Open type earphone audio optimization method based on adaptive compensation algorithm

Abstract

The invention provides an open earphone audio optimization method based on a self-adaptive compensation algorithm, and aims to improve the audio auditory experience of a user. The method comprises the following steps: firstly, acquiring an environmental noise signal through an earphone microphone, receiving a target audio signal, analyzing auditory perception of a user in a current environment by combining an ISO226 loudness curve and the like, and determining auditory sensitivity of the user to different frequency bands (including high frequency, intermediate frequency and low frequency); and then, enhancing the high-frequency part, deepening the low-frequency part and performing smooth transition compensation on the intermediate-frequency part by using a self-adaptive compensation algorithm. And then, dynamically adjusting the low-frequency part by using a hyperbolic filter, generating an adjusted audio signal, and generating a harmonic signal based on the amplitude and frequency characteristics to further optimize the tone quality. Finally, the optimized audio signal and the generated harmonic signal are provided for a user together, and the effects of automatically optimizing audio output in different environments and enhancing tone quality performance are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of audio optimization, and in particular to an open-type earphone audio optimization method based on an adaptive compensation algorithm. Background Art

[0002] In the existing technology, open-back headphones are widely used in daily life and are favored by users for their comfortable wearing and wide sound field. Such headphones usually allow external ambient sound to enter naturally through an open structure, thereby reducing wearing fatigue while maintaining a good sound quality experience. Existing open-back headphone technology improves sound quality through basic noise suppression and audio enhancement algorithms. Many devices also integrate simple sound filtering functions to reduce the interference of ambient noise on audio playback.

[0003] However, existing open-earphone technology still has significant limitations in terms of sound quality optimization. Its audio enhancement algorithm often fails to make adaptive adjustments to different environmental noises and the user's hearing sensitivity, resulting in poor clarity and balance of audio output. At the same time, traditional audio processing technology fails to fully consider the optimization needs of different frequency bands, especially the inability to achieve a detailed transition and balance between high, medium and low frequencies, affecting the overall sound quality.

[0004] Therefore, there is an urgent need to develop a new open-earphone audio optimization method. Summary of the invention

[0005] The present application provides an open headphone audio optimization method based on an adaptive compensation algorithm to improve the headphone sound quality.

[0006] The present application provides an open-earphone audio optimization method based on an adaptive compensation algorithm, comprising:

[0007] The ambient noise signal is collected through the microphone of the open-type headset, and the input target audio signal is received;

[0008] Based on the collected environmental noise signal and the input target audio signal, the user's auditory perception in the current environment is analyzed using the ISO226 equal loudness curve to determine the user's auditory sensitivity to different frequency bands; wherein the different frequency bands include high frequency, medium frequency and low frequency;

[0009] Using the determined auditory sensitivity, an adaptive compensation algorithm is used to enhance the high frequency part of the target audio signal, deepen the low frequency part, and perform smooth transition compensation on the intermediate frequency part;

[0010] For the audio signal processed by the adaptive compensation algorithm, a hyperbolic filter is applied to dynamically adjust the low-frequency part to obtain an adjusted audio signal; and a corresponding harmonic signal is generated according to the amplitude and frequency characteristics of the adjusted audio signal;

[0011] The adjusted audio signal and the generated corresponding harmonic signal are provided to the user as an optimized audio signal.

[0012] Furthermore, collecting the environmental noise signal through the microphone of the open-type earphone includes:

[0013] Use multiple distributed microphone arrays, located in the left and right channels of the headset, to improve the positioning accuracy of different noise sources;

[0014] The collected noise signal is processed by time-space filtering to eliminate unnecessary environmental echo signals and ensure the purity of the noise signal;

[0015] According to the sound source direction information of the noise signal, the relative position of the main noise source is determined, and the collection range of the noise signal is automatically adjusted according to the user's head posture.

[0016] Furthermore, the receiving of the input target audio signal includes:

[0017] Receive a target audio signal via Bluetooth or a wired connection, wherein the audio signal includes multiple channels corresponding to high-frequency, medium-frequency and low-frequency signals respectively;

[0018] After receiving the target audio signal, the audio signal is pre-processed, including volume standardization and preliminary filtering of background noise;

[0019] The initial volume is automatically adjusted according to the audio preferences set by the user, and the optimal decoding algorithm is selected according to the signal format to ensure high-fidelity sound transmission.

[0020] Furthermore, the use of ISO226 equal loudness curves to analyze the user's auditory perception in the current environment includes:

[0021] Generate a personalized user auditory perception model based on the user's historical audio usage data;

[0022] The ISO226 equal loudness curve is combined with the personalized model to analyze the user's hearing sensitivity in the current environment in real time, and dynamically adjust the hearing sensitivity curve according to the noise environment in different frequency bands.

[0023] Furthermore, the analyzing the user's auditory perception in the current environment using ISO226 equal loudness curves also includes:

[0024] Monitor the user's physiological data in real time to detect the user's current emotional state and adjust the auditory perception model based on the emotional state;

[0025] When it is detected that the user is in a high-stress state, the balance of the audio output is adjusted so that the output intensity of the high-frequency part is moderately weakened to reduce the user's auditory fatigue, and the volume of the low-frequency and mid-frequency parts is enhanced.

[0026] Furthermore, the applying of the hyperbolic filter to dynamically adjust the low-frequency part includes:

[0027] According to the frequency distribution characteristics of the current environmental noise, the parameters of the hyperbolic filter are adjusted so that the degree of low-frequency enhancement changes dynamically with the change of the intensity of the low-frequency noise in the environment;

[0028] When it is detected that the low-frequency noise in the environment is enhanced, the hyperbolic filter reduces the gain of the low-frequency part to avoid sound quality distortion caused by excessive low-frequency amplification.

[0029] Furthermore, the applying of the hyperbolic filter to dynamically adjust the low-frequency part also includes:

[0030] The user's motion state is detected by integrating an acceleration sensor and a position sensor in the headset;

[0031] When the user is stationary, the dynamic adjustment of the hyperbolic filter is used to maintain the clarity and purity of the low frequencies; when the user is detected to be in motion, the hyperbolic filter is used to enhance the dynamic response of the low-frequency part and provide a stronger bass effect to meet the auditory needs during motion.

[0032] Furthermore, generating a corresponding harmonic signal according to the amplitude and frequency characteristics of the adjusted audio signal includes:

[0033] Analyze the adjusted audio signal, extract the signal characteristics of the low frequency, medium frequency and high frequency bands, and determine the amplitude and frequency information of each frequency band;

[0034] In the low frequency band, a double frequency signal is generated to enhance the depth and thickness of the low frequency signal;

[0035] In the mid-frequency and high-frequency bands, the double-frequency signal is generated to enhance the signal details and avoid distortion. By reducing the order of the double-frequency signal, the clarity and natural sound quality of the mid-frequency and high-frequency bands are ensured.

[0036] When it is detected that the strength of the low-frequency signal exceeds a preset threshold, the order of the low-frequency multiplication signal is increased to further enhance the expressiveness of the low-frequency band.

[0037] Furthermore, the adaptive compensation algorithm comprises the following steps:

[0038] The high frequency part of the target audio signal is enhanced by the following formula 1:

[0039]

[0040] Where H(f) is the signal amplitude after high frequency enhancement; G h is the high-frequency gain coefficient, which is determined by the user's hearing sensitivity; f is the frequency; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, used to calculate the signal intensity at frequency f under the reference standard curve; k h is the weight parameter of high-frequency compensation, which controls the intensity of high-frequency enhancement; β h is the high-frequency sensitivity adjustment coefficient, which controls the distribution of high-frequency signals; f h is the center frequency of the high frequency band;

[0041] The low-frequency part is deepened by the following formula 2:

[0042]

[0043] Where L(f) is the signal amplitude after low-frequency deepening processing; G l is the low-frequency gain coefficient, which is determined by the user's hearing sensitivity; f is the frequency; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, used to calculate the signal intensity at frequency f under the reference standard curve; k l is the weight parameter of low-frequency compensation, which is used to control the strength of low-frequency enhancement; α l The coefficient for adjusting the smoothness of low-frequency compensation; γ l is the enhancement factor of low frequency compensation; f l is the center frequency of the low frequency band;

[0044] The smooth transition compensation for the intermediate frequency part is achieved by the following formula 3:

[0045]

[0046] Where M(f) is the signal amplitude after intermediate frequency smoothing compensation; G m is the intermediate frequency gain coefficient; α m f is the adjustment parameter for controlling the intermediate frequency smoothing compensation; m is the center frequency of the intermediate frequency band; f c is the transition bandwidth of the mid-frequency band, which is used to control the width of the frequency transition; S u(f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, which is used to calculate the signal intensity at frequency f under the reference standard curve.

[0047] Furthermore, the intensity function S of the target audio signal u (f) is achieved by the following formula 3:

[0048]

[0049] Among them, A u β is the maximum intensity of the user's current audio signal, indicating the maximum volume perceived by the user; u To adjust the sensitivity coefficient of the audio signal strength change; f 0u is the center frequency of the audio signal; C u To adjust the coefficient of the periodic component related to the frequency of the target audio signal, used to simulate the amplitude fluctuation of the signal at different frequencies; f is the frequency;

[0050] The intensity function S of the reference standard signal r (f) is achieved by the following formula 4:

[0051]

[0052] Among them, A r is the maximum strength of the reference signal; B r To adjust the coefficient of the frequency square term, it is used to control the response speed of the reference signal strength in the frequency change; α r is the attenuation coefficient of the reference signal when the frequency changes; f is the frequency.

[0053] This application has the following beneficial technical effects:

[0054] (1) The adaptive compensation algorithm combines the ambient noise and the user's hearing sensitivity to achieve dynamic optimization of different frequency bands (high frequency, medium frequency and low frequency), so that the audio signal can be automatically adjusted in different environments, providing a more balanced sound quality performance and enhancing the user's hearing experience. (2) Using the ISO226 equal loudness curve, it can accurately analyze the user's auditory perception in different environments, and enhance or smooth the high frequency, medium frequency and low frequency bands respectively to ensure that the sound of each frequency band is clear and natural, and reduce the imbalance or abruptness between frequencies. (3) The low frequency part is dynamically adjusted through the hyperbolic filter, so that the low frequency signal shows a stronger sense of depth and power, especially in a noisy environment. The degree of low frequency enhancement can be automatically adjusted to ensure the purity and stability of the sound quality. (4) Harmonic signals are generated based on the amplitude and frequency characteristics of the adjusted audio signal to further enhance the audio details and layering. In particular, the optimization of the harmonic signal makes the low frequency fuller, the high frequency clearer, and the overall sound quality richer. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flowchart of an open-earphone audio optimization method based on an adaptive compensation algorithm provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0056] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0057] The first embodiment of the present application provides an open-earphone audio optimization method based on an adaptive compensation algorithm. Figure 1 , which is a flow chart of the first embodiment of the present application. Figure 1 The first embodiment of the present application provides an open-earphone audio optimization method based on an adaptive compensation algorithm and is described in detail.

[0058] S101: Collecting an environmental noise signal through a microphone of an open-ear headset and receiving an input target audio signal.

[0059] In step S101, an ambient noise signal is collected through a microphone of an open-ear headset, and an input target audio signal is received. This process is described in detail as follows.

[0060] First of all, open headphones are usually equipped with multiple microphones to capture noise signals in the environment. The microphone system can include one or more microphone arrays, which can be distributed in the left and right channels of the headphones or the outside and inside of the headphones to collect the surrounding environmental noise. Specifically, the layout of the microphones can improve the ability to capture noise from different directions through array design, and synchronously collect noise signals in multiple directions to ensure that the noise signal can fully reflect the current noise environment. The noise signal collected by the microphone can be a full-band ambient sound, including different types of sounds such as human voices, vehicle noise, wind noise, etc.

[0061] While the noise signal is being collected, the headset also needs to receive the input target audio signal. The target audio signal refers to the audio content that the user wants to listen to, and the signal can be received through a wired connection (such as a 3.5mm audio interface) or wireless transmission (such as Bluetooth, Wi-Fi, etc.). The signal is usually transmitted in a digital format, and common audio formats include MP3, FLAC, AAC, etc. The audio processing system in the headset is responsible for decoding the received target audio signal and converting it into an audio stream signal for further processing.

[0062] The ambient noise signal and the target audio signal are simultaneously transmitted to the audio processing module of the headset. In this module, the ambient noise signal is usually stored separately for subsequent noise analysis and compensation. The target audio signal needs to be converted into an audio signal through a decoder and further processed as input to the optimization algorithm. To ensure the synchronous processing of the noise signal and the target audio signal, the headset system will continuously collect and update the ambient noise signal to ensure that each time the noise signal is compensated, it is based on the most real-time noise situation in the current environment. This design ensures that even if the user moves to a different noise environment, the headset can adaptively adjust the noise compensation effect to optimize the sound quality experience.

[0063] Therefore, the implementation of S101 not only involves the microphone layout and signal acquisition in hardware, but also covers the process of receiving and processing the target audio signal, while ensuring that the noise signal and the target audio signal can be simultaneously, real-time and accurately input into the audio optimization algorithm, which becomes the basis for subsequent steps.

[0064] Furthermore, collecting the environmental noise signal through the microphone of the open-type earphone includes:

[0065] Use multiple distributed microphone arrays, located in the left and right channels of the headset, to improve the positioning accuracy of different noise sources;

[0066] The collected noise signal is processed by time-space filtering to eliminate unnecessary environmental echo signals and ensure the purity of the noise signal;

[0067] According to the sound source direction information of the noise signal, the relative position of the main noise source is determined, and the collection range of the noise signal is automatically adjusted according to the user's head posture.

[0068] In this embodiment, based on the open headphone audio optimization method of the adaptive compensation algorithm, collecting environmental noise signals through microphones is a key step in realizing the optimization process. Specifically, multiple distributed microphone arrays are used in the headphone system, and these microphones are arranged on the left and right channels of the headphone. The microphone of each channel can independently collect noise signals from different directions. This distributed microphone design can significantly improve the positioning accuracy of the headphone for different noise sources in the environment. Each microphone array can not only capture the overall environmental noise, but also detect the direction, distance and other information of the noise source to form more detailed noise data.

[0069] The collected environmental noise signals will be further processed by spatiotemporal filtering to ensure that these signals are sufficiently pure. In the process of spatiotemporal filtering, the system will combine the time domain and space domain information, especially by analyzing the changes of noise signals at different time and space positions, to filter out signals or environmental echoes that are not related to the main noise source. Spatiotemporal filtering is a method that uses the time and space characteristics of noise signals to optimize the noise elimination effect, which helps to eliminate unnecessary noise interference such as reflected sound and echo, thereby obtaining a relatively pure and clear noise signal, ensuring that the subsequent audio processing process is more accurate.

[0070] In addition, the system will further determine the relative position of the main noise source based on the sound source direction information of the collected noise signal. Specifically, by analyzing the signal differences between different microphones, the headphones can infer the direction and distance of the noise source, and then locate which noise sources are most in need of processing. After determining the location of the main noise source, the system can automatically adjust the collection range of the noise signal according to the user's head posture (such as turning the head, lowering the head, side face, etc.). This adjustment is to ensure that no matter how the user's head moves, the system can always accurately capture and analyze the main noise source, to prevent the user's head movement from weakening the noise processing effect.

[0071] This process enables the headset to not only cope with complex environmental noise, but also provide a continuous and stable noise optimization effect when the user is moving. This technology based on microphone array noise collection, spatiotemporal filtering processing and automatic adjustment of the collection range ensures that the headset can provide users with high-quality audio output while effectively suppressing unnecessary environmental noise.

[0072] Furthermore, the receiving of the input target audio signal includes:

[0073] Receive a target audio signal via Bluetooth or a wired connection, wherein the audio signal includes multiple channels corresponding to high-frequency, medium-frequency and low-frequency signals respectively;

[0074] After receiving the target audio signal, the audio signal is pre-processed, including volume standardization and preliminary filtering of background noise;

[0075] The initial volume is automatically adjusted according to the audio preferences set by the user, and the optimal decoding algorithm is selected according to the signal format to ensure high-fidelity sound transmission.

[0076] In the present embodiment, receiving the input target audio signal is one of the core steps to optimize the audio quality. Specifically, the target audio signal can be transmitted via Bluetooth or wired connection. Both methods are currently commonly used audio signal transmission methods, among which Bluetooth connection can provide wireless convenience, while wired connection can usually ensure higher stability and accuracy of signal transmission. Whether it is Bluetooth or wired connection, the target audio signal can contain multiple channels, and different channels transmit high-frequency, medium-frequency and low-frequency signals respectively. This ensures that the details of the audio signal in different frequency ranges are fully retained, thereby providing sufficient frequency information support for subsequent audio processing and optimization.

[0077] After receiving the target audio signal, the system will first pre-process it. This process includes standardizing the volume to ensure that the input audio signal has a consistent volume level in different playback environments. Since audio signals from different sources may have different volume ranges, standardization can adjust the volume to a unified baseline to prevent the user from not being able to hear clearly due to too low volume, or the acoustic system of the headset from being overloaded by too high volume. At the same time, the system will also perform preliminary filtering of background noise on the audio signal. Although the target audio signal itself is usually noise-free, it may be accompanied by certain noise components during the transmission process or due to quality issues of the signal source. By initially filtering out background noise, the system can ensure that the transmitted audio signal is as pure as possible, thus laying a good foundation for subsequent sound quality optimization.

[0078] Next, based on the user's pre-set audio preferences, the system automatically adjusts the initial volume of the target audio signal. This step is to ensure that the personalized volume setting can meet the user's needs. For example, some users may prefer a lower volume output to maintain ear comfort, while other users may prefer a higher volume for a stronger immersive experience. The system will automatically adjust the volume based on these user preferences to ensure that the audio playback effect meets the user's expectations.

[0079] In addition, when receiving audio signals, the system will automatically select the optimal decoding algorithm according to the format of the signal to ensure high fidelity of sound quality transmission. At present, common audio formats include MP3, FLAC, AAC, etc. Different audio formats have different compression algorithms and decoding requirements. The system will intelligently identify the format of the target audio signal and match the most appropriate decoding algorithm. Efficient decoding can minimize the loss of audio signals that may occur during transmission, ensure the integrity and high fidelity of audio signals, and enable users to experience the most authentic sound quality effects. Through this series of receiving and processing steps, the target audio signal can be optimized on multiple levels, providing users with a clear, natural and high-fidelity listening experience.

[0080] S102: Based on the collected environmental noise signal and the input target audio signal, the user's auditory perception in the current environment is analyzed using the ISO226 equal loudness curve to determine the user's auditory sensitivity to different frequency bands; wherein the different frequency bands include high frequency, medium frequency and low frequency.

[0081] In step S102, based on the collected ambient noise signal and in combination with the input target audio signal, the ISO226 equal loudness curve is used to analyze the user's auditory perception in the current environment, and determine the user's auditory sensitivity to different frequency bands.

[0082] First, the collected ambient noise signal and target audio signal are transmitted to the audio processing module of the headset. In this module, the frequency distribution and intensity of the noise signal are analyzed. A common practice is to divide the noise signal into different frequency bands, such as low frequency, medium frequency and high frequency. The noise intensity of each frequency band is measured by Fourier transform or other frequency domain analysis techniques to obtain the noise characteristics of each frequency band in the current environment.

[0083] At the same time, the input target audio signal will also be divided into corresponding frequency bands and processed in combination with the collected noise characteristics. At this time, the ISO226 equal loudness curve is used to evaluate the user's auditory perception. The ISO226 curve describes the sensitivity of the human ear at different frequencies on the loudness perception curve of the human ear for different frequencies. During implementation, the audio processing module will superimpose the user's current ambient noise with the target audio signal to simulate the user's possible auditory experience, and correct the volume perception of different frequency bands based on the ISO226 curve.

[0084] Specifically, the system calculates the user's perception threshold for each frequency band in the current environment based on the ISO226 curve. In this step, the headset processor dynamically adjusts the benchmark of the loudness curve to match the noise environment the user is in. For example, in a low-noise environment, the user's perception of high frequencies will be more sensitive, while in a high-noise environment (such as in places with more traffic noise), low-frequency noise will obscure the user's perception of low-frequency audio signals, so the headset will dynamically increase the volume of the low-frequency part to maintain a balanced listening experience.

[0085] Next, by analyzing the user's auditory perception sensitivity in different frequency bands, the system can accurately determine the user's perception sensitivity to high, medium and low frequencies in the current environment. The high frequency part usually involves sharper and clearer sounds, such as the high notes of musical instruments or the high frequency components of language, while the low frequency part corresponds to bass drums or other heavy parts. The medium frequency is the main frequency range of human voices or musical instruments, which determines the clarity and warmth of the sound.

[0086] By combining the ISO226 curve and the real-time noise environment analysis, the headphone system generates a personalized frequency sensitivity model for the user. The model is adaptive and can be updated at any time according to different environmental conditions to ensure that each frequency band of the audio signal matches the user's actual auditory perception. This model will serve as the basis for the next audio compensation and optimization steps to ensure that the audio output can be dynamically adjusted according to the user's auditory sensitivity, thereby providing the user with the most comfortable listening experience.

[0087] In summary, the implementation of S102 combines the ambient noise signal with the target audio signal, uses the ISO226 curve to analyze the user's auditory perception, and dynamically adjusts different frequency bands in the audio output to match the user's perception ability.

[0088] Furthermore, the use of ISO226 equal loudness curves to analyze the user's auditory perception in the current environment includes:

[0089] Generate a personalized user auditory perception model based on the user's historical audio usage data;

[0090] The ISO226 equal loudness curve is combined with the personalized model to analyze the user's hearing sensitivity in the current environment in real time, and dynamically adjust the hearing sensitivity curve according to the noise environment in different frequency bands.

[0091] Furthermore, the analyzing the user's auditory perception in the current environment using ISO226 equal loudness curves also includes:

[0092] Monitor the user's physiological data in real time to detect the user's current emotional state and adjust the auditory perception model based on the emotional state;

[0093] When it is detected that the user is in a high-stress state, the balance of the audio output is adjusted so that the output intensity of the high-frequency part is moderately weakened to reduce the user's auditory fatigue, and the volume of the low-frequency and mid-frequency parts is enhanced.

[0094] In this embodiment, the use of ISO226 equal loudness curves to analyze the user's auditory perception in the current environment is an important step in real-time adjustment of audio output by combining the user's personalized audio usage habits and environmental noise conditions. Specifically, first, the system generates a personalized user auditory perception model based on the user's historical audio usage data. The user's audio usage data may include information such as their preferred volume, the type of music they often listen to, and the noise characteristics of the environment. These data are gradually accumulated through the use records of the headphones to form a model of the user's sensitivity to audio in different scenarios. This model can reflect the user's personal preferences for different frequency bands. For example, some users may be more sensitive to the high-frequency part, while others prefer to enhance the low-frequency part.

[0095] Based on this personalized model, the system combines the ISO226 and other loudness curves to further analyze the user's auditory perception in the current environment. The ISO226 curve is a standardized loudness perception curve that describes the human ear's sound perception threshold for different frequencies. By combining the user's personalized auditory model with the ISO226 standard curve, the system can analyze in real time the user's auditory sensitivity to different frequency bands in the current noisy environment. For example, in a quiet indoor environment, the user is more sensitive to the high frequency band, so the signal in the high frequency part may not need additional enhancement, while in a noisy outdoor environment, the high frequency band may need to be further enhanced to offset the interference of external noise on auditory perception.

[0096] In addition, the system will dynamically adjust the user's hearing sensitivity curve based on the noise characteristics of the current environment. In a noisy environment, low-frequency and mid-frequency signals are easily masked by environmental noise, so the system will automatically increase the volume of these frequency bands to ensure that the user can clearly perceive these audio contents. On the contrary, in a quiet environment, the system will appropriately reduce the volume of these frequency bands to avoid excessive amplification and distortion of sound quality.

[0097] Further improvements include real-time monitoring of the user's physiological data to detect the user's emotional state. The sensors in the headphones can collect physiological signals related to the user's emotional state, such as heart rate, skin conductivity, etc. When the system detects that the user is in a high-stress state, it automatically adjusts the balance of the audio output. At this time, the system will appropriately reduce the volume output of the high-frequency part, because high-frequency signals may increase the user's auditory burden in some cases, leading to increased fatigue. In order to allow users to have a more comfortable listening experience under high-stress conditions, the system will enhance the volume of the low-frequency and mid-frequency parts to make the audio output calmer and softer, helping users relieve stress.

[0098] This series of real-time monitoring and dynamic adjustment mechanisms ensure that the system can continuously optimize the audio output according to the user's emotional state and the current noise environment. Whether in a noisy outdoor environment or a quiet indoor scene, the system can provide personalized audio adjustments to ensure the comfort and clarity of the sound quality. This implementation process ensures the flexibility and accuracy of the audio optimization method in practical applications, thereby meeting the personalized needs of users.

[0099] In the process of generating a personalized user auditory perception model, the system needs to rely on the user's historical audio usage data and related algorithms to achieve personalized model generation. This process needs to consider multiple factors, including the user's volume preference, frequency band sensitivity, common noise environment, and the user's emotional state. The following details the steps for generating this model.

[0100] First, the system continuously collects the user's audio usage data through headphones or related devices. This data includes but is not limited to the volume settings of the user when using headphones in different environments, the types of music or audio that the user often listens to (such as music with heavy bass, voice calls, etc.), and the audio adjustment behavior in different noise environments (such as whether the user often adjusts the volume). By collecting and recording this data, the system can accumulate the user's auditory preference data.

[0101] Secondly, the system can generate a frequency response curve through multiple acquisitions of data. This curve describes the user's preference for different frequency bands. For example, the user may prefer to enhance the performance of the low-frequency band, or want the high-frequency band to maintain a lower volume. By analyzing the user's volume adjustment history in various environments, the user's specific sensitivity to different frequency bands can be obtained. Based on this data, the system will set a baseline sensitivity value for each frequency band, and these baseline values ​​will be updated as the user's usage habits change.

[0102] Then, combined with machine learning or adaptive algorithms, the system can further adjust and optimize the user's auditory perception model. The specific implementation can be based on supervised learning or unsupervised learning methods. The system will automatically adjust the parameters of the model according to the user's behavior pattern (such as manually increasing the volume of the low-frequency part in a noisy environment many times) to gradually tend to the user's actual needs. For this process, the system can use algorithms such as K-means clustering or neural networks to cluster and classify the user's data, thereby identifying the user's preferences for different audio scenes, and apply these preferences for optimization in future audio processing.

[0103] To further improve the accuracy of the model, the system can also combine noise data from different environments. For example, the user's auditory sensitivity will change with the change of environmental noise. The system collects the user's auditory response in quiet, noisy or mixed noise environments to determine the user's different needs for high, medium and low frequencies in different environments. These needs will be dynamically integrated into the personalized auditory model, so that the model not only adapts to the user's long-term preferences, but also can be flexibly adjusted according to environmental changes.

[0104] Finally, the personalized auditory perception model will be used in conjunction with the ISO226 equal loudness curve to ensure that the generated model is not only based on the user's historical behavior data, but also fully considers the natural perception of the human ear to sounds of different frequencies. In this way, the model can more accurately reflect the user's personal auditory preferences and match them with the standard loudness curve, so that the optimization of audio output not only adapts to the user's individual needs, but also maintains the balance of overall sound quality.

[0105] In summary, generating a personalized user auditory perception model is a comprehensive process based on user historical data, machine learning algorithms, frequency sensitivity analysis, and environmental noise adjustment. Through in-depth analysis and calculation of these factors, the system is able to generate a highly personalized, dynamically adjusted auditory perception model, thereby providing users with the best audio optimization effect.

[0106] S103: using the determined auditory sensitivity, adopting an adaptive compensation algorithm to enhance the high frequency part of the target audio signal, deepen the low frequency part, and perform smooth transition compensation on the intermediate frequency part.

[0107] In step S103, based on the user's auditory sensitivity determined in the previous step, the system begins to apply the adaptive compensation algorithm to specifically process the target audio signals in different frequency bands. This process aims to optimize the high frequency, low frequency and medium frequency parts of the target audio signal according to the user's actual auditory perception to achieve the best auditory effect.

[0108] First, for the signal processing of the high-frequency part, the system will identify the signal components belonging to the high-frequency range in the target audio signal according to the user's sensitivity to high frequencies, and enhance them. High frequencies usually refer to higher-pitched sounds, covering the high-frequency sounds of musical instruments, sharp sounds, or the treble parts of human voices. The adaptive compensation algorithm will dynamically adjust the gain of the high-frequency signals to ensure that these sounds can still be clearly distinguished in a noisy environment. When the noise environment is more complex, such as when there is too much traffic noise or wind noise, the headphone system will especially enhance these high-frequency signals to compensate for the user's reduced sensitivity to high-frequency sounds under noise interference.

[0109] Secondly, for the processing of the low-frequency part, the system will deepen the low-frequency signal according to the user's auditory perception sensitivity to low frequencies. The low-frequency part usually covers sound effects such as heavy bass and drums. These sounds are in the lower frequency range and are usually easily disturbed by environmental low-frequency noise, such as car engine sounds or lower-frequency noise in the crowd. The adaptive compensation algorithm will deepen the performance of the low-frequency part, by increasing the amplitude of the low-frequency signal, making it more present in a complex noise background, while also ensuring that the user can feel the thickness and shock of the low-frequency signal. In addition, the algorithm will take into account the changes in the low-frequency signal under different noise conditions and automatically adjust the gain to avoid audio distortion.

[0110] For the intermediate frequency part, the system adopts a smooth transition compensation method. The intermediate frequency signal is usually the main frequency range of human voice and musical instruments, and it is also the part of sound most commonly perceived by users. In order to ensure the naturalness of the audio output, the adaptive compensation algorithm will not over-enhance or weaken the intermediate frequency signal, but ensure the balance and coherence of the intermediate frequency signal between different frequencies through a smooth transition method. Specifically, the system will adjust the transition area between high frequency and low frequency in the audio signal according to the user's sensitivity to the intermediate frequency part, so that the switching between high frequency, intermediate frequency and low frequency is more natural, without sudden jumps or sudden changes in timbre. In addition, the smooth transition compensation method can avoid the distortion caused by over-enhancement of a certain frequency band, making the overall performance of the audio more coordinated.

[0111] The entire adaptive compensation process is dynamic, and the system will continuously adjust according to the real-time collected environmental noise and the user's hearing sensitivity. Through this multi-band compensation processing of high-frequency enhancement, low-frequency deepening, and smooth transition of mid-frequency, the system can ensure that the audio output can not only adapt to the user's personalized hearing needs, but also maintain stable sound quality performance under different environmental conditions.

[0112] Furthermore, the adaptive compensation algorithm comprises the following steps:

[0113] The high frequency part of the target audio signal is enhanced by the following formula 1:

[0114]

[0115] Where H(f) is the signal amplitude after high frequency enhancement; G h is the high-frequency gain coefficient, which is determined by the user's hearing sensitivity; f is the frequency; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, used to calculate the signal intensity at frequency f under the reference standard curve; k h is the weight parameter of high-frequency compensation, which controls the intensity of high-frequency enhancement; β h is the high-frequency sensitivity adjustment coefficient, which controls the distribution of high-frequency signals; f h is the center frequency of the high frequency band;

[0116] The low-frequency part is deepened by the following formula 2:

[0117]

[0118] Where L(f) is the signal amplitude after low-frequency deepening processing; G l is the low-frequency gain coefficient, which is determined by the user's hearing sensitivity; f is the frequency; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, used to calculate the signal intensity at frequency f under the reference standard curve; k l is the weight parameter of low-frequency compensation, which is used to control the strength of low-frequency enhancement; α l The coefficient for adjusting the smoothness of low-frequency compensation; γ l is the enhancement factor of low frequency compensation; f l is the center frequency of the low frequency band;

[0119] The smooth transition compensation for the intermediate frequency part is achieved by the following formula 3:

[0120]

[0121] Where M(f) is the signal amplitude after intermediate frequency smoothing compensation; G m is the intermediate frequency gain coefficient; α m f is the adjustment parameter for controlling the intermediate frequency smoothing compensation; m is the center frequency of the intermediate frequency band; f c is the transition bandwidth of the mid-frequency band, which is used to control the width of the frequency transition; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r(f) is the intensity function of the reference standard signal, which is used to calculate the signal intensity at frequency f under the reference standard curve.

[0122] Furthermore, the intensity function S of the target audio signal u (f) is achieved by the following formula 3:

[0123]

[0124] Among them, A u β is the maximum intensity of the user's current audio signal, indicating the maximum volume perceived by the user; u To adjust the sensitivity coefficient of the audio signal strength change; f 0u is the center frequency of the audio signal; C u To adjust the coefficient of the periodic component related to the frequency of the target audio signal, used to simulate the amplitude fluctuation of the signal at different frequencies; f is the frequency;

[0125] The intensity function S of the reference standard signal r (f) is achieved by the following formula 4:

[0126]

[0127] Among them, A r is the maximum strength of the reference signal; B r To adjust the coefficient of the frequency square term, it is used to control the response speed of the reference signal strength in the frequency change; α r is the attenuation coefficient of the reference signal when the frequency changes; f is the frequency.

[0128] The adaptive compensation algorithm of this embodiment includes specific compensation processing for the high frequency, low frequency and medium frequency parts of the target audio signal to achieve optimization of different frequency bands. These steps are intended to enhance the frequency response of the audio signal and adjust it in combination with the user's auditory perception sensitivity and the strength of the reference standard signal. The following is a detailed description of each step and related formulas.

[0129] First, for the enhancement of the high frequency part, the system calculates it by the following formula:

[0130]

[0131] In this formula:

[0132] H(f) represents the enhanced high-frequency signal amplitude. This value reflects the energy of the processed high-frequency signal.

[0133] G his the high frequency gain factor, which represents the overall gain of the high frequency part according to the user's hearing sensitivity. This factor can be set by the user's personal preference for high frequency sounds, usually based on the user's historical audio usage data.

[0134] f is the current frequency, which indicates the frequency of the input signal.

[0135] S u (f) is the intensity function of the target audio signal, which calculates the target signal intensity at frequency f. This function is used to describe the audio signal intensity perceived by the user. The specific formula is described in detail later.

[0136] S r (f) is the intensity function of the reference standard signal, which calculates the intensity of the reference signal at frequency f. This is the reference signal intensity calculated based on the standard curve.

[0137] k h It is the weight parameter of high frequency compensation, which controls the strength of high frequency enhancement. It is used to adjust the amplification or reduction of the high frequency signal to ensure that the signal is not over-enhanced.

[0138] β h It is the high-frequency sensitivity adjustment coefficient, which controls the distribution of high-frequency signals. This coefficient determines the accuracy of high-frequency enhancement. The higher it is, the more concentrated the enhancement effect is near the center frequency.

[0139] f h It is the center frequency of the high frequency band, indicating the center frequency of the high frequency band enhancement.

[0140] The formula is obtained by amplifying the high-frequency signal, combining the contrast between the target signal and the reference signal, and the exponential decay term.

[0141]

[0142] To ensure that the enhanced high-frequency signal is not distorted and the enhancement effect is concentrated in the required frequency band.

[0143] For the deepening processing of the low-frequency part, the system uses the following formula:

[0144]

[0145] In this formula:

[0146] L(f) represents the amplitude of the low-frequency signal after deepening processing.

[0147] G l is the low frequency gain factor, which controls the amount of gain in the low frequency portion, based on the user's hearing sensitivity setting.

[0148] k lIt is the weight parameter of low-frequency compensation, which controls the strength of low-frequency enhancement.

[0149] α l It is a coefficient for adjusting the smoothness of low-frequency compensation. The larger it is, the wider the enhancement range will be.

[0150] γ l It is the enhancement coefficient of low-frequency compensation, which is used to improve the enhancement effect of the low-frequency part.

[0151] f l It is the center frequency of the low-frequency band, indicating the core area of ​​low-frequency enhancement.

[0152] The processing of the low-frequency signal also includes using the sine function sin(πf) to smooth the changes of the low-frequency signal, and adjusting the enhancement range of the low-frequency signal through the exponential term to ensure a natural transition of the enhancement effect.

[0153] For smooth transition compensation of the intermediate frequency part, the system uses the following formula:

[0154]

[0155] In this formula:

[0156] M(f) is the signal amplitude after intermediate frequency compensation.

[0157] G m It is the intermediate frequency gain coefficient, which controls the gain amount of the intermediate frequency band.

[0158] α m It is the adjustment parameter that controls the intermediate frequency smoothing compensation and determines the smoothness of the intermediate frequency enhancement.

[0159] f m is the center frequency of the mid-band.

[0160] f c It is the transition bandwidth of the mid-frequency band, which controls the transition range of the signal from low frequency to high frequency.

[0161] The smoothing compensation of the intermediate frequency signal is achieved by the cosine function This is achieved by ensuring that the signal transitions naturally between different frequencies and avoiding abrupt changes in the transition band.

[0162] Next, the intensity function S of the target audio signal u (f) is used to calculate the strength of the target signal, and the formula is as follows:

[0163]

[0164] In this formula:

[0165] A uIt is the maximum strength of the user's current audio signal, indicating the maximum volume perceived by the user.

[0166] β u It is the sensitivity coefficient that adjusts the change in audio signal strength and determines the speed at which the signal is enhanced during frequency changes.

[0167] f 0u It is the center frequency of the audio signal, indicating the main frequency band of the signal.

[0168] C u It is the coefficient that adjusts the frequency-related periodic components of the target audio signal and is used to simulate the amplitude fluctuation of the signal at different frequencies.

[0169] Finally, the intensity function S of the reference standard signal r (f) is calculated by the following formula:

[0170]

[0171] In this formula:

[0172] A r is the maximum strength of the reference signal.

[0173] B r To adjust the coefficient of the frequency square term, the response speed of the control signal changes with frequency.

[0174] α r It is the attenuation coefficient of the signal when the frequency changes, which determines the gradual attenuation of the reference signal as the frequency increases.

[0175] Through the interaction of these formulas, the system can dynamically adjust the enhancement effect of the target audio signal in different frequency bands according to the user's personalized needs and standard reference curve, ensuring that the audio signal is transmitted to the user naturally, clearly, and without distortion.

[0176] In this embodiment, the target audio signal and the reference standard signal play different roles, which are explained in detail as follows:

[0177] The target audio signal is the audio content that the user wants to hear through the headset, that is, the input original audio signal. These audio signals can come from various audio sources, such as music, podcasts, phone calls, etc., and are transmitted to the headset via Bluetooth or wired. The target audio signal is usually unprocessed sound data, which includes sound components in multiple frequency bands, such as high frequency, medium frequency, and low frequency. In order to enable users to clearly hear these sounds in different environments (such as a quiet indoor or noisy outdoor), the adaptive compensation algorithm in this embodiment will perform frequency band enhancement, compensation and other processing on the target audio signal.

[0178] The frequency response characteristics of the target audio signal (i.e. S u (f)) is obtained through a series of processing and calculations, describing the audio intensity at a certain frequency, reflecting the sound intensity perceived by the user in the current environment. u (f) is used to represent the audio intensity currently perceived by the user. It is combined with the user's auditory sensitivity and volume preference and input into the compensation algorithm to dynamically adjust the performance of the audio signal at different frequencies.

[0179] The reference standard signal represents a reference signal that is used to compare with the target audio signal to help the system determine how to adjust the target audio signal to achieve the best hearing effect. The reference standard signal is usually based on standards such as the ISO226 equal loudness curve, which describes the standardized audio intensity at different frequencies. The ISO 226 curve provides the human ear's perception of different frequencies and describes the volume required to hear the same loudness at different frequencies.

[0180] The role of the reference standard signal is to provide the system with an ideal audio intensity data (i.e. S r (f)) so that the system can make adjustments based on the difference between the target audio signal and the standard signal. For example, in some cases where the ambient noise is large, the system may find that certain frequency bands (such as low frequency or high frequency) of the target audio signal are weak. At this time, the reference standard signal can be used as a comparison to guide the system to appropriately enhance or compensate these frequency bands to match the standard loudness perception.

[0181] In short, the target audio signal is the audio content that the user wants to hear through the headphones, which is optimized after being processed by the system to adapt it to the user's current auditory environment. The reference standard signal is based on the standard auditory curve of the human ear (such as ISO226) and is used to compare the frequency response of the target audio signal in order to dynamically adjust the audio output.

[0182] By comparing the target audio signal with the reference standard signal, the system can determine the frequency range in which the audio signal needs to be enhanced or adjusted to provide a natural, clear and optimized listening experience.

[0183] S104: applying a hyperbolic filter to dynamically adjust the low-frequency part of the audio signal processed by the adaptive compensation algorithm to obtain an adjusted audio signal; and generating a corresponding harmonic signal according to the amplitude and frequency characteristics of the adjusted audio signal.

[0184] In step S104, a hyperbolic filter is applied to the audio signal processed by the adaptive compensation algorithm, and dynamic adjustment is performed on the low-frequency part in particular, in order to further optimize the low-frequency performance, making it clearer and fuller, while reducing distortion or poor sound effects caused by excessive low-frequency enhancement.

[0185] First, the audio signal processed by step S103 already contains the compensation results for different frequency bands. However, the low-frequency part is still easily affected by environmental noise or the structure of the headphone. In order to solve this problem, the system further processes the low-frequency signal through a hyperbolic filter. The hyperbolic filter is a specially designed filter that can dynamically adjust the frequency response according to the frequency characteristics and amplitude of the input signal. Low-frequency signals usually contain deeper sounds, such as bass drums, low-frequency background music or ambient sounds, which are often interfered with by external noise in open headphones. The hyperbolic filter can automatically identify these low-frequency signals and dynamically adjust their gain according to their amplitude to avoid excessive enhancement or weakening of the low-frequency band.

[0186] In specific implementation, the hyperbolic filter monitors the amplitude and frequency characteristics of the low-frequency signal in real time. When the amplitude of the low-frequency signal is small, the filter will slightly increase the gain of the low-frequency part to ensure that the low-frequency band is not masked by the high or medium frequency, thereby enhancing the expressiveness of the low frequency and increasing the depth of the bass. When the amplitude of the low-frequency signal is large, the filter will moderately compress or attenuate the low-frequency part to prevent distortion caused by excessive low-frequency enhancement, especially in an environment with strong low-frequency noise (such as vehicle engine sound or large mechanical noise). This adjustment can significantly improve the clarity of the sound quality.

[0187] In addition, the dynamic adjustment function of the hyperbolic filter can flexibly adjust its operating parameters according to changes in environmental noise. The microphone in the headset continuously monitors changes in the surrounding noise and feeds this data back to the filter, which changes its response curve to low-frequency signals based on the characteristics of the real-time environmental noise. For example, when it is detected that the low-frequency component of the external noise is strong, the filter will reduce the gain of the low-frequency band accordingly to prevent the external noise from conflicting with the low-frequency part of the target audio signal.

[0188] After the low-frequency signal is processed, the system will further analyze the amplitude and frequency characteristics of the adjusted audio signal and generate the corresponding harmonic signal accordingly. Harmonic signals are a commonly used technology in the audio processing process. By generating signals with multiple frequencies or other harmonic frequencies, the layering of the audio can be further enriched. For example, the harmonic signal in the low-frequency band can increase the resonance of the low frequency, making it sound more powerful without distortion, while the harmonics in the high-frequency band can make the audio sound more delicate and clear.

[0189] The dynamic adjustment of the entire step is carried out in real time, ensuring that the audio signal can be optimally optimized under different environmental conditions, so that users can always enjoy the best low-frequency sound experience. In this process, the introduction of the hyperbolic filter not only improves the quality of the audio signal, but also ensures that the system can flexibly respond to different environmental noises, providing a more immersive auditory experience.

[0190] Furthermore, the applying of the hyperbolic filter to dynamically adjust the low-frequency part includes:

[0191] According to the frequency distribution characteristics of the current environmental noise, the parameters of the hyperbolic filter are adjusted so that the degree of low-frequency enhancement changes dynamically with the change of the intensity of the low-frequency noise in the environment;

[0192] When it is detected that the low-frequency noise in the environment is enhanced, the hyperbolic filter reduces the gain of the low-frequency part to avoid sound quality distortion caused by excessive low-frequency amplification.

[0193] In this embodiment, the core of applying the hyperbolic filter to dynamically adjust the low-frequency part is to be able to adjust the processing method of the low-frequency part in real time according to the changes in the low-frequency noise in the environment, so as to ensure that the sound quality is not disturbed by the environmental noise, while avoiding the distortion problem caused by excessive enhancement of the low-frequency signal. Specifically, this process relies on real-time monitoring of the current environmental noise, especially a detailed analysis of the frequency distribution characteristics of the low-frequency noise in the environment.

[0194] First, the microphone in the headphone system will continuously collect the surrounding environmental noise and perform frequency analysis on the noise signal. Through fast Fourier transform (FFT) or similar frequency analysis technology, the system can determine the frequency distribution of the current environmental noise, especially the energy distribution of the low-frequency part. The low-frequency part of the environmental noise may come from traffic noise, the operating sound of mechanical equipment, or even the background noise in the crowd. These low-frequency noises are usually more continuous and have greater intensity, which will interfere with the user's perception of the low-frequency components in the target audio signal.

[0195] Next, the system will automatically adjust the parameters of the hyperbolic filter according to the frequency distribution characteristics of the detected environmental low-frequency noise. The design of the hyperbolic filter enables it to flexibly enhance or compress low-frequency signals, and the adjustment of specific parameters determines the size of the gain of the low-frequency part. When the low-frequency noise in the environment is weak, the hyperbolic filter will increase the gain of the low-frequency part of the target audio signal accordingly, making the low-frequency part more prominent and rich, thereby ensuring that users can hear clear low-frequency sound effects. Conversely, when the low-frequency component in the environmental noise is strong, the system will reduce the gain of the low-frequency part to prevent the low-frequency noise from overlapping with the low-frequency part of the target audio signal, resulting in auditory turbidity or distortion.

[0196] When the system detects that the low-frequency noise in the environment suddenly increases, the hyperbolic filter will respond immediately and reduce the gain of the low-frequency part. The purpose of this dynamic adjustment is to prevent the low-frequency signal in the target audio from being over-amplified, thereby avoiding sound quality distortion caused by interference from external environmental noise. For example, if the user is in an environment full of low-frequency noise (such as a subway car or a construction site), the system can reduce the sound quality loss in the low-frequency band by reducing the low-frequency gain, thereby maintaining the clarity and naturalness of the audio.

[0197] The entire process is adjusted in real time, and the system can update the parameter settings of the hyperbolic filter at any time according to changes in the noise environment. This ensures that even in complex and changing noise environments, users can still enjoy balanced, distortion-free audio effects. Through this adaptive low-frequency adjustment method, the system effectively balances the low-frequency performance in the target audio signal and the interference of external low-frequency noise, achieving dynamic optimization of the low-frequency part of the audio signal.

[0198] Furthermore, the applying of the hyperbolic filter to dynamically adjust the low-frequency part also includes:

[0199] The user's motion state is detected by integrating an acceleration sensor and a position sensor in the headset;

[0200] When the user is stationary, the dynamic adjustment of the hyperbolic filter is used to maintain the clarity and purity of the low frequencies; when the user is detected to be in motion, the hyperbolic filter is used to enhance the dynamic response of the low-frequency part and provide a stronger bass effect to meet the auditory needs during motion.

[0201] In this embodiment, the user's motion state is detected by the accelerometer and position sensor in the headset, thereby optimizing the sound performance of the low-frequency part. This method not only adjusts the audio output according to the ambient noise, but also takes into account the user's actual motion. This dynamic adjustment mechanism further enhances the user's auditory experience in different activity scenarios, ensuring that the appropriate low-frequency effect can be obtained whether listening to music in a stationary state or enjoying audio while exercising.

[0202] Specifically, the accelerometer and position sensor integrated in the headset are responsible for real-time monitoring of the user's motion status. The accelerometer can detect the user's acceleration, deceleration, head turning and other movements, while the position sensor provides information about the user's current posture and position changes. The function of these sensors is to monitor whether the user is stationary (such as sitting or standing) or in motion (such as running or walking). The system analyzes the sensor data to identify the user's activity pattern and make dynamic audio adjustments accordingly.

[0203] When the user is stationary, the main function of the hyperbolic filter is to maintain the clarity and purity of the low-frequency signal. In this state, the user's auditory needs are usually focused on accurately capturing the details of the sound, so the system will prioritize ensuring that the low-frequency part is not over-enhanced to avoid low-frequency distortion or blur. The hyperbolic filter will ensure that the low-frequency signal remains balanced and natural according to the needs in the noisy environment and the stationary state, so that the audio output has an elegant and clean low-frequency performance.

[0204] However, when the user is in motion, the system will enhance the dynamic response of the low-frequency part through a hyperbolic filter. The user's auditory needs when exercising are different from those when stationary, and usually require a stronger low-frequency effect to counter the surrounding environmental noise and sound interference caused by movement. At the same time, when in motion, users tend to feel the impact of bass, so the system will enhance the response of low frequencies to make the low-frequency part more powerful and deep. This adjustment is achieved by increasing the low-frequency gain through the filter, ensuring that users can still experience dynamic and rhythmic bass effects while exercising.

[0205] The whole process is dynamic and real-time. The system constantly evaluates the user's status through sensor feedback data and makes corresponding low-frequency adjustments. In this way, the headphones can provide users with the best low-frequency performance in different scenarios. When stationary, the low-frequency sound quality is delicate and clear; when exercising, the low-frequency sound is strong and powerful, meeting the different needs of users and thus improving the overall audio experience.

[0206] Furthermore, generating a corresponding harmonic signal according to the amplitude and frequency characteristics of the adjusted audio signal includes:

[0207] Analyze the adjusted audio signal, extract the signal characteristics of the low frequency, medium frequency and high frequency bands, and determine the amplitude and frequency information of each frequency band;

[0208] In the low frequency band, a double frequency signal is generated to enhance the depth and thickness of the low frequency signal;

[0209] In the mid-frequency and high-frequency bands, the double-frequency signal is generated to enhance the signal details and avoid distortion. By reducing the order of the double-frequency signal, the clarity and natural sound quality of the mid-frequency and high-frequency bands are ensured.

[0210] When it is detected that the strength of the low-frequency signal exceeds a preset threshold, the order of the low-frequency multiplication signal is increased to further enhance the expressiveness of the low-frequency band.

[0211] In this embodiment, a corresponding harmonic signal is generated based on the amplitude and frequency characteristics of the adjusted audio signal. The purpose is to further optimize the various frequency bands of the audio output by generating and adjusting the double-frequency signal so that it has ideal performance in the low-frequency, medium-frequency and high-frequency bands.

[0212] First, the system will further analyze the audio signal after the adaptive compensation algorithm and filtering. The analysis process includes extracting the signal features of the low-frequency, medium-frequency and high-frequency bands. Specifically, the amplitude and frequency of each frequency band are measured and calculated. Through the extraction of this signal feature, the system can understand the specific performance of each frequency band, including the strength distribution of the audio signal in different frequency bands, the dynamic range of each frequency band and other information. Based on these analyses, the system can perform more targeted optimization processing on signals in different frequency bands.

[0213] In the low frequency band, the system generates a double frequency signal to enhance the depth and weight of the low frequency signal. The low frequency signal usually determines the bass effect of the audio and is a vital part of the auditory experience. By doubling the low frequency components, the double frequency signal can add more layers and resonance to the original low frequency signal, making the bass sound thicker and deeper. This processing method is particularly suitable for scenes that require strong low frequency performance, such as the thumping of drums in music or the sound effect experience in movies, ensuring that the expressiveness of the low frequency band is significantly improved.

[0214] At the same time, in the mid-frequency and high-frequency bands, the system also generates double-frequency signals, but their purposes are different. For the mid-frequency and high-frequency bands, the system focuses more on enhancing the details and clarity of the audio signal. The double-frequency signals can improve the delicacy of the sound in these frequency bands, allowing users to hear more details, especially in the high-frequency sounds of musical instruments or the mid-frequency parts of human voices. At the same time, the system ensures that the signal is not over-amplified by reducing the order of the double-frequency signal, avoiding distortion or overly bright sound effects in the high-frequency part, thereby maintaining natural sound quality. This processing helps to avoid hearing fatigue in the high-pitched area, while keeping the human voice and instrument sounds in the mid-frequency band soft and natural.

[0215] When the system detects that the strength of the low-frequency signal exceeds a preset threshold, it will further increase the order of the low-frequency double-frequency signal. This means that when the low-frequency signal is strong, the system will dynamically adjust the level of the low-frequency double-frequency signal to increase the resonance effect of the bass. This enhancement helps to further enhance the expressiveness of the low frequency in scenes where the low-frequency signal is more prominent, making the bass more shocking and powerful, and adapting to various audio content that requires strong bass performance.

[0216] In this way, the system realizes personalized processing of signals in each frequency band, and the frequency doubling effect of generating harmonic signals not only enhances the layering of the audio, but also effectively avoids distortion or unbalanced sound quality problems in different frequency bands, thus providing users with a more immersive and delicate listening experience. This process ensures that the performance of audio signals in all frequency bands can reach an ideal state, and can be accurately adjusted dynamically according to the characteristics of different signals.

[0217] S105: providing the adjusted audio signal and the generated corresponding harmonic signal as an optimized audio signal to a user.

[0218] In step S105, the audio signal processed in the previous steps has been optimized, and the system will finally transmit the optimized audio signal and the generated harmonic signal to the user. This process is intended to ensure that the user can receive the audio output after multi-band optimization and harmonic processing to achieve the best auditory experience.

[0219] First, the adjusted audio signal has been processed by the adaptive compensation algorithm. The high frequency, mid-frequency and low frequency parts of the target audio signal are enhanced, deepened or smoothly transitioned according to the user's hearing sensitivity. At the same time, the low frequency part is further dynamically adjusted through the hyperbolic filter to ensure that the low frequency signal is still clear and full under the influence of environmental noise, and to avoid distortion caused by over-enhancement. At this point, the audio signal has enough layering and detail.

[0220] In addition to the directly adjusted audio signal, the system also generates a harmonic signal corresponding to the audio signal. Harmonic signals are generated by analyzing the amplitude and frequency characteristics of the audio signal, which can enhance the richness of the audio, especially in the high and low frequency parts. Harmonic signals can compensate for the frequency loss or insufficient energy areas that may exist in the target audio. For users, harmonic signals can increase the stereoscopic and spatial sense of the audio, making the audio performance fuller without distortion.

[0221] Next, the system combines the adjusted audio signal with the generated harmonic signal to form the final optimized audio signal. In order to ensure that the audio signal received by the user has the best sound quality performance in various auditory environments, the system will continuously monitor the changes in ambient noise and adjust the audio output in real time. Through this dynamic optimization, the headphones can maintain high-quality audio performance in various scenarios. Whether in a quiet indoor environment or in a noisy outdoor scene, users can feel clear and natural sound output.

[0222] Finally, the optimized audio signal is played to the user through the sound unit of the headset. Due to the special design of open-type headphones, the sound can not only propagate in the ear canal, but also naturally mix with the external ambient sound. Therefore, the combination of the adjusted audio signal and the harmonic signal can ensure that the layering and details of the audio are preserved, giving the user a more immersive listening experience.

[0223] A second embodiment of the present application provides an electronic device, the electronic device comprising:

[0224] processor;

[0225] The memory is used to store a program, and when the program is read and executed by the processor, the open-earphone audio optimization method based on the adaptive compensation algorithm provided in the first embodiment of the present application is executed.

[0226] The third embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the open-earphone audio optimization method based on the adaptive compensation algorithm provided in the first embodiment of the present application is executed.

[0227] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. An open-earphone audio optimization method based on an adaptive compensation algorithm, characterized in that: include: The ambient noise signal is collected through the microphone of the open-type headset, and the input target audio signal is received; Based on the collected environmental noise signal and the input target audio signal, the user's auditory perception in the current environment is analyzed using the ISO226 equal loudness curve to determine the user's auditory sensitivity to different frequency bands; wherein the different frequency bands include high frequency, medium frequency and low frequency; Using the determined auditory sensitivity, an adaptive compensation algorithm is used to enhance the high frequency part of the target audio signal, deepen the low frequency part, and perform smooth transition compensation on the intermediate frequency part; For the audio signal processed by the adaptive compensation algorithm, a hyperbolic filter is applied to dynamically adjust the low-frequency part to obtain an adjusted audio signal; and a corresponding harmonic signal is generated according to the amplitude and frequency characteristics of the adjusted audio signal; The adjusted audio signal and the generated corresponding harmonic signal are provided to the user as an optimized audio signal.

2. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 1, characterized in that: The collecting of the ambient noise signal by the microphone of the open-type headset comprises: Use multiple distributed microphone arrays, located in the left and right channels of the headset, to improve the positioning accuracy of different noise sources; The collected noise signal is processed by time-space filtering to eliminate unnecessary environmental echo signals and ensure the purity of the noise signal; According to the sound source direction information of the noise signal, the relative position of the main noise source is determined, and the collection range of the noise signal is automatically adjusted according to the user's head posture.

3. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 1, characterized in that: The receiving an input target audio signal comprises: Receive a target audio signal via Bluetooth or a wired connection, wherein the audio signal includes multiple channels corresponding to high-frequency, medium-frequency and low-frequency signals respectively; After receiving the target audio signal, the audio signal is pre-processed, including volume standardization and preliminary filtering of background noise; The initial volume is automatically adjusted according to the audio preferences set by the user, and the optimal decoding algorithm is selected according to the signal format to ensure high-fidelity sound transmission.

4. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 1, characterized in that: The use of ISO226 equal loudness curves to analyze the user's auditory perception in the current environment includes: Generate a personalized user auditory perception model based on the user's historical audio usage data; The ISO226 equal loudness curve is combined with the personalized model to analyze the user's hearing sensitivity in the current environment in real time, and dynamically adjust the hearing sensitivity curve according to the noise environment in different frequency bands.

5. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 4, characterized in that: The use of ISO226 equal loudness curves to analyze the user's auditory perception in the current environment also includes: Monitor the user's physiological data in real time to detect the user's current emotional state and adjust the auditory perception model based on the emotional state; When it is detected that the user is in a high-stress state, the balance of the audio output is adjusted so that the output intensity of the high-frequency part is moderately weakened to reduce the user's auditory fatigue, and the volume of the low-frequency and mid-frequency parts is enhanced.

6. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 1, characterized in that: The method of applying a hyperbolic filter to dynamically adjust the low-frequency part includes: According to the frequency distribution characteristics of the current environmental noise, the parameters of the hyperbolic filter are adjusted so that the degree of low-frequency enhancement changes dynamically with the change of the intensity of the low-frequency noise in the environment; When it is detected that the low-frequency noise in the environment is enhanced, the hyperbolic filter reduces the gain of the low-frequency part to avoid sound quality distortion caused by excessive low-frequency amplification.

7. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 6, characterized in that: The method of applying a hyperbolic filter to dynamically adjust the low-frequency part also includes: The user's motion state is detected by integrating an acceleration sensor and a position sensor in the headset; When the user is stationary, the dynamic adjustment of the hyperbolic filter is used to maintain the clarity and purity of the low frequencies; when the user is detected to be in motion, the hyperbolic filter is used to enhance the dynamic response of the low-frequency part and provide a stronger bass effect to meet the auditory needs during motion.

8. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 1, characterized in that: The step of generating a corresponding harmonic signal according to the amplitude and frequency characteristics of the adjusted audio signal comprises: Analyze the adjusted audio signal, extract the signal characteristics of the low frequency, medium frequency and high frequency bands, and determine the amplitude and frequency information of each frequency band; In the low frequency band, a double frequency signal is generated to enhance the depth and thickness of the low frequency signal; In the mid-frequency and high-frequency bands, the double-frequency signal is generated to enhance the signal details and avoid distortion. By reducing the order of the double-frequency signal, the clarity and natural sound quality of the mid-frequency and high-frequency bands are ensured. When it is detected that the strength of the low-frequency signal exceeds a preset threshold, the order of the low-frequency multiplication signal is increased to further enhance the expressiveness of the low-frequency band.

9. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 1, characterized in that: The adaptive compensation algorithm comprises the following steps: The high frequency part of the target audio signal is enhanced by the following formula 1: Among them, H(f) is the signal amplitude after high-frequency enhancement; G h is the high-frequency gain coefficient, which is determined by the user's hearing sensitivity; f is the frequency; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, used to calculate the signal intensity at frequency f under the reference standard curve; k h is the weight parameter of high-frequency compensation, which controls the intensity of high-frequency enhancement; β h is the high-frequency sensitivity adjustment coefficient, which controls the distribution of high-frequency signals; f h is the center frequency of the high frequency band; The low-frequency part is deepened by the following formula 2: Where L(f) is the signal amplitude after low-frequency deepening processing; G l is the low-frequency gain coefficient, which is determined by the user's hearing sensitivity; f is the frequency; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, used to calculate the signal intensity at frequency f under the reference standard curve; k l is the weight parameter of low-frequency compensation, which is used to control the strength of low-frequency enhancement; α l The coefficient for adjusting the smoothness of low-frequency compensation; γ l is the enhancement coefficient of low-frequency compensation; f l is the center frequency of the low frequency band; The smooth transition compensation for the intermediate frequency part is achieved by the following formula 3: Where M(f) is the signal amplitude after intermediate frequency smoothing compensation; G m is the intermediate frequency gain coefficient; α m f is the adjustment parameter for controlling the intermediate frequency smoothing compensation; m is the center frequency of the intermediate frequency band; f c is the transition bandwidth of the mid-frequency band, which is used to control the width of the frequency transition; S u (f) is the intensity function of the target audio signal, which is used to calculate the target signal intensity at frequency f; S r (f) is the intensity function of the reference standard signal, which is used to calculate the signal intensity at frequency f under the reference standard curve.

10. The open-earphone audio optimization method based on the adaptive compensation algorithm according to claim 9, characterized in that: The intensity function S of the target audio signal u (f) is achieved by the following formula 3: Among them, A u β is the maximum intensity of the user's current audio signal, indicating the maximum volume perceived by the user; u To adjust the sensitivity coefficient of the audio signal strength change; f 0u is the center frequency of the audio signal; C u To adjust the coefficient of the periodic component related to the frequency of the target audio signal, used to simulate the amplitude fluctuation of the signal at different frequencies; f is the frequency; The intensity function S of the reference standard signal r (f) is achieved by the following formula 4: Among them, A r is the maximum strength of the reference signal; B r To adjust the coefficient of the frequency square term, it is used to control the response speed of the reference signal strength in the frequency change; α r is the attenuation coefficient of the reference signal when the frequency changes; f is the frequency.

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