54 results about "Roadway noise" patented technology

Techniques for automatic parameter tuning of active road noise cancellation systems are described herein. The system can automatically search for an optimal set of algorithm parameters based on recorded data. An active road noise cancellation algorithm and simulation can be embedded in an auto-differentiation framework, which allows gradients of the algorithm parameters to guide the automatic search and calculations of the algorithm parameters.

A method, an apparatus, a storage medium and a computer program product for noise prediction of a vehicle model. There is provided a method for noise prediction of a vehicle model, which includes: training a noise prediction model based on historical test data of the vehicle and corresponding historical road noise data; obtaining predictive road noise data by inputting test data of the vehicle model into a trained noise prediction model; and obtaining contributions of various parameters in the test data to the predictive road noise data by analyzing the predictive road noise data with a SHAP explaining module.

Various implementations include audio systems and related approaches for providing road noise cancelation (RNC). Certain implementations include a method of training a machine learning (ML) based road noise cancelation (RNC) system for a vehicle, the method including: providing inputs to the ML based RNC system, the inputs obtained from: a set of ear-mounted microphones on a user of the vehicle, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, wherein the inputs from the set of ear-mounted microphones on the user approximate detected road noise by the user; adapting a set of parameters defining noise cancelation signals in the ML based RNC system based on the inputs; and generating noise cancelation signals for output by the at least one transducer based on the adapted set of parameters.

Various implementations include a method of training a road noise cancelation (RNC) system for a vehicle, including: providing inputs to RNC system, the inputs obtained from: a set of ear-mounted microphones on a user, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, the inputs from the set of ear-mounted microphones on the user approximating a signal detected by the ears of the user; adapting a set of parameters in the RNC system defining an estimated signal detected at respective ears of the user based on the inputs; and generating at least one of the following for input during an operating mode of the RNC system: estimated ear microphone signals based on the adapted set of parameters, or a set of projection filters for use in determining an estimated ear signal at the respective ears of the user.

This invention discloses a method for predicting traffic conditions in large-scale urban road networks based on graph neural networks. The method first acquires road network topology and traffic time-series data, extracts road hierarchy information, and constructs graph data. The core lies in building a spatiotemporal graph neural network model that integrates dedicated road hierarchy components: a road hierarchy embedding layer maps discrete levels to continuous vectors to enhance feature semantics; a hierarchical global context module aggregates the global states of similar roads with linear complexity; and a dynamic cross-level attention module models the dynamic interaction dependencies between roads of different levels. This method suppresses noise interference from low-level roads through a hierarchical differentiation strategy and uses a separate weighted loss function for training. This invention effectively solves the problems of insufficient global perception, missing interaction modeling, and significant noise interference caused by neglecting road hierarchy semantics in existing technologies, significantly improving the accuracy and robustness of traffic condition prediction for large-scale urban road networks.

A hydrophone may be deployed in a wellbore to collect sounds that may be used to identify whether a wellbore is safe to operate. This hydrophone may include acoustic sensors that sense noise generated by motion of the hydrophone and may sense noise indicative of a defect that could lead to catastrophic failure of a wellbore. Noise generated by movement of the hydrophone may be classified as "road noise" and noise associated with wellbore defects may be classified being "signals of interest." The presence of "road noise" may interfere with the collection of "signals of interest" and because of this, evaluations performed on data that includes "road noise" may result in inaccurate determinations and a decrease in safety. As such, systems and methods of the present disclosure are directed to improving safety of a wellbore by removing "road noise" more effectively while increasing quality of "signals of interest."

This application discloses a road noise cancellation method, apparatus, device, and readable storage medium. The method includes the steps of: acquiring a first vibration signal of real-time noise from a single-axis accelerometer; generating a first anti-vibration signal based on the first vibration signal; and using the first anti-vibration signal to control a loudspeaker to play the first anti-noise signal to eliminate road noise. This application reduces the path for acquiring vibration signals, thereby lowering the bandwidth requirements of multi-axis accelerometers during data transmission and improving signal transmission efficiency.

The invention discloses an automobile road noise processing method and device, and relates to the technical field of noise reduction, and the method comprises the steps: dynamically determining a target amplitude limiting parameter value according to obtained influence factor information, the influence factor information comprising environment state information and / or sensor state information, the environment state information is information representing the environment state inside and / or outside the automobile, and the sensor state information is information representing the working state of a sensor used for collecting a reference signal and / or an error signal; generating an anti-noise signal based on the input first time domain reference signal and the first time domain error signal by adopting a pre-adaptive algorithm; and performing amplitude limiting on the anti-noise signal according to the target amplitude limiting parameter value, and outputting the anti-noise signal subjected to the amplitude limiting so as to control the road noise. According to the invention, the system stability and safety are ensured while a good noise reduction effect is ensured.

The utility model provides a kind of road noise barrier based on sound-absorbing material, including barrier support assembly and barrier body, barrier support assembly includes the barrier support post of pair arrangement, and the sliding slot upper end of barrier support post is connected with buffer sliding block, and one barrier hanging pole is connected between buffer sliding block, barrier hanging pole is connected with barrier assembly support by hinged link, and barrier assembly support is connected with barrier body by bolt.The utility model in barrier body is converted into heat energy dissipation by the sound hole hole of sound hole board body, and the sound-absorbing micropore on microporous attraction cloth layer is based on micro-perforated plate theory, and the sound-absorbing coefficient is excellent in full frequency band, especially in low frequency part, further improve sound-absorbing effect, significantly improve the sound-absorbing performance of low frequency band by sound-absorbing cotton layer, and the porous structure of sound-absorbing cotton layer effectively insulates internal air convection and heat radiation, significantly reduce heat loss, while large aperture area absorbs and reflects sound wave, effectively reduce noise.

Disclosed are a vehicle road noise control method and system based on active noise cancellation, electronic equipment, and a storage medium. The vehicle road noise control method comprises: collecting a multi-channel reference signal of vehicle road noise; generating a control signal according to a filter coefficient at current moment and the multi-channel reference signal, and feeding the control signal to a sound reproduction device of a vehicle; collecting sound signals at a plurality of sampling positions in a vehicle compartment to obtain a vector of an error signal; filtering the reference signal to obtain a filtered reference signal; writing the filtered reference signal into a matrix form; and updating the filter coefficient according to the matrix form of the filtered reference signal and the vector of the error signal. Active noise cancellation can be carried out on road noise caused by friction between vehicle tires and the road surface, noise pollution in the vehicle is reduced, and a relatively high convergence speed and a relatively excellent accuracy are achieved.

The present application relates to a kind of computing method for realizing wide range dynamic noise map system fast update, comprising the following steps: using the data information of system database, based on existing day and night period and city road grade, simplify the structure of traditional road sound level mechanism model;Using big data technology, using hybrid modeling method, regression each grade road sound source (day and night) sound level contribution amount prediction model;Road noise fast calculation method based on sound level contribution amount prediction model is constructed, the fast update of dynamic noise map is realized.Compared with prior art, while ensuring the calculation precision, the operation and update efficiency of noise map system is greatly improved, and the technical constraints of foreign core computing software are broken, the localization of noise map core computing technology is realized, which provides technical support for wide range deep development of noise map.

The present disclosure relates to an information processing device, an information processing method, and a program with which it is possible to realize superior noise cancellation. A tracking information acquisition unit acquires tracking information pertaining to the head of an occupant inside the compartment of a vehicle. A filter selection unit selects, from among noise cancellation filters generated on the basis of position information pertaining to the head, a noise cancellation filter corresponding to the tracking information. A noise cancellation unit outputs, from a speaker provided within the compartment, a noise cancellation signal generated on the basis of the selected noise cancellation filter and a sensor value obtained from an acceleration sensor mounted on the vehicle. The technology according to the present disclosure can be applied to, for example, a road noise cancellation system.

Various implementations include noise cancelation (NC) systems and related approaches for NC. Certain implementations include a system including: a vehicle audio system including at least one transducer for providing an audio output to an occupant in a vehicle; a position sensor for indicating a position of the occupant in the vehicle; a set of additional sensors including an accelerometer; and a machine learning (ML) based road noise cancelation (RNC) system connected with the vehicle audio system and the position sensor, the ML based RNC system configured to: receive inputs from the vehicle audio system, the position sensor, and the accelerometer; apply a set of parameters defining noise cancelation signals based on the inputs; and generate noise cancelation signals for output by the at least one transducer based on the applied set of parameters.

The utility model discloses an urban road noise reduction device, which belongs to the technical field of traffic noise treatment and comprises a road body, sound insulation and noise reduction components are arranged on two sides of the road body, two sound insulation barrier arc-shaped covers are centripetally arranged, and arc centers face the road body. An air bag noise reduction bag used for absorbing noise and reducing sound wave propagation vibration is bonded to the inner wall of each sound insulation barrier arc-shaped cover, a top noise reduction cover is further arranged between the top ends of the two sound insulation barrier arc-shaped covers, and a noise reduction and energy absorption assembly is arranged in the middle of the opposite side face of each sound insulation barrier plate. The two noise reduction and energy absorption assemblies each comprise a first noise reduction and sound absorption filter screen, and the two first noise reduction and sound absorption filter screens are located on the two sides of the road body correspondingly, so that vehicle noise in the road body is subjected to sound absorption and noise reduction through the first noise reduction and sound absorption filter screens, the air bag noise reduction bag and the top noise reduction cover. Noise can be effectively reflected and blocked, and the noise can be guided to enter the internal sound absorption assembly to be processed.

The invention provides a road noise reduction control method and system based on traffic state statistics, and relates to the field of road noise reduction control, and the method comprises the steps: collecting a real-time traffic state and a real-time associated traffic state of a target road, and predicting and obtaining a predicted traffic state of the target road in a future time period through a traffic state predictor; through a noise feature simulation channel, a simulated noise feature sequence is obtained through analysis according to the predicted traffic state; and setting a reverse sound wave control scheme of the active noise reduction equipment by taking elimination of the analog noise feature sequence as a target, and executing noise reduction control. The objective of the invention is to solve the technical problem that the timeliness and precision of road noise suppression are insufficient due to the fact that a traditional noise reduction control method is difficult to cope with the dynamic change of road traffic noise, and can significantly improve the timeliness and precision of noise reduction scheme setting through real-time traffic prediction and dynamic reverse sound wave control. The problems of time-varying characteristics and complexity of road traffic noise are effectively solved, and accurate road noise reduction control is realized.

An assist band is set to a standard band when there is no large road noise, and is set to a treble band not including a low frequency band but including a high frequency band as compared with the standard band when there is a large road noise. In audio data of a front seat microphone, components outside the assist band are cut by an IIR filter, downsampled by a downsampling unit to a sampling frequency that is twice the upper limit of the assist band, processed by an echo canceller and a howling canceller, upsampled by an upsampling unit, and output to a rear seat speaker. When there is a large road noise, the operation of the howling canceller is invalidated, and the input is output as it is.

The invention discloses a road sound environment emotion influence evaluation method and device, and relates to the technical field of environment evaluation, and the method comprises the steps: carrying out the sampling of various urban road sound environment functional regions, determining the effective dominant audio and dominant sound pressure levels which a plurality of road dominant sound source types belong to, and classifying the effective dominant audio and dominant sound pressure levels into a plurality of road noise types; building a questionnaire platform in combination with the effective dominant audio and collecting subjective scores of a plurality of subjective indexes; acquiring electroencephalogram signals according to the effective dominant audio triggering of each road noise type; correspondingly determining subjective key principal components based on the subjective score of each road noise type; extracting a plurality of spectrum features of each electroencephalogram signal, and correspondingly determining objective key principal components according to the spectrum features of each road noise type; and correspondingly and comprehensively determining the emotion comprehensive index of each road noise type by adopting the subjective key principal component, the objective key principal component, the effective dominant audio and the dominant sound pressure level. And the actual influence of the urban road sound environment on human emotion and cognition can be revealed more comprehensively and accurately.

Various implementations include a method of training a road noise cancelation (RNC) system for a vehicle, including: providing inputs to RNC system, the inputs obtained from: a set of ear-mounted microphones on a user, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, the inputs from the set of ear-mounted microphones on the user approximating a signal detected by the ears of the user; adapting a set of parameters in the RNC system defining an estimated signal detected at respective ears of the user based on the inputs; and generating at least one of the following for input during an operating mode of the RNC system: estimated ear microphone signals based on the adapted set of parameters, or a set of projection filters for use in determining an estimated ear signal at the respective ears of the user.

Techniques for automatic parameter tuning of an active road noise cancellation system are described herein. The system can automatically search a group of optimal algorithm parameters according to recorded data. Active road noise cancellation algorithms and simulations may be embedded in an automatic differential framework that allows gradients of algorithm parameters to guide automatic search and calculation of algorithm parameters.

This invention discloses an in-vehicle selective noise reduction method based on sound field reconstruction, relating to the field of in-vehicle safety warning technology. The method includes: collaboratively collecting in-vehicle acoustic and vibration data through sensors; separating the acoustic and vibration data using a blind source separation algorithm to obtain a voiceprint feature tensor; inputting the voiceprint feature tensor into a convolutional neural network for voiceprint recognition, outputting a safety sound category and a safety sound hazard level; generating adaptive noise reduction parameters based on vehicle speed and safety sound category; assigning the final adaptive noise reduction parameters to seats in different regions; and issuing a physical vibration warning based on the safety sound hazard level to complete the in-vehicle selective noise reduction. This invention significantly improves the detection and recognition accuracy of critical safety sounds in road noise environments; it balances noise suppression and active safety assurance, effectively enhancing the vehicle's perception and response capabilities to emergency sound sources while improving passenger comfort.

The present invention provides a method for evaluating the impact of road traffic noise around a sound-sensitive area. The method divides the road around the sound-sensitive area into multiple noise monitoring units, performs noise monitoring in each noise monitoring unit, and obtains the road noise spectrum of each noise monitoring unit. An environmental noise receiving point is set in the sound-sensitive area; a transfer function T is decentralized and an objective function is established. The covariance matrix of the objective function is obtained, and an eigenvalue vector λ is obtained after principal component analysis. The noise impact contribution estimate w of the road is used. t The calculation model is used to calculate the contribution value of each road, so that the noise impact contribution of different roads around a certain sound-sensitive area to a certain location in the area can be evaluated.

1. Name of the product in this design: Road Noise Testing Vehicle. 2. Purpose of this design: To test the noise generated by road surfaces and bridge expansion joints on automobile tires. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key features: the front view. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted.

The application relates to the technical field of road noise prediction, and discloses a road traffic noise prediction method based on a cellular transmission model, which comprises the following steps: dividing a road into cells, and homogenizing vehicles in the same cell; constructing a motion characteristic data prediction model according to the number of vehicle exchanges of adjacent cells and the average speed of vehicles in the cells; calculating motion characteristic data of vehicles in the cells at the next moment according to the characteristic data prediction model, wherein the motion characteristic data comprises the number of vehicles in the cells, the average speed of the vehicles and the acceleration of the vehicles; obtaining noise data of each cell at a standard distance according to the motion characteristic data and a motor vehicle noise emission model; performing distance attenuation on the noise data of the cells at an effective distance; superimposing vehicle noises in the form of cells to obtain noise values of the cells; and realizing road scale traffic noise prediction, which can effectively solve the problem of prediction accuracy while taking into account the scale of road traffic noise research.

One or more embodiments of the present disclosure provide a simulation test system and method for vehicle road noise cancellation. The simulation test system can include a vehicle road noise cancellation simulation system and a power amplifier. The vehicle road noise cancellation simulation system is configured to simulate a road noise cancellation system in a vehicle environment. The power amplifier is configured to execute a road noise cancellation algorithm and can be in data communication with the vehicle road noise cancellation simulation system. Wherein the vehicle road noise cancellation simulation system transmits acceleration data representative of an acceleration signal and microphone data representative of a microphone signal to the power amplifier as inputs to the road noise cancellation algorithm in the power amplifier. And the vehicle road noise cancellation simulation system can receive loudspeaker data representative of a loudspeaker signal from the power amplifier. Wherein the vehicle road noise cancellation simulation system includes a secondary path simulation model and a signal flow simulation model.

A vehicle-implemented noise cancellation system includes: a noise cancellation system disposed in the vehicle, the noise cancellation system including an adaptive filter that is adjusted based on a reference signal and an error signal, the adaptive filter outputting a noise cancellation signal that, when converted by a speaker into a noise cancellation audio signal, cancels road noise in at least one zone within a cabin of the vehicle; and an adjustment module configured to change a power of the noise cancellation signal or an adaptation rate of the adaptive filter from a first value to a second value through at least one intermediate value between the first and second values ​​based on a time-varying signal indicative of a signal-to-noise ratio of the reference signal relative to a first standard.

The present disclosure provides a system and method for road roughness measurement. A vehicle having a plurality of road noise cancellation sensors captures vibration data as it travels along a road. The vehicle checks whether a set of operating conditions of the vehicle are satisfied. If the set of operating conditions is satisfied, the vehicle determines a portion of the vibration data captured during a time range in which the set of operating conditions is satisfied. The vehicle may then use the portion of the vibration data to extract feature data of the one or more features. The vehicle then uses feature data associated with the one or more features to determine a roughness indicator for the road on which the vehicle is currently traveling. The road roughness indicator measurements may be used to trigger further analysis of the vibration data to determine other parameters of the vehicle.

Various implementations include audio systems and related approaches for providing road noise cancelation (RNC). Certain implementations include a method of training a machine learning (ML) based road noise cancelation (RNC) system for a vehicle, the method including: providing inputs to the ML based RNC system, the inputs obtained from: a set of ear-mounted microphones on a user of the vehicle, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, wherein the inputs from the set of ear-mounted microphones on the user approximate detected road noise by the user; adapting a set of parameters defining noise cancelation signals in the ML based RNC system based on the inputs; and generating noise cancelation signals for output by the at least one transducer based on the adapted set of parameters.

Methods and mechanisms are provided for emulating automotive audio bus (A2B) based accelerometers. A port of an electronic device, which may be implemented using a Field-Programmable Gate Array (FPGA), may be connected downstream from an A2B port of an A2B node of a vehicle. A set of emulated accelerometer data at the electronic device may be selected, and the set of emulated accelerometer data may then be transmitted from the A2B port of the electronic device to the A2B port of the A2B node.

This invention belongs to the field of automotive technology, specifically a method for selecting reference signals in an active control system for vehicle road noise. It includes: 1. Data acquisition; 2. Determining the frequency band to be reduced and the evaluation indicators; 3. Selecting an initial reference signal with the highest coherence to the target noise; 4. Selecting reference signals with the largest information content and including them in the initial reference signal set; 5. Updating the initial reference signal set until all reference signals are included in the initial reference signal set in sequence; 6. Determining the required number of input reference signals, thereby identifying the necessary reference signals from the initial sensor set; 7. Outputting the final reference signal selection result and inputting the result into the active noise control system for effect verification. This invention is based on the Fisher information matrix method, which not only selects the optimal combination of reference signals more efficiently but also provides a basis for determining the number of reference signals.

The present application relates to the technical field of road noise monitoring, in particular to a road noise monitoring device, which comprises a linkage photographing mechanism, a tracking monitoring mechanism, a built-in charging mechanism and a pre-buried wiring mechanism, the linkage photographing mechanism is connected with the tracking monitoring mechanism on one side, the built-in charging mechanism is arranged inside the tracking monitoring mechanism, the pre-buried wiring mechanism is connected with the linkage photographing mechanism on one end, the tracking monitoring mechanism comprises a pre-buried bridge, a meandering track, a launching pile, a climbing launching group, a reset launching group and a noise monitoring frame, the meandering track is distributed on the inner walls of the pre-buried bridge on both sides, the present application can realize the sliding launching process of sequential arrangement through the cooperation of the pre-buried bridge and the meandering track with three groups of noise monitoring frames, so as to track and monitor the road vehicles in the interval after deceleration, and realize the backup and accurate monitoring record of the model data and noise conditions of a single vehicle.