-to-noise ratio, and control the hearing aid to perform audiometry according to the audiometric configuration. The audiometric configuration may include parameters such as gain setting, frequency response adjustment, noise suppression level, noise reduction depth, and sound amplification strategy. These parameters are adjusted based on the user's specific hearing needs and ambient noise conditions. For example, for a user with high-frequency hearing loss, in a noisy environment, the hearing aid can increase the gain of the high-frequency band while reducing the gain of the low-frequency band to reduce the interference of low-frequency noise. Furthermore, the hearing aid can dynamically adjust these parameters based on changes in ambient noise to provide the best listening experience. For example, in a quiet library environment, the hearing aid can reduce the gain to avoid amplifying unnecessary background noise; while on a noisy street, the hearing aid can increase the gain to ensure that the user can hear important sound signals. This provides users with more personalized and adaptable hearing support.

[0113] Step S103, i.e., adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio, includes:

[0114] Step S1031: Adjust the audiometric configuration of the hearing aid according to the ambient noise energy and the signal-to-noise ratio. The process of obtaining the ambient noise energy is described above.

[0115] In some optional specific implementations, step S1031, i.e., adjusting the audiometric configuration of the hearing aid according to the ambient noise energy and the signal-to-noise ratio, includes:

[0116] Step S10311: Obtain the magnitude relationship between the environmental noise energy and a preset noise energy threshold.

[0117] Step S10312: If it is determined that the current environment is suitable for audiometry based on the magnitude relationship, the noise reduction depth in the audiometry configuration is adjusted according to the signal-to-noise ratio.

[0118] In this embodiment, the suitability of the current sound field environment for audiometry is determined by first comparing the ambient noise energy with a preset noise energy threshold. If the ambient noise energy is lower than the preset threshold, the current ambient noise level is low and suitable for audiometry. Conversely, if the ambient noise energy is higher than the preset threshold, measures may be needed to reduce the noise impact, or audiometry may be conducted in a quieter environment.

[0119] Specifically, step S10312, i.e., adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio, includes:

[0120] Step S103121, obtaining a preset signal-to-noise ratio threshold corresponding to the signal-to-noise ratio;

[0121] Step S103122: if the signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold, reducing the noise reduction depth in the audiometry configuration;

[0122] Step S103123: If the signal-to-noise ratio is lower than the preset signal-to-noise ratio threshold, increase the noise reduction depth in the audiometry configuration.

[0123] In this embodiment of the present invention, after determining that the sound field environment is suitable for audiometry, the hearing aid's noise reduction depth is adjusted based on the signal-to-noise ratio (SNR) and a preset SNR threshold. SNR is an important indicator of signal quality, reflecting the relative strength of the signal and background noise. By adjusting the noise reduction depth, the hearing aid's output can be optimized, ensuring a clear hearing experience for users in a variety of sound field environments.

[0124] The preset signal-to-noise ratio threshold is set based on the hearing aid's performance and the user's hearing needs to ensure the hearing aid provides optimal hearing performance in different environments. Through these steps, the hearing aid can automatically adjust its audiometric configuration based on the real-time sound field environment and noise conditions, thereby providing personalized hearing support for the user.

[0125] In this embodiment, by comparing the signal-to-noise ratio (SNR) with a preset SNR threshold, the hearing aid can automatically adjust its noise reduction function to adapt to different ambient noise levels. When the SNR is high, it indicates that the signal in the environment is relatively strong and the noise impact is small. Therefore, the noise reduction depth can be appropriately reduced to maintain the naturalness and clarity of the signal. Conversely, if the SNR is low, it indicates that the noise has a greater impact on the signal. In this case, increasing the noise reduction depth helps reduce noise interference and improve speech intelligibility. Through this dynamic adjustment, the hearing aid can provide users with a clearer and more comfortable listening experience, while ensuring optimal listening effects in various sound field environments.

[0126] In actual applications, for example, if the sound field environment is suitable for audiometry and the signal-to-noise ratio is 10dB, which is lower than the preset signal-to-noise ratio threshold of 15dB, the hearing aid will automatically increase the noise reduction depth to enhance the speech signal and reduce the interference of background noise. In this way, the user can get a clearer hearing experience even in noisy environments. This adaptive adjustment mechanism of the hearing aid ensures that it can provide optimal hearing support in different noise environments and meet the personalized needs of users.

[0127] In some other optional specific implementations, after obtaining the ambient noise energy of the key frequency in the sound spectrum, the method further includes:

[0128] Based on the frequency (i.e., noise frequency) and intensity corresponding to the ambient noise energy, the masking threshold of the target signal of the ambient sound in each frequency band is calculated; the target signal is the audio signal used for audiometry, that is, the signal of a specific frequency band; specifically, the target signal refers to the sound signal that the hearing aid user wants to hear, usually an important signal such as speech or music; the intensity of the ambient noise energy is usually expressed in the form of a power value, and the power spectral density or power value can more directly reflect the energy distribution of the signal; the frequency and intensity corresponding to the ambient noise energy, that is, the noise frequency and the corresponding intensity.

[0129] The target signal of the ambient sound is compared with a corresponding masking threshold, and the audibility of the target signal of the ambient sound is modified based on the comparison result.

[0130] In this embodiment of the present invention, the step of correcting the audibility of the target signal of the ambient sound may be performed only if the current environment is determined to be suitable for audiometry. If the current environment is determined to be unsuitable for audiometry, the step of correcting the audibility of the target signal of the ambient sound may be performed only if the current environment is determined to be unsuitable for audiometry. The process of determining whether the current environment is suitable for audiometry is described above.

[0131] In this embodiment, the masking threshold refers to the threshold used to determine whether the ambient noise energy at a specific frequency affects the audibility of the target signal. The masking effect is a phenomenon in human ear perception, whereby a stronger sound can mask a weaker sound, making the latter difficult to detect. By calculating the masking threshold, the minimum intensity that the target signal needs to reach in each frequency band under the current ambient noise level can be determined to ensure its audibility. When calculating the masking threshold, the energy distribution and frequency characteristics of the ambient noise, as well as the frequency characteristics of the target signal, need to be considered. After calculating the masking threshold, the target signal can be adjusted accordingly to ensure its audibility in a noisy environment. For example, if the ambient noise energy in a certain frequency band is higher, the masking threshold of the target signal in that frequency band will also increase accordingly, requiring the signal intensity in that frequency band to overcome the influence of the noise. In this way, the hearing aid can provide the user with a clearer and more comfortable listening experience.

[0132] Among them, the target signal refers to the sound that the user wants to hear, usually refers to important signals such as speech or music. In this embodiment, the target signal refers to the speech signal corresponding to the audiometry. Among them, the calculation of the masking threshold of the target signal is crucial to optimizing the auditory experience. By accurately calculating the masking threshold, the hearing aid can adjust the frequency response of the output signal, thereby highlighting the target signal in a noisy environment and reducing the interference of background noise. For example, if the target signal is masked by ambient noise in a certain frequency band, the hearing aid can increase the gain of this frequency band to make the target signal more prominent, so that the user can more easily recognize and understand the sound content.

[0133] The masking threshold can be calculated as follows:

[0134] First, analyze the energy distribution of ambient noise: This involves preprocessing the ambient sound to extract Mel-Frequency Cepstral Coefficients (MFCCs), generating corresponding audio feature vectors, and then using neural network classification and evaluation to determine the sound field environment. Then, based on the sound spectrum data of this sound field environment, calculate the noise energy value at each key frequency point, thereby obtaining the ambient noise energy at the key frequencies in the sound spectrum.

[0135] Next, determine the frequency characteristics of the target signal: The target signal is typically the sound the user wants to hear, such as speech or music. Understanding the frequency characteristics of the target signal helps predict which frequency bands may be affected by noise.

[0136] Next, psychoacoustic models are applied: Psychoacoustic models can be used to calculate the masking threshold. Psychoacoustic models simulate the human hearing characteristics and account for the masking effect. For example, when a pure tone is masked by continuous noise with a certain bandwidth centered around the same frequency, if the power of the pure tone when it is heard is equal to the noise power within this frequency band, then this bandwidth is the critical hearing bandwidth.

[0137] Finally, the masking threshold is calculated: Based on the above information, combined with the ambient noise energy and the characteristics of the target signal, the masking threshold on each frequency band is calculated.

[0138] Correcting the audibility of the target signal of the ambient sound is mainly achieved by adjusting the gain of the target signal in each frequency band:

[0139] First, the target signal is matched to the masking threshold: the target signal is compared with the calculated masking threshold. If the target signal strength in a certain frequency band is lower than the masking threshold for that frequency band, it indicates that the target signal in that frequency band may not be heard by the user and needs to be enhanced.

[0140] Then, adjust the target signal gain: Based on the comparison results, appropriately increase the gain of the target signal in the affected frequency band so that its intensity exceeds the masking threshold, thereby ensuring that the user can clearly hear the target signal.

[0141] Adjusting the gain of the target signal in each frequency band also constitutes adjusting the audiometric configuration of the hearing aid.

[0142] In other optional specific implementations, the hearing aid audiometry method provided in this embodiment further includes:

[0143] If there are multiple mixed sounds in the sound field environment, the direction of the test sound source is determined based on the direction estimation algorithm, and the signal reception strength in the corresponding direction is controlled and enhanced. The test sound source is the source (sound-emitting device) of the audio used in the test.

[0144] Controlling and enhancing the signal reception strength in the corresponding direction also belongs to adjusting the audiometric configuration of the hearing aid.

[0145] In this embodiment, if the sound field environment is a reverberant environment and there is a demand for sound from a specific direction, such as in a conference room, the hearing aid can use a directional estimation algorithm to identify and enhance sounds from a specific direction. This directional estimation algorithm can analyze sound signals from different directions in the sound field and strengthen the sound source of interest to the user by adjusting the receiving mode of the hearing aid. For example, if the user wants to focus on the voice of the speaker in front, the hearing aid can reduce interference from other directions, such as noise from the sides or rear, thereby improving the clarity and discernibility of the target sound. In addition, the directional algorithm can also help users better locate the sound source in a noisy environment, which is crucial for understanding multi-person conversations or identifying specific sounds in a complex sound field.

[0146] Among them, the following methods can be used to determine whether there are multiple mixed sounds in the sound field environment:

[0147] 1. Using microphone array detection

[0148] Hearing aids are typically equipped with multiple microphones that can receive signals from different directions. By comparing the signal strength and phase difference received by different microphones, it is possible to preliminarily determine whether there are multiple sound sources.

[0149] If the signals received by different microphones have significant time delays or phase differences, this may indicate the presence of multiple sound sources. For example, the time delay between the signals of two microphones can be estimated using the Generalized Cross-Correlation (GCC) method.

[0150] 2. Spectrum Analysis

[0151] Perform spectrum analysis on the received audio signal to check for multiple frequency components or uneven energy distribution within the frequency band. If multiple peaks appear in the spectrum, this may be a sign of multiple sound sources.

[0152] Using Short-Time Fourier Transform (STFT) or other time-frequency analysis tools can help identify multiple frequency components in a signal.

[0153] 3. Energy detection

[0154] By monitoring the energy changes of the signal, it is possible to detect the presence of multiple sound sources. If the signal energy fluctuates greatly within a period of time, this may mean that there are multiple sound sources alternating.

[0155] Energy detection can be achieved by calculating the square value of the signal and averaging it. This method can quickly respond to changes in the sound source.

[0156] In addition, the direction of the sound source can be determined in the following ways:

[0157] 1. Method based on Time Difference of Arrival (TDOA)

[0158] Time Difference of Arrival (TDOA) is one of the most commonly used techniques for sound source localization. By measuring the time difference between sound waves arriving at different microphones, the direction of the sound source can be calculated.

[0159] 2. Generalized Cross-Correlation (GCC) Method

[0160] The generalized cross-correlation (GCC) method is an improved time delay estimation technique that suppresses the effects of noise and reverberation by weighting the cross power spectrum in the frequency domain.

[0161] GCC-PHAT (Phase Transform) is a variant of GCC. It normalizes the amplitude of the cross-spectrum to make the GCC in the time domain sharper, which helps to estimate the delay more accurately.

[0162] 3. MUSIC algorithm

[0163] The Multiple Signal Classification (MUSIC) algorithm is a high-resolution Direction of Arrival (DOA) estimation method particularly well-suited for use in high-noise environments. Using subspace decomposition technology, the MUSIC algorithm leverages the large number of known signals to effectively separate the signal from background noise, accurately capturing the directional information of each sound source.

[0164] The audiometry method for a hearing aid provided in this embodiment obtains the ambient sound of the user's current environment, then determines the signal-to-noise ratio corresponding to the ambient sound, and finally adjusts the audiometry configuration of the hearing aid according to the signal-to-noise ratio, and controls the hearing aid to perform audiometry according to the adjusted audiometry configuration. That is, the hearing aid can automatically adjust its audiometry configuration according to the real-time sound field environment and noise conditions, thereby realizing hearing testing of hearing aid users under different noise conditions, improving the accuracy and reliability of test results, adapting to the actual environment of the user, and providing the user with personalized hearing support, thereby providing the user with clearer and more accurate sound enhancement effects.

[0165] This embodiment also provides an audiometric device for a hearing aid, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0166] This embodiment provides an audiometric device for a hearing aid, such as Figure 2 As shown, including:

[0167] Ambient sound acquisition module 201, used to acquire the ambient sound of the current environment;

[0168] A signal-to-noise ratio determination module 202 is configured to determine a signal-to-noise ratio corresponding to the ambient sound;

[0169] The audiometry module 203 is configured to adjust the audiometry configuration of the hearing aid according to the signal-to-noise ratio, and control the hearing aid to perform audiometry according to the audiometry configuration.

[0170] In some optional embodiments, the audiometric device of the hearing aid further includes:

[0171] A sound spectrum acquisition module, used to acquire the sound spectrum corresponding to the ambient sound;

[0172] an environmental noise energy acquisition module, configured to acquire environmental noise energy at a key frequency in the sound spectrum, where the key frequency is a predetermined hearing aid audiometry frequency;

[0173] The audiometry module 203 is specifically configured to adjust the audiometry configuration of the hearing aid according to the ambient noise energy and the signal-to-noise ratio.

[0174] In some optional specific implementations, the environmental noise energy acquisition module includes:

[0175] an acquiring unit, configured to acquire an average power value of a power spectrum at each time point and a power value corresponding to the key frequency, wherein the power spectrum at each time point is a power spectrum of each short-time frame of the ambient sound segmentation;

[0176] a noise distinction threshold setting unit, configured to set a noise distinction threshold based on the average power value;

[0177] a first comparing unit, configured to compare the power value corresponding to each of the key frequencies with the noise discrimination threshold value one by one;

[0178] a noise frequency identification unit, configured to identify the key frequency having a power value exceeding the noise discrimination threshold as a noise frequency;

[0179] The environmental noise energy determining unit is configured to determine the environmental noise energy based on the power value of the noise frequency.

[0180] In some optional specific implementations, the audiometry module 203 includes:

[0181] A second comparing unit, configured to obtain a magnitude relationship between the ambient noise energy and a preset noise energy threshold;

[0182] The noise reduction depth adjustment unit is configured to adjust the noise reduction depth in the audiometry configuration according to the signal-to-noise ratio if it is determined that the current environment is suitable for audiometry based on the magnitude relationship.

[0183] In some optional specific implementations, the audiometry module 203 includes:

[0184] a signal-to-noise ratio threshold acquiring unit, configured to acquire a preset signal-to-noise ratio threshold corresponding to the signal-to-noise ratio;

[0185] a reducing unit, configured to reduce the noise reduction depth in the audiometry configuration if the signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold;

[0186] The enhancing unit is configured to enhance the noise reduction depth in the audiometry configuration if the signal-to-noise ratio is lower than the preset signal-to-noise ratio threshold.

[0187] In some optional embodiments, the audiometric device of the hearing aid further includes:

[0188] a masking threshold calculation module, configured to calculate the masking threshold of the target signal of the ambient sound in each frequency band according to the frequency and intensity corresponding to the ambient noise energy; the target signal is the audio signal used for audiometry;

[0189] The audibility correction module is configured to compare the target signal of the ambient sound with a corresponding masking threshold, and correct the audibility of the target signal of the ambient sound based on the comparison result.

[0190] In some optional specific implementations, the sound spectrum acquisition module includes:

[0191] A sound field environment acquisition unit, configured to acquire the sound field environment corresponding to the ambient sound using a neural network model;

[0192] The sound spectrum acquisition unit is configured to acquire the sound spectrum corresponding to the sound field environment as the sound spectrum corresponding to the ambient sound.

[0193] In some optional specific implementations, the sound field environment acquisition unit includes:

[0194] An extraction subunit, configured to extract Mel-frequency cepstral coefficients of the ambient sound;

[0195] A feature generation subunit, configured to generate a corresponding audio feature vector based on the Mel-frequency cepstral coefficients;

[0196] The classification prediction subunit is used to use the neural network model to classify and evaluate the audio feature vector to obtain the corresponding sound field environment.

[0197] In some optional specific embodiments, the extraction subunit is specifically used to divide the ambient sound into multiple short-time frames; apply a preset window function to each of the short-time frames; perform a fast Fourier transform on each of the short-time frames to which the preset window function is applied, and calculate the power spectrum of each of the short-time frames; pass the power spectrum of each of the short-time frames through a set of Mel filter groups to generate multiple filter outputs; take the logarithm of the energy output of any filter, and perform a discrete cosine transform on the energy value after taking the logarithm to generate cepstral coefficients; select the first N cepstral coefficients as the Mel-frequency cepstral coefficients of the ambient sound.

[0198] In some optional embodiments, the audiometric device of the hearing aid further includes:

[0199] The enhancement module is used to determine the direction of the test sound source based on the directionality estimation algorithm if there are multiple mixed sounds in the sound field environment, and control and enhance the signal reception strength in the corresponding direction.

[0200] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0201] The audiometric device of the hearing aid in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0202] The embodiment of the present invention also provides a hearing aid having the above Figure 2 The audiometric device of the hearing aid shown.

[0203] See also Figure 3 , Figure 3 : is a structural diagram of a hearing aid provided by an optional embodiment of the present invention, such as Figure 3 As shown, the hearing aid includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed and low-speed interfaces. The various components communicate with each other using different buses and can be mounted on a common motherboard or in other ways as needed. The processor processes instructions executed within the hearing aid. In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple storage devices, if desired. Figure 3 A processor 10 is taken as an example.

[0204] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0205] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0206] The memory 20 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function; the data storage area may store data generated based on the use of the hearing aid. Furthermore, the memory 20 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10. Such remote memory may be connected to the hearing aid via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0207] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0208] The hearing aid further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The example of a bus connection is shown in FIG. The input device may include, for example, a microphone for collecting sounds from the environment and converting them into electrical signals. The hearing aid may also include an amplifier for receiving the electrical signals converted by the microphone and processing and enhancing these signals. The amplifier can adjust the sound intensity of different frequencies in a targeted manner according to the user's hearing loss characteristics. The output device 40 may include a speaker for converting the amplified electrical signals back into sound signals and transmitting them to the user's ears.

[0209] The hearing aid may further comprise a communication interface for the hearing aid to communicate with other devices or a communication network.

[0210] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0211] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0212] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A hearing aid audiometry method, characterized in that: The method comprises: Get the ambient sound of the current environment; Determining a signal-to-noise ratio corresponding to the ambient sound; According to the signal-to-noise ratio, an audiometry configuration of the hearing aid is adjusted, and the hearing aid is controlled to perform audiometry according to the audiometry configuration.

2. The method according to claim 1, characterized in that Before adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio, the method further includes: Obtaining a sound spectrum corresponding to the ambient sound; Acquiring environmental noise energy at a key frequency in the sound spectrum, where the key frequency is a predetermined hearing aid audiometry frequency; The adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio includes: The audiometric configuration of the hearing aid is adjusted according to the ambient noise energy and the signal-to-noise ratio.

3. The method according to claim 2, characterized in that The obtaining of the ambient noise energy of the key frequency in the sound spectrum includes: Obtaining an average power value of the power spectrum at each time point and a power value corresponding to the key frequency, wherein the power spectrum at each time point is the power spectrum of each short-time frame of the ambient sound segmentation; Setting a noise discrimination threshold based on the average power value; Comparing the power value corresponding to each of the key frequencies with the noise discrimination threshold one by one; identifying the key frequency having a power value exceeding the noise discrimination threshold as a noise frequency; The environmental noise energy is determined based on the power value of the noise frequency.

4. The method according to claim 2, characterized in that The adjusting the audiometric configuration of the hearing aid according to the ambient noise energy and the signal-to-noise ratio includes: Obtaining a magnitude relationship between the ambient noise energy and a preset noise energy threshold; If it is determined that the current environment is suitable for audiometry based on the magnitude relationship, the noise reduction depth in the audiometry configuration is adjusted according to the signal-to-noise ratio.

5. The method according to any one of claims 1 to 4, characterized in that The adjusting the audiometric configuration of the hearing aid according to the signal-to-noise ratio includes: Obtaining a preset signal-to-noise ratio threshold corresponding to the signal-to-noise ratio; If the signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold, reducing the noise reduction depth in the audiometry configuration; If the signal-to-noise ratio is lower than the preset signal-to-noise ratio threshold, the noise reduction depth in the audiometry configuration is increased.

6. The method according to claim 2 or 4, characterized in that After obtaining the ambient noise energy of the key frequency in the sound spectrum, the method further includes: Calculating the masking threshold of the target signal of the ambient sound in each frequency band according to the frequency and intensity corresponding to the ambient noise energy; the target signal is the audio signal used for audiometry; The target signal of the ambient sound is compared with a corresponding masking threshold, and the audibility of the target signal of the ambient sound is modified based on the comparison result.

7. The method according to claim 2, characterized in that The obtaining of the sound spectrum corresponding to the ambient sound includes: Using a neural network model to obtain the sound field environment corresponding to the ambient sound; A sound spectrum corresponding to the sound field environment is obtained as a sound spectrum corresponding to the ambient sound.

8. The method according to claim 7, characterized in that The using a neural network model to obtain the sound field environment corresponding to the ambient sound includes: Extracting Mel-frequency cepstral coefficients of the ambient sound; Based on the Mel-frequency cepstral coefficients, generating a corresponding audio feature vector; The neural network model is used to classify and evaluate the audio feature vector to obtain the corresponding sound field environment.

9. The method according to claim 8, characterized in that The extracting of the Mel-frequency cepstral coefficients of the ambient sound includes: Segmenting the ambient sound into multiple short time frames; Applying a preset window function to each of the short time frames; Performing a fast Fourier transform on each of the short-time frames to which the preset window function is applied, and calculating a power spectrum of each of the short-time frames; Passing the power spectrum of each of the short-time frames through a set of Mel filter banks to generate a plurality of filter outputs; Take the logarithm of the energy output by any filter and perform discrete cosine transform on the energy value after taking the logarithm to generate the cepstral coefficient; The first N cepstral coefficients are selected as the Mel-frequency cepstral coefficients of the ambient sound.

10. The method according to claim 1, characterized in that Also includes: If there are multiple mixed sounds in the sound field environment, the direction of the test sound source is determined based on the directionality estimation algorithm, and the signal reception strength in the corresponding direction is controlled and enhanced.

11. An audiometric device for a hearing aid, characterized in that: The device comprises: Ambient sound acquisition module, used to obtain the ambient sound of the current environment; a signal-to-noise ratio determination module, configured to determine a signal-to-noise ratio corresponding to the ambient sound; The audiometry module is configured to adjust the audiometry configuration of the hearing aid according to the signal-to-noise ratio, and control the hearing aid to perform audiometry according to the audiometry configuration.

12. A hearing aid, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the hearing aid audiometry method according to any one of claims 1 to 10 by executing the computer instructions.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the hearing aid audiometry method according to any one of claims 1 to 10.

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