Method, device and equipment for enhancing sound effect of automobile sound equipment adaptive to road conditions
By generating environmental feature vectors and dynamically adjusting the audio system parameters, the problem of insufficient acoustic adaptation accuracy in existing technologies is solved, and a high-performance and low-energy-consumption audio system is realized in complex environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- VOYAH AUTOMOBILE TECH CO LTD
- Filing Date
- 2026-01-15
- Publication Date
- 2026-04-10
AI Technical Summary
Existing car audio sound enhancement systems struggle to accurately identify instantaneous changes in the acoustic environment under complex and ever-changing driving conditions, resulting in decreased acoustic adaptation accuracy and lower sound quality.
By integrating vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals to generate an environmental feature vector, the equalizer and noise reduction parameters of the audio system are dynamically adjusted, the sound effect compensation function is calculated, and the sound effect is enhanced through filter compensation values.
It achieves high-precision acoustic adaptation in complex driving environments, improves sound quality, reduces system energy consumption, and extends the service life of the audio system.
Smart Images
Figure CN121842589A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle data processing, specifically to a method, apparatus, device, and computer-readable storage medium for enhancing the sound effects of a car audio system that adapts to road conditions. Background Technology
[0002] In the wave of upgrading the intelligent cockpit experience, car audio sound enhancement systems have become one of the core technologies for high-end models and new energy vehicles to enhance product competitiveness. They directly affect the auditory comfort and immersive experience of users during the driving process, and have extremely high application value and market demand. Current mainstream car audio sound enhancement technologies primarily revolve around three core methods: active noise cancellation algorithms, multi-channel equalization adjustment, and preset sound effect modes. By integrating an in-vehicle microphone array to collect ambient noise signals in real time, and combining this with vehicle driving status data obtained from inertial sensors, the system can achieve basic ambient noise suppression and dynamic sound pressure level adjustment. In steady-state scenarios such as highway cruising and city driving at constant speeds, it can effectively cancel out low-frequency external noise, maintain relatively stable sound quality output, and meet users' basic auditory experience needs. However, existing technical solutions have significant limitations, the core issue being the overly simplistic environmental perception dimension. Current systems can only perceive and respond to limited parameters such as vehicle speed and basic noise intensity, failing to comprehensively capture the complex and ever-changing characteristics of the actual driving environment. For example, in dynamic scenarios such as vehicle acceleration for overtaking, driving on bumpy roads, changes in the number of occupants, and switching between open and closed windows, the system struggles to accurately identify instantaneous changes in the acoustic environment, leading to a significant decrease in acoustic adaptation accuracy. This manifests as fluctuations in noise suppression, sound distortion, and sound field positioning deviation, failing to provide users with a consistently stable, high-quality auditory experience. This severely restricts further upgrades to the acoustic experience of intelligent cockpits, necessitating a breakthrough in the existing perception dimension's technical bottlenecks to construct a more comprehensive environmental perception and acoustic adaptation system. Summary of the Invention
[0003] This application provides a method, apparatus, device, and computer-readable storage medium for enhancing the sound effect of a car audio system that adapts to road conditions. This can solve the technical problem that existing car audio enhancement systems have too simple an environmental perception dimension, resulting in decreased acoustic adaptation accuracy and lower sound quality.
[0004] In a first aspect, embodiments of this application provide a method for enhancing car audio sound effects in response to road conditions, including: By fusing the collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals, environmental feature vector information is generated. The vehicle status information includes suspension vibration signals, vehicle speed, and door status, and the environmental parameters include humidity. The equalizer parameters and noise reduction parameters of the audio system are dynamically adjusted based on the environmental feature vector information to obtain the target equalizer parameters and target noise reduction parameters, so as to calculate the sound effect compensation function. Based on the sound effect compensation function, obtain the frequency response deviation matrix; Based on the obtained occupant distribution model, the speaker power in the audio system is distributed to obtain the speaker power allocation matrix; Based on the loudspeaker power distribution matrix and the frequency response deviation matrix, the filter compensation value is calculated to enhance the sound effect emitted by the audio system.
[0005] In conjunction with the first aspect, in one embodiment, calculating the filter compensation value based on the loudspeaker power distribution matrix and the frequency response deviation matrix to enhance the sound effect emitted by the audio system includes: The minimum mean square algorithm is used to iteratively optimize the loudspeaker power allocation matrix and the frequency response deviation matrix using a finite impulse response structure to calculate the filter compensation value. The sound effects emitted by the audio system are enhanced based on the filter compensation value.
[0006] In conjunction with the first aspect, in one implementation, obtaining the frequency response deviation matrix based on the sound effect compensation function includes: The frequency response deviation value is calculated based on the measured frequency response and the factory-calibrated frequency response of the vehicle. The frequency response deviation matrix is obtained based on the frequency response deviation value and the sound effect compensation function.
[0007] In conjunction with the first aspect, in one implementation, the step of distributing the speaker power in the audio system according to the acquired occupant distribution model to obtain a speaker power allocation matrix includes: An existence matrix is established based on the pressure values of each seat and the visual recognition information of each seat. Based on the existence matrix, the speaker power distribution in the audio system is calculated to obtain the speaker power allocation matrix for the seat.
[0008] In conjunction with the first aspect, in one implementation, the step of dynamically adjusting the equalizer parameters and noise reduction parameters of the audio system based on the environmental feature vector information to obtain target equalizer parameters and target noise reduction parameters, and then calculating the sound effect compensation function, includes: Based on the obtained door state matrix and target transfer function, obtain the sound transfer function library; Based on the environmental feature vector information and the sound transfer function library, the equalizer parameters and noise reduction parameters of the audio system are dynamically adjusted to obtain the target equalizer parameters and target noise reduction parameters. Based on the target equalizer parameters and target noise reduction parameters, the sound effect compensation function is calculated.
[0009] In conjunction with the first aspect, in one implementation, calculating the sound effect compensation function based on the target equalizer parameters and the target noise reduction parameters includes: Obtain the formula for the preset sound effect compensation function; Based on the preset sound effect compensation function formula, the target equalizer parameters, and the target noise reduction parameters, the sound effect compensation function is calculated.
[0010] In conjunction with the first aspect, in one implementation, the step of obtaining an environmental feature vector by fusing the collected vehicle state information, environmental parameters, occupant distribution data, and high-frequency vibration signals includes: Obtain the preset Kalman filter; The pre-set Kalman filter is used to fuse the collected vehicle state information, environmental parameters, occupant distribution data and high-frequency vibration signals to obtain environmental feature vectors.
[0011] Secondly, embodiments of this application provide an adaptive road condition car audio sound enhancement device, the adaptive road condition car audio sound enhancement device comprising: The generation module is used to generate environmental feature vector information by fusing the collected vehicle status information, environmental parameters, occupant distribution data and high-frequency vibration signals. The vehicle status information includes suspension vibration signals, vehicle speed and door status, and the environmental parameters include humidity. The acquisition and calculation module is used to dynamically adjust the equalizer parameters and noise reduction parameters of the audio system based on the environmental feature vector information, acquire the target equalizer parameters and target noise reduction parameters, and calculate the sound effect compensation function. The first acquisition module is used to acquire the frequency response deviation matrix according to the sound effect compensation function; The second acquisition module is used to distribute the speaker power in the audio system according to the acquired occupant distribution model, and obtain the speaker power allocation matrix; The calculation and enhancement module is used to calculate the filter compensation value based on the speaker power distribution matrix and the frequency response deviation matrix, so as to enhance the sound effect emitted by the audio system.
[0012] Thirdly, embodiments of this application provide an adaptive road condition car audio sound enhancement device, which includes a processor, a memory, and an adaptive road condition car audio sound enhancement program stored in the memory and executable by the processor. When the adaptive road condition car audio sound enhancement program is executed by the processor, it implements the steps of the adaptive road condition car audio sound enhancement method as described above.
[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing an adaptive road condition car audio sound enhancement program, wherein when the adaptive road condition car audio sound enhancement program is executed by a processor, it implements the steps of the adaptive road condition car audio sound enhancement method as described above.
[0014] The beneficial effects of the technical solutions provided in this application include: By fusing collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals, an environmental feature vector is generated. The vehicle status information includes suspension vibration signals, vehicle speed, and door status; the environmental parameters include humidity. Based on this environmental feature vector, the equalizer and noise reduction parameters of the audio system are dynamically adjusted to obtain target equalizer and noise reduction parameters, which are then used to calculate a sound effect compensation function. Based on the sound effect compensation function, a frequency response deviation matrix is obtained. The speaker power in the audio system is distributed according to the obtained occupant distribution model to obtain a speaker power allocation matrix. Based on the speaker power allocation matrix and the frequency response deviation matrix, filter compensation values are calculated to enhance the sound effect emitted by the audio system. This solves the technical problem in existing automotive audio sound enhancement systems where the environmental perception dimension is too singular, leading to decreased acoustic fit accuracy and lower sound quality. This approach improves both acoustic fit accuracy and sound quality. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the first embodiment of the adaptive road condition car audio sound enhancement method of this application. Figure 2 This is a schematic diagram of the functional modules of an embodiment of the adaptive road condition car audio sound enhancement device of this application; Figure 3 This is a schematic diagram of the hardware structure of the adaptive road condition car audio sound enhancement device involved in the embodiments of this application. Detailed Implementation
[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.
[0017] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.
[0018] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0019] In a first aspect, embodiments of this application provide a method for enhancing the sound effects of a car audio system that adapts to road conditions.
[0020] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the adaptive road condition car audio sound enhancement method of this application. Figure 1 As shown, the adaptive road condition car audio sound enhancement method includes: Step S10: Based on the fusion processing of the collected vehicle status information, environmental parameters, occupant distribution data and high-frequency vibration signals, an environmental feature vector information is generated. The vehicle status information includes suspension vibration signals, vehicle speed and door status, and the environmental parameters include humidity. This system, as an example, utilizes the CAN bus to acquire suspension vibration signals, vehicle speed, and door opening / closing status. A newly added vibration sensor supplements this with high-frequency road surface information. Simultaneously, it integrates with the air conditioning system's humidity sensor and navigation weather data. Seat pressure sensors and an onboard camera detect occupant position and head displacement. After spatiotemporal alignment processing, this data generates a comprehensive feature vector including road condition type, ambient humidity, and occupant distribution. The CAN bus refers to the Controller Area Network Communication Protocol, which can be implemented using the ISO11898 standard. It is used to acquire vehicle driving status parameters in real time, providing foundational data for acoustic environment modeling. The vibration sensor is a piezoelectric accelerometer, which can be implemented using MEMS technology. It captures the high-frequency road surface vibration spectrum, supplementing features on uneven road surfaces such as gravel roads that cannot be covered by suspension sensors. The humidity sensor is a capacitive humidity detection device, which can be implemented using HIH-4000 series components. It monitors changes in in-vehicle air humidity, providing input parameters for sound wave propagation speed compensation. Spatiotemporal alignment processing refers to the timestamp calibration and spatial coordinate unification of multi-source data. This can be achieved using a Kalman filter algorithm to eliminate data asynchrony caused by differences in sensor sampling frequencies, ensuring spatiotemporal consistency of physical quantities in each dimension of the feature vector. During vehicle operation, suspension vibration amplitude, vehicle speed pulse signals, and door opening / closing angles are continuously acquired via the CAN bus. Simultaneously, newly added vibration sensors capture road impact signals with frequencies higher than 200Hz. The humidity sensor built into the air conditioning system monitors the relative humidity inside the vehicle in real time, and weather data provided by the navigation system is updated synchronously with external environmental parameters. A seat pressure sensor array detects the occupant's seating posture distribution in a matrix format, and an onboard camera tracks the occupant's head coordinates using image recognition algorithms. After all sensor data undergoes timestamp synchronization and spatial coordinate system transformation, a multi-dimensional feature vector containing road roughness level, air humidity percentage, and occupant position coordinates is generated, providing environmental perception input for subsequent acoustic parameter adjustments.
[0021] Specifically, the step of fusing the collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals to obtain an environmental feature vector includes: obtaining a preset Kalman filter; and fusing the collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals according to the preset Kalman filter to obtain an environmental feature vector.
[0022] As an example, the existing sensor signals on the vehicle are defined as ,in: This is a suspension vibration signal. For vehicle speed, The car door is in the current position. For air conditioning humidity, For seat pressure distribution. The newly added lightweight sensor signal is... ,in High-frequency vibration signal Environmental feature vector generation: Multi-source signals are fused using a Kalman filter to generate a comprehensive feature vector. :
[0023] in Road condition characteristics (such as bump level). For ambient humidity, This provides the coordinates for occupant distribution. It effectively solves the problem of insufficient sound field adaptation accuracy caused by the single dimension of environmental perception in existing technologies. By integrating multimodal data such as high-frequency vibration, humidity, and occupant position, it provides accurate environmental feature input for dynamic sound effect adjustment, improves the acoustic compensation effect under bumpy road surfaces and humidity change scenarios, and eliminates the asynchronicity of multi-source data by using spatiotemporal alignment processing to ensure the real-time performance and accuracy of sound field control parameters.
[0024] Step S20: Dynamically adjust the equalizer parameters and noise reduction parameters of the audio system according to the environmental feature vector information, obtain the target equalizer parameters and target noise reduction parameters, and calculate the sound effect compensation function. As an example, based on real-time acquired environmental feature vectors, a pre-stored acoustic boundary condition model is invoked, and a fuzzy logic algorithm is used to generate an equalizer frequency band gain adjustment strategy. For instance, when high-speed wind noise is detected, high-frequency noise bands are automatically attenuated while mid-to-low frequency music signals are enhanced; when encountering bumpy road sections, low-frequency resonance noise is suppressed and the dynamic compression ratio is optimized to ensure consistent sound quality under different road conditions. The acoustic boundary condition model refers to a pre-established dataset containing acoustic transfer functions under different door opening and closing states. This model can be constructed using experimental measurements or numerical simulations to reflect the characteristics of the in-vehicle acoustic environment as the physical structure changes. The fuzzy logic algorithm is a reasoning method for handling uncertainties and nonlinear relationships. It can be implemented using membership functions and rule bases to map multi-dimensional environmental parameters to equalizer adjustment strategies. The equalizer frequency band gain adjustment strategy refers to the amplification or attenuation scheme for different frequency ranges of the audio signal. This can be implemented using digital signal processing algorithms to specifically suppress noise bands and enhance effective signals.
[0025] During vehicle operation, environmental feature vectors are input to the dynamic sound effect adjustment module in real time. The acoustic boundary condition model calls the corresponding acoustic transfer function based on the current door state, and the fuzzy logic algorithm converts road condition type, vibration intensity, and noise spectrum characteristics into frequency band gain adjustment parameters. For example, when high-speed driving increases the high-frequency components of wind noise, the system automatically reduces the high-frequency gain of the equalizer while increasing the output intensity of mid-to-low frequency music signals to maintain music clarity; in low-frequency resonance scenarios caused by bumpy roads, a dynamic compression algorithm suppresses energy peaks in specific frequency bands to prevent distortion in the audio system.
[0026] Compared to existing technologies, which rely solely on vehicle speed or noise signals for sound effect compensation, this approach fails to comprehensively address the multi-dimensional coupling relationship between door status, vibration spectrum, and noise characteristics. This solution integrates acoustic boundary condition models and fuzzy logic algorithms to achieve dynamic adaptation to complex acoustic environments, resolving the insufficient adjustment accuracy of traditional methods in scenarios with changing door opening / closing states or complex noise. This application can automatically match the optimal frequency band adjustment strategy under different driving conditions, effectively suppressing specific frequency band interference caused by wind noise and road vibration, while maintaining the intensity of the main frequency band of the music signal, ensuring stable output of in-vehicle sound quality under various road conditions. Furthermore, this application proposes a passenger presence detection and resource scheduling step, including determining whether each seat has passengers using seat pressure sensors and visual recognition, dividing the vehicle interior into independent acoustic control zones, automatically reducing the power of the corresponding speaker and redistributing computing power to occupied areas when a zone is empty, and dynamically adjusting the drive power based on speaker temperature to avoid overheating losses. Seat pressure sensors are pressure sensing devices used to detect passenger weight, specifically employing piezoelectric thin film or strain gauge sensors, determining seat occupancy through changes in pressure distribution. Visual recognition refers to capturing occupant contours and head movements using in-vehicle cameras. This can be achieved using infrared cameras combined with image recognition algorithms to assist in confirming occupant presence and head position. Independent acoustic control zones refer to sound field control units divided based on occupant distribution. This can be achieved through speaker group division and independent configuration of acoustic parameters, ensuring that acoustic optimization in each zone does not interfere with each other. Computational power reallocation refers to dynamically adjusting audio processing resources based on occupant distribution. This can be achieved using multi-core processor task scheduling strategies, concentrating computing resources from idle areas to occupied areas to improve sound processing accuracy. Dynamic temperature-adjusted drive power refers to adjusting output power based on speaker temperature. This can be achieved using temperature sensors combined with a power amplifier closed-loop control circuit to prevent performance degradation or damage due to overheating.
[0027] Specifically, occupant presence detection is achieved through a collaboration between seat pressure sensors and an onboard camera. The pressure sensors detect changes in seat pressure, while the camera captures the occupant's silhouette and head position. The data from both systems is fused to generate an occupant distribution profile. When an empty seating area is detected, the speaker power in that area is reduced to a preset threshold, for example, by shutting down the subwoofer or reducing high-frequency gain. Simultaneously, computing resources for that area are reallocated to occupied areas, such as by increasing the frequency of the sound field localization algorithm in occupied areas. Furthermore, the speaker drive power is dynamically adjusted based on real-time temperature data. When the temperature sensor detects that the speaker temperature exceeds a safe threshold, the drive current is automatically reduced and a cooling mechanism is activated to ensure safe hardware operation.
[0028] Compared to existing technologies, current audio systems continue to drive the corresponding speakers at full power even when some seats are empty, leading to energy waste and thermal management challenges. This solution, through occupant presence detection and dynamic resource scheduling, maintains high power output and high computing power only in occupied areas, while switching to low-power mode in unoccupied areas, effectively reducing overall energy consumption and heat accumulation. Simultaneously, the dynamic temperature adjustment mechanism prevents speaker performance degradation or hardware damage due to overheating, extending equipment lifespan. This application solves the energy waste and thermal management problems caused by rigid hardware resource scheduling in existing technologies, achieving dynamic power allocation and computing resource optimization based on occupant distribution. It reduces system energy consumption while ensuring sound quality and improves hardware reliability through an adaptive temperature adjustment mechanism, extending the audio system's lifespan.
[0029] Specifically, based on the obtained door state matrix and target transfer function, a sound transfer function library is obtained; based on the environmental feature vector information and the sound transfer function library, the equalizer parameters and noise reduction parameters of the audio system are dynamically adjusted to obtain target equalizer parameters and target noise reduction parameters; based on the target equalizer parameters and target noise reduction parameters, a sound effect compensation function is calculated.
[0030] Exemplary acoustic boundary condition model: defining the door state matrix ( (Indicates door opening and closing), corresponding to the sound transfer function library. ,in Indicates loudspeaker Take your seat The transfer function. Fuzzy logic adjustment strategy: input feature vector. Equalizer adjustment matrix is generated using a fuzzy rule base. and noise reduction parameters :
[0031] in This is the frequency band gain matrix. This is the noise reduction coefficient.
[0032] Specifically, the step of calculating the sound effect compensation function based on the target equalizer parameters and the target noise reduction parameters includes: obtaining a preset sound effect compensation function formula; and calculating the sound effect compensation function based on the preset sound effect compensation function formula, the target equalizer parameters, and the target noise reduction parameters.
[0033] Exemplary sound compensation function Defined as:
[0034] in This indicates parameter coupling operations, used to suppress noise bands and enhance effective signals.
[0035] Step S30: Obtain the frequency response deviation matrix according to the sound effect compensation function; As an example, when the vehicle starts or idles, a wideband test signal is played through the built-in microphone to collect the actual response of each speaker and compare it with the factory calibration data. The frequency response deviation is calculated, and an adaptive compensation filter is generated based on this deviation to correct the amplifier output signal in real time. This compensates for sound quality degradation caused by aging of interior materials or speaker performance decline, and the self-calibration process does not affect the real-time processing of audio signals. The wideband test signal refers to a swept-frequency signal covering the audible frequency range of 20Hz to 20kHz, which can be generated using linear frequency modulation or a pseudo-random sequence, and is used to comprehensively test the speaker response characteristics in different frequency bands. The factory calibration data refers to the speaker frequency response curves recorded under standard acoustic conditions when the vehicle leaves the factory. This can be obtained through measurements in an anechoic chamber and serves as an acoustic performance benchmark for subsequent deviation calculations. The frequency response deviation refers to the difference between the speaker frequency response in actual use and the factory scale. This can be calculated using a fast Fourier transform to determine the frequency domain energy difference, and is used to quantify the deformation of interior materials or the degree of speaker aging.
[0036] Specifically, obtaining the frequency response deviation matrix based on the sound effect compensation function includes: calculating the frequency response deviation value based on the measured frequency response and the factory-calibrated frequency response of the vehicle; and obtaining the frequency response deviation matrix based on the frequency response deviation value and the sound effect compensation function.
[0037] Exemplary method for obtaining measured frequency response Calculate the measured frequency response The difference between the values is taken as the frequency response deviation value. The frequency response deviation value is then used to calculate the frequency response deviation matrix using the sound effect compensation function. .
[0038] Step S40: Distribute the speaker power in the audio system according to the obtained occupant distribution model to obtain the speaker power allocation matrix; The exemplary occupant presence detection and resource scheduling steps include using seat pressure sensors and visual recognition to determine whether each seat is occupied, dividing the vehicle interior into independent acoustic control zones. When a zone is empty, the corresponding speaker power is automatically reduced and computing power is redistributed to occupied zones. Simultaneously, drive power is dynamically adjusted based on speaker temperature to prevent overheating and power loss. The seat pressure sensor is a pressure sensing device used to detect occupant weight, which can be implemented using piezoelectric thin film or strain gauge sensors. Changes in pressure distribution indicate whether a seat is occupied. Visual recognition involves capturing occupant contours and head movements using an onboard camera, which can be implemented using an infrared camera combined with image recognition algorithms to assist in confirming occupant presence and head position. The independent acoustic control zone refers to a sound field control unit divided based on occupant distribution. This can be achieved through speaker group division and independent configuration of acoustic parameters, ensuring that acoustic optimization in each zone does not interfere with each other. Computing power redistribution refers to dynamically adjusting audio processing resources based on occupant distribution. This can be achieved using multi-core processor task scheduling strategies, concentrating computing resources from idle zones to occupied zones to improve sound processing accuracy. The dynamic temperature-adjusted drive power refers to adjusting the output power based on the speaker temperature. This can be achieved using a temperature sensor in conjunction with a closed-loop control circuit for the power amplifier, preventing performance degradation or damage due to overheating. Occupant presence detection is accomplished through a combination of seat pressure sensors and an onboard camera. The pressure sensor detects changes in seat pressure, while the camera captures the occupant's silhouette and head position. The data is then fused to generate an occupant distribution profile. When an empty seating area is detected, the speaker power in that area is reduced to a preset threshold, for example, by shutting down the subwoofer or reducing high-frequency gain. Simultaneously, computing resources for that area are reallocated to occupied areas, such as increasing the frequency of the sound field localization algorithm in occupied areas. Furthermore, the speaker drive power is dynamically adjusted based on real-time temperature data. When the temperature sensor detects that the speaker temperature exceeds a safe threshold, the drive current is automatically reduced and a cooling mechanism is activated to ensure safe hardware operation.
[0039] Existing audio systems continue to drive the corresponding speakers at full power when some seats are empty, leading to energy waste and thermal management challenges. This solution, through occupant presence detection and dynamic resource scheduling, maintains high power output and high computing power only in occupied areas, while switching to low-power mode in unoccupied areas, effectively reducing overall energy consumption and heat accumulation. Simultaneously, the dynamic temperature adjustment mechanism prevents speaker performance degradation or hardware damage due to overheating, extending equipment lifespan. This application solves the energy waste and thermal management problems caused by rigid hardware resource scheduling in existing technologies, achieving dynamic power allocation and computing resource optimization based on occupant distribution. It reduces system energy consumption while maintaining sound quality and improves hardware reliability through an adaptive temperature adjustment mechanism, extending the audio system's lifespan.
[0040] Specifically, the step of distributing the speaker power in the audio system according to the acquired occupant distribution model to obtain a speaker power allocation matrix includes: establishing an existence matrix based on the acquired pressure values of each seat and the visual recognition information of each seat; and distributing the speaker power in the audio system according to the existence matrix to obtain a speaker power allocation matrix for each seat.
[0041] Exemplary occupant presence detection and resource scheduling Crew distribution model: Seat pressure and visual recognition Establish an existence matrix :
[0042] in Indicates seat Are there any passengers?
[0043] Speaker power distribution matrix: based on Generate power allocation matrix ,satisfy:
[0044] in .
[0045] Step S50: Calculate the filter compensation value based on the loudspeaker power distribution matrix and the frequency response deviation matrix to enhance the sound effect emitted by the audio system.
[0046] An exemplary adaptive compensation filter refers to a digital filter with inverse frequency response characteristics. Specifically, it can employ a finite impulse response filter structure, dynamically adjusting the filter coefficients using a least mean square algorithm to offset detected frequency response deviations. Real-time correction of the amplifier output signal refers to superimposing a compensation signal during audio playback. This can be achieved using a feedforward control architecture, performing compensation filtering operations during digital signal processing to avoid introducing additional delays. Specifically, during vehicle startup or prolonged idling, a wideband test signal is generated by a built-in sweep frequency signal generator in the audio system and played sequentially through each speaker. Built-in microphones located on the roof or dashboard synchronously collect the acoustic response, comparing the collected signal with pre-stored factory calibration data in the frequency domain. By calculating the sound pressure level differences at each frequency point, a frequency response deviation matrix containing frequency offset and phase error is generated. Based on this deviation matrix, the compensation filter parameters are iteratively updated using a least mean square error algorithm, forming an inverse compensation curve that matches the current acoustic environment. A compensation filter is embedded in the audio processing link to perform real-time pre-distortion processing on the music signal output by the power amplifier, compensating for frequency response abnormalities caused by changes in the sound absorption coefficient of interior materials or aging of speaker diaphragms. The self-calibration process is automatically executed when the vehicle is not in motion, and a time-division multiplexing mechanism is used to ensure that audio playback and self-test signals do not interfere with each other.
[0047] Existing audio systems rely solely on fixed acoustic parameters at the factory, lacking periodic self-checking mechanisms and failing to detect sound field distortion caused by speaker performance degradation or interior material aging. This solution establishes a closed-loop acoustic detection system through a built-in test signal generation and response acquisition module, enabling dynamic identification of hardware performance changes and generating targeted compensation strategies. Compared to traditional manual calibration methods, this solution achieves fully automated online calibration, avoiding the need for interior material disassembly or reliance on professional equipment, while ensuring consistent sound quality through real-time filtering correction. This application effectively solves the problem of sound field distortion caused by speaker performance degradation and interior material deformation due to long-term use. Through periodic self-checking and dynamic compensation mechanisms, it maintains stable frequency response characteristics of the audio system throughout its entire lifespan, avoiding sound quality degradation phenomena such as high-frequency attenuation or low-frequency resonance. The seamless integration of the compensation filter and audio signal processing link ensures that the sound optimization process does not affect the real-time performance of music playback, while reducing the frequency of hardware replacement and lowering maintenance costs.
[0048] Specifically, the step of calculating the filter compensation value based on the speaker power allocation matrix and the frequency response deviation matrix to enhance the sound effect emitted by the audio system includes: using a finite impulse response structure to perform a minimum mean square algorithm iterative optimization calculation on the speaker power allocation matrix and the frequency response deviation matrix to calculate the filter compensation value; and enhancing the sound effect emitted by the audio system based on the filter compensation value.
[0049] Exemplary, adaptive compensation filter: based on Generate compensation filter A finite impulse response (FIR) structure is adopted:
[0050] in The filter coefficients are iteratively optimized using the Least Mean Square (LMS) algorithm.
[0051] In this embodiment, environmental feature vector information is generated by fusing collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals. The vehicle status information includes suspension vibration signals, vehicle speed, and door status, while the environmental parameters include humidity. The equalizer and noise reduction parameters of the audio system are dynamically adjusted based on the environmental feature vector information to obtain target equalizer and noise reduction parameters, which are then used to calculate a sound effect compensation function. A frequency response deviation matrix is obtained based on the sound effect compensation function. The speaker power in the audio system is distributed according to the obtained occupant distribution model to obtain a speaker power allocation matrix. Finally, a filter compensation value is calculated based on the speaker power allocation matrix and the frequency response deviation matrix to enhance the sound effect emitted by the audio system. This solves the technical problem in existing automotive audio sound enhancement systems where the environmental perception dimension is too singular, leading to decreased acoustic fit accuracy and lower sound quality. This approach improves both acoustic fit accuracy and sound quality.
[0052] Secondly, embodiments of this application also provide an adaptive car audio sound enhancement device.
[0053] In one embodiment, reference is made to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of the adaptive road condition car audio sound enhancement device of this application. Figure 2 As shown, the adaptive road condition car audio sound enhancement device includes: The generation module 10 is used to generate environmental feature vector information by fusing the collected vehicle status information, environmental parameters, occupant distribution data and high-frequency vibration signals. The vehicle status information includes suspension vibration signals, vehicle speed and door status, and the environmental parameters include humidity. The acquisition and calculation module 20 is used to dynamically adjust the equalizer parameters and noise reduction parameters of the audio system according to the environmental feature vector information, acquire the target equalizer parameters and target noise reduction parameters, and calculate the sound effect compensation function. The first acquisition module 30 is used to acquire the frequency response deviation matrix according to the sound effect compensation function; The second acquisition module 40 is used to distribute the speaker power in the audio system according to the acquired occupant distribution model and obtain the speaker power allocation matrix. The calculation and enhancement module 50 is used to calculate the filter compensation value based on the loudspeaker power distribution matrix and the frequency response deviation matrix, so as to enhance the sound effect emitted by the audio system.
[0054] Furthermore, in one embodiment, the calculation and enhancement module 50 is used for: The minimum mean square algorithm is used to iteratively optimize the loudspeaker power allocation matrix and the frequency response deviation matrix using a finite impulse response structure to calculate the filter compensation value. The sound effects emitted by the audio system are enhanced based on the filter compensation value.
[0055] Furthermore, in one embodiment, the second acquisition module 40 is used for: The frequency response deviation value is calculated based on the measured frequency response and the factory-calibrated frequency response of the vehicle. The frequency response deviation matrix is obtained based on the frequency response deviation value and the sound effect compensation function.
[0056] Furthermore, in one embodiment, the first acquisition module 30 is used for: An existence matrix is established based on the pressure values of each seat and the visual recognition information of each seat. Based on the existence matrix, the speaker power distribution in the audio system is calculated to obtain the speaker power allocation matrix for the seat.
[0057] Furthermore, in one embodiment, the acquisition and calculation module 20 is used for: Based on the obtained door state matrix and target transfer function, obtain the sound transfer function library; Based on the environmental feature vector information and the sound transfer function library, the equalizer parameters and noise reduction parameters of the audio system are dynamically adjusted to obtain the target equalizer parameters and target noise reduction parameters. Based on the target equalizer parameters and target noise reduction parameters, the sound effect compensation function is calculated.
[0058] Furthermore, in one embodiment, the adaptive road condition car audio enhancement device also includes a new module for: Obtain the formula for the preset sound effect compensation function; Based on the preset sound effect compensation function formula, the target equalizer parameters, and the target noise reduction parameters, the sound effect compensation function is calculated.
[0059] Furthermore, in one embodiment, the generation module 10 is used to: Obtain the preset Kalman filter; The pre-set Kalman filter is used to fuse the collected vehicle state information, environmental parameters, occupant distribution data and high-frequency vibration signals to obtain environmental feature vectors.
[0060] The functions of each module in the aforementioned adaptive road condition car audio sound enhancement device correspond to the steps in the aforementioned adaptive road condition car audio sound enhancement method embodiment, and their functions and implementation processes will not be described in detail here.
[0061] Thirdly, this application provides an adaptive road condition car audio enhancement device, which can be a personal computer (PC), laptop computer, server or other device with data processing capabilities.
[0062] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the adaptive road condition car audio enhancement device involved in the embodiments of this application. In the embodiments of this application, the adaptive road condition car audio enhancement device may include a processor, a memory, a communication interface, and a communication bus.
[0063] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.
[0064] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the adaptive road condition car audio enhancement device, as well as interfaces used for interconnecting the adaptive road condition car audio enhancement device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.
[0065] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0066] The processor can be a general-purpose processor, which can call the adaptive road condition car audio enhancement program stored in memory and execute the adaptive road condition car audio enhancement method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the adaptive road condition car audio enhancement program is called can be referred to the various embodiments of the adaptive road condition car audio enhancement method of this application, and will not be repeated here.
[0067] Those skilled in the art will understand that Figure 3 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0068] Fourthly, embodiments of this application also provide a computer-readable storage medium.
[0069] The present application provides a computer-readable storage medium storing an adaptive road condition car audio enhancement program, wherein when the adaptive road condition car audio enhancement program is executed by a processor, it implements the steps of the adaptive road condition car audio enhancement method described above.
[0070] The method implemented when the adaptive road condition car audio sound enhancement program is executed can be referred to in the various embodiments of the adaptive road condition car audio sound enhancement method of this application, and will not be repeated here.
[0071] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0072] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.
[0073] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.
[0074] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.
[0075] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.
[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.
[0077] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for enhancing car audio sound effects in response to road conditions, characterized in that, include: By fusing the collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals, environmental feature vector information is generated. The vehicle status information includes suspension vibration signals, vehicle speed, and door status, and the environmental parameters include humidity. The equalizer parameters and noise reduction parameters of the audio system are dynamically adjusted based on the environmental feature vector information to obtain the target equalizer parameters and target noise reduction parameters, so as to calculate the sound effect compensation function. Based on the sound effect compensation function, obtain the frequency response deviation matrix; Based on the obtained occupant distribution model, the speaker power in the audio system is distributed to obtain the speaker power allocation matrix; Based on the loudspeaker power distribution matrix and the frequency response deviation matrix, the filter compensation value is calculated to enhance the sound effect emitted by the audio system.
2. The adaptive road condition car audio sound enhancement method as described in claim 1, characterized in that, The step of calculating filter compensation values based on the loudspeaker power distribution matrix and the frequency response deviation matrix to enhance the sound effects emitted by the audio system includes: The minimum mean square algorithm is used to iteratively optimize the loudspeaker power allocation matrix and the frequency response deviation matrix using a finite impulse response structure to calculate the filter compensation value. The sound effects emitted by the audio system are enhanced based on the filter compensation value.
3. The adaptive road condition car audio sound enhancement method as described in claim 1, characterized in that, The step of obtaining the frequency response deviation matrix based on the sound effect compensation function includes: The frequency response deviation value is calculated based on the measured frequency response and the factory-calibrated frequency response of the vehicle. The frequency response deviation matrix is obtained based on the frequency response deviation value and the sound effect compensation function.
4. The adaptive road condition car audio sound enhancement method as described in claim 1, characterized in that, The step of distributing the speaker power in the audio system according to the obtained occupant distribution model to obtain a speaker power allocation matrix includes: An existence matrix is established based on the pressure values of each seat and the visual recognition information of each seat. Based on the existence matrix, the speaker power distribution in the audio system is calculated to obtain the speaker power allocation matrix for the seat.
5. The adaptive road condition car audio sound enhancement method as described in claim 1, characterized in that, The step of dynamically adjusting the equalizer parameters and noise reduction parameters of the audio system based on the environmental feature vector information, obtaining the target equalizer parameters and target noise reduction parameters, and calculating the sound effect compensation function includes: Based on the obtained door state matrix and target transfer function, obtain the sound transfer function library; Based on the environmental feature vector information and the sound transfer function library, the equalizer parameters and noise reduction parameters of the audio system are dynamically adjusted to obtain the target equalizer parameters and target noise reduction parameters. Based on the target equalizer parameters and target noise reduction parameters, the sound effect compensation function is calculated.
6. The adaptive road condition car audio sound enhancement method as described in claim 5, characterized in that, The step of calculating the sound effect compensation function based on the target equalizer parameters and the target noise reduction parameters includes: Obtain the formula for the preset sound effect compensation function; Based on the preset sound effect compensation function formula, the target equalizer parameters, and the target noise reduction parameters, the sound effect compensation function is calculated.
7. The adaptive road condition car audio sound enhancement method as described in claim 1, characterized in that, The step involves fusing the collected vehicle status information, environmental parameters, occupant distribution data, and high-frequency vibration signals to obtain an environmental feature vector, including: Obtain the preset Kalman filter; The pre-set Kalman filter is used to fuse the collected vehicle state information, environmental parameters, occupant distribution data and high-frequency vibration signals to obtain environmental feature vectors.
8. A car audio sound enhancement device that adapts to road conditions, characterized in that, The adaptive road condition car audio sound enhancement device includes: The generation module is used to generate environmental feature vector information by fusing the collected vehicle status information, environmental parameters, occupant distribution data and high-frequency vibration signals. The vehicle status information includes suspension vibration signals, vehicle speed and door status, and the environmental parameters include humidity. The acquisition and calculation module is used to dynamically adjust the equalizer parameters and noise reduction parameters of the audio system based on the environmental feature vector information, acquire the target equalizer parameters and target noise reduction parameters, and calculate the sound effect compensation function. The first acquisition module is used to acquire the frequency response deviation matrix according to the sound effect compensation function; The second acquisition module is used to distribute the speaker power in the audio system according to the acquired occupant distribution model, and obtain the speaker power allocation matrix; The calculation and enhancement module is used to calculate the filter compensation value based on the speaker power distribution matrix and the frequency response deviation matrix, so as to enhance the sound effect emitted by the audio system.
9. A car audio sound enhancement device that adapts to road conditions, characterized in that, The adaptive road condition car audio enhancement device includes a processor, a memory, and an adaptive road condition car audio enhancement program stored in the memory and executable by the processor, wherein when the adaptive road condition car audio enhancement program is executed by the processor, it implements the steps of the adaptive road condition car audio enhancement method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an adaptive road condition car audio enhancement program, wherein when the adaptive road condition car audio enhancement program is executed by a processor, it implements the steps of the adaptive road condition car audio enhancement method as described in any one of claims 1 to 7.