71 results about "Transient noise" patented technology

The invention relates to the technical field of carrier communication, and particularly discloses a power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model.The power line carrier channel simulation method based on the dynamic multi-dimensional interference model.The power line carrier channel simulation method based on the dynamic multi-dimensional interference model comprises the steps that a power line noise sample is collected, and characteristic parameters of periodic interference, transient noise and background noise are extracted; classifying and marking according to noise types and corresponding power grid working conditions, and establishing a noise database containing a mapping relation between noise characteristics and working conditions; and based on the noise database, respectively establishing a periodic interference model, a transient noise model and a background noise model, and forming a multi-dimensional noise interference model. According to the power line carrier channel simulation method based on the dynamic multi-dimensional interference model, through multi-dimensional noise modeling and dynamic environment adaptation, the precision, the real-time performance and the scene coverage capability of power line carrier communication simulation are remarkably improved, and the simulation bottleneck in a complex power grid environment in the prior art is solved.

The invention relates to the technical field of industrial laser precision measurement, in particular to a laser centering device for a coupler and a using method of the laser centering device for the coupler. Light spot displacement signals and environment vibration signals are synchronously collected through a laser transmitter and an optical sensor array, and a frequency domain feature decoupling module is adopted to separate a shafting displacement component and an interference component; the time domain correlation verification module is used for calculating signal time sequence correlation and generating a deviation vector, and finally the adaptive compensation decision module is used for analyzing the deviation vector and outputting an adjustment instruction. According to the invention, effective separation of real misalignment and transient noise in a dynamic environment is realized, the technical problem of measurement misalignment of an existing laser centering device in a complex industrial environment is solved, and the coupling installation precision and the equipment operation stability are improved.

The invention discloses a load switching detection method and system based on a dynamic window and anti-interference verification. The method belongs to the technical field of non-intrusive load monitoring. The technical problems that in the prior art, complete event capturing capacity for multi-stage power changes during equipment starting is lacked, and the multi-stage power changes are easily misjudged as multiple independent switching events are solved. Detecting a power abrupt change event through a sliding window bilateral CUSUM algorithm, dynamically adjusting a window overlapping proportion and a judgment threshold according to a power change rate, and distinguishing climbing start and step start; introducing a delay dual-check mechanism, and filtering instantaneous noise interference in combination with steady-state judgment and trend consistency verification; according to the multi-stage power change of the frequency conversion equipment, event classification judgment logic assisted by transient characteristics is designed, and wrong event segmentation is avoided. Through the collaborative design of the dynamic window strategy and the anti-interference verification, the detection precision and robustness of the load switching event in the household power consumption scene are remarkably improved, and the method is particularly suitable for low-frequency sampling and high-noise environments.

The present application relates to pipeline system damping and noise reduction technical field, provide a kind of active noise cancellation pressure relief valve, comprising: valve body, with the liquid inlet cavity and pressure relief cavity being communicated are formed;Bypass pipe, with liquid inlet cavity is communicated;Piezoelectric actuator and pressure relief component, pressure relief component includes adjusting piece, pressure relief elastic piece and pressure relief valve core, pressure relief valve core and pressure relief elastic piece are set in pressure relief cavity, adjusting piece is movably arranged on valve body, piezoelectric actuator is arranged on adjusting piece, pressure relief elastic piece is pressed between piezoelectric actuator and pressure relief valve core;Piezoelectric actuator is configured to drive pressure relief elastic piece, to eliminate the transient noise caused by pulsating pressure when pressure relief valve core moves.The active noise cancellation pressure relief valve provided by the present application introduces active noise cancellation technology into the active noise cancellation pressure relief valve pulsating noise control by arranging piezoelectric actuator, can be adjusted by pressure relief elastic piece The force exerted on the pressure relief valve core can eliminate the transient noise caused by pulsating pressure when the pressure relief valve core moves.

The invention relates to the technical field of digital image processing and computer vision, and discloses a strip detection method based on image data processing, which comprises the following steps: acquiring an original image matrix of the surface of a carrier to be detected; dynamically adjusting diffusion flux according to a pixel neighborhood gradient modulus value, and processing an original image matrix to generate a reference matrix; applying spatial perturbation to the reference matrix and generating virtual residual distribution; performing differential operation on the original image matrix and the reference matrix to construct a feature matrix; and calculating an energy offset vector between the feature matrix and the virtual residual distribution, identifying a non-target noise cluster according to the linearity difference of the energy offset vector relative to a preset displacement amount, and reducing the pixel energy weight of the non-target noise cluster. According to the method, decoupling of physical entity features and transient noise features is realized, and weak damage features are reserved while non-uniform illumination and water mist noise are suppressed.

The invention relates to the technical field of power equipment on-line monitoring and intelligent diagnosis, in particular to an extra-high voltage transformer multi-modal data conflict fault misjudgment analysis method based on a DS evidence theory and graph enhancement. According to the invention, under the unified time reference and the electromagnetic-thermal physical topology constraint, the consistency fusion of the three-mode data of electromagnetic, acoustic vibration and distributed optical fiber temperature is realized, and the layered suppression and traceable positioning are carried out on evidence conflicts caused by coupling uncertainty and transient noise in the fusion process. Therefore, reliable fault confidence evaluation is output, and an accurate misjudgment traceability basis is provided. A traceable fault confidence coefficient is output in a unified time reference and a physical topology constraint through an electromagnetic-thermal dynamic coupling map which is synchronously acquired by hardware-level three modes and is updated in real time along with working conditions, and a layered D-S evidence conflict suppression and traceability algorithm.

The invention provides a hearing aid intelligent protection device and method integrated on a headgear, and the method comprises the steps: converting an environment sound wave signal into a frequency domain signal through a signal processor in an interlayer of the headgear, and separating a noise dominant frame and a voice dominant frame in a hearing aid; determining a voice masking index between the environmental noise and the voice in the environmental sound wave signal according to a distribution association relationship between the noise dominant frame and the voice dominant frame; according to the energy distribution characteristics of the noise spectrum in the frequency domain signal, determining the noise interference suffered by the hearing aid in the current wearing scene, and determining the transient noise dynamic characteristics of the environment sound wave signal under the current noise interference; and performing disturbance trend prediction on voice signal noise in the hearing aid according to the transient noise dynamic characteristics and the voice masking index, and further updating an adaptive filtering coefficient of the hearing aid according to a prediction result. By adopting the scheme of the invention, the prediction of the noise disturbance trend in a complex and changeable environment can be realized so as to adaptively adjust the noise reduction filter coefficient of the hearing aid.

The invention relates to the technical field of language data processing, and discloses a language data preprocessing method in a multilingual and complex scene, which is a voice data preprocessing system in the multilingual and complex scene based on an AutoPrep framework, integrates five modules, namely a voice enhancement module, a voice segmentation module, a speaker clustering module, a target voice extraction module and a quality filtering module. According to the scheme, differential suppression of steady-state noise and transient-state noise in multilingual voice signals is realized, and particularly in a small language (such as Kazakh and Talanx) scene, the voice signal-to-noise ratio and the language independence of voice features are effectively improved, so that the voice data can be automatically and structurally processed, and the voice signal-to-noise ratio and the language independence of the voice features can be effectively improved. The problem that in the prior art, the phoneme mapping error rate is high due to the fact that small languages lack an exclusive phoneme system processing module is solved, and the availability and the processing effect of low-resource language data are enhanced.

The invention relates to the technical field of electrical variable measurement, in particular to an intelligent leakage current real-time monitoring and diagnosis system and method, and the method comprises the steps: obtaining a real-time current signal of a target regional power grid and a corresponding current signal timestamp, and carrying out the intelligent processing of a real-time electric signal, and obtaining a corresponding current signal sequence; performing wavelet packet transformation and intelligent processing on the current signal sequence to obtain a current signal sub-sequence; for each current signal sub-sequence dynamic window sequence set, intelligent feature analysis is carried out on the dynamic window sequence set, and steady-state features and transient-state features of the current signals are obtained; and establishing a current diagnosis model, and performing diagnosis and early warning on the real-time current based on the current diagnosis model. According to the method, the problem of missing detection or false alarm caused by decomposition of a single frequency band, difficulty in accurately distinguishing high-frequency transient noise and low-frequency steady-state abnormity is solved, the threshold value is adjusted according to the actual risk duration, real-time dynamic prediction is realized, and the health management level of a regional power grid is improved.

This invention discloses a preprocessing method for highly polluted pediatric EEG data, belonging to the field of EEG signal processing technology. The invention proposes a robust motion artifact detection method based on a multi-channel voting mechanism. This method overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision-making, automatically identifying time periods requiring restoration. A comprehensive preprocessing workflow integrating PCHIP interpolation restoration, adaptive Bayesian wavelet denoising, bandpass filtering, and independent component analysis is designed. This workflow is validated using real pediatric continuous task EEG data. This invention demonstrates excellent performance in preserving frequency band information and improving the weighted signal-to-noise ratio, achieving an average accuracy of 96.4% (205 test cases) and a maximum of 100% based on permutation entropy feature classification. This invention effectively solves the preprocessing challenge of pediatric EEG data in high-noise environments, significantly improving the accuracy and reliability of subsequent classification tasks.

The present application relates to the technical field of new energy vehicle NVH testing, and discloses a range extender transient noise sound quality evaluation method based on physiological signal correction, which constructs a double-branch neural network by using in-vehicle noise audio signals, subject EEG signals and subjective scores under the transient working condition of the range extender, the audio branch outputs the objective benchmark score, and the EEG branch outputs the subjective bias correction amount; the model is trained by using a joint loss function that fuses the score fitting loss, the objective consistency constraint loss and the bias regularization loss; in the inference stage, the objective sound quality score can be output by only inputting the audio features. The present application decouples the noise objective quality and the evaluator's physiological bias, effectively eliminates the interference of individual differences, emotions and fatigue states on the evaluation results, improves the evaluation stability and repeatability, realizes efficient, accurate and robust objective evaluation of the range extender transient noise without relying on EEG, and is suitable for NVH performance testing and optimization in the vehicle development stage.

The present invention discloses an Internet of Things-based power equipment status monitoring system, which relates to the technical field of wireless communication networks. It includes: a spectrum sensing module that detects and identifies the noise characteristics in electromagnetic signals through a quantum spectrum sensing array to obtain corresponding spectrum data and classification results; a dynamic evaluation module that filters out a list of available channels through dynamic channel survivability, and determines corresponding channels for signal transmission from the list of available channels according to the classification results of the SVW classifier; a frequency hopping optimization module that determines a globally optimal frequency hopping sequence through the list of available channels and the total energy between different channels. The present invention realizes the capture of microsecond-level transient noise through a superconducting resonator and a quantum compressive sensing algorithm, and at the same time, real-time filters out anti-interference channels through dynamic channel survivability and obtains an optimal channel combination, enabling it to complete channel switching within 10 ms.

The application provides a kind of intelligent voice interaction system and method, it is related to electronic information technical field, including: acquisition calibration module, for by multichannel acoustic sensor array real-time acquisition mixed audio signal in environment, and set four reference positioning points located at corner point on sensor array, form quadrilateral structure;Based on the area characteristics of quadrilateral generation geometric correction value, and synchronous extraction noise frequency band feature and speech short-time energy distribution characteristics, and utilize geometric correction value to extract the feature and calibrate;Noise identification module is used for based on noise frequency band feature and speech short-time energy distribution characteristics, identify noise type, including wide-band high-intensity noise, low-frequency persistent noise, multi-source human voice interference and burst transient noise, generate label vector, and dynamically adjust noise reduction strategy according to label vector.The application is collected and calibrated audio signal by multichannel acoustic sensor array, improves the accuracy and fluency of voice interaction.

The present invention relates to the field of carrier communication technologies, and specifically discloses a power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model. The power line carrier channel simulation method based on the dynamic multi-dimensional interference model includes: collecting power line noise samples, extracting characteristic parameters of periodic interference, transient noise, and background noise, classifying and labeling them according to the noise type and the corresponding power grid operating conditions, and establishing a noise database containing the mapping relationship between noise characteristics and operating conditions; based on the noise database, respectively establishing a periodic interference model, a transient noise model, and a background noise model to form a multi-dimensional noise interference model. The power line carrier channel simulation method based on the dynamic multi-dimensional interference model in the embodiments of the present invention significantly improves the accuracy, real-time performance, and scenario coverage ability of power line carrier communication simulation through multi-dimensional noise modeling and dynamic environment adaptation, and solves the simulation bottleneck of the prior art in a complex power grid environment.

The invention relates to the technical field of vehicles, in particular to a noise reduction method and device, a vehicle and a storage medium, and aims at avoiding privacy leakage of passengers while reducing noise in the vehicle. The method comprises the following steps: acquiring an in-vehicle sound signal collected by a first microphone array; acquiring a noise signal outside the vehicle, which is acquired by a second microphone array; under the condition that the sound signal comprises a voice signal, sending a first interference signal to the outside of the vehicle through a first loudspeaker array based on the voice signal; the first interference signal is used for covering the voice signal; under the condition that the noise signal is the steady-state noise, executing an algorithm corresponding to the steady-state noise, and generating a second interference signal for counteracting the noise signal; under the condition that the noise signal is transient noise, executing an algorithm corresponding to the transient noise to generate a second interference signal; and sending the second interference signal to the outside of the vehicle through the first loudspeaker array.

The application discloses a micro bearing production and manufacturing detection data analysis method and system based on big data, relates to the technical field of data processing, and pre-trains a recurrent time sequence network through collection of vibration signals of bearings in a known state, obtains an evolution feature vector of bearings in the same batch through the network combined with a transient noise suppression operator, extracts a static feature vector combined with a machining time sequence and an end face image, corrects a visual missing sample after splicing, constructs a reconstructed kernel feature matrix, obtains a feature transformation and a mapping matrix based on two-way alternate optimization of the matrix and a real label, establishes a bearing state database, collects multi-modal data of a bearing to be detected to generate an initial to-be-detected feature vector, calculates the similarity of the to-be-detected feature vector with database samples after mapping, and finally outputs a detection result. The application deeply fuses multi-modal features, effectively overcomes interference such as workshop visual pollution, thermal expansion and group tolerance drift, and greatly improves detection accuracy and robustness.

The invention relates to a pipeline leakage detection method, in particular to a pipeline leakage detection method based on deep learning. The method comprises the following steps: acquiring a pipeline inspection video; capturing a leakage target; comparing the current frame and the historical frame of the leakage target; capturing the change and spatial position of the leakage target in the time dimension, and carrying out feature fusion; carrying out modeling and motion trail prediction on the motion trail of the leakage target; performing feature enhancement on the leakage target after the motion trail prediction to obtain enhanced features; and carrying out detection head monitoring on the enhanced features, and outputting the leakage probability. According to the invention, a temporarily appearing false target and continuously existing real leakage can be intelligently distinguished, the space-time fusion detection mechanism significantly improves the anti-interference capability of the system, and the problem that the transient noise is sensitive and the continuous leakage is slow in the prior art is solved.

The application discloses a pre-processing method based on high-pollution children's electroencephalogram data and belongs to the technical field of electroencephalogram signal processing. The application proposes a robust motion artifact detection method based on a multi-channel voting mechanism, overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision, and automatically identifies the repair period. A set of comprehensive preprocessing procedures are designed, which integrate PCHIP interpolation repair, adaptive Bayesian wavelet denoising, band-pass filtering and independent component analysis. The procedures are verified based on real children's continuous task electroencephalogram data. The application performs excellently in preserving frequency band information and improving weighted signal-to-noise ratio, and the average accuracy rate based on permutation entropy feature classification reaches 96.4% (205 test sets), and the highest is 100%. The application effectively solves the pre-processing problem of children's electroencephalogram data in a high-noise environment, and significantly improves the accuracy and reliability of subsequent classification tasks.

The invention provides an active noise reduction method and device, and relates to the technical field of data processing.The method comprises the steps that real-time driving state data of a vehicle are input into a mode recognition model, the current vehicle condition mode category is determined, and the real-time driving state data at least comprise the engine rotating speed; obtaining a target filter coefficient corresponding to the vehicle condition mode category from a preset filter coefficient lookup table; the target filter coefficient is utilized to update a filter coefficient of an adaptive filter in the active noise reduction system, and the adaptive filter generates an anti-noise signal to offset engine noise based on the updated filter coefficient and a reference signal corresponding to the engine rotating speed. The active noise reduction system can respond quickly when the working conditions of the vehicle are switched quickly, and the transient noise reduction performance and the stability of the overall noise reduction effect under the complex driving working conditions are remarkably improved.

A method includes providing, by a signal source circuit of a sensing circuit, a signal to a sensor via a conductor. When the sensor is exposed to a condition and is receiving the signal, an electrical characteristic of the sensor affects the signal. The signal includes at least one of: a direct current (DC) component and an oscillating component. When the sensing circuit is in a noisy environment, transient noise couples with the signal to produce a noisy signal. The method further includes comparing, by a transient circuit of the sensing circuit, the noisy signal with a representation of the noisy signal. When the noisy signal compares unfavorably with the representation of the noisy signal, supplying, by the transient circuit, a compensation signal to the conductor. A level of the compensation signal corresponds to a level at which the noisy signal compares unfavorably with the representation of the noisy signal.

The application discloses a wet granulator fault identification method and relates to the technical field of industrial process control. Through a dynamic scaling factor and a sliding window mechanism, the application can adapt to normal fluctuations of raw material properties in real time, significantly reduce the false alarm risk caused by slight changes in particle size or moisture content, adopt an incremental learning and forgetting strategy for an online updated multivariate statistical process control model, effectively handle data drift and working condition switching, avoid model rigidity, and improve stability in continuous production. Through continuous monitoring and double threshold value design, an abnormal score and hysteresis alarm mechanism can distinguish between transient noise and real faults, reduce alarm jitter, and ensure the timeliness of fault identification. The model robustness and transferability are further enhanced by integrating multi-source data such as near-infrared spectral characteristics and referring to experience tolerance in a knowledge base, and reliable monitoring can be realized without hardware modification.

The invention discloses a rotating machine fault classification method based on semi-supervised transfer learning, and relates to the field of fault diagnosis, and the method comprises the following steps: S1, collecting a vibration signal containing transient noise and a small number of fault labels; s2, positioning and removing transient noise by using a first-order Markov model, and dividing a 80% training set and a 20% test set; s3, building a semi-supervised transfer learning model; s4, training a semi-supervised transfer learning model; s5, the model is tested, and it needs to be ensured that the accuracy rate is larger than or equal to 95% under the label rate of 5 And S6, performing online application. According to the method, semi-supervised and transfer learning are fused, the non-label data value is effectively mined, transient noise is suppressed, the blank of low-label and high-interference working condition diagnosis is filled, a data driving method is promoted to move from a laboratory to an industrial complex scene, and key technical support is provided for field improvement of fault detection accuracy and reliability and predictive maintenance upgrading.

The invention discloses a real-time ball target detection method and system based on artificial intelligence. The method comprises the steps of initial detection of a ball target, bounding box zooming, ball adaptive matching degree enhancement, ball credibility index enhancement, ball motion orbit diameter repair, motion orbit diameter smoothing and output of a real-time detection result. The invention belongs to the field of target detection, and particularly relates to a real-time ball target detection method and system based on artificial intelligence. According to the scheme, scaling factors are dynamically adjusted based on credible indexes, direction sensitive scaling is introduced, and insufficient scaling in the high-speed motion direction is avoided; reducing matching failures during rate mutation based on dynamic scaling distance enhancement; shape misjudgment caused by insufficient pixels is reduced based on micro weighted shape optimization; introducing previous three frames of historical credible exponential attenuation items, and filtering instantaneous noise by using historical trajectory continuity; and focusing candidate matching based on the survivability and carrying out rate enhancement fusion, thereby improving the ball target detection effect.

This application discloses a method and system for real-time inference and parameter inversion of gravitational wave events based on deep learning. The method includes: constructing a physics-instrument dual-manifold decoupled network; extracting physics signal features and instrument noise features from time-frequency spectra; introducing physical manifold constraints and orthogonally decoupling the features in the latent space; performing parameter inversion on the decoupled physics features using a conditionally normalized flow model; and verifying the consistency of the inference results by regenerating the waveform in reverse and analyzing the residuals. This application distinguishes between high-eccentricity gravitational wave signals and detector transient noise, improving recognition accuracy and reducing false alarm rate.

This invention relates to a keyword detection method in noisy environments based on multi-task joint learning, belonging to the fields of speech signal processing and natural language processing. It extracts multi-layer acoustic representations from noisy signals using a pre-trained audio encoder model, then performs temporal and inter-layer aggregation through global average pooling layers to generate highly compact and noise-resistant shared features, eliminating local interference from transient noise. Finally, the features are input in parallel into detection and classification branches, and a multi-task joint optimization strategy is used for training to enhance the model's ability to distinguish between speech and background noise. A keyword detection test dataset in a real noisy environment is constructed, and a cascaded decision logic of detection followed by classification is adopted in the inference stage to effectively filter invalid inputs. This invention combines the generalization representation power of the pre-trained model with a multi-task collaborative learning mechanism, significantly improving the model's recognition accuracy, system stability, and engineering practicality in low signal-to-noise ratio and real non-stationary noise environments.

The present application relates to pipeline system damping and noise reduction technical field, provide a kind of pulsating noise active noise cancellation pressure relief valve, comprising: valve body, formed with the liquid inlet cavity and pressure relief cavity that communicate with each other;Bypass pipe, with liquid inlet cavity communication;Pressure relief assembly, including adjusting piece, pressure relief elastic element and pressure relief valve core, pressure relief valve core and pressure relief elastic element are set in pressure relief cavity, adjusting piece is movably arranged on valve body, pressure relief elastic element is pressed between adjusting piece and pressure relief valve core;Electromagnetic actuator and noise reduction elastic element, electromagnetic actuator is arranged in liquid inlet cavity, noise reduction elastic element is pressed between the drive end of electromagnetic actuator and pressure relief valve core.The pulsating noise active noise cancellation pressure relief valve provided by the present application, by the way of arranging electromagnetic actuator, active noise cancellation technology is introduced into pulsating noise active noise cancellation pressure relief valve pulsating noise control, can be adjusted by noise reduction elastic element The force exerted on pressure relief valve core can eliminate the transient noise caused by pulsating pressure when the pressure relief valve core moves.

The invention discloses a camera image data processing method and system based on a noise reduction technology, and relates to the technical field of camera data processing, and the method comprises the steps: collecting multi-phase charge quantity data, converting the data into a voltage signal matrix, and generating a multi-frequency measurement tensor after frequency grouping processing; performing phase compensation and windowing processing on the extended frequency domain tensor, then performing inverse Fourier transform, enhancing the time domain resolution through interpolation processing, suppressing transient noise by adopting median filtering, and generating a transient time domain image; extracting time domain waveform features from the transient time domain image, segmenting a confidence region through a confidence threshold, distinguishing effective reflection signals in combination with multi-peak detection and a random forest, and outputting a depth probability graph; and performing channel-space double attention weighting on the depth probability graph, modeling neighborhood relevance by using graph convolution and performing hybrid coding, and outputting an anti-noise enhanced depth graph. According to the method, hybrid coding is implemented through the uncertainty matrix, and the data redundancy is reduced while the precision of the depth map is ensured.

The invention discloses a karaoke audio processing method based on human voice separation and restoration, and the method comprises the steps: obtaining a time-frequency matrix of a karaoke audio signal, and judging whether a noise component exists in the karaoke audio signal or not; identifying a noise basis matrix and an effective audio basis matrix, calculating overlapping coefficients of noise and effective audio on different frequency bands, and determining a cross frequency band; identifying transient noise and steady-state noise according to the noise basis matrix and the corresponding activation matrix; designing a filter, and filtering the noise of each frame in real time; judging lost phase information, interpolating a missing region phase by using a linear difference value, and compensating time delay through time-frequency matrix translation; and judging a de-noising effect and an audio repairing effect, and formulating and executing a karaoke audio signal processing improvement scheme. The problems of noise interference, audio distortion, phase loss, delay compensation and the like in the current karaoke application can be effectively solved, the audio quality is remarkably improved, and better and more personalized karaoke experience is provided for users.

The present invention discloses a camera image data processing method and system based on noise reduction technology, which relates to the field of camera data processing technology. The method comprises the following steps: collecting multi-phase charge quantity data and converting it into a voltage signal matrix, generating a multi-frequency measurement tensor after frequency grouping processing; performing phase compensation and windowing on the extended frequency domain tensor, then performing an inverse Fourier transform, enhancing the time domain resolution through interpolation, and suppressing transient noise using median filtering to generate a transient time domain image; extracting time domain waveform features from the transient time domain image, segmenting confidence regions using confidence thresholds, and combining multi-peak detection with random forests to distinguish valid reflection signals, thereby outputting a depth probability map; performing channel-space dual attention weighting on the depth probability map, modeling neighborhood correlation using graph convolution, and implementing hybrid coding to output a noise-resistant depth map. The present invention implements hybrid coding using an uncertainty matrix, reducing data redundancy while ensuring depth map accuracy.

Embodiments of the present disclosure provide a signal-to-noise ratio estimation method, apparatus, medium, and computing device. The method includes: dividing a target audio into an audio frame sequence, where the target audio is obtained by superimposing a reference speech and a reference transient noise in the time domain; determining speech frames and superimposed frames in the audio frame sequence according to the timing information of the reference speech and the reference transient noise; respectively determining the speech energy and the noise energy in the superimposed frames according to the audio energy of each of the speech frames and the superimposed frames; and calculating the signal-to-noise ratio of the target audio based on the speech energy and the noise energy in the superimposed frames. Applying this method to perform active signal-to-noise ratio estimation on an audio containing a transient noise signal, the estimation result has a high accuracy.