Sound pressure adjusting method and system based on human body induction sound box

Abstract

The invention relates to a sound pressure adjusting method and system based on a human body induction sound box. The method comprises the following steps: acquiring user motion data through a human body induction sensor, and performing triangulation positioning analysis on the user motion data through an MCU (Microprogrammed Control Unit) chip to obtain position coordinate data and motion track data; inputting a sound field analysis model in the DSP chip for characteristic analysis to obtain surrounding sound field characteristic parameters; the method comprises the following steps: performing multichannel separation and time domain equalization processing on an original audio signal by a DSP chip to obtain a multichannel preprocessed signal; inputting into a DSP chip for sound pressure balance calculation to obtain an initial sound channel gain control parameter; performing power amplification on the multichannel pre-processed signal, and driving a loudspeaker array to output a surround sound signal; and the DSP chip dynamically optimizes the initial sound channel gain control parameter to obtain a target sound channel gain control parameter. According to the invention, real-time optimization and dynamic updating of sound pressure parameters are realized, and the response speed of the human body induction sound box to environmental changes is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human body induction speakers, and in particular to a sound pressure adjustment method and system based on human body induction speakers. Background Art

[0002] During playback, traditional speakers usually use a fixed sound pressure output method and are unable to automatically adjust the sound pressure distribution according to the user's position in the room. This results in significant differences in the sound pressure of music heard by users at different positions, seriously affecting the listening experience.

[0003] Although the speaker systems on the market have begun to adopt surround sound technology, most systems still lack real-time perception of user positions and intelligent adjustment functions. In this case, even with advanced audio processing algorithms, it is difficult to provide a balanced sound field experience for users in different positions, especially when the user is moving, the problem of uneven sound pressure distribution is more prominent. Existing speaker sound pressure adjustment technology mainly relies on manual adjustment or preset mode, and lacks adaptive adjustment capabilities. At the same time, due to the lack of effective human body sensing and sound field feedback mechanism, real-time optimization and dynamic adjustment of sound pressure cannot be achieved, which means that users often need to repeatedly adjust speaker parameters when enjoying music in different positions in the room, reducing the user experience. Summary of the invention

[0004] The main purpose of the present invention is to provide a sound pressure adjustment method and system based on a human body induction speaker. The present invention realizes real-time optimization and dynamic update of sound pressure parameters, and improves the response speed of the human body induction speaker to environmental changes.

[0005] To achieve the above object, the present invention provides a method for adjusting sound pressure based on a human body induction speaker, comprising the following steps:

[0006] The user's motion data is collected through the human body sensing sensor, and the MCU chip performs triangulation positioning analysis on the user's motion data to obtain position coordinate data and motion trajectory data;

[0007] Inputting the position coordinate data and the motion trajectory data into a sound field analysis model in a DSP chip for feature analysis to obtain surround sound field feature parameters;

[0008] The original audio signal is received through the BT chip, and the DSP chip performs multi-channel separation and time domain equalization processing on the original audio signal to obtain a multi-channel pre-processed signal;

[0009] Inputting the surround sound field characteristic parameters and the multi-channel pre-processed signal into a DSP chip to perform sound pressure balance calculation to obtain initial channel gain control parameters;

[0010] Amplifying the multi-channel preprocessed signal according to the initial channel gain control parameter, and driving the speaker array to output a surround sound signal;

[0011] The spatial sound field data of the surround sound signal is detected by an acoustic sensor, and the initial channel gain control parameter is dynamically optimized by a DSP chip to obtain a target channel gain control parameter.

[0012] The present invention also provides a sound pressure adjustment system based on a human body induction speaker, comprising:

[0013] The acquisition module is used to collect user motion data through a human body sensing sensor, and the MCU chip performs triangulation analysis on the user motion data to obtain position coordinate data and motion trajectory data;

[0014] An analysis module, used for inputting the position coordinate data and the motion trajectory data into a sound field analysis model in a DSP chip for feature analysis to obtain surround sound field feature parameters;

[0015] A processing module is used to receive the original audio signal through the BT chip, and perform multi-channel separation and time domain equalization processing on the original audio signal by the DSP chip to obtain a multi-channel pre-processed signal;

[0016] A calculation module, used for inputting the surround sound field characteristic parameters and the multi-channel pre-processed signal into a DSP chip to perform sound pressure balance calculation to obtain initial channel gain control parameters;

[0017] an amplification module, configured to amplify the multi-channel preprocessed signal according to the initial channel gain control parameter, and drive a speaker array to output a surround sound signal;

[0018] The optimization module is used to detect the spatial sound field data of the surround sound signal through an acoustic sensor, and dynamically optimize the initial channel gain control parameters through a DSP chip to obtain target channel gain control parameters.

[0019] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0020] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0021] In summary, the technical solution provided by the present invention realizes precise positioning and trajectory tracking of the user's position by adopting a combination of MCU chip and human body sensing sensor, thereby ensuring the positioning accuracy of sound pressure adjustment; uses DSP chip for sound field analysis and multi-channel processing to realize intelligent balanced adjustment of sound pressure, ensuring that users can obtain consistent listening experience at any position in the room; uses BT chip for wireless transmission and processing of audio signals, thereby improving the connection stability and audio processing efficiency of the system; through the real-time feedback optimization mechanism of acoustic sensor and DSP chip, dynamic adjustment of sound pressure parameters is realized, and the adaptive performance of the system is enhanced; multi-channel separation and time domain equalization processing technology are integrated to optimize the sound field restoration effect of audio signals; a complete sound pressure adjustment closed-loop control system is established to realize intelligent control of the whole process from user position perception to sound pressure output; through the precise management of power amplification and drive control, the stability and consistency of each channel output are ensured; real-time optimization and dynamic update of sound pressure parameters are realized, and the response speed of human body sensing speaker to environmental changes is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the steps of a sound pressure adjustment method based on a human body induction speaker in one embodiment of the present invention;

[0023] Figure 2 It is a structural block diagram of a sound pressure adjustment system based on a human body induction speaker in one embodiment of the present invention;

[0024] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0025] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Reference Figure 1 This embodiment provides a sound pressure adjustment method based on a human body induction speaker, comprising the following steps:

[0028] S1, collects user motion data through human body sensing sensors, and uses the MCU chip to perform triangulation positioning analysis on the user motion data to obtain position coordinate data and motion trajectory data;

[0029] Among them, the motion state of the user is sampled by the human body sensing sensor to obtain a motion sampling signal. The human body sensing sensor uses infrared, radar or ultrasonic technology to obtain signals by detecting the user's body temperature changes, motion trajectory or body position. The motion sampling signal is input into the digital filter in the MCU (microcontroller unit) chip for noise elimination and signal enhancement. The digital filter removes high-frequency noise and low-frequency interference in the signal by setting appropriate filtering parameters, and at the same time enhances the useful signal part to obtain more accurate user motion data. The purified user motion data is triangulated and spatially mapped. Through the triangulation positioning method, the user's position coordinates in three-dimensional space are obtained through geometric calculation using data from different angles provided by multiple sensors. The position coordinate data reflects the user's instant position. Through the process of spatial mapping, the position coordinates are mapped to the actual sound field area, taking into account the physical layout of the sound system and the relative position of the user, thereby improving the accuracy of positioning. In order to enhance the understanding of the motion state, the position coordinate data is analyzed for spatiotemporal characteristics. By combining time and space information, the regularity and trend of user movement are extracted. For example, the user's acceleration change, velocity vector change, etc. These spatiotemporal features can help the system understand the user's motion pattern more accurately and obtain a motion feature vector, which contains key features of the motion, such as motion direction, speed change, trajectory turning, and other information. Based on the motion feature vector, the user's motion trajectory is fitted and analyzed in terms of direction. Trajectory fitting uses methods such as polynomial fitting and spline fitting to infer the user's possible motion path through algorithms. At the same time, direction analysis can help the system determine the direction of the user's motion and understand its relative motion changes. Through fitting calculation and direction analysis, a motion trajectory vector is generated to represent the user's motion path and direction. The position coordinate data and the motion trajectory vector are weighted fused. A preliminary data set is synthesized by assigning different weights to the position and trajectory data. The preliminary fused data is used to identify the motion state. By analyzing the changes in the user's motion pattern, it is possible to identify the user in different motion states, such as stillness, walking, running, or other complex motion forms. Motion state parameters are numerical features that characterize the user's current motion state, such as speed, acceleration, motion intensity, etc. These parameters help the sound system determine the current sound adjustment needs. Combine the preliminary fusion data and motion state parameters to integrate the motion trajectory and obtain the final motion trajectory data.

[0030] S2, inputting the position coordinate data and the motion trajectory data into the sound field analysis model in the DSP chip for feature analysis to obtain surround sound field feature parameters;

[0031] Specifically, the position coordinate data is input into the spatial mapping layer of the sound field analysis model in the DSP chip to perform three-dimensional sound field modeling and room acoustic characteristics analysis. The sound field distribution in the user's environment is simulated. Through the acoustic model, a three-dimensional sound field distribution matrix is ​​established according to the actual position of the user and the physical characteristics of the room (such as the material and shape of the wall, ceiling, and floor) to describe the propagation and reflection of the sound source in the space. This matrix reflects the different paths of sound waves from the speaker to the user's position, as well as the attenuation and reflection effects of sound in these paths. The motion trajectory data is input into the motion prediction layer of the sound field analysis model for Kalman filtering and trajectory fitting analysis. Kalman filtering is a prediction algorithm used in dynamic systems that optimizes the motion trajectory under uncertain conditions to more accurately predict the user's future position. Through Kalman filtering, the system can estimate the user's position change trend based on historical motion data and reduce the impact caused by sensor errors or external interference. Trajectory fitting smoothes the user's motion trajectory and infers the user's motion path through a mathematical model, which helps to improve the accuracy of the prediction and obtain the user's position prediction vector. This vector describes the user's position at a certain moment in the future. Combined with the position coordinate data and motion trajectory prediction, it provides more accurate positioning information for subsequent sound field adjustment. The sound field spatial distribution matrix is ​​subjected to sound path difference calculation and sound pressure attenuation analysis to obtain the sound path compensation parameters. The sound path difference calculation is based on the distance difference between different speakers and the user's position, and calculates the time difference for the sound to reach the user. Since the sound wave will attenuate as the distance increases during the propagation process, the sound pressure attenuation analysis can estimate the change in sound intensity on different paths. Through calculation, the sound path compensation parameters are obtained to ensure that the sound signal emitted by each speaker has equal intensity when it reaches the user's ear, avoiding the volume imbalance caused by the distance difference. The sound path compensation parameters and the user position prediction vector are input into the delay calculation layer of the sound field analysis model for phase difference analysis and group delay calculation. Phase difference analysis can detect the phase difference between different speakers, ensure that the sound signals output by multiple channels can be accurately aligned in time, avoid audio phase interference, and ensure the clarity of sound quality. The group delay calculation focuses on the signal propagation delay in different frequency bands, and ensures the consistency of the audio signal in the frequency domain by adjusting the delay. Get the multi-channel time delay parameters. Perform frequency domain decomposition and energy distribution analysis on the sound field spatial distribution matrix to obtain the sound pressure equalization coefficient. Frequency domain decomposition decomposes the audio signal into different frequency components, so as to analyze the energy distribution of each frequency band. The sound propagation characteristics of different frequencies are different. Some frequency bands attenuate faster, while some frequency bands propagate farther. Through energy distribution analysis, according to the characteristics of different frequency bands, the sound pressure equalization coefficient of each frequency band is calculated. The multi-channel time delay parameters and sound pressure equalization coefficient are input into the gain calculation layer of the sound field analysis model to perform channel energy balance and dynamic range control to obtain the multi-channel gain matrix.According to the delay and sound pressure equalization coefficient of each channel, the gain values ​​of different channels are adjusted to ensure that the sound output by each channel achieves the best energy balance in the space. Dynamic range control is to avoid excessive compression or distortion of sound signals and ensure that the volume is always kept within a reasonable range in different scenarios, thereby ensuring natural and clear sound quality. The multi-channel delay parameters and multi-channel gain matrix are optimized for the sound field to obtain the sound field optimization vector. By combining delay and gain adjustment, the output of each channel is optimized to achieve the best spatial sound field effect. The propagation path and attenuation characteristics of the sound are corrected by the algorithm to ensure that users can get the best surround sound experience at any position. The optimized sound field optimization vector is processed by feature fusion and parameter normalization to obtain the surround sound field characteristic parameters.

[0032] S3, receiving the original audio signal through the BT chip, and performing multi-channel separation and time domain equalization processing on the original audio signal by the DSP chip to obtain a multi-channel pre-processed signal;

[0033] It should be noted that the original audio signal is received through the BT chip, and the sampling rate conversion and error correction are performed on it to obtain the preprocessed audio signal. The BT chip receives wireless audio signals from Bluetooth devices (such as mobile phones, computers, etc.). Since the received audio signals may have different sampling rates and noise interference, the audio signals are sampled and converted to ensure that they meet the requirements of audio processing. At the same time, during the receiving process, the audio signal will be affected by interference and errors. The error correction algorithm is used to correct the deviation in the signal to obtain a more accurate preprocessed audio signal. The preprocessed audio signal is input into the preamplifier of the DSP chip for gain compensation and dynamic range compression to obtain a gain compensation signal. The function of the preamplifier is to increase the amplitude of the signal to ensure that the signal strength in the subsequent processing process is moderate. Dynamic range compression is used to reduce the difference in the extreme high and low parts of the audio signal to avoid excessive or small volume changes during sound output. After gain compensation and dynamic range compression, the amplitude of the signal is more balanced. The gain compensation signal is bandpass filtered and Gaussian filtered to obtain a noise suppression signal. The bandpass filter removes low-frequency and high-frequency noise in the signal to ensure that the main frequency band of the signal is retained, while the Gaussian filter helps to remove noise generated by the external environment or the system itself to obtain a clearer and purer signal. Independent component analysis and blind source separation calculation are performed on the noise suppression signal to obtain the separation parameters of each channel. Independent component analysis is a method for processing mixed signals that can effectively separate independent components from multiple mixed signals. In the audio processing process, the audio signals of multiple channels will have cross-interference. Through the blind source separation algorithm, the independent signals of each channel are restored from the mixed signal. Based on the separation parameters of each channel, channel separation is performed to ensure that the audio signals of each channel no longer interfere with each other. The separated channel signals are input into the multi-channel equalizer of the DSP chip, and the equalization compensation parameters are obtained by adjusting the frequency response curve and phase characteristics. The equalizer adjusts its frequency response according to the characteristics of each channel to ensure the consistency and coordination of the output of each channel within the frequency range. Adjusting the phase characteristics effectively avoids phase interference between signals of different channels and improves the overall sound quality. According to the equalization compensation parameters, the signals of each channel are subjected to time domain equalization processing to obtain a time domain equalized signal. The time-domain equalized signal is subjected to waveform reconstruction and channel alignment to obtain a synthesized audio signal. Waveform reconstruction is to ensure the naturalness and continuity of the sound by restoring the details in the audio signal, while channel alignment is to adjust the audio signal according to the delay differences between different channels to ensure that the sound can reach the user's position synchronously when it is transmitted from each channel, thereby enhancing the surround sound effect and sense of space. The synthesized audio signal is subjected to multi-channel formatting and digital quantization processing to obtain a multi-channel pre-processed signal. Multi-channel formatting converts the signal of each channel into a format suitable for speaker array playback, while digital quantization converts analog signals into digital signals to ensure that the audio signal can be efficiently processed and played in a digital audio system.

[0034] S4, inputting the surround sound field characteristic parameters and the multi-channel pre-processed signal into the DSP chip to perform sound pressure balance calculation to obtain the initial channel gain control parameters;

[0035] Specifically, the multi-channel spatial distribution is modeled and analyzed according to the surround sound field characteristic parameters to obtain the sound pressure distribution matrix. The surround sound field characteristic parameters include the user's position information and motion trajectory data, reflecting the relative relationship between the sound source and the user and the acoustic characteristics of the room. Through these parameters, the audio output is spatially modeled to analyze the sound pressure performance of each channel at different spatial positions. The sound pressure distribution matrix describes the propagation and attenuation of the sound emitted by different speakers in the space, including the sound pressure difference and propagation path between multiple channels. On this basis, the sound pressure distribution matrix is ​​mapped in three-dimensional space, considering the actual sound pressure attenuation at each position in the space. Since the sound waves are reflected and absorbed by objects such as walls and ceilings during propagation, these factors will cause the attenuation of sound pressure. Through spatial mapping, the attenuation parameters of the sound pressure are estimated, and the sound output is adjusted according to these attenuation data to ensure that the volume of each channel remains balanced. The multi-channel pre-processed signal is input into the DSP chip, and the spectrum analysis of Fourier transform is performed to obtain the frequency domain energy vector. Fourier transform is an effective method to convert time domain signals into frequency domain signals, which can reveal the energy distribution of each frequency component in the audio signal. Through spectrum analysis, the energy distribution of each frequency band is identified, and the impact of different frequency bands on sound pressure and sound quality is analyzed. Energy compensation calculation is performed on the sound pressure attenuation parameters and frequency domain energy vectors. By comparing the attenuation parameters and frequency domain energy vectors, the energy compensation coefficient of each channel is calculated to compensate for the volume difference caused by spatial attenuation and frequency imbalance. The result of the energy compensation calculation is the channel compensation coefficient, which reflects the adjustment requirements of each channel in different frequency bands and spatial positions. The channel compensation coefficient is used to balance the energy between channels to ensure that the energy of the sound output by different channels is consistent in space. By balancing the energy of each channel, it is avoided that the volume of some channels is too large or too small, and each channel can be output at an appropriate volume. Based on the surround sound field characteristic parameters, the channel balance parameters are optimized for the sound field. By analyzing the sound pressure distribution and frequency domain energy characteristics, the audio output of each channel is adjusted to ensure that users can get the best listening experience at any position. After optimization, the channel gain vector is obtained, which represents the gain value of each channel and is dynamically adjusted according to the change of the user's position. Perform dynamic range control on the channel gain vector to obtain the gain control coefficient. Dynamic range control can prevent the volume from being too high or too low, ensuring the naturalness and clarity of the audio signal. Through dynamic range control, the sound signal is ensured not to be distorted at various volume levels, and can be flexibly adjusted according to different environments and user needs. Perform real-time optimization and phase correction on the gain control coefficient to obtain the target gain data. Real-time optimization dynamically adjusts the gain control coefficient by continuously monitoring the user's motion status and environmental changes, ensuring that the sound system always outputs audio signals with the best performance.Phase correction adjusts the phase difference between channels to ensure that the sound is synchronized when it is transmitted from different speakers to the user's ears, avoiding audio distortion and phase interference. Based on the target gain data, channel synchronization processing is performed to ensure that the audio signals of multiple channels are accurately aligned and output synchronously to obtain the initial channel gain control parameters.

[0036] S5, power amplifying the multi-channel pre-processed signal according to the initial channel gain control parameter, and driving the speaker array to output a surround sound signal;

[0037] Among them, the low-frequency, intermediate-frequency and high-frequency channels in the multi-channel preprocessed signal are divided according to the initial channel gain control parameters to obtain the divided-frequency gain data. Since the frequency range of the audio signal is wide, the propagation characteristics of sounds in different frequency bands are different in space, so it is necessary to effectively divide the low-frequency, intermediate-frequency and high-frequency channels. The divided-frequency processing can split the audio signal according to the frequency, and the signal of each frequency band enters a different processing path, and the gain adjustment and power amplification are performed respectively. By adjusting the gain of each frequency band, the sound of different frequency components has a suitable volume and clarity when output. According to the initial channel gain control parameters, the divided-frequency gain data reflects the gain requirement of each frequency band signal, ensuring that the intensity of the audio signal in each frequency band is balanced. The power distribution calculation is performed on the divided-frequency gain data to obtain the power control matrix. The power control matrix is ​​used to describe how to reasonably distribute the power of the audio signal between different channels and frequency bands. Since the sounds of different frequency bands have different energy requirements in the speaker, the power control matrix helps the system to dynamically adjust the power distribution according to the channel gain and frequency band characteristics to ensure that the sound of each frequency band can be properly driven. Through the power control matrix, accurate power regulation is achieved on each channel to ensure the energy balance between low frequency, medium frequency and high frequency of the output sound. The multi-channel pre-processed signal is amplified in sections according to the power control matrix to obtain the pre-amplified signal. In this process, according to the power allocation result of each frequency band, the audio signals of different frequency bands are amplified in sections to increase the amplitude of the signal and ensure that the output of the audio signal can reach the appropriate volume level. The pre-amplified signal is clipped and overloaded to avoid distortion caused by excessive signal or overload. Peak clipping ensures that the peak value of the audio signal does not exceed the set maximum value, thereby avoiding sound distortion or system damage. At the same time, the overload protection mechanism will automatically limit the signal when the amplitude is too high to protect the system from overload damage. Through these protection measures, the output of the audio signal is always within a safe working range. The power limit data is temperature compensated and voltage adjusted. Temperature compensation is because the working state of the power amplifier will be affected by the ambient temperature, and voltage regulation is to ensure that the system can still work stably under different voltage conditions. These compensation and adjustment measures help maintain the stability of the system and avoid performance degradation due to temperature changes or voltage fluctuations. A protection control signal is obtained to reflect the control requirements after temperature, power limit and voltage adjustment. Impedance matching and phase compensation are performed based on the protection control signal to obtain driving data. Impedance matching is to ensure impedance matching between the speaker and the power amplifier, thereby improving power transmission efficiency and reducing energy loss. Phase compensation is to ensure that the signal phases between different speaker units are consistent to avoid phase interference and ensure clear and stable sound. Through impedance matching and phase compensation, the best driving conditions are provided for each speaker unit.Based on the driving data, power is allocated to the woofer, midrange unit and tweeter in the speaker array. Audio signals of different frequency bands correspond to different units of the speaker array. Low-frequency signals drive the woofer, mid-frequency signals drive the midrange unit, and high-frequency signals drive the tweeter. Through reasonable power distribution, it is ensured that each speaker unit can effectively output the required frequency components, ensuring the balance of the surround sound effect and the optimization of the sound quality. The speaker driving signal is input into the power driving circuit for amplification and output to obtain the surround sound signal. The function of the power driving circuit is to amplify the signal and provide enough power to drive each unit in the speaker array, and finally obtain the surround sound signal. These surround sound signals can drive the speaker array to produce high-quality sound.

[0038] S6, detecting the spatial sound field data of the surround sound signal through the acoustic sensor, and dynamically optimizing the initial channel gain control parameters through the DSP chip to obtain the target channel gain control parameters.

[0039] Specifically, the sound pressure level and frequency distribution of the surround sound signal are collected through the acoustic sensor array to obtain the spatial sound field sampling data. The acoustic sensor array collects sound signals at different positions in the space through multiple sensors, and can capture the intensity (sound pressure) and frequency distribution of the sound. The signal processing unit of the DSP chip performs signal-to-noise ratio analysis and spectrum extraction on the spatial sound field sampling data to obtain the spatial sound field feature data. Signal-to-noise ratio analysis is used to evaluate the clarity and quality of the signal in the collected sound field data, and can remove unnecessary interference introduced by environmental noise or the noise of the device itself. Spectrum extraction converts the sound field data into frequency domain representation and analyzes the energy distribution and response characteristics of different frequency bands. Through spectrum analysis, it is identified which frequency bands have stronger sounds in the space, and which frequency bands are unclear due to attenuation or reflection problems. The sound field uniformity analysis and sound pressure distribution calculation of the spatial sound field feature data are performed to obtain real-time sound field distribution parameters. The sound field uniformity analysis aims to evaluate whether the sound distribution in different areas is balanced to avoid the phenomenon that the sound in some areas is too strong or too weak. The sound pressure distribution calculation quantifies the propagation and attenuation of sound in space to ensure that the volume level at each location can be reasonably controlled. The real-time sound field distribution parameters can accurately reflect the state of the current sound field, including the distribution of sound pressure changes and frequency responses. The real-time sound field distribution parameters are compared with the preset sound field balance model to obtain the sound field deviation data. The preset sound field balance model is a standard sound field model obtained through engineering design and testing based on the sound field distribution characteristics under ideal conditions. By comparing the real-time sound field distribution with this standard model, the unbalanced or distorted parts in the sound field are identified and the deviation data is calculated. The gain compensation coefficient is calculated and the channel balance analysis is performed on the sound field deviation data to obtain the gain correction vector. By analyzing the deviation data, the gain adjustment required for each channel and frequency band is determined to compensate for the unbalanced sound field. The channel balance analysis ensures that the audio signals between multiple channels can be output in a coordinated manner to avoid some channels being too prominent or uneven in volume. Through this step, the gain correction value of each channel in different frequency bands is calculated. The gain correction vector and the initial channel gain control parameter are updated to obtain the updated gain parameter. The updated gain parameters are subjected to inter-channel phase synchronization and energy balance calculation to obtain the channel balance matrix. Inter-channel phase synchronization ensures that the sound output of each speaker is completely consistent in time, avoiding audio distortion or loss of spatial sense caused by phase differences. Energy balance calculation ensures that the volume output of different channels is balanced in the entire space, avoiding that some channels are too strong or too weak. Through the channel balance matrix, each channel can be accurately adjusted. The target channel gain control parameters are generated according to the channel balance matrix.

[0040] In one example, the user's motion data is collected through a human body sensing sensor, and the MCU chip performs triangulation positioning analysis on the user's motion data to obtain position coordinate data and motion trajectory data, including:

[0041] The motion state of the user is sampled through the human body sensing sensor to obtain a motion sampling signal, and the motion sampling signal is input into the digital filter in the MCU chip for noise elimination and signal enhancement to obtain the user's motion data;

[0042] Perform triangulation coordinate solution and spatial mapping on the user's motion data to obtain position coordinate data, and perform spatiotemporal feature analysis on the position coordinate data to obtain a motion feature vector;

[0043] Based on the motion feature vector, the user's motion trajectory is fitted and analyzed in terms of direction to obtain the motion trajectory vector, and the position coordinate data and the motion trajectory vector are weightedly fused to obtain the initial fused data;

[0044] The motion state of the initial fusion data is identified to obtain motion state parameters, and the motion trajectory of the initial fusion data and the motion state parameters is integrated to obtain motion trajectory data.

[0045] In this example, the motion state of the user is sampled by a human body sensing sensor to obtain a motion sampling signal, and the motion sampling signal is subjected to noise elimination and signal enhancement to improve data quality. The human body sensing sensor senses the user's motion state by measuring acceleration, angular velocity or force sensors. The motion sampling signal is input to the digital filter in the MCU chip to achieve noise elimination. The digital filter filters out high-frequency noise or irrelevant low-frequency components in the input signal and retains important motion information. Filtering methods include low-pass filtering, high-pass filtering or band-pass filtering. Assume that the input motion signal is , after passing through the filter, the output signal is , expressed as: ;

[0046] in, represents the transfer function of the filter, is the input raw motion signal, is the filtered signal. After noise elimination, the obtained signal is used as the user's motion data The MCU chip performs triangulation coordinate calculation and spatial mapping on the motion data. Triangulation determines the target position by measuring the relative position relationship between multiple sensors. In a multi-sensor system, the user's position coordinates are calculated by triangulation using the known sensor coordinates and the signal strength or arrival time received by the sensor. Assume that there are three sensors located , and The user's actual location is , triangulation positioning is calculated using the following formula:

[0047] ;

[0048] ;

[0049] ;

[0050] in, , and are the distances between the user and the three sensors. By solving these three equations, we can get the user's position coordinates . Perform spatiotemporal feature analysis on the position coordinate data to extract important information related to motion, such as speed, acceleration, and motion direction. In order to perform spatiotemporal feature analysis, a combination of time domain analysis and frequency domain analysis is used to extract spatiotemporal features in motion signals. The spatiotemporal feature is represented as a feature vector ,in Indicates the user's speed, represents the user's acceleration, Indicates the direction of the user's movement. The velocity and acceleration are obtained through the first-order derivative and the second-order derivative respectively:

[0051] ;

[0052] ;

[0053] Through these spatiotemporal features, the user's motion state can be better described. , perform fitting calculation and direction analysis on the user's motion trajectory to obtain the motion trajectory vector. Motion trajectory vector ,in and Indicates the user's location coordinates at a certain moment. Indicates the user's movement direction, and is fitted and calculated through the user's movement data. Trajectory fitting methods include polynomial fitting or least squares method. Assuming that the least squares method is used to fit the trajectory curve, the goal is to use a set of time series observation data Minimize the error function: ;

[0054] in, represents the trajectory prediction value calculated according to the motion model, is the actual observed value. By minimizing the error function , and get the best fitting value of the motion trajectory. After obtaining the motion trajectory vector, the position coordinate data and the motion trajectory vector are weighted fused to obtain the initial fusion data. The weighted fusion is performed by weighted averaging method. Assuming that the position coordinate data is , the motion trajectory vector is , weighted fusion is expressed as: ;

[0055] in, is a weighting coefficient between 0 and 1. Through weighted fusion, the system can comprehensively consider the position coordinates and motion trajectory information to obtain a more accurate initial fusion data. The initial fusion data is used to identify the motion state and determine the user's current motion state, such as still, walking, or running. The motion state parameters are identified by a classification method based on machine learning. For example, by training a classifier, such as a support vector machine or a random forest, the current motion state is predicted based on previous motion data. Assume that the motion state parameters output by the classifier are , indicating the current motion state of the user. The initial fusion data and motion state parameters are integrated into the motion trajectory to obtain the final motion trajectory data. The trajectory data under different motion states are processed to adapt to the user's current behavior mode. For example, if the user is currently in a stationary state, the motion trajectory data will change slightly; if the user is walking or running, the trajectory data will change significantly.

[0056] In one example, the position coordinate data and the motion trajectory data are input into the sound field analysis model in the DSP chip for feature analysis to obtain surround sound field feature parameters, including:

[0057] The position coordinate data is input into the space mapping layer of the sound field analysis model in the DSP chip to perform three-dimensional sound field modeling and room acoustic characteristics analysis to obtain the sound field spatial distribution matrix;

[0058] The motion trajectory data is input into the motion prediction layer of the sound field analysis model for Kalman filtering and trajectory fitting analysis to obtain the user position prediction vector;

[0059] Calculate the sound path difference and analyze the sound pressure attenuation on the sound field spatial distribution matrix to obtain the sound path compensation parameters;

[0060] Input the sound path compensation parameter and the user position prediction vector into the delay calculation layer of the sound field analysis model to perform phase difference analysis and group delay calculation to obtain multi-channel delay parameters;

[0061] Perform frequency domain decomposition and energy distribution analysis on the sound field spatial distribution matrix to obtain the sound pressure equalization coefficient;

[0062] Input the multi-channel delay parameters and sound pressure equalization coefficients into the gain calculation layer of the sound field analysis model to perform channel energy balance and dynamic range control, and obtain a multi-channel gain matrix;

[0063] The multi-channel delay parameters and the multi-channel gain matrix are optimized for the sound field to obtain the sound field optimization vector, and the sound field optimization vector is subjected to feature fusion and parameter normalization processing to obtain the surround sound field feature parameters.

[0064] In this example, the position coordinate data is input into the sound field analysis model in the DSP chip for three-dimensional sound field modeling and room acoustic characteristics analysis. The three-dimensional sound field modeling is based on physical models or numerical simulation methods such as acoustic ray tracing, finite difference method or boundary element method. These methods accurately describe the propagation of sound waves in the room. When modeling the sound field, the position coordinate data is input into the sound field analysis model in the DSP chip for three-dimensional sound field modeling and room acoustic characteristics analysis. Input the spatial mapping layer of the sound field analysis model. The model calculates the propagation path and reflection of sound waves in space by combining the position of the user or sound source with the physical characteristics of the room. Represented as a matrix describing the sound field distribution at different points in space. Each element Represents the source location To the receiving point The sound field intensity is related to the direction of the sound source, the reflective surface of the room, and the sound absorption characteristics. Assume that the position of the sound source is The location of the receiving point is , the sound field distribution is approximated by the following formula: ;

[0065] in, represents the sound absorption and reflection coefficient from the source to the receiving point, is the distance from the source to the receiving point, usually calculated as The motion trajectory data is input into the motion prediction layer of the sound field analysis model for Kalman filtering and trajectory fitting analysis to obtain the prediction vector of the user's position. In motion analysis, Kalman filtering is a recursive algorithm used for dynamic system prediction and state estimation, which can estimate the optimal value of the system state based on time series data. Assumptions Represents the user's state vector, including the user's coordinates and speed. The prediction formula of the Kalman filter is: ;

[0066] in, It is at the moment The state prediction, is the state transition matrix, is the control input, is the input matrix. The user position prediction vector is obtained by performing Kalman filtering on the motion trajectory data Provides a prediction of the user's location at the current moment. Performs path difference calculation and sound pressure attenuation analysis on the sound field spatial distribution matrix to obtain path compensation parameters. Path difference calculation refers to the effect of path differences from the source to the receiving point on the propagation of sound waves. By measuring the path length differences between different sound sources and receiving points , determine the acoustic path difference. The acoustic path difference affects the propagation time of the sound wave. The relative position difference of each sound source in space will cause the sound pressure to attenuate. Acoustic path compensation parameters Calculated by the following formula: ;

[0067] in, It is the distance from the sound source to the receiving point, reflecting the degree of sound pressure attenuation. The sound path compensation parameters and the user position prediction vector are input into the delay calculation layer of the sound field analysis model for phase difference analysis and group delay calculation to obtain the multi-channel delay parameters. Group delay refers to the delay difference caused by different frequency components during signal propagation. Phase difference analysis determines the propagation delay of the signal by calculating the phase difference between the source and the receiving point. Assuming that the two sound sources and The acoustic phases are and , then the phase difference It is expressed as: ;

[0068] in, is the distance difference between the sound sources, is the wavelength of the sound wave. Group delay By integrating the frequency, the formula for group delay is: ;

[0069] Through these calculations, the sound field model will obtain multi-channel delay parameters, which are used to control the synchronization of multi-channel audio signals to ensure the coordination of sound between different channels. The sound field spatial distribution matrix is ​​subjected to frequency domain decomposition and energy distribution analysis to obtain the sound pressure equalization coefficient. Frequency domain decomposition is to convert the sound field signal from the time domain to the frequency domain to better analyze the impact of different frequency components on the sound field. Through Fourier transform, the frequency domain representation of the sound field is obtained. ,in is the frequency, the sound pressure equalization coefficient Used to compensate for uneven frequency response. The equalization coefficients are optimized based on the target frequency response and actual measurement data. Assuming the target frequency response is , the actual frequency response is , the calculation formula of the equalization coefficient is: ;

[0070] The multi-channel delay parameters and sound pressure equalization coefficients are input into the gain calculation layer of the sound field analysis model to perform channel energy balance and dynamic range control, and obtain the multi-channel gain matrix. The purpose of the gain matrix is ​​to ensure that the sound output by each channel is evenly distributed in the space and to control the dynamic range of the sound. Assume that the gain of each channel is , the gain matrix is ​​expressed as: ;

[0071] By adjusting the multi-channel gain matrix, the audio system can achieve energy balance between different channels to ensure that the output sound will not be distorted or overly loud. The multi-channel delay parameters and multi-channel gain matrix are optimized for the sound field. The layout of the sound source and the audio output are optimized according to the user's position and movement trajectory, so that the sound propagates evenly and has good directionality in the space, and the sound field optimization vector is obtained. The sound field optimization vector is subjected to feature fusion and parameter normalization processing to finally obtain the surround sound field feature parameters.

[0072] In one example, an original audio signal is received through a BT chip, and a DSP chip performs multi-channel separation and time domain equalization processing on the original audio signal to obtain a multi-channel pre-processed signal, including:

[0073] The original audio signal is received through the BT chip, and the sampling rate is converted and the error is corrected for the original audio signal to obtain a pre-processed audio signal;

[0074] The pre-processed audio signal is input into the pre-amplifier of the DSP chip for gain compensation and dynamic range compression to obtain a gain compensation signal, and the gain compensation signal is subjected to bandpass filtering and Gaussian filtering to obtain a noise suppression signal;

[0075] Performing independent component analysis and blind source separation calculation on the noise suppression signal to obtain channel separation parameters, and performing channel separation based on the channel separation parameters;

[0076] The separated channel signals are input into the multi-channel equalizer of the DSP chip, and the equalization compensation parameters are obtained by adjusting the frequency response curve and the phase characteristics. The time domain equalization processing is performed on the channel signals according to the equalization compensation parameters to obtain the time domain equalization signal;

[0077] The time domain equalization signal is subjected to waveform reconstruction and channel alignment to obtain a synthesized audio signal, and the synthesized audio signal is subjected to multi-channel formatting and digital quantization processing to obtain a multi-channel preprocessed signal.

[0078] In this example, the original audio signal is received through the BT chip, and the sampling rate conversion and error correction are performed on it to obtain the preprocessed audio signal. The Bluetooth audio signal is compressed and transmitted through the Bluetooth protocol stack, which will affect the quality and accuracy of the signal. After receiving the original audio signal, the sampling rate conversion is performed on it to adapt to the subsequent processing flow. The sampling frequency of the audio signal is adjusted to a standard frequency suitable for audio processing. For example, if the sampling rate of the received audio signal is 44.1kHz, and the DSP chip requires a sampling frequency of 48kHz, the signal is sampled and converted by interpolation. Assume that the original signal is , the signal after sampling rate conversion The calculation is done by the following formula: ;

[0079] in, It is the scaling factor of the sampling rate conversion. At the same time, the error correction algorithm is used to adjust the distortion caused by signal compression or transmission process. The error correction method estimates the error in the transmission process through filters and models, and corrects the signal to obtain an audio signal that is closer to the real sound. The pre-processed audio signal is input to the pre-amplifier of the DSP chip for gain compensation and dynamic range compression to obtain a gain compensation signal. The purpose of gain compensation is to adjust the strength of the audio signal to adapt it to subsequent processing. Gain compensation signal It is expressed by the following formula: ;

[0080] in, is a gain factor, which is determined according to the maximum value of the audio signal. Dynamic range compression reduces the dynamic range of the audio signal, so that smaller signals are amplified and larger signals are limited to a set range. Dynamic range compression is achieved by setting a threshold. and compression ratio When the amplitude of the audio signal exceeds the threshold, the compressor adjusts the signal according to the set compression ratio: ;

[0081] in, is the compression threshold, is the compression ratio. After gain compensation and dynamic range compression, the obtained signal is subjected to bandpass filtering and Gaussian filtering for noise suppression. Bandpass filtering is used to retain signals in a specific frequency band (for example, mid-high frequency parts) while removing low-frequency and high-frequency noise. The transfer function of the bandpass filter is expressed as: ;

[0082] in, and is the frequency range of the bandpass filter, and is the center frequency. The Gaussian filter is used to smooth the signal and remove random noise. The filter function is expressed as: ;

[0083] in, is the standard deviation of the Gaussian filter, which controls the smoothness of the filter. After these filtering processes, the noise suppression signal is . Perform independent component analysis and blind source separation calculations on the noise suppression signal to obtain the separation parameters of each channel. Independent component analysis is a statistical signal processing method used to separate independent source signals from multiple mixed signals. In audio signal processing, independent component analysis is used to extract independent channel signals from mixed multi-channel signals. Assume is a mixed audio signal. The goal of independent component analysis is to find a matrix , so that: ;

[0084] in, is the separated independent source signal, is a mixed signal. Through the blind source separation algorithm, the separation parameters of each channel and the respective channel signals are obtained. The separated channel signals are input into the multi-channel equalizer of the DSP chip for processing. The goal of the equalizer is to adjust the frequency response curve and phase characteristics of each channel so that the final audio signal meets the preset sound characteristics. The parameters of the equalizer are adjusted according to the target frequency response, including gain, frequency range and bandwidth. Assume that the target frequency response is The actual frequency response is , then the equalization compensation parameter Calculated by the following formula: ;

[0085] The adjusted signal is compensated by equalization to obtain a time domain equalized signal Compensate accordingly in the time domain. Perform waveform reconstruction and channel alignment on the time domain equalized signal to obtain a synthesized audio signal. Waveform reconstruction is to restore the equalized and processed audio signal to a smooth waveform, so that the audio signal remains coherent in time and avoids mutations or distortion. When performing channel alignment, taking into account the time delay differences between channels, the audio signals of different channels are time-aligned to ensure that they are correctly coordinated and played in space. Aligned signal The calculation is done by the following formula: ;

[0086] in, is a time delay, indicating the alignment deviation of different channel signals. The synthesized audio signal is subjected to multi-channel formatting and digital quantization processing to obtain a multi-channel pre-processed signal. The audio signal is organized into multiple channel data streams for subsequent processing by the audio playback system. Digital quantization converts the continuous analog signal into a discrete digital signal for further operation by the digital audio processing system. The process of digital quantization is expressed as: ;

[0087] in, is the quantization bit depth, and round is the rounding operation. After the above steps, the final multi-channel pre-processed signal is Used to drive speaker arrays for surround sound output.

[0088] In one example, the surround sound field characteristic parameters and the multi-channel pre-processed signal are input into the DSP chip to perform sound pressure balance calculation to obtain the initial channel gain control parameters, including:

[0089] Modeling and analyzing the multi-channel spatial distribution according to the characteristic parameters of the surround sound field to obtain the sound pressure distribution matrix, and performing three-dimensional spatial mapping on the sound pressure distribution matrix to obtain the sound pressure attenuation parameters;

[0090] Input the multi-channel pre-processed signal into the DSP chip for Fourier transform spectrum analysis to obtain the frequency domain energy vector;

[0091] Performing energy compensation calculation on the sound pressure attenuation parameter and the frequency domain energy vector to obtain a channel compensation coefficient, and performing energy balance between channels on the channel compensation coefficient to obtain a channel balance parameter;

[0092] Performing sound field optimization processing on the channel balance parameters based on the surround sound field characteristic parameters to obtain a channel gain vector, and performing dynamic range control on the channel gain vector to obtain a gain control coefficient;

[0093] The gain control coefficient is optimized and phase corrected in real time to obtain target gain data, and channel synchronization processing is performed based on the target gain data to obtain initial channel gain control parameters.

[0094] In this example, the multi-channel spatial distribution is modeled and analyzed based on the surround sound field characteristic parameters. The sound pressure of different channels is analyzed using the sound field theory and environmental acoustic model to obtain the sound pressure distribution matrix. The sound field analysis model establishes the sound field spatial distribution matrix by analyzing the sound source distribution in the space, room acoustic characteristics, reflection and attenuation, and other factors. , whose elements represent the sound pressure at each point in space. Assume that a certain point in space Sound pressure Affected by multiple sound sources, the sound pressure distribution is calculated by the following formula: ;

[0095] in, It is The sound pressure intensity of a sound source, Is the sound source To point The distance is the number of sound sources. The spatial distribution of the sound field is obtained by calculation, and three-dimensional spatial mapping is performed. The sound pressure distribution matrix Mapped into a three-dimensional space coordinate system, taking into account the location of the sound source and the geometry of the room, the sound pressure attenuation parameters are obtained. It is expressed as: ;

[0096] in, The point to which the sound is transmitted The distance is the attenuation factor of the room, which is related to the sound absorption characteristics and frequency of the room. Through the attenuation parameter, the sound pressure attenuation at each point in the space can be more accurately predicted. On this basis, the multi-channel pre-processed signal is input into the DSP chip, and the spectrum analysis of Fourier transform is performed to obtain the frequency domain energy vector . Convert the time domain signal to the frequency domain signal in order to analyze the signal's spectrum. The formula for Fourier transform is: ;

[0097] in, is the time domain signal, is a frequency domain signal. Through Fourier transform, we get the frequency domain energy vector It is the energy value corresponding to each frequency point in the spectrum. This energy vector describes the energy distribution of the signal in the frequency domain. The sound pressure attenuation parameter and the frequency domain energy vector are used to perform energy compensation calculation to obtain the channel compensation coefficient. . The spectral energy of each channel Compensation is performed to balance the energy of different channels and avoid the phenomenon that some channels are too strong or too weak. Channel compensation coefficient Calculated by the following formula: ;

[0098] in, It is The frequency domain energy of each channel, is the attenuation factor corresponding to the channel. By performing energy compensation on each channel, the compensation coefficient obtained ensures the energy balance between channels. The channel compensation coefficient is used to balance the energy between channels to obtain the channel balance parameter The goal of channel balance is to ensure that different channels can produce balanced volume during playback, avoiding the sound of a certain channel being too loud or too soft. Channel balance is calculated using the following formula: ;

[0099] in, It is The sound pressure of each channel, is the channel compensation coefficient. Based on the surround sound field characteristic parameters, the channel balance parameters are optimized, and the frequency response of the channel is adjusted through the optimization algorithm, making the final audio effect more natural and realistic. In channel optimization, the phase difference between different channels, frequency response curve, and spatial layout are considered, and finally an optimized channel gain vector is obtained. . Channel gain vector Used to adjust the volume of each channel to improve the sound quality and enhance the surround feeling. The formula for gain adjustment is: ;

[0100] in, is the attenuation parameter of the corresponding channel, is the sound pressure of the channel, which is adjusted by , optimize the frequency response of the channel and make dynamic adjustments. Perform dynamic range control to obtain the gain control coefficient . Limit the maximum gain of the audio signal to avoid sound overload or distortion. Control coefficient Calculated by the following formula: ;

[0101] in, and These are the minimum and maximum gain limits. Through dynamic range control, the gain of the channel is ensured to be within a reasonable range to prevent signal distortion. Perform real-time optimization and phase correction to obtain target gain data. The purpose of phase correction is to eliminate the phase difference between different channels so that the audio signal can reach the listener's ears more accurately and enhance the sense of space and direction. Phase correction is performed through time delay or phase compensation filter. The final target gain data It is an optimized gain value used to drive the speaker for accurate audio playback. Channel synchronization is performed based on the target gain data to obtain the initial channel gain control parameters.

[0102] In one example, power amplifying a multi-channel preprocessed signal according to an initial channel gain control parameter and driving a speaker array to output a surround sound signal includes:

[0103] Perform frequency division processing on the low-frequency, intermediate-frequency and high-frequency channels in the multi-channel preprocessed signal according to the initial channel gain control parameter to obtain frequency division gain data;

[0104] Performing power allocation calculation on the frequency division gain data to obtain a power control matrix, and performing segmented amplification processing on the multi-channel pre-processed signal according to the power control matrix to obtain a pre-amplified signal;

[0105] Perform peak clipping, amplitude limiting and overload protection on the pre-amplified signal to obtain power limit data, and perform temperature compensation and voltage regulation on the power limit data to obtain a protection control signal;

[0106] Perform impedance matching and phase compensation based on the protection control signal to obtain driving data, and distribute power to the woofer, midrange unit and tweeter in the speaker array according to the driving data to obtain a speaker driving signal;

[0107] The speaker driving signal is input into the power driving circuit for amplification and output to obtain a surround sound signal.

[0108] In this example, the low-frequency, middle-frequency and high-frequency channels in the multi-channel pre-processed signal are divided according to the initial channel gain control parameters, and the signal is filtered in different frequency bands to process the low-frequency (usually 20Hz to 250Hz), middle-frequency (250Hz to 2kHz) and high-frequency (2kHz to 20kHz) signals separately. Assume that the original audio signal is , and its frequency domain expression is , the signal is divided by a bandpass filter to obtain signals of different frequency bands:

[0109] ;

[0110] ;

[0111] ;

[0112] in, and Represents the frequency division results of low frequency, medium frequency and high frequency signals respectively. These filters select the signals of the corresponding frequency bands by setting appropriate cutoff frequencies. The gain of low frequency, medium frequency and high frequency signals is adjusted according to the initial channel gain control parameters. Gain control parameters It is derived based on the surround sound field characteristics, channel compensation coefficients, and spatial sound pressure distribution, and represents the gain adjustment value of each channel. Assume that the initial gain control parameter is , ,and , these gain control parameters are used to adjust the gain of signals in different frequency bands. The gain adjustment process is expressed by the following formula:

[0113] ;

[0114] ;

[0115] ;

[0116] Through gain adjustment, appropriate gain is provided for low-frequency, medium-frequency and high-frequency signals respectively to meet the system's requirements for each channel frequency band. Power distribution calculation is performed on the crossover gain data to obtain the power control matrix , and according to the matrix, the multi-channel pre-processed signal is amplified in sections, optimizing the power distribution of each frequency band in the system so that the power of the output signal can be reasonably distributed to different channels. Power Control Matrix Calculated by the following formula: ;

[0117] in, and They are the power distribution values ​​of each channel in the low frequency, mid-frequency and high frequency parts respectively. is the number of channels. According to the preset gain coefficient and system requirements, the power is reasonably allocated to ensure the volume consistency of each channel. According to the power control matrix, segmented amplification processing is performed to obtain the pre-amplified signal. Appropriate gain processing is performed on the divided audio signal to ensure that the audio signal reaches the desired volume level without distortion. The audio signal of each channel The power allocation matrix and gain control coefficient are used for weighted processing to achieve segmented amplification:

[0118] ;

[0119] ;

[0120] ;

[0121] The pre-amplified signal is subjected to peak clipping and overload protection to prevent the peak value of the audio signal from exceeding the maximum output capacity of the system, thereby avoiding distortion and overload. Peak clipping is performed using hard or soft limiting methods. Assume that the amplitude of the pre-amplified signal is , the peak clipping and amplitude limiting process is realized by the following formula:

[0122] ;

[0123] in, is the maximum amplitude that the system can withstand. This formula is used to control the amplitude of the signal not to exceed , to avoid audio signal overload. Temperature compensation and voltage adjustment are performed during peak clipping to cope with changes in system performance under different environmental conditions. Temperature and voltage will affect the working state of the amplifier, and compensation must be performed before power driving. Temperature compensation and voltage regulation The sensor measures in real time and then adjusts the drive signal according to the actual situation:

[0124] ;

[0125] ;

[0126] Through temperature and voltage compensation, the system can ensure stable operation under different environmental conditions. The compensation signal is integrated into the protection control signal , which is used for subsequent impedance matching and phase compensation. Impedance matching and phase compensation are performed based on the protection control signal to obtain driving data. Impedance matching ensures that the impedance of the speaker matches the output impedance of the power amplifier, thereby improving efficiency and avoiding power loss. Phase compensation corrects the phase difference between different speaker units to ensure that signals in different frequency bands can reach the listener's ears synchronously. After impedance matching and phase compensation, the driving data obtained To the woofers, midrange drivers, and tweeters in the speaker array:

[0127] ;

[0128] ;

[0129] ;

[0130] These drive signals ensure that the woofer, midrange unit and tweeter work in synchronization with the required power and phase. The drive signal is input to the power drive circuit for amplification and output, and finally the surround sound signal is obtained. The power drive circuit uses a class D or class AB amplifier to amplify the drive signal to meet the output requirements of the speaker.

[0131] In one example, the spatial sound field data of the surround sound signal is detected by an acoustic sensor, and the initial channel gain control parameter is dynamically optimized by a DSP chip to obtain the target channel gain control parameter, including:

[0132] The sound pressure level and frequency distribution of the surround sound signal are collected through an acoustic sensor array to obtain spatial sound field sampling data;

[0133] The signal processing unit of the DSP chip performs signal-to-noise ratio analysis and spectrum extraction on the spatial sound field sampling data to obtain spatial sound field feature data;

[0134] Perform sound field uniformity analysis and sound pressure distribution calculation on the spatial sound field characteristic data to obtain real-time sound field distribution parameters, and perform error calculation between the real-time sound field distribution parameters and the preset sound field balance model to obtain sound field deviation data;

[0135] Calculating the gain compensation coefficient and analyzing the channel balance of the sound field deviation data to obtain a gain correction vector, and updating the gain correction vector and the initial channel gain control parameter to obtain an updated gain parameter;

[0136] Inter-channel phase synchronization and energy balance calculation are performed on the updated gain parameters to obtain a channel balance matrix, and target channel gain control parameters are generated according to the channel balance matrix.

[0137] In this example, the spatial sound field data of the surround sound signal is obtained through an acoustic sensor array. The acoustic sensor array is arranged in different spatial positions, which can monitor the propagation of audio signals in all directions in real time and capture the sound pressure level and frequency distribution in the space. The sensor converts the sound pressure value of each sampling point into an electrical signal, and forms spatial sound field sampling data through the signal acquisition system. The sampling data contains the sound pressure intensity and frequency components at different positions, constituting a preliminary representation of a spatial sound field. The sound field sampling data is input into the signal processing unit of the digital signal processing (DSP) chip for analysis. The DSP chip performs signal-to-noise ratio (SNR) analysis on the sampling data to evaluate the quality of the collected data. Signal-to-noise ratio analysis helps to identify and remove background noise, ensuring the accuracy of data for subsequent processing. The signal-to-noise ratio SNR is calculated by the following formula: ;

[0138] in, is the power of the signal, is the power of noise. Through analysis, low signal-to-noise ratio noise data is filtered out and high-quality spatial sound field sampling data is retained. The DSP chip extracts the spectrum of high-quality spatial sound field data, converts the time domain signal into frequency domain representation using Fourier transform, and obtains the energy distribution of different frequency components. Based on the extracted spectrum and sound pressure data, the sound field uniformity analysis is performed to evaluate the uniform distribution of the sound field in space. If the sound field uniformity is poor, the sound quality in some areas will be too high or too low, thus affecting the listening effect. By analyzing the uniformity of the sound field, the calculation results of the sound pressure distribution are obtained, and the real-time sound field distribution parameters are obtained to describe the sound intensity and frequency characteristics of the sound field at different spatial points. The real-time sound field distribution parameters are compared with the preset sound field balance model for error calculation. The preset sound field balance model is a standard model derived from the idealized sound field distribution, representing an ideal surround sound field. By calculating the error between the real-time sound field and the preset model, the sound field deviation data is obtained, revealing the gap between the current actual sound field and the ideal sound field. According to the sound field deviation data, the gain compensation coefficient is calculated. The gain compensation coefficient adjusts the gain of different channel signals according to the deviation data, thereby correcting the balance of the sound field. By calculating and optimizing the compensation coefficient, the unbalanced channels are corrected to ensure that the sound of each channel can be output with appropriate gain to restore the ideal sound distribution. Based on the compensation coefficient, the DSP chip performs channel balance analysis, analyzes the relative strength between the channels, and ensures that their volume is evenly distributed in space. Calculate the gain correction vector. The gain correction vector is a vector that reflects the adjustment amount of the gain of each channel. Update the gain correction vector with the initial channel gain control parameter to obtain the updated gain parameter. Perform phase synchronization and energy balance calculation between channels on the updated gain parameter. Phase synchronization ensures that the signals of different channels are consistent in time to avoid sound interference or sound quality problems caused by phase differences. By adjusting the phase of the signals of each channel, it is ensured that the sound of all channels reaches the listener's position synchronously to avoid sound field distortion caused by different phases between channels. Energy balance means ensuring that the energy output of each channel is relatively balanced, avoiding some channels from outputting too much energy while other channels output too little, and optimizing the overall sound quality. Through the above optimization processing, the channel balance matrix is ​​obtained, which describes the gain, phase and energy distribution of each channel. According to the channel balance matrix, the target channel gain control parameters are generated, thereby achieving precise control of each channel output.

[0139] Reference Figure 2 This embodiment provides a sound pressure adjustment system based on a human body induction speaker, comprising:

[0140] The acquisition module 1 is used to collect user motion data through the human body sensing sensor, and the MCU chip performs triangulation analysis on the user motion data to obtain position coordinate data and motion trajectory data;

[0141] Analysis module 2, used for inputting the position coordinate data and the motion trajectory data into the sound field analysis model in the DSP chip for feature analysis to obtain surround sound field feature parameters;

[0142] The processing module 3 is used to receive the original audio signal through the BT chip, and perform multi-channel separation and time domain equalization processing on the original audio signal by the DSP chip to obtain a multi-channel pre-processed signal;

[0143] Calculation module 4, used for inputting surround sound field characteristic parameters and multi-channel pre-processing signals into the DSP chip to perform sound pressure balance calculation and obtain initial channel gain control parameters;

[0144] an amplifier module 5, for amplifying the multi-channel pre-processed signal according to the initial channel gain control parameter, and driving the speaker array to output a surround sound signal;

[0145] The optimization module 6 is used to detect the spatial sound field data of the surround sound signal through the acoustic sensor, and dynamically optimize the initial channel gain control parameters through the DSP chip to obtain the target channel gain control parameters.

[0146] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0147] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0148] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0149] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0151] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0152] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A sound pressure adjustment method based on a human body induction speaker, characterized in that: The following steps are involved: The user's motion data is collected through the human body sensing sensor, and the MCU chip performs triangulation positioning analysis on the user's motion data to obtain position coordinate data and motion trajectory data; Inputting the position coordinate data and the motion trajectory data into a sound field analysis model in a DSP chip for feature analysis to obtain surround sound field feature parameters; The original audio signal is received through the BT chip, and the DSP chip performs multi-channel separation and time domain equalization processing on the original audio signal to obtain a multi-channel pre-processed signal; Inputting the surround sound field characteristic parameters and the multi-channel pre-processed signal into a DSP chip to perform sound pressure balance calculation to obtain initial channel gain control parameters; Amplifying the multi-channel preprocessed signal according to the initial channel gain control parameter, and driving the speaker array to output a surround sound signal; The spatial sound field data of the surround sound signal is detected by an acoustic sensor, and the initial channel gain control parameter is dynamically optimized by a DSP chip to obtain a target channel gain control parameter.

2. The sound pressure adjustment method based on the human body induction speaker according to claim 1, characterized in that: The user motion data is collected by the human body sensing sensor, and the MCU chip performs triangulation positioning analysis on the user motion data to obtain position coordinate data and motion trajectory data, including: The motion state of the user is sampled by a human body sensing sensor to obtain a motion sampling signal, and the motion sampling signal is input into a digital filter in an MCU chip for noise elimination and signal enhancement to obtain user motion data; Performing triangulation coordinate solution and space mapping on the user motion data to obtain position coordinate data, and performing spatiotemporal feature analysis on the position coordinate data to obtain a motion feature vector; Perform fitting calculation and direction analysis on the user's motion trajectory based on the motion feature vector to obtain a motion trajectory vector, and perform weighted fusion on the position coordinate data and the motion trajectory vector to obtain initial fusion data; The initial fusion data is subjected to motion state recognition to obtain motion state parameters, and the initial fusion data and the motion state parameters are subjected to motion trajectory integration to obtain motion trajectory data.

3. The sound pressure adjustment method based on the human body induction speaker according to claim 2 is characterized in that: The step of inputting the position coordinate data and the motion trajectory data into a sound field analysis model in a DSP chip for feature analysis to obtain surround sound field feature parameters includes: Inputting the position coordinate data into the space mapping layer of the sound field analysis model in the DSP chip to perform three-dimensional sound field modeling and room acoustic characteristic analysis to obtain a sound field spatial distribution matrix; The motion trajectory data is input into the motion prediction layer of the sound field analysis model to perform Kalman filtering and trajectory fitting analysis to obtain a user position prediction vector; Performing sound path difference calculation and sound pressure attenuation analysis on the sound field spatial distribution matrix to obtain sound path compensation parameters; Inputting the sound path compensation parameter and the user position prediction vector into the delay calculation layer of the sound field analysis model to perform phase difference analysis and group delay calculation to obtain multi-channel delay parameters; Performing frequency domain decomposition and energy distribution analysis on the sound field spatial distribution matrix to obtain a sound pressure equalization coefficient; Inputting the multi-channel delay parameter and the sound pressure equalization coefficient into the gain calculation layer of the sound field analysis model to perform channel energy balance and dynamic range control to obtain a multi-channel gain matrix; The multi-channel delay parameters and the multi-channel gain matrix are subjected to sound field optimization to obtain a sound field optimization vector, and the sound field optimization vector is subjected to feature fusion and parameter normalization processing to obtain surround sound field feature parameters.

4. The sound pressure adjustment method based on the human body induction speaker according to claim 3 is characterized in that: The method of receiving the original audio signal through the BT chip and performing multi-channel separation and time domain equalization processing on the original audio signal by the DSP chip to obtain a multi-channel pre-processed signal includes: The original audio signal is received through the BT chip, and the sampling rate is converted and the error is corrected for the original audio signal to obtain a preprocessed audio signal; Inputting the pre-processed audio signal into a pre-amplifier of a DSP chip for gain compensation and dynamic range compression to obtain a gain compensation signal, and performing bandpass filtering and Gaussian filtering on the gain compensation signal to obtain a noise suppression signal; Performing independent component analysis and blind source separation calculation on the noise suppression signal to obtain channel separation parameters, and performing channel separation based on the channel separation parameters; Input the separated channel signals into the multi-channel equalizer of the DSP chip, obtain the equalization compensation parameters by adjusting the frequency response curve and the phase characteristics, and perform time domain equalization processing on the channel signals according to the equalization compensation parameters to obtain the time domain equalized signals; The time domain equalization signal is subjected to waveform reconstruction and channel alignment to obtain a synthesized audio signal, and the synthesized audio signal is subjected to multi-channel formatting and digital quantization processing to obtain a multi-channel preprocessed signal.

5. The sound pressure adjustment method based on human body induction speaker according to claim 4 is characterized in that: The step of inputting the surround sound field characteristic parameters and the multi-channel pre-processed signal into a DSP chip to perform sound pressure balance calculation to obtain initial channel gain control parameters includes: Modeling and analyzing the multi-channel spatial distribution according to the surround sound field characteristic parameters to obtain a sound pressure distribution matrix, and performing three-dimensional spatial mapping on the sound pressure distribution matrix to obtain a sound pressure attenuation parameter; Inputting the multi-channel pre-processed signal into a DSP chip for Fourier transform spectrum analysis to obtain a frequency domain energy vector; Performing energy compensation calculation on the sound pressure attenuation parameter and the frequency domain energy vector to obtain a channel compensation coefficient, and performing energy balance between channels on the channel compensation coefficient to obtain a channel balance parameter; Performing sound field optimization processing on the channel balance parameter based on the surround sound field characteristic parameter to obtain a channel gain vector, and performing dynamic range control on the channel gain vector to obtain a gain control coefficient; The gain control coefficient is optimized and phase corrected in real time to obtain target gain data, and channel synchronization processing is performed based on the target gain data to obtain initial channel gain control parameters.

6. The sound pressure adjustment method based on human body induction speaker according to claim 5, characterized in that: The method of power-amplifying the multi-channel pre-processed signal according to the initial channel gain control parameter and driving the speaker array to output a surround sound signal includes: Perform frequency division processing on the low-frequency, intermediate-frequency and high-frequency channels in the multi-channel preprocessed signal according to the initial channel gain control parameter to obtain frequency division gain data; Performing power allocation calculation on the frequency division gain data to obtain a power control matrix, and performing segmented amplification processing on the multi-channel pre-processed signal according to the power control matrix to obtain a pre-amplified signal; Performing peak clipping, amplitude limiting and overload protection on the pre-amplified signal to obtain power limit data, and performing temperature compensation and voltage regulation on the power limit data to obtain a protection control signal; Perform impedance matching and phase compensation based on the protection control signal to obtain driving data, and distribute power to the woofer, midrange unit and tweeter in the speaker array according to the driving data to obtain a speaker driving signal; The speaker driving signal is input into a power driving circuit for amplification and output to obtain a surround sound signal.

7. The sound pressure adjustment method based on human body induction speaker according to claim 6, characterized in that: The detecting the spatial sound field data of the surround sound signal by an acoustic sensor and dynamically optimizing the initial channel gain control parameter by a DSP chip to obtain the target channel gain control parameter includes: The sound pressure level and frequency distribution of the surround sound signal are collected by an acoustic sensor array to obtain spatial sound field sampling data; The signal processing unit of the DSP chip performs signal-to-noise ratio analysis and spectrum extraction on the spatial sound field sampling data to obtain spatial sound field feature data; Performing sound field uniformity analysis and sound pressure distribution calculation on the spatial sound field characteristic data to obtain real-time sound field distribution parameters, and performing error calculation between the real-time sound field distribution parameters and a preset sound field balance model to obtain sound field deviation data; Performing gain compensation coefficient calculation and channel balance analysis on the sound field deviation data to obtain a gain correction vector, and performing parameter update on the gain correction vector and the initial channel gain control parameter to obtain an updated gain parameter; Inter-channel phase synchronization and energy balance calculation are performed on the updated gain parameters to obtain a channel balance matrix, and target channel gain control parameters are generated according to the channel balance matrix.

8. A sound pressure adjustment system based on a human body induction speaker, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the system comprises: The acquisition module is used to collect user motion data through a human body sensing sensor, and the MCU chip performs triangulation analysis on the user motion data to obtain position coordinate data and motion trajectory data; An analysis module, used for inputting the position coordinate data and the motion trajectory data into a sound field analysis model in a DSP chip for feature analysis to obtain surround sound field feature parameters; A processing module is used to receive the original audio signal through the BT chip, and perform multi-channel separation and time domain equalization processing on the original audio signal by the DSP chip to obtain a multi-channel pre-processed signal; A calculation module, used for inputting the surround sound field characteristic parameters and the multi-channel pre-processed signal into a DSP chip to perform sound pressure balance calculation to obtain initial channel gain control parameters; an amplification module, configured to amplify the multi-channel preprocessed signal according to the initial channel gain control parameter, and drive a speaker array to output a surround sound signal; The optimization module is used to detect the spatial sound field data of the surround sound signal through an acoustic sensor, and dynamically optimize the initial channel gain control parameters through a DSP chip to obtain target channel gain control parameters.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images