Sound delay detection method of hearing aid

Abstract

The invention relates to the field of hearing aids, in particular to a sound delay detection method of a hearing aid, which comprises a detection system, and the detection system comprises a signal acquisition module and a feature extraction module. The invention discloses a sound delay detection method for a hearing aid, which comprises a signal acquisition module, a feature extraction module, a feature selection module, a model training module, an extension detection module, a result optimization module and a user module, and the signal acquisition module and the feature extraction module. Noise and complex audio environments can be better adapted, and false detection and missing detection are reduced; the real-time requirement is considered during design, user requirements can be quickly responded, and the method is suitable for real-time audio processing; through feature selection and model optimization, the calculation complexity is reduced, and the calculation efficiency is improved; the method can adapt to different types of audio signals and application scenes, has high adaptive capacity, and improves the overall application efficiency of detection.

Description

Technical Field

[0001] This invention relates to the field of hearing aid technology, specifically to a method for detecting sound delay in hearing aids. Background Technology

[0002] Hearing aids are assistive devices used by people with hearing impairments to compensate for the deficiencies caused by hearing loss, thereby improving their ability to communicate and converse with others. A hearing aid consists of a microphone, amplifier, receiver, and power supply. Hearing aids are categorized by sound amplification level: low-power, medium-power, and high-power. Based on the electroacoustic characteristics of the hearing aid and its relationship to the patient's hearing characteristics and needs, a suitable product should be selected. After wearing the hearing aid, it is best to familiarize yourself with various background sounds in a quiet environment and try to distinguish each sound.

[0003] With the development of technology and the progress of society, people have higher and higher requirements for quality of life, and the dependence of hearing-impaired people on hearing aids is also gradually increasing. As an assistive tool for hearing-impaired people, hearing aids have become an indispensable part of their daily lives. However, in existing hearing aid products, sound delay is a common phenomenon, which brings a lot of inconvenience to users. In conversations, sound delay will cause the time between the speaker and the listener to be out of sync, affecting comprehension and the natural fluency of communication. Users may gradually adapt to prolonged exposure to delayed sound environments, but they may feel uncomfortable in the initial stage. Delayed sound may mix with the original sound, producing feedback and causing auditory confusion, which affects the user experience. Therefore, it is necessary to propose a sound delay detection method for hearing aids. Summary of the Invention

[0004] To address the problems in the prior art, this invention provides a method for detecting sound delay in hearing aids.

[0005] The technical solution adopted by this invention to solve its technical problem is: a sound delay detection method for hearing aids, including a detection system. The detection system includes a signal acquisition module, a feature extraction module, a feature selection module, a model training module, a delay detection module, a result optimization module, and a user module. The signal acquisition module, feature extraction module, feature selection module, model training module, delay detection module, result optimization module, and user module are electrically connected to each other, and each hardware module can operate independently without affecting each other. The signal acquisition module collects different external sounds through a microphone connected to the external part of the hearing aid to capture environmental sounds; it also collects airborne sound signals through a capture component in the microphone and performs signal conversion input.

[0006] The feature extraction module extracts delay-related features from the audio signal, such as frequency components and energy distribution. Phase analysis is used to analyze the phase information of audio signals and the delay is determined by comparing the phase differences at different times. At the same time, short-time Fourier transform is used to perform short-time Fourier transform on the audio signals to observe the time delay of different frequency components, which facilitates subsequent feature selection.

[0007] The feature selection module uses feature selection technology to filter out the most useful features for delay detection, thereby reducing computational complexity. Correlation analysis is used to calculate the correlation between data features and latency, and features with high correlation are selected. At the same time, the original features are projected onto a low-dimensional space, and the main components are selected as useful feature data, which increases the efficiency of data screening and facilitates the construction of data models in the later stage.

[0008] The model training module selects a suitable machine learning or deep learning model, such as a convolutional neural network or a recurrent neural network. Based on the audio signals and corresponding delay information data collected and organized by the signal acquisition module, a main feature covering the relevant extraction is built. At the same time, a time series data model is processed in conjunction with convolutional neural networks and recurrent neural networks. The extracted features and corresponding delay data are used to continuously train the model. To improve detection accuracy, a model capable of recognizing sound delays is trained using machine learning or deep learning techniques. The model is trained with a large amount of labeled data to ensure that it can effectively recognize sound delays.

[0009] The delay detection module applies the trained model to the actual audio signal and determines the existence and magnitude of the delay through the model output. The actual audio signal collected and detected is input into the pre-trained model. The model extracts data characteristics from the input audio signal, performs logical input, compares with different training sets, outputs the predicted delay, and then analyzes and interprets the prediction results to determine the actual delay.

[0010] The result optimization module processes the audio signal based on the detection results, such as adjusting the volume and synchronization, to improve the audio quality. Based on the detected delay, the audio signal is compensated for the delay accordingly, making the audio signal more aligned in time. Based on the characteristics of the audio signal, its dynamic range is adjusted to make the sound clearer and fuller. At the same time, the phase of the audio signal is corrected to improve the clarity and stereo effect of the sound.

[0011] The user module allows users to adjust settings, such as latency compensation level and sound mode, and users can adjust system settings according to their own auditory needs to optimize their auditory experience.

[0012] The test signal is responsible for generating a signal that meets the testing requirements of the hearing aid. Based on the frequency range and dynamic range of the hearing aid, the parameters of the test signal are designed, including frequency, volume, waveform, etc. In addition, the duration and interval of the signal must be considered to meet the needs of different testing scenarios; The signal input is responsible for inputting the test signal into the hearing aid; By setting the parameters of the hearing aid, such as volume and frequency, the hearing aid is put into normal use. In this process, it is necessary to ensure that the settings of the hearing aid match the actual use scenario in order to more realistically simulate the user's usage. The signal output is responsible for measuring the signal output by the hearing aid and comparing it with the input signal; This requires the use of professional audio analysis instruments, such as oscilloscopes and audio analysis software, to obtain accurate output signal data. The main task of the signal output module is to calculate the time difference between the output signal and the input signal, thereby obtaining the sound delay of the hearing aid.

[0013] The results analysis analyzes the sound delay phenomenon of hearing aids based on the calculated time difference; This process requires testing hearing aids of different models and brands to obtain comprehensive sound delay data. By analyzing the test data, we can identify the general patterns of hearing aid sound delay, providing a basis for optimizing hearing aid design and improving user satisfaction.

[0014] The main functions are concentrated in the following aspects: Data collection involves gathering different types of audio samples, including sounds from different scenes and sources, to simulate real-world application scenarios. Data annotation: The collected audio samples are annotated to distinguish between delayed and non-delayed components in order to facilitate model training and evaluation; Model training: Train the model using labeled data and adjust model parameters to improve detection performance; Experimental validation will be conducted in real-world application scenarios to evaluate the accuracy and robustness of the new detection method. Delay detection: The system can detect and quantify the delay of sound signals during processing within the hearing aid; Delay compensation: Once a delay is detected, the system will immediately take measures to compensate for it, ensuring that the sound heard by the user is real-time and natural; Real-time monitoring: The system continuously monitors the sound processing flow to ensure that latency is continuously optimized; User customization: Users can adjust system settings according to their own auditory needs to optimize their auditory experience.

[0015] The method for using the sound delay detection method for hearing aids includes the following steps: Step 1: First, we need to collect a series of audio signals; these signals include input signals and output signals; the input signal is the raw signal generated by the sound source, and the output signal is the signal output by the hearing aid. The collected signals should cover a variety of different sound scenarios and frequency ranges in order to comprehensively evaluate the performance of the hearing aid. Step 2: Next, we will perform signal processing on the acquired input and output signals; Since hearing aids may perform differently at different frequencies, we need to filter the input and output signals to match the frequency response of the hearing aid. Specifically, we can use the frequency response of the hearing aid as the weight of the filter to filter the input and output signals. Since hearing aids may perform time-domain adjustments on the input signal, we need to synchronize the input and output signals. Specifically, we can use the cross-correlation method to calculate the correlation between the input and output signals and compare it with a threshold. If the correlation is higher than the threshold, the input and output signals are considered to be synchronized. Once the input and output signals are synchronized, we can calculate the delay between them. Specifically, we can use the cross-correlation method to calculate the delay between the input and output signals. The delay can be determined by finding the maximum correlation between the input and output signals. Step 3: Finally, we analyze the calculated delay to evaluate the performance of the hearing aid. Specifically, we can calculate the average delay between the input and output signals and compare it with the delay claimed by the manufacturer. If the calculated delay is higher than the delay claimed by the manufacturer, it is considered that the hearing aid has a sound delay problem.

[0016] The beneficial effects of this invention are: The sound delay detection method for hearing aids described in this invention employs more advanced feature extraction and selection techniques. This new method can more accurately identify sound delay, better adapt to noisy and complex audio environments, and reduce false positives and false negatives. Designed with real-time requirements in mind, it can quickly respond to user needs and is suitable for real-time audio processing. Through feature selection and model optimization, computational complexity is reduced and computational efficiency is improved. It can adapt to different types of audio signals and application scenarios, possessing strong adaptability and increasing the overall application efficiency of the detection. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Figure 1A schematic diagram of the flow structure of the sound delay detection method for hearing aids provided by the present invention; Figure 2 This is a schematic diagram of the system structure in the sound delay detection method for hearing aids provided by the present invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0020] like Figures 1-2 As shown, the sound delay detection method for hearing aids according to the present invention includes a detection system. The detection system includes a signal acquisition module, a feature extraction module, a feature selection module, a model training module, a delay detection module, a result optimization module, and a user module. The signal acquisition module, feature extraction module, feature selection module, model training module, delay detection module, result optimization module, and user module are electrically connected to each other, and each hardware module can operate independently without affecting each other. The signal acquisition module collects different external sounds through a microphone connected to the external part of the hearing aid to capture environmental sounds; it also collects airborne sound signals through a capture component in the microphone and performs signal conversion input.

[0021] The feature extraction module extracts delay-related features from the audio signal, such as frequency components and energy distribution. Phase analysis is used to analyze the phase information of audio signals and the delay is determined by comparing the phase differences at different times. At the same time, short-time Fourier transform is used to perform short-time Fourier transform on the audio signals to observe the time delay of different frequency components, which facilitates subsequent feature selection.

[0022] The sound delay detection method for hearing aids according to claim 1 is characterized in that: the feature selection module uses feature selection technology to filter out the most useful features for delay detection, thereby reducing computational complexity; Correlation analysis is used to calculate the correlation between data features and latency, and features with high correlation are selected. At the same time, the original features are projected onto a low-dimensional space, and the main components are selected as useful feature data, which increases the efficiency of data screening and facilitates the construction of data models in the later stage.

[0023] The model training module selects a suitable machine learning or deep learning model, such as a convolutional neural network or a recurrent neural network. Based on the audio signals and corresponding delay information data collected and organized by the signal acquisition module, a main feature covering the relevant extraction is built. At the same time, a time series data model is processed in conjunction with convolutional neural networks and recurrent neural networks. The extracted features and corresponding delay data are used to continuously train the model. To improve detection accuracy, a model capable of recognizing sound delays is trained using machine learning or deep learning techniques. The model is trained with a large amount of labeled data to ensure that it can effectively recognize sound delays.

[0024] The delay detection module applies the trained model to the actual audio signal and determines the existence and magnitude of the delay through the model output. The actual audio signal collected and detected is input into the pre-trained model. The model extracts data characteristics from the input audio signal, performs logical input, compares with different training sets, outputs the predicted delay, and then analyzes and interprets the prediction results to determine the actual delay.

[0025] The result optimization module processes the audio signal based on the detection results, such as adjusting the volume and synchronization, to improve the audio quality. Based on the detected delay, the audio signal is compensated for the delay accordingly, making the audio signal more aligned in time. Based on the characteristics of the audio signal, its dynamic range is adjusted to make the sound clearer and fuller. At the same time, the phase of the audio signal is corrected to improve the clarity and stereo effect of the sound.

[0026] The user module allows users to adjust settings, such as latency compensation level and sound mode, and users can adjust system settings according to their own auditory needs to optimize their auditory experience.

[0027] The test signal is responsible for generating a signal that meets the testing requirements of the hearing aid. Based on the frequency range and dynamic range of the hearing aid, the parameters of the test signal are designed, including frequency, volume, waveform, etc. In addition, the duration and interval of the signal must be considered to meet the needs of different testing scenarios; The signal input is responsible for inputting the test signal into the hearing aid; By setting the parameters of the hearing aid, such as volume and frequency, the hearing aid is put into normal use. In this process, it is necessary to ensure that the settings of the hearing aid match the actual use scenario in order to more realistically simulate the user's usage. The signal output is responsible for measuring the signal output by the hearing aid and comparing it with the input signal; This requires the use of professional audio analysis instruments, such as oscilloscopes and audio analysis software, to obtain accurate output signal data. The main task of the signal output module is to calculate the time difference between the output signal and the input signal, thereby obtaining the sound delay of the hearing aid.

[0028] The results analysis analyzes the sound delay phenomenon of hearing aids based on the calculated time difference; This process requires testing hearing aids of different models and brands to obtain comprehensive sound delay data. By analyzing the test data, we can identify the general patterns of hearing aid sound delay, providing a basis for optimizing hearing aid design and improving user satisfaction.

[0029] The main functions are concentrated in the following aspects: Data collection involves gathering different types of audio samples, including sounds from different scenes and sources, to simulate real-world application scenarios. Data annotation: The collected audio samples are annotated to distinguish between delayed and non-delayed components in order to facilitate model training and evaluation; Model training: Train the model using labeled data and adjust model parameters to improve detection performance; Experimental validation will be conducted in real-world application scenarios to evaluate the accuracy and robustness of the new detection method. Delay detection: The system can detect and quantify the delay of sound signals during processing within the hearing aid; Delay compensation: Once a delay is detected, the system will immediately take measures to compensate for it, ensuring that the sound heard by the user is real-time and natural; Real-time monitoring: The system continuously monitors the sound processing flow to ensure that latency is continuously optimized; User customization: Users can adjust system settings according to their own auditory needs to optimize their auditory experience.

[0030] In practical use: First, we need to collect a series of audio signals; these signals include input signals and output signals; the input signal is the original signal generated by the sound source, and the output signal is the signal output by the hearing aid. The collected signals should cover various sound scenarios and frequency ranges in order to comprehensively evaluate the performance of the hearing aid. Next, we will perform signal processing on the acquired input and output signals; Since hearing aids may perform differently at different frequencies, we need to filter the input and output signals to match the frequency response of the hearing aid. Specifically, we can use the frequency response of the hearing aid as the weight of the filter to filter the input and output signals. Since hearing aids may perform time-domain adjustments on the input signal, we need to synchronize the input and output signals. Specifically, we can use the cross-correlation method to calculate the correlation between the input and output signals and compare it with a threshold. If the correlation is higher than the threshold, the input and output signals are considered to be synchronized. Once the input and output signals are synchronized, we can calculate the delay between them. Specifically, we can use the cross-correlation method to calculate the delay between the input and output signals. The delay can be determined by finding the maximum correlation between the input and output signals. Finally, we analyze the calculated delay to evaluate the performance of the hearing aid. Specifically, we can calculate the average delay between the input and output signals and compare it with the delay claimed by the manufacturer. If the calculated delay is higher than the manufacturer's claimed delay, the hearing aid is considered to have a sound delay problem.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. Use of a hearing aid sound delay detection method, characterized in that: The method comprises the following steps: First step: First, we need to collect a series of audio signals; These signals include input signals and output signals; The input signal is the original signal generated by the sound source, and the output signal is the signal output by the hearing aid; The collected signals should cover a variety of different sound scenes and frequency ranges in order to comprehensively evaluate the performance of the hearing aid; Second step: Next, we process the collected input signals and output signals; Because the performance of the hearing aid may vary at different frequencies, we need to filter the input signals and output signals to match the frequency response of the hearing aid; Specifically, we can use the frequency response of the hearing aid as the weight of the filter to filter the input signals and output signals; Because the hearing aid may adjust the time domain of the input signal, we need to synchronize the input signal and the output signal; Specifically, we can use the cross-correlation method to calculate the correlation between the input signal and the output signal, and compare it with a threshold value; If the correlation is higher than the threshold value, it is considered that the input signal and the output signal have been synchronized; Once the input signal and the output signal are synchronized, we can calculate the delay between them; Specifically, we can use the cross-correlation method to calculate the delay between the input signal and the output signal; The delay can be determined by finding the maximum correlation between the input signal and the output signal; Third step: Finally, we analyze the calculated delay to evaluate the performance of the hearing aid; Specifically, we can calculate the average delay between the input signal and the output signal, and compare it with the delay claimed by the manufacturer; If the calculated delay is higher than the delay claimed by the manufacturer, it is considered that the hearing aid has a sound delay problem.

2. A method of sound delay detection for a hearing aid, characterized in that The detection system comprises a signal collection module, a feature extraction module, a feature selection module, a model training module, a delay detection module, a result optimization module, and a user module. The signal collection module, the feature extraction module, the feature selection module, the model training module, the delay detection module, the result optimization module, and the user module are electrically connected to each other, and each hardware module can be independently operated without affecting each other. The signal collection module collects different external sounds through the microphone connected externally to the hearing aid to capture the sound in the environment; The air sound signal is collected through the capture component in the microphone and is converted into a signal input. The feature extraction module extracts delay-related features from the audio signal, such as frequency components and energy distribution.

3. The method of claim 1, wherein: The phase analysis is used to analyze the phase information of the audio signal, and the delay is determined by comparing the phase differences at different times; At the same time, the short-time Fourier transform is used to observe the delay of different frequency components in time, which facilitates subsequent feature selection. The feature selection module selects the most useful features for delay detection through feature selection technology, reducing the computational complexity.

4. The method of claim 1, wherein: ​ The correlation between the data characteristics and the delay is calculated by correlation analysis, and the characteristics with high correlation are selected; at the same time, the original characteristics are projected into a low-dimensional space, and the main components are selected as useful feature data to increase the data screening efficiency and facilitate the construction of the data model.

5. The method of claim 1, wherein: The model training module selects appropriate machine learning or deep learning models, such as convolutional neural networks and recurrent neural networks; The main features extracted by the signal acquisition module are used as the basis for building a model, and the convolutional neural network and recurrent neural network are used to process time series data models. The use of machine learning or deep learning technology can improve the accuracy of detection by training a model that can identify sound delays.

6. The method of claim 1, wherein: The delay detection module applies the trained model to actual audio signals and determines the presence and size of the delay through model output. The collected actual audio signals are input into the trained model, and the model extracts data characteristics from the input audio signals and performs logical operations.

7. The method of claim 1, wherein: The result optimization module processes the audio signals according to the detection results, such as adjusting the volume, synchronizing, etc., to improve the audio quality; according to the detected delay size, the audio signal is compensated for the delay, so that the audio signal is more aligned in time; according to the characteristics of the audio signal, the dynamic range is adjusted to make the sound clearer and fuller, and the phase of the audio signal is corrected to improve the clarity and stereoscopic effect of the sound.

8. The method of claim 1, wherein: The user module allows users to adjust settings, allowing users to adjust system parameters such as delay compensation level, sound mode, etc. Users can adjust system settings according to their hearing needs to optimize the hearing experience.

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