Noise adjustment method for an audiometer and noise generator therefor

Abstract

The application relates to a noise adjustment method of an audiometer and a noise generator thereof, comprising the following steps: constructing a noise database, the types of the noise including simulated noise, random noise and mixed noise; acquiring frequency range and amplitude data when pure tone is output, and generating a preselected scheme of masking noise adapted to the pure tone from the corresponding type according to an input instruction, the preselected scheme including the duration, the expected frequency range and the expected amplitude of the selected masking noise; superimposing and outputting the selected masking noise and the pure tone, and acquiring the mixed tone segment signal again at the eardrum, and generating a waveform spectrum through time-frequency conversion; based on the waveform spectrum, removing the waveform spectrum calculated according to the frequency range and the amplitude data of the pure tone, and recalculating the actual frequency range and the actual amplitude of the masking noise, and adjusting the frequency range and the amplitude of the masking noise output according to the actual frequency range and the actual amplitude, so that the actual frequency range and the actual amplitude are infinitely close to the expected frequency range and the expected amplitude respectively.

Description

Technical Field

[0001] This invention relates to the technical field of sound-generating device control, specifically to a noise adjustment method for an audiometer and its noise generator. Background Technology

[0002] An audiometer is an electronic instrument used to test hearing function and is widely used in the examination and diagnosis of hearing impairment. The noise generator within an audiometer is a device that generates random or predetermined sound waves. Typically, in hearing tests, noise generators are used to simulate ambient noise or as masking noise to assess a subject's hearing level in different noise environments. However, in existing audiometer and other sound-generating devices, differences in circuitry or other hardware, coupled with internal or external noise interference, affect the quality of the sound output. Audiometers require a stable noise output during testing to mix with pure tones to determine the user's hearing status. Traditional noise generators only generate a noise signal and output it, then superimpose it with pure tones, further using filtering circuits to remove redundant noise at specific frequencies. However, these conventional methods have limitations. They cannot solve the problem of redundant noise generated within the device, and the noise signal is relatively complex. After transmission loss, some local noise signals are lost, or when used as ambient noise, they can easily overlap with or even cover the pure tone output signal, failing to provide the subject with a stable and consistent superimposed sound, ultimately affecting the accuracy of the test results. Summary of the Invention

[0003] To address the problems existing in the prior art, one objective of this invention is to provide a noise adjustment method for an audiometer. This method can adjust and accurately generate corresponding masking noise in real time, effectively avoiding the influence of redundant noise caused by internal equipment, etc., and, in conjunction with pure tone superposition output, makes the test results of the subject more accurate. A second objective of this invention is to provide a noise generator for an audiometer. This device, in conjunction with the above method, adjusts the frequency range and amplitude of the masking noise in real time to better adapt to the formation of masking noise for each pure tone superposition.

[0004] The noise adjustment method for an audiometer according to the present invention includes the following steps:

[0005] S1. Construct a noise database, wherein the types of noise include analog noise, random noise, and mixed noise;

[0006] S2. Obtain the frequency range and amplitude data when the pure tone is output, and generate a pre-selection scheme of masking noise adapted to the pure tone from the corresponding type according to the input instruction. The pre-selection scheme includes the duration, desired frequency range and desired amplitude of the selected masking noise.

[0007] S3. The selected masking noise is superimposed with the pure tone and output, and the mixed tone signal is acquired again at the diaphragm. The waveform spectrum is generated by time-frequency conversion.

[0008] S4. Based on the waveform spectrum, remove the waveform spectrum calculated from the frequency range and amplitude data of the pure tone, then calculate the actual frequency range and actual amplitude of the masking noise, and adjust the frequency range and amplitude of the masking noise output accordingly, so that the actual frequency range and actual amplitude are infinitely close to the desired frequency range and desired amplitude, respectively.

[0009] In one embodiment, in step S4, the specific method for adjusting the frequency range and amplitude of the masking noise output is as follows: the 20Hz to 20kHz frequency range is divided into several frequency bands according to the corresponding octaves, and the signal generation of the masking noise is adjusted by using a PID algorithm. Each frequency band is independently set with PID parameter combinations, and the records are stored to form a PID parameter combination library.

[0010] In one embodiment, the method further includes: constructing an LSTM neural network to record control effect data in real time; when the reduction rate is less than 10% in multiple consecutive control cycles, generating a virtual signal to explore the parameter space; and updating the PID parameter combination library through reinforcement learning.

[0011] In one embodiment, the simulated noise is a collection of noises collected from the outside world and simulated to form under different environments.

[0012] In one embodiment, the random noise includes pink noise, speech noise, broadband white noise, and narrowband white noise.

[0013] In one embodiment, the mixed noise is an overlapping noise formed by randomly mixing two or more segments of the simulated noise and the random noise.

[0014] In one embodiment, in step S2, the waveform spectra of the pure tone and the masking noise are calculated based on frequency range and amplitude data. When the overlap rate of the two waveform spectra is higher than 25%, a prompt is issued suggesting that the input command be reset.

[0015] In one embodiment, before the masking noise is combined with the pure tone for output, the masking noise is filtered to remove unwanted frequency components according to the input instruction.

[0016] The present invention discloses a noise generator for an audiometer, which applies the aforementioned noise adjustment method for an audiometer. The noise generator comprises:

[0017] The control unit is used to acquire the data of the pure tone, the data of the masking noise, and the data of the pure tone and the masking noise superimposed and output, and then adjust them after PID calculation;

[0018] A signal source, connected to the control unit, adjusts the generated noise signal based on input commands and feedback from the control unit;

[0019] A frequency control module, connected to both the control unit and the signal source, is used to adjust the frequency range of the noise signal by changing the filter settings.

[0020] An amplitude control module is connected to both the control unit and the signal source, and is used to adjust the intensity or amplitude of the noise signal.

[0021] A filter, connected to the signal source, is used to filter out unwanted frequency components according to input instructions.

[0022] In one embodiment, the noise generator includes an analog noise generator and a digital noise generator.

[0023] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0024] This invention provides a noise adjustment method for an audiometer. Through modeling and learning, a corresponding masking noise is adapted using a noise database and combined with pure tone output. The output sound is then collected again to adjust the frequency range and amplitude of the masking noise in real time. This ensures that the masking noise output is stable when the audiometer is assessing the subject's hearing status, and that the pure tone output is not affected by redundant noise, thus providing reliable test results. This is of great significance for accurately assessing the subject's hearing health status and for the timely detection and diagnosis of hearing diseases.

[0025] This invention also provides a noise generator for an audiometer. This device uses the above-described method to dynamically adjust the frequency range and amplitude of the masking noise in real time to better adapt to the formation of masking noise for each pure tone superposition. Simultaneously, the noise generator incorporates PID technology to adjust parameters, corresponding to different noise sets under different commands, such as pink noise, speech noise, broadband white noise, and narrowband white noise, and constructs an LSTM neural network reinforcement learning system to update the PID parameter combination library. Attached Figure Description

[0026] Figure 1 This is an overall flowchart of a noise adjustment method for an audiometer according to the present invention;

[0027] Figure 2 This is a connection diagram of a noise generator for an audiometer according to the present invention. Detailed Implementation

[0028] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0029] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The technical solution of this invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] like Figure 1 As shown, a noise adjustment method for an audiometer according to the present invention includes the following steps:

[0031] S1. Construct a noise database, including analog noise, random noise, and mixed noise.

[0032] S2. Obtain the frequency range and amplitude data when the pure tone is output, and generate a pre-selection scheme of masking noise adapted to the pure tone from the corresponding type according to the input instruction. The pre-selection scheme includes the duration, desired frequency range and desired amplitude of the selected masking noise.

[0033] S3. The selected masking noise is superimposed on the pure tone and output, and the mixed tone signal is acquired again at the diaphragm. The waveform spectrum is generated by time-frequency conversion.

[0034] S4. Based on the waveform spectrum, remove the waveform spectrum calculated from the frequency range and amplitude data of the pure tone, then calculate the actual frequency range and actual amplitude of the masking noise, and adjust the frequency range and amplitude of the masking noise output accordingly, so that the actual frequency range and actual amplitude are infinitely close to the desired frequency range and desired amplitude, respectively.

[0035] This invention provides a noise adjustment method for an audiometer. Through modeling and learning, a corresponding masking noise is adapted using a noise database and combined with pure tone output. The output sound is then collected again to adjust the frequency range and amplitude of the masking noise in real time. This method is mainly based on the waveform obtained at the diaphragm. After removing the pure tone portion from the waveform, the accuracy of the masking noise is analyzed to determine if there is redundant noise and whether the actual frequency range and amplitude reach the expected frequency range and amplitude, respectively. This ensures that the masking noise output is stable when the audiometer assesses the subject's hearing status and that the pure tone output is not affected by redundant noise, thus providing reliable test results. This is of great significance for accurately assessing the subject's hearing health status and for the timely detection and diagnosis of hearing diseases.

[0036] Further, in step S4, the specific method for adjusting the frequency range and amplitude of the masking noise output is as follows: the 20Hz~20kHz range is divided into several frequency bands according to the corresponding octaves. A PID algorithm is used to adjust the signal generation of the masking noise. PID parameter combinations are independently set for each frequency band, and these combinations are stored to form a PID parameter combination library. PID control is a feedback control technology widely used in industrial control systems. Specifically, the PID algorithm calculates the error between the setpoint and the current value, and adjusts the control input based on this error to achieve stable and precise system control. It mainly optimizes the controller's performance by adjusting the proportional gain Kp, integral gain Ki, and derivative gain Kd to achieve fast response, reduce steady-state error, and improve system stability. An octave is the space occupied by a frequency doubling. Since the starting frequency is random, the spectral space occupied by the octaves at different starting frequency points is different. In this step, the corresponding octave bands differ for different masking noises. For example, speech noise and broadband white noise use a 1 / 3 octave band, while narrowband white noise uses a 5 / 12 octave band. Based on the corresponding octave bands, the masking noise segment can be divided into multiple frequency bands. Each frequency band is calculated according to independently set PID parameters, resulting in multiple PID parameter combinations. Adjusting the independent PID parameters for each frequency band allows for more natural and precise dynamic adjustment of the entire masking noise segment. The adjustment of the frequency range and amplitude of the masking noise differs in practical applications. Frequency range adjustment first determines the upper and lower limits of the masking noise frequency, then primarily uses a filter to filter out unwanted frequency components, keeping them within a certain range. For dynamic adjustment of the masking noise amplitude, the adjustment signal is output according to the following PID algorithm formula:

[0037] u(0) = 0;

[0038] Δu(k)=K p ·[e(k)-e(k-1)]+Ki ·e(k)+K d ·[e(k)-2e(k-1)+e(k-2)];

[0039] u(k)=u(k-1)+Δu(k),

[0040] e(k) represents the input deviation between the given value and the measured value, and k represents the kth sampling period. The adjustment signal u(k) is calculated and output to achieve dynamic adjustment.

[0041] Furthermore, this method includes step S5: constructing an LSTM neural network to record control effect data in real time. When the reduction rate is less than 10% within, for example, 5 consecutive control cycles, a virtual signal is generated to explore the parameter space, and the PID parameter combination library is updated through reinforcement learning. This step introduces an embedded online learning system, which mainly uses three gated logic units: a forget gate, an input gate, and an output gate to control the flow and updating of information. Through its unique structure, LSTM can effectively handle long-term dependencies in sequence data, thereby achieving autonomous evolution of control parameters.

[0042] Specifically, simulated noise is a collection of noises collected from the outside world and simulated under different environments, such as the sound of rain, city noise, or the roar of machines in a factory. Random noise includes pink noise, speech noise, broadband white noise, and narrowband white noise. This set mainly consists of relatively more regular masking noise. For example, pink noise contains equal noise energy in each equal bandwidth band over a wide frequency range; speech noise is white noise that has been specially filtered, resulting in equal energy between 250Hz and 1000Hz, and a 12dB decrease in energy per octave between 1000Hz and 6000Hz; white noise contains equal noise energy in each equal bandwidth band over a wide frequency range. White noise is further divided into: broadband white noise, which is white noise fixed within a certain frequency range after filtering, with its broadband measured at 3dB below the peak value; and narrowband white noise, which is white noise whose center frequency is a pure audio frequency after filtering. Mixed noise is a set of overlapping noises that are randomly mixed from two or more segments of simulated noise and random noise, in order to better adapt to the pure tone mixed test of the audiometer and form a variety of test modes.

[0043] Furthermore, to prevent masking noise from affecting the output of the pure tone during selection and adjustment, in step S2, the waveform spectra of the pure tone and masking noise are calculated based on the frequency range and amplitude data. When the overlap rate of the two waveform spectra is higher than 25%, a prompt is issued, suggesting that the input command be reset. This effectively avoids the pure tone becoming blurred and affecting the actual test results after being superimposed with the masking noise selected and adjusted by the noise database itself. When the coverage rate is higher than 25% during superposition, a prompt is added to the input command to confirm that the pure tone will be partially masked in this test. Unless this setting is specifically set for the subject, a different command should be re-entered, such as changing the noise type or manually adjusting the required frequency range and amplitude, to ensure that the output of the pure tone is not affected by the same-frequency coverage of the masking noise. Moreover, before the masking noise and pure tone are combined and output, the masking noise will be filtered to remove unwanted frequency components according to the input command. This is mainly to further prevent the adjustment of the frequency range from easily exceeding the required range of the original masking noise, and also to prevent redundant noise generated inside the device from forming these unwanted frequency components.

[0044] like Figure 2 As shown, the present invention provides a noise generator for an audiometer, which applies the aforementioned noise conditioning method for an audiometer. The noise generator includes:

[0045] The control unit is used to acquire pure tone data, masking noise data, and the output data after the pure tone and masking noise are superimposed. After PID calculation, the data is fed back and adjusted. It is mainly used to operate and set various parameters of the noise generator, especially to adjust the different parameter combinations of each frequency band divided according to the corresponding octave in the above PID algorithm, so as to adapt to the real-time adjustment of the pure tone corresponding to the masking noise and its superposition effect.

[0046] The signal source, connected to the control unit, adjusts the generated noise signal based on input commands and feedback from the control unit. This is the basic part of generating noise signals, including the aforementioned analog noise, random noise, and mixed noise.

[0047] The frequency control module is connected to the control unit and the signal source respectively. It is used to adjust the frequency range of the noise signal by changing the filter settings. This module, combined with the above-mentioned PID algorithm, first needs to determine the upper and lower limit frequencies for dynamic adjustment of the masking noise frequency range. Within the range of these upper and lower limit frequencies, the output frequency range is limited by adjusting the filter, thereby adjusting the frequency of the masking noise.

[0048] The amplitude control module is connected to the control unit and the signal source respectively. It is used to adjust the intensity or amplitude of the noise signal. This module also combines the above-mentioned PID algorithm. The real-time dynamic adjustment of the intensity or amplitude of the masking noise is mainly due to the change of the parameter combination in the formula. According to different masking noises, several frequency bands are divided according to the corresponding octaves. The PID parameters are set independently for different frequency bands to form the parameter combination.

[0049] A filter is connected to a signal source and is used to filter out unwanted frequency components according to input instructions.

[0050] The device of the present invention uses the above method to dynamically adjust the frequency range and amplitude of the masking noise in real time, so as to better adapt to the formation of masking noise for each pure tone superposition.

[0051] Specifically, noise generators include analog noise generators and digital noise generators. Analog noise generators use analog circuits to generate noise signals, while digital noise generators use digital signal processing technology to generate noise signals. Using them together can more easily collect or generate the aforementioned analog noise, random noise, and even mixed noise, thus creating more possible combinations for audiometer testing and improving test accuracy.

[0052] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application.

[0053] The positional relationships described in the figures are for illustrative purposes only and should not be construed as limiting this patent. Clearly, the above embodiments of the present invention are merely examples to clearly illustrate the invention and are not intended to limit the implementation of the invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of this invention.

Claims

1. A noise adjustment method for an audiometer, characterized in that, Includes the following steps: S1. Construct a noise database, wherein the types of noise include analog noise, random noise, and mixed noise; S2. Obtain the frequency range and amplitude data when the pure tone is output, and generate a pre-selection scheme of masking noise adapted to the pure tone from the corresponding type according to the input instruction. The pre-selection scheme includes the duration, desired frequency range and desired amplitude of the selected masking noise. S3. The selected masking noise and the pure tone are superimposed and output, and the mixed tone signal of the masking noise and the pure tone superimposed and output is obtained again at the diaphragm. The waveform spectrum is generated by time-frequency conversion. S4. Based on the waveform spectrum, remove the waveform spectrum calculated from the frequency range and amplitude data of the pure tone, then calculate the actual frequency range and actual amplitude of the masking noise, and adjust the frequency range and amplitude of the masking noise output accordingly, so that the actual frequency range and actual amplitude are infinitely close to the desired frequency range and desired amplitude, respectively; wherein, the specific method of adjusting the frequency range and amplitude of the masking noise output is as follows: divide 20Hz~20kHz into several frequency bands according to the corresponding octaves, use a PID algorithm to adjust the signal generation of the masking noise, set PID parameter combinations independently for each frequency band, and store the records to form a PID parameter combination library; S5. Construct an LSTM neural network to record control effect data in real time. When the reduction rate is less than 10% in multiple consecutive control cycles, generate a virtual signal to explore the parameter space and update the PID parameter combination library through reinforcement learning.

2. The noise adjustment method for an audiometer according to claim 1, characterized in that, The simulated noise is a collection of noises collected from the outside world and simulated to form under different environments.

3. The noise adjustment method for an audiometer according to claim 1, characterized in that, The random noise includes pink noise, speech noise, broadband white noise, and narrowband white noise.

4. The noise adjustment method for an audiometer according to claim 1, characterized in that, The mixed noise is an overlapping noise formed by randomly mixing two or more segments of the simulated noise and the random noise.

5. The noise adjustment method for an audiometer according to claim 1, characterized in that, In step S2, the waveform spectra of the pure tone and the masking noise are calculated based on the frequency range and amplitude data. When the overlap rate of the two waveform spectra is higher than 25%, a prompt is issued, suggesting that the input command be reset.

6. The noise adjustment method for an audiometer according to claim 5, characterized in that, Before the masking noise is combined with the pure tone for output, the masking noise is filtered to remove unwanted frequency components according to the input command.

7. A noise generator for an audiometer, characterized in that, The noise conditioning method for an audiometer as described in any one of claims 1-6, wherein the noise generator comprises: The control unit is used to acquire data of pure tone, data of masking noise, and data of the superposition of the pure tone and the masking noise, respectively, and then adjust the data after PID calculation; A signal source, connected to the control unit, adjusts the generated noise signal based on input commands and feedback from the control unit; A frequency control module, connected to both the control unit and the signal source, is used to adjust the frequency range of the noise signal by changing the filter settings. An amplitude control module is connected to both the control unit and the signal source, and is used to adjust the intensity or amplitude of the noise signal. A filter, connected to the signal source, is used to filter out unwanted frequency components according to input instructions.

8. The noise generator for an audiometer according to claim 7, characterized in that, The noise generator includes an analog noise generator and a digital noise generator.

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