117 results about "Noise field" patented technology

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention proposes an optical cable burial depth detection method based on a distributed optical fiber sensing technology, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting the burial state change data of an optical cable, and the burial state change data comprise temperature data and vibration data; constructing a spatio-temporal data matrix corresponding to the temperature data, carrying out data processing on the spatio-temporal data matrix according to a sliding window algorithm, obtaining spatial temperature distribution data, selecting a plurality of reference features of the spatial temperature distribution data in any time period to construct a feature matrix, and sending the feature matrix into an optical cable temperature-burial depth relation model, obtaining an optical cable prediction depth corresponding to the temperature data; constructing a ground noise environment model based on the vibration data, obtaining a noise field distribution characteristic function corresponding to the ground noise environment model based on the ground noise environment model, performing Fourier transform on the noise field distribution characteristic function to obtain a noise correlation function, and obtaining a ground environment parameter according to the ground environment parameter and the noise correlation function. Calculating an optical cable prediction depth corresponding to the vibration data; and calculating the burying depth of the optical cable based on the optical cable prediction depth corresponding to the temperature data, the optical cable prediction depth corresponding to the vibration data and a depth prediction algorithm. According to the invention, the accuracy of predicting the embedding depth of the optical cable can be improved.

The invention relates to the field of adaptive noise reduction, in particular to an adaptive noise reduction method and system for a multi-mode audio SoC main control chip. The method comprises the following steps: acquiring an original audio input signal according to an SoC main control chip, performing time-frequency domain dual deconstruction and cross-frequency domain noise interference structure analysis, and constructing a full-frequency domain noise interference topology table; carrying out multi-mode interference factor separation on the full-frequency-domain noise interference topology table, carrying out multi-noise environment modeling, and constructing a real-time noise scene model; time sequence noise slope fluctuation modeling is carried out on the real-time noise scene model, dynamic noise reduction response learning optimization is carried out, and a dynamic noise reduction optimization strategy is constructed; and performing neural coding gain and audio boundary line reconstruction according to the original audio input signal to obtain a coding gain key audio signal and a weak audio optimization signal. According to the invention, by flexibly adjusting the noise reduction intensity of the audio signal, the real-time scene noise reduction performance is optimized.

The invention relates to a differential privacy position protection method based on a dynamic attenuation noise field, and the method comprises the steps: obtaining a plurality of candidate regions based on a user track, recognizing different sensitive regions from the plurality of candidate regions based on the behavior characteristics of a user, and calculating the dynamic sensitivity of each sensitive region; wherein the behavior characteristics of the user comprise a stay identifier and access time of the user in the candidate area; constructing a three-dimensional dynamic noise model for generating a dynamically attenuated space-time noise field; the method comprises the following steps: acquiring a position coordinate of a target position in a sensitive area and a corresponding timestamp, generating a corresponding noise amplitude by adopting a three-dimensional dynamic noise model, generating a two-dimensional position offset based on the noise amplitude by adopting a Laplace mechanism, and disturbing the position coordinate of the target position based on the two-dimensional position offset. According to the scheme, adaptive adjustment of the noise amplitude can be realized, and collaborative optimization of strong protection of a high-sensitivity area and high precision of a low-sensitivity area is realized.

The invention discloses a two-dimensional angle estimation method for a random array in a non-uniform noise scene, and the method comprises the steps: obtaining response signals collected by an EMVS array in random geometric distribution, and obtaining a covariance matrix of the response signals; removing the covariance influence of non-uniform noise in the covariance matrix to obtain a noise-free signal covariance tensor estimated value; the noise-free covariance matrix is recovered based on tensor filling, and a third-order PARAFAC tensor is obtained; and decomposing the third-order PARAFAC tensor based on a parallel factorization algorithm to obtain an azimuth angle and a polarization angle of the source signal. According to the method, the two-dimensional angle of the random array in the non-uniform noise scene is estimated based on tensor filling and parallel factorization, the two-dimensional direction-of-arrival estimation precision in the non-uniform noise environment can be improved while the aperture of the array is kept, and the defects in the aspects of noise suppression, calculation complexity and estimation precision in the prior art are overcome.

The invention relates to the technical field of noise recognition, and discloses an intelligent recognition method and system for Bluetooth headset environment noise, and the method comprises the steps: collecting two paths of original audio of a Bluetooth headset, determining the dynamic evolution characteristics of environment noise components, carrying out the frequency domain cepstrum transformation of the environment noise components, and obtaining the frequency domain noise characteristics; carrying out attention fusion on the dynamic evolution features and the frequency domain noise features to obtain fused audio features, and constructing an association feature matrix of the fused audio features; constructing a noise scene candidate set of the Bluetooth headset, and performing noise type screening on the noise of the Bluetooth headset by using the associated feature matrix and the noise scene candidate set to obtain a noise type candidate list; and performing adversarial enhancement processing on the noise corresponding to each noise type in the noise type candidate list to obtain an enhanced noise set, extracting a dynamic characteristic spectrum of the enhanced noise set, and identifying the environmental noise of the Bluetooth headset. According to the invention, the recognition precision of the environment noise of the Bluetooth earphone can be improved.

The invention provides an electrocardiosignal detection method and device, equipment and a storage medium. The electrocardiosignal detection method comprises the following steps: in response to an acquired electrocardiosignal, periodically detecting a target wave in the electrocardiosignal according to a set window; in response to periodically detecting that a target wave in the electrocardiosignal passes a first time, determining abnormal parameter attributes in the target wave based on the parameter attributes of the first number, and determining a second number of the abnormal parameter attributes; based on the second number and the first number of the abnormal parameter attributes of the target waves, resetting the parameter attributes of all the target waves; and detecting a target wave in the electrocardiosignal after the first time based on the reset parameter attribute. By resetting all the parameter attributes of the target wave, the purpose of detecting whether the target wave in the electrocardiosignal is normal or not in real time is achieved, so that it is guaranteed that the detected target wave is a normal signal, and the electrocardiosignal detection accuracy in a high-noise scene is improved.

The invention discloses a noise reduction processing method and device, a vehicle and a storage medium, and relates to the technical field of intelligent cabins, and the method comprises the steps: carrying out the recognition based on an error signal and an accelerometer signal of the vehicle, and obtaining a noise scene where the vehicle is located; decomposing the accelerometer signal into a plurality of sub-band signals which are not overlapped with one another; for each sub-band signal, determining a noise reduction processing parameter corresponding to each sub-band signal based on the energy analysis and noise scene of the sub-band signal; based on the noise reduction processing parameter corresponding to each sub-band signal, performing adaptive filtering on each sub-band signal in combination with the error signal to obtain a noise reduction signal corresponding to each sub-band signal; and carrying out fusion reconstruction on the noise reduction signals corresponding to the plurality of sub-band signals to obtain a full-band noise reduction signal. According to the invention, adaptive filtering and noise reduction are carried out on each sub-band signal based on the noise scene where the sub-band signal is located, efficient division and treatment and response can be carried out on complex and multi-spectral structure noise, the noise reduction effect is improved, and the vehicle use experience feeling of a user is further improved.

The invention discloses a broadband array gain forecasting method based on sound field spatial correlation correction. The method comprises the following steps: carrying out sound field modeling by utilizing marine environment data, calculating a channel frequency response function from a target to each array element position, estimating a correlation coefficient of an array element receiving signal after a useful signal is subjected to channel distortion, and forming a signal correlation coefficient matrix; an isotropic noise field model is used for estimating inter-channel noise correlation coefficients after the processing frequency band deviates from the ideal frequency, and a noise correlation coefficient matrix is formed; and finally calculating the array gain by using the two correlation coefficient matrixes. According to the method, the coupling condition of the underwater acoustic array and the actual marine environment in the working process can be effectively reflected, and the accuracy degree of the array gain calculation result can be improved.

Provided herein are various embodiments related to the design, creation, and use of a folded sheet scaffold for medical devices. The scaffold comprises a shellular porous pseudorandom orientable architecture composed of a continuous folded sheet of topological genus-n. Methods for designing the scaffold involve driving noise from an infinite 3D noise field to define a field with real space, tuning field values based on user input, and subtracting to create thin wall geometry. The methods can be computer-implemented or executed by a processor following instructions stored on a non-transitory computer-readable medium. A computer system for designing the scaffold includes modules for noise-driving, tuning, and subtracting. The disclosure also includes a folded sheet scaffold formed by these methods and a medical device comprising the folded sheet scaffold.

The invention discloses an audio signal measurement method and device, an electronic device and a storage medium, and relates to the field of audio signal measurement, and the method comprises the steps: deploying a detection microphone nearby a tested device, and arranging a reference microphone in a noise field, so as to synchronously collect a mixed signal containing a test signal and noise, and a pure ambient noise reference signal. After the noise of the two signals is subjected to correlation evaluation and the consistency of noise sources is confirmed, dynamically changing noise components are accurately stripped from the signals of the tested equipment by utilizing real-time noise data acquired by the reference microphone and through an adaptive noise cancellation algorithm, so that pure test signals are restored. Therefore, the problem of non-stationary noise pollution is solved through an active signal processing means instead of depending on imperfect physical sound insulation, and accurate and efficient measurement of the performance of the microphone in a noisy environment is realized.

The invention relates to a DAS signal positioning method based on adaptive tensor decomposition and dynamic correction. The method comprises the steps of converting phase difference data into strain rate data, constructing a three-dimensional tensor based on a time domain signal of a space point, solving an optimization problem after constraint modeling, and extracting a de-noised DAS signal. Calculating an initial fault position, and calculating an actual optical path and an apparent position after temperature change based on a thermal expansion effect and a thermo-optic effect; and a dynamic correction algorithm is set, a system coordinate reference is adaptively calibrated, and a final positioning result is calculated. According to the method, original signals are separated into a low-rank background field, a sparse event field and a structured noise field through unsupervised tensor decomposition, and the defects of dependence on labeled data and insufficient complex noise separation are overcome; by establishing a temperature-optical path coupling physical model and a dynamic correction algorithm for real-time cross-correlation calibration, positioning drift caused by environmental factors is accurately compensated, and high-fidelity denoising and accurate positioning of cable line events in a complex environment are realized.

The invention provides an unmanned aerial vehicle identification and tracking method and system based on multi-modal fusion and trajectory modeling, and relates to the technical field of target detection and tracking. Infrared / visible light images and audio signals are synchronously collected in an airport key area, visual and acoustic features are extracted through a deep network after time alignment, and the target detection and tracking accuracy is improved. Outputting respective unmanned aerial vehicle category probabilities and candidate target frames; afterwards, weights are distributed adaptively according to confidence coefficients of audio and visual classification results, category fusion is carried out on a probability level, and unified multi-modal fusion representation is constructed on a feature level, so that relatively high recognition reliability is still kept in a small-target, weak-texture and strong-noise scene; on the basis, the multi-modal fusion feature sequence serves as input, the position and speed of the unmanned aerial vehicle in each time step are constructed into a trajectory state sequence, a sequence generation model outputs a complete trajectory in an autoregressive mode under conditional constraints, and continuous tracking, occlusion interval complementation and future trend prediction of the motion of the unmanned aerial vehicle are achieved.

The invention discloses a large-depth hydrophone amplitude response measurement method based on deep sea environment noise field depth distribution, and the method comprises the steps: receiving a field measurement result of a noise spectrum level through matching a calibrated hydrophone or a standard hydrophone under low pressure, and carrying out the measurement of the amplitude response of a large-depth hydrophone based on a noise field modeling result of a marine environment parameter and a sound field model in a measurement period; the noise field full-depth distribution rule of the tested sea area is obtained, then the difference value between the noise field full-depth distribution rule and the actual measurement result of the hydrophone to be tested is calculated, the difference value is compensated to the amplitude response nominal value of the hydrophone to be tested, and the amplitude response of the hydrophone to be tested in the large-depth environment can be obtained. The invention provides a new way for obtaining the sensitivity of the hydrophone in a deep-sea large-depth environment.

The invention discloses a vibration noise digital twinborn modeling method and system for a large complex, relates to the technical field of computer data processing, and aims at a target complex to perform sensing array deployment and twinborn reconstruction, construct a three-dimensional twinborn body and determine a vibration noise source of the target complex. Discrete aggregation is carried out in a vibration mode, a vibration equation of each aggregation class is mined, an explicit scene migration framework is introduced, modeling explicit requirements of a vibration noise scene are determined, the vibration equations are assisted, vibration noise distribution based on acoustic medium boundaries is carried out on the three-dimensional twin body, and a vibration noise model is generated; the method and the device are used for solving the technical problem of insufficient accuracy and adaptability of dynamic modeling caused by insufficient refinement degree and flexibility of vibration noise modeling for complex building scenes in the prior art. The vibration noise fine modeling and dynamic visual presentation oriented to complex building scenes are realized, and the method has the remarkable advantages of high accuracy and high adaptability.

The invention relates to the technical field of acoustic metamaterial, in particular to an acoustic metamaterial integrating vibration reduction, sound insulation and sound absorption and a preparation method of the acoustic metamaterial. The acoustic metamaterial capable of giving consideration to vibration reduction, sound insulation and sound absorption comprises a sound absorption functional layer, a sound insulation functional layer and a vibration reduction functional layer, the sound absorption function layer adopts a series-parallel Helmholtz resonant cavity coupling porous material and is used for realizing a broadband sound absorption effect; the sound insulation functional layer adopts a polyurethane filling gradient gradual change minimal curved surface and is used for realizing a broadband sound insulation effect; the vibration reduction function layer is coupled based on a band gap mechanism and used for achieving the broadband vibration reduction effect. The function of comprehensively controlling the environmental noise by means of vibration reduction, sound insulation and sound absorption can be achieved, the efficient control capability on the environmental noise is achieved, the design and manufacturing complexity is low, and the device can be conveniently used in various noise occasions.

The invention relates to the technical field of intelligent fault diagnosis, in particular to a bearing fault diagnosis method based on multi-modal fusion and a multi-scale residual network, and the method comprises the steps: building a subway train traction motor bearing experiment platform, collecting the multi-modal data of a motor bearing, carrying out the preprocessing of the multi-modal data based on principal component analysis, and carrying out the fault diagnosis of the motor bearing. A data fusion method is constructed to convert collected multi-modal data into RGB images to serve as input of a model, meanwhile, a multi-scale noise reduction module is constructed, the network is helped to explore multi-scale features and filter out irrelevant information, and the adaptability of a residual network in a noise scene is improved; a dual-scale residual block is constructed, deep and shallow layer features are learned on different scales, and bearing fault information of different spatial dimensions is captured. Building a multi-scale residual network, carrying out the training and verification through the preprocessed multi-modal data, and carrying out the evaluation of a bearing multi-modal sample through a trained model, and obtaining a fault classification result of the subway train traction motor bearing.

The application discloses a rotating machinery fault diagnosis method based on time-frequency domain joint modeling and self-supervised learning, and belongs to the technical field of rotating machinery state monitoring and fault diagnosis, and comprises a vibration signal preprocessing module, a time-frequency domain joint feature modeling module, a self-supervised learning diagnosis module, a confidence check closed loop module and an incremental iteration optimization module; the vibration signal preprocessing module is used for receiving a raw vibration signal of rotating machinery, performing signal standardization preprocessing, generating three types of input data in a time domain, a frequency domain and a time-frequency domain, adaptively weighting fault sensitive dimensions through a channel attention mechanism, comprehensively representing fault evolution information, significantly improving fault feature distinguishability in a variable working condition and a strong noise scene, and guaranteeing the identification ability of early weak faults.

This invention discloses a digital twin modeling method and system for vibration and noise in large-scale complexes, relating to the field of computer data processing technology. For the target complex, a sensor array is deployed and a twin is reconstructed to build a three-dimensional twin. The vibration and noise sources of the target complex are identified. Vibration modes are discretized and aggregated, and vibration equations for each aggregate class are mined. An explicit scene transfer framework is introduced to determine the explicit modeling requirements of the vibration and noise scene. Assisting the vibration equations, vibration and noise distribution based on acoustic medium boundaries is performed on the three-dimensional twin to generate a vibration and noise model. This invention addresses the technical problem in existing technologies where the level of detail and flexibility in vibration and noise modeling for complex architectural scenes is insufficient, leading to inadequate accuracy and adaptability in dynamic modeling. It achieves detailed vibration and noise modeling and dynamic visualization for complex architectural scenes, possessing significant advantages of high accuracy and high adaptability.

The invention provides a cable joint partial discharge fiber bragg grating sound wave intelligent sensing and detection method, and relates to the technical field of cable partial discharge detection, and the method comprises the steps: obtaining multi-channel time-domain spectral response data of a detected cable joint through a fiber bragg grating sensing array, and demodulating the data to obtain a multi-dimensional feature vector group; decomposing the waveform signal into narrowband intrinsic mode components, extracting energy density through Hilbert transform and executing singular value decomposition, and screening dominant mode components to reconstruct the waveform signal to obtain stress wave time sequence characteristics; calculating a waveform high-order cross-correlation tensor as a hyperedge weight, extracting a time-frequency amplitude feature vector as an initial node feature, constructing a space-time hypergraph, and generating a node embedding vector through graph convolution; and initializing a Gaussian noise field based on a node embedding vector and setting condition information, executing reverse sampling denoising to generate a three-dimensional space probability density field to identify an initial positioning coordinate, and iteratively correcting the positioning coordinate and a wave velocity value until a residual error converges.

The present invention relates to a spatial noise reduction technology, and discloses a spatial noise reduction method based on a stepped classroom, comprising: performing similarity recognition on a sound signal and an input signal respectively obtained by a main microphone and a noise reducer of the stepped classroom, and deleting similar signals in the input signal according to the result of the similarity recognition to obtain a noise signal; calculating a resonance frequency according to the three-dimensional information of the stepped classroom, and calculating a control noise signal according to the resonance frequency and a noise suppression algorithm; filtering the noise signal according to a proportional coefficient determined by the control noise signal and the spatial position of the noise reducer to obtain an inverted noise signal, and transmitting the sound signal and the inverted noise signal to a speaker. The present invention also proposes a spatial noise reduction device, an electronic device, and a storage medium based on a stepped classroom. The present invention can improve the noise reduction effect and the accurate noise reduction of the noise field under mixed content.

The invention discloses an ocean three-dimensional temperature and salt field reconstruction method and system, electronic equipment and a medium. The method comprises the steps that a target field and an observation field are constructed; sequentially adding multiple levels of Gaussian noise to each piece of sample data in the standardized target field, and constructing a noise field sample data set; constructing an ocean three-dimensional temperature and salt field reconstruction model comprising a convolution local dependence modeling module, a patch division module, a multi-head attention global dependence modeling module, a patch restoration module and a convolution reconstruction module; training the ocean three-dimensional temperature and salt field reconstruction model by adopting the noise field sample data set to obtain a trained ocean three-dimensional temperature and salt field reconstruction model; and denoising the pure Gaussian noise field by adopting the ocean three-dimensional temperature and salt field reconstruction model which is guided and trained by the observation field to obtain an ocean three-dimensional temperature and salt field reconstruction result, and carrying out de-standardization on the ocean three-dimensional temperature and salt field reconstruction result to obtain a reconstructed ocean three-dimensional temperature and salt field. According to the invention, the accuracy of ocean three-dimensional temperature and salt field reconstruction can be improved.

The invention relates to the technical field of unmanned aerial vehicle acoustic measurement, in particular to a method for acoustically measuring environmental noise of unmanned aerial vehicle mounting equipment, which comprises the following steps of: during the flight of an unmanned aerial vehicle, continuously acquiring original noise signals, and synchronously recording rotating speed, attitude, acceleration and track position data; generating a harmonic sequence according to the rotating speed of the rotor wing, establishing a rotor wing echo template, and adjusting template parameters in combination with a track position and a reflection path; carrying out difference on the original noise and the rotor echo template in a time-frequency domain to obtain an echo residual signal, and carrying out local interpolation on the gap to form a continuous first noise sequence; identifying a wind shear event segment and reconstructing the event segment according to front and back normal segment trends to obtain a second noise sequence; a sound pressure consistency entropy value is calculated in a multi-track intersection area, and a minimum entropy measuring point is selected as a calibration anchor point; and carrying out trend correction on the adjacent track sequences by taking the anchor points as references, constructing a measurement point network diagram, fusing multi-track data, and finally obtaining a noise field of the target area.

The application discloses a speech detection method and device, a terminal equipment and a computer readable storage medium. The method comprises the following steps: performing frame processing on an original audio signal to obtain each frame signal; calculating an energy gradient factor of each frame signal, wherein the energy gradient factor is the sum of energy gradients between adjacent two frequency points in each frequency point of the frame signal; determining a target energy gradient factor threshold corresponding to a target frame signal in each frame signal; and determining that the target frame signal is a speech signal when the energy gradient factor of the target frame signal is greater than or equal to the target energy gradient factor threshold. The application provides a speech detection scheme using frequency domain gradient information of a signal as a feature to improve the stability of a VAD algorithm in a noise scene.

The invention relates to the technical field of artificial intelligence, and discloses a text data preprocessing method suitable for a large financial model, and the method comprises the steps: receiving an original financial text sequence sorted according to timestamps; performing word segmentation and part-of-speech tagging; dynamically calculating an emotional fluctuation index of each text unit based on the sliding time window; self-adaptively setting an emotion noise judgment threshold value and marking candidate noise units; semantic stability is evaluated through embedding space cosine similarity, and noise is confirmed for the second time; after the confirmation noise is removed, anaphora resolution and logic connection reconstruction are executed to repair context coherence; and outputting the pure text sequence. The system comprises corresponding function modules. According to the method, through a dynamic threshold and semantic dual verification mechanism, the effective information retention rate and the noise elimination accuracy rate are remarkably improved in a high-noise scene, and the quality and the processing throughput of financial large model input data are guaranteed.

The application discloses a voice wake-up method, a training method of an acoustic model and related devices. The voice wake-up method comprises the following steps: receiving a voice signal to be recognized, and obtaining an acoustic feature of the voice signal to be recognized; inputting the acoustic feature into a trained acoustic model to obtain a probability of a wake-up word; wherein the step of training the acoustic model comprises the following steps: constructing an enhanced model, the enhanced model comprising a first branch which is the same as the acoustic model; training the enhanced model and the acoustic model together by using training data, and synchronously updating and keeping the same parameters of the first branch and the acoustic model; and determining whether to wake up based on the probability. In the foregoing manner, the application can improve the frame classification accuracy of the acoustic model in a noise scene, thereby greatly improving the wake-up rate in the noise scene.

The invention provides a method for analyzing a high-frequency aerodynamic noise field in a closed space, which comprises the following steps of: 1) based on a CAD (Computer Aided Design) model, carrying out computational domain cutting and grid division on a flow field outside the model, dividing the flow field into a sound source area and a noise radiation area, and carrying out local grid encryption on the sound source area to ensure the subsequent turbulence resolution; 2) solving a flow field by adopting a DDES method, and capturing near-field sound source pulsation; 3, the generalized Green function analysis form in the limited space is introduced into an FW-H equation for integration, and far-field noise considering wall and ground boundary reflection is output. The concept is reasonable, the prediction accuracy, comparable with APE, in the tunnel can be obtained, and a new feasible way is provided for rapid evaluation and optimization of high-speed train tunnel acoustic performance.

The present invention discloses a method and system for estimating a direction of arrival in an impulse noise scenario. The method repeatedly iterates and updates impulse noise parameters and a sparse sound source signal matrix according to an overcomplete array manifold matrix, a received data vector matrix, an initial sparse sound source signal matrix, an initial impulse noise matrix, an initial impulse noise distribution accuracy parameter matrix and an initial impulse noise distribution degree of freedom parameter until an iteration condition is satisfied, thereby obtaining a final sparse sound source signal matrix, and estimating a direction of arrival according to the final sparse sound source signal matrix, so as to better adapt to a complex impulse noise environment, thereby improving the estimation accuracy of the direction of arrival, and solving the problem that, in a complex impulse noise statistical characteristic environment, a fitting error matrix with a p value selected by experience cannot fit the impulse noise well, resulting in low impulse noise fitting accuracy, thereby resulting in low estimation accuracy of the direction of arrival.

A machine learning network is trained to generate noise field images from radiographic (x-ray) images. The training includes accessing a number of previously acquired radiographic images, duplicating the previously acquired radiographic images, and conditioning each of the duplicated images with simulated noise content to form a plurality of simulated low-exposure images. Each of the simulated low-exposure images is paired with its corresponding previously acquired image to form a learning pair. The machine learning network is trained to generate noise field images using the learning pairs of images. A noise-suppressed image of a subject can be generated by applying a scaling factor to at least a portion of a corresponding noise field image and combining the scaled noise field image with a currently captured image of the subject.