41 results about "Noise classification" patented technology

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention relates to the technical field of medical monitoring, and provides a high-precision sleep electrocardio continuous monitoring system and method.The high-precision sleep electrocardio continuous monitoring system and method.A modular closed-loop framework is adopted, and synchronous acquisition of electrocardio, movement and respiration signals is achieved through a multi-modal sensing and high-precision synchronous acquisition module; the signal quality multi-dimensional traceability diagnosis module performs feature extraction and noise classification on the input signal; the dynamic traceability filtering processing module intelligently calls a corresponding filtering algorithm to perform signal purification according to the noise type; and the intelligent output and self-adaptive resource management module outputs high-quality electrocardiosignals and realizes dynamic optimization of system power consumption. By establishing a complete signal quality evaluation system and an intelligent processing mechanism, the whole process optimization of the sleep electrocardiosignals from collection to output is realized, and the accuracy of monitoring data and the cruising ability of the system are remarkably improved.

The invention discloses a time-frequency fusion power line noise classification modeling method, system and device based on deep learning, and a medium, and belongs to the technical field of power line noise classification modeling, and the method comprises the steps: constructing a data set containing multiple types of typical power line noise signals; performing short-time Fourier transform on the noise signal to obtain a spectrogram, and extracting local texture features in the spectrogram; extracting global time sequence characteristics of the original time domain waveform of the noise signal; splicing the local texture features and the global time sequence features to form a fusion feature vector, inputting the fusion feature vector into a full-connection neural network, and outputting a multi-label noise prediction result; and training the full-connection neural network, and updating network parameters by optimizing a binary cross entropy loss function. According to the method, different characteristics and generation mechanisms of each type of noise are considered, through fusion of time domain information and frequency domain information, the characteristics can be extracted more comprehensively, the space relation and mode of the time domain and the frequency domain are effectively captured, and the adaptability and accuracy of the model to a noise classification task are improved.

The invention discloses an environmental noise classification method based on adaptive joint parameter space optimization, and the method comprises the steps: collecting a plurality of noise signals, enabling each type of signals to correspond to a specific environmental noise type, and constructing a data set; defining a joint parameter space comprising a plurality of optimization variables, wherein the joint parameter space comprises a data enhancement parameter subspace, a model network parameter subspace and a model training hyper-parameter subspace; according to training data characteristics, environmental noise classification task complexity and model deployment constraint, adaptively calculating each parameter range space; and constructing an objective function, and searching a multi-parameter optimal collaborative combination by using Bayesian optimization. According to the method, a joint parameter space is constructed, and Bayesian optimization is utilized to adaptively search a multi-parameter optimal collaborative combination of a data enhancement parameter, a model network parameter and a training hyper-parameter in a multi-model training process, so that synchronous dynamic optimization of data enhancement, a model structure and model training is realized; and finally, the performance of the neural network model in environmental noise classification is improved.

The invention discloses an AI-driven vehicle-mounted sound field real-time modeling and voice separation method, and relates to the technical field of voice signal processing. A main control unit comprising a time sequence synchronizer, a resource scheduler and a health monitor is constructed. 3D sound field modeling is carried out by adopting a lightweight STCN + bidirectional LSTM network, adaptive updating of the model is realized through EWC incremental learning, and a CNN-LSTM noise classification network and targeted suppression algorithms such as ANF / spectral subtraction are developed. The voice separation module adopts an improved Conv-TasNet architecture, 3D spatial constraint and a multi-task loss function are fused, and low delay is realized under INT8 quantization and pipeline processing. The system dynamically optimizes parameters through a real-time regulation and control unit, supports scene self-adaption, finally achieves a separation effect in a mixed noise scene, reduces the delay of the whole system, and effectively improves the definition and stability of vehicle-mounted voice interaction.

The invention discloses a medical data intelligent identification method and apparatus. The method comprises the steps of obtaining to-be-identified target data; inputting the target data into a pre-trained intelligent recognition model to obtain a recognition result output by the intelligent recognition model, the recognition result at least comprising all sample categories of the target data and statistics of the number of the categories; wherein the intelligent identification model is obtained by training and optimizing a pre-constructed neural network by using a training data set, the training data set trains the neural network to obtain an initial network, and the initial model is optimized by using a preset optimization target to obtain the intelligent identification model. According to the intelligent recognition model based on feature distribution robustness optimization, by optimizing feature distribution, automatically exploring and constructing an optimal and most robust feature space and introducing anti-noise classification loss, the problems of medical data imbalance, annotation noise and generalization in the prior art are solved.

This invention discloses a multi-source electromagnetic noise suppression method based on noise classification and deep learning, belonging to the field of geophysical electromagnetic exploration technology. The method includes: segmenting and preprocessing the original electromagnetic observation sequence; using an improved U-Net network with an encoder embedded in a Mamba time-series modeling module for low-frequency noise suppression; identifying strong noise types in the data segments using a ROCKET classifier; based on the classification results, calling a second improved U-Net network trained for the corresponding noise type for class-based targeted denoising; and finally, splicing the data segments to obtain complete, high-quality data. This invention, through a phased processing framework of "low-frequency pre-suppression—noise classification—class-based denoising," combined with the strong time-series modeling capabilities of the Mamba module and the efficient classification performance of ROCKET, significantly improves the suppression accuracy and signal fidelity for complex, multi-source electromagnetic noise, and is particularly suitable for processing ground and airborne electromagnetic exploration data.

The utility model discloses a sound scene enhancing device based on environmental perception, and relates to the technical field of acoustics. The system comprises a microphone array, a signal conditioning circuit, an analog-to-digital converter, a DSP processor, a sound scene database memory, a master control MCU, an FPGA beam controller, an audio synthesis chip, a power amplifier, a loudspeaker array and a noise classification coprocessor, the microphone array is electrically connected with the signal conditioning circuit, and the signal conditioning circuit is electrically connected with the analog-to-digital converter. The analog-to-digital converter is electrically connected with the DSP processor, the analog-to-digital converter is electrically connected with the master control MCU, the DSP processor is electrically connected with the sound scene database memory, the DSP processor is electrically connected with the noise classification coprocessor, the noise classification coprocessor is electrically connected with the master control MCU, the master control MCU is electrically connected with the sound scene database memory, and the master control MCU is electrically connected with the FPGA beam controller. The sound scene can be automatically adjusted according to environment changes.

The invention relates to the technical field of railway vehicle noise analysis and processing, and particularly discloses a railway vehicle noise distinguishing and extracting device and method.The device comprises an acquisition module, a processing module and an output module, and the acquisition module is used for acquiring carriage mixed noise in the running process of a railway vehicle; the processing module is used for performing frequency decomposition, noise classification and independent loudness analysis of various types of classified noise on the collected mixed noise of the carriage; the output module is used for outputting and / or storing the loudness values and the frequency characteristics of various types of noise after classification; according to the method, wheel track noise, passenger voice and train station reporting voice can be accurately separated and subjected to loudness analysis in real time, the problem that the passenger voice and the train station reporting voice are difficult to separate and distinguish is solved, and a scientific noise monitoring and management means is provided for rail transit operation enterprises; the passenger comfort level is improved, the station reporting volume and the operation service quality are optimized, and complaint and transformation cost caused by noise is reduced.

An ultrasonic radar noise recognition method and system are presented. The method includes: transmitting ultrasonic waves by an ultrasonic radar and receiving corresponding echoes; extracting echo data from the received echoes to form raw data; processing the raw data according to noise echo features to obtain feature data, wherein the noise echo features represent waveform features associated with echoes generated by noise; and inputting the feature data into a trained noise classifier to obtain a noise recognition result.

The invention provides an image mixed noise self-adaptive suppression method and device of an ultrasonic endoscope, which are applied to electronic equipment in the ultrasonic endoscope, and the ultrasonic endoscope comprises the electronic equipment and a probe. The method comprises: controlling a probe to collect an ultrasonic image of an object lung area; performing noise feature extraction on each local image in the ultrasonic image to obtain a first feature, a second feature and a third feature of each local image; for each local image, correcting the first feature and the second feature by using a depth value of each pixel point in the local image, a depth gain compensation relationship and a lung ultrasonic signal attenuation relationship; determining a noise classification result of the target local image with noise based on the third feature of each local image and the corrected feature; determining a noise reduction scheme of each target local image by using the noise classification result of each target local image; and performing noise reduction on each target local image by using each noise reduction scheme. The denoising effect of the ultrasonic image can be improved.

This application discloses a method for determining model parameters, a method for classifying vehicle abnormal noises, an apparatus, and a device. The method includes: acquiring a training dataset of vehicle abnormal noise features; generating an initial population based on the training dataset; the population containing multiple individuals; constructing a kernel principal component analysis (KPC) model based on preset model parameters; the KPC model is used to extract target abnormal noise feature vectors from the vehicle's abnormal noise features for vehicle abnormal noise classification; determining an objective function based on the KPC model; iteratively updating the initial population based on the objective function to obtain an updated population; determining the optimal individual from the updated population; and determining the target model parameters of the KPC model based on the optimal individual and a preset parameter range. This method can determine the target model parameters of the KPC model most suitable for the current abnormal noise environment, thereby improving the classification accuracy of vehicle abnormal noise classification.

This invention belongs to the field of electrocardiogram (ECG) signal classification technology and discloses an automatic identification and classification method for ECG signal quality. The method involves performing discrete wavelet decomposition on the ECG signal to obtain low-frequency sub-bands, reconstructed sub-bands, and high-frequency sub-bands. Time-domain features are then extracted from these sub-bands. Based on the results of the time-domain feature extraction, global noise classification and local noise classification are performed. This invention organically combines wavelet analysis, time-domain feature extraction, and fault diagnosis technology, enabling automatic identification and classification of ECG signal quality without relying on event features such as QRS complexes and RR intervals.

The invention relates to the technical field of earphone noise reduction, in particular to a low-delay Bluetooth earphone audio resampling method based on a deep convolutional neural network, and the method comprises the steps: 1, building an end-to-end multi-task deep learning earphone noise reduction model; 2, selecting an audio sample; step 3, training a to-be-trained multi-task deep learning noise reduction model by using the audio sample obtained in the step 2; 4, after training is completed, audio data are input into the model, an output end outputs a denoised signal, and the audio data are noiseless data and comprise voice of a target dialogue; and step 5, calculating a confidence score of the noise reduction signal output in the step 4, the confidence score being used for evaluating the audio quality, and through a multi-task learning framework, significantly improving the adaptability of the model to different noise environments. The basic noise reduction model focuses on the noise reduction effect, the noise classification model assists in recognizing the noise type, and the audible noise reduction output signal model ensures the naturalness of output voice.

This invention discloses an AI-driven dynamic audio fence control method, specifically including the following steps: Step 1: Signal acquisition and preprocessing using a beamforming microphone array and a DSP processor; Step 2: AI feature extraction and speech noise classification of standardized multi-channel digital audio signals; Step 3: MVDR dynamic beamforming processing based on speech noise classification labels and noise feature masks; Step 4: 3D audio rendering of the beamformed single-channel target speech signal; Step 5: Effect monitoring and parameter iteration based on the target speech signal and feedback signals from the audio output module; Step 6: Pure target speech output playback based on the 3D rendered time-domain target speech signal. The advantages of this invention are: ensuring a continuous improvement in the target speech signal-to-noise ratio (SNR), ultimately transmitting pure target speech to the speaker, forming a personal sound bubble-like virtual audio fence around the target object.

This invention belongs to the field of electromyography (EMG) signal classification and detection technology, and discloses an EMG signal classification and detection method combining SNN and HDC. This invention proposes an EMG signal classification and detection framework combining SNN and HDC, aiming to achieve ultra-low power operation. In this framework, SNN utilizes random untrained weights to perform event-driven feature extraction, thereby minimizing computational overhead; HDC achieves noise-resistant classification through high-dimensional representation. The integration of the two not only achieves energy-efficient real-time detection, but is also particularly suitable for resource-constrained scenarios such as wearable devices. The proposed method achieves an average accuracy of 95% (peak accuracy of 96.44%) in three fatigue recognition tasks, with a training speed 5.7 times faster than one-dimensional convolutional neural networks (1D-CNN) and 45 times faster than five-dimensional long short-term memory networks (5D-LSTM). Even using only 20% of the training data, the method still maintains an accuracy of over 90%, fully demonstrating its efficiency and robustness in practical deployment.

The invention relates to an ultrasonic radar signal identification method and system, and belongs to the technical field of vehicle perception identification, and the method comprises the steps: obtaining a first original signal detected by an ultrasonic radar; identifying the first original signal through a pre-constructed noise classification model to obtain a first ultrasonic radar signal; calculating the power spectral density of the first ultrasonic radar signal, and dynamically updating a noise baseline according to the power spectral density to obtain a second ultrasonic radar signal; and identifying an effective ultrasonic radar signal of the second ultrasonic radar signal through a pre-constructed environment filtering mapping model to obtain an ultrasonic radar signal. According to the invention, interference noise in the environment can be effectively removed, and the accuracy and reliability of ultrasonic radar signal identification are improved.

The application discloses a high-speed maneuvering target detection method based on deep learning and generalized Radon-Fourier transformation, and the method comprises the following steps: obtaining three-dimensional data through echo signal preprocessing; inputting the three-dimensional data into a target / noise classification network to obtain the probability of existence of a target in each distance unit; inputting the data of the distance unit in which the target may exist into a velocity acceleration classification network to output the search interval of the velocity and acceleration of the target; accumulating energy in the search interval by using generalized Radon-Fourier transformation; and finally obtaining a detection result through constant false alarm detection. The application combines the neural network and the generalized Radon-Fourier transformation, and can effectively balance the detection performance and the calculation amount. The application has a high accuracy in estimating the distance unit where the target is located and the motion parameters of the target; in addition, unnecessary search operations can be reduced according to the rough estimation result of the network, so that the calculation amount is greatly reduced.

The invention relates to the technical field of noise recognition, in particular to a ship noise recognition method and system based on deep learning, and the method comprises the following steps: collecting a ship noise signal, and generating a two-dimensional time-frequency spectrogram of the ship noise signal through adaptive time-frequency transformation; according to the two-dimensional time-frequency spectrogram, generating a time sequence feature vector sequence through a global time sequence perception convolutional neural network; inputting the time sequence feature vector sequence into a multi-scale time sequence mode extraction network to obtain a multi-scale time sequence fusion feature of the ship noise signal; and inputting the multi-scale time sequence fusion features into a noise classification network to realize noise identification. According to the method, a more adaptive time-frequency spectrogram can be generated through adaptive time-frequency transformation, the capability of capturing a noise periodic mode and the sensitivity of anomaly detection are improved through the global time sequence perception convolutional neural network and the multi-scale time sequence mode extraction network, and the accuracy and the intelligent degree of noise recognition in a complex marine environment are improved.

The application discloses a kind of high-efficiency energy-saving air conditioner noise reduction method and device, noise frequency interval and decibel reference value are stored in advance in controller, detect the activity state of indoor personnel as the precondition of noise reduction start condition.All noise reduction structures are closed when there is no one in the room, and only temperature regulation is performed;When there is someone in the room, collect the operating noise and divide it into low-frequency noise, environmental noise and high-frequency noise, and do not process the environmental noise.The application adopts differentiated regulation and control for different noises, controls active noise reduction structure according to low-frequency noise, and cooperatively controls passive noise reduction structure and fan state in combination with temperature difference and high-frequency noise.Through pre-detection and noise classification, false triggering and invalid operation of the noise reduction system are avoided, energy saving and consumption reduction and silent optimization are realized, temperature control performance and indoor comfort are considered, and it belongs to the field of energy-saving and environment-friendly high-efficiency energy-saving technology.

The invention relates to a multi-modal deep learning marine ship noise classification method based on bilinear fusion, and belongs to the technical field of acoustic signal processing. The multi-modal deep learning marine ship noise classification method based on bilinear fusion comprises the steps of data preprocessing, construction of a double-flow feature extraction network, bilinear feature fusion and classification. And the bilinear feature fusion step comprises the steps of performing dimension reduction processing on the waveform feature vector and the spectrum feature vector respectively, calculating an outer product weighted sum of the waveform feature vector after dimension reduction and the spectrum feature vector after dimension reduction through bilinear interaction, obtaining a fusion feature vector, and performing nonlinear processing on the fusion feature vector to obtain a final fusion feature vector. According to the method, fine-grained interaction of the waveform features and the spectrum features is realized through bilinear fusion, complementary information of two modes can be more fully mined, and the expression ability and discrimination of the fused features are improved.

The invention relates to the technical field of audio signal processing, and discloses a real-time mute detection method based on zero-crossing rate and energy value optimization, which comprises the following steps: calculating an energy value and a noise stability value of each frame of audio signal, classifying noise, and dividing noise intensity grades; performing framing processing on the input signal, and calculating a current frame signal peak value; setting a zero-crossing amplitude threshold value to obtain an optimized zero-crossing rate; generating a self-adaptive energy value according to the dynamic threshold value; constructing a feature pair sequence, calculating a correlation coefficient of the feature pair sequence and judging a feature relationship; dynamically calculating a zero-crossing rate threshold value; judging a mute candidate state and a non-mute candidate state according to the dynamic threshold value and the zero-crossing rate threshold value; and outputting a judgment result according to the states of two continuous frames. According to the method, the zero-crossing rate calculation mode is dynamically adjusted, the self-adaptive energy threshold value is designed, the double-feature collaborative decision logic is constructed, and high-precision and low-delay silence detection in the complex noise environment is achieved.

The invention relates to the technical field of transformers, and discloses a transformer DC bias vibration and noise evaluation method, system and device and a medium, and the method comprises the steps: building a multi-physics field coupling analysis model comprising an electromagnetic field, structural mechanics and acoustics based on key parameters of a target transformer, and configuring an inter-field coupling relation in the model; constructing a field-circuit combined driving source of the target transformer under a direct current bias working condition, inputting the field-circuit combined driving source into the analysis model for transient electromagnetic structure coupling solution, obtaining a structure vibration response, converting the structure vibration response into a boundary condition through an inter-field coupling relationship, and inputting the structure vibration response into the analysis model for acoustic transient solution to obtain an acoustic response; obtaining harmonic distribution of the target transformer and a structural inherent frequency of the target transformer in a frequency domain to determine a modal harmonic overlap ratio, and determining a vibration and noise grading evaluation result of the target transformer in combination with a structural vibration response and an acoustic response; and the vibration and noise of the transformer are accurately evaluated by adopting a step-by-step coupling calculation strategy.

The application belongs to the technical field of computer vision and medical image processing, and particularly relates to a retinal blood vessel segmentation method and device in fundus images, aiming to solve the problems of poor adaptability to different image qualities, weak anti-interference ability to composite noise and low blood vessel reconstruction accuracy in the prior art. The method comprises the following steps: acquiring a fundus image to be processed; analyzing the fundus image to determine prior analysis indexes and noise classification information, and then determining processing parameters; processing the fundus image by using the processing parameters and performing noise removal processing to generate an initial blood vessel segmentation map; identifying breakpoints and candidate destination points in the initial blood vessel segmentation map; performing multi-dimensional checking between the breakpoints and each candidate destination point to determine the optimal connection and generate a reconstructed retinal blood vessel segmentation map. The application significantly improves the accuracy, robustness and adaptability to complex lesion images through adaptive parameter adjustment and multi-dimensional checking reconstruction.

This invention relates to the field of electronic digital data processing technology, specifically to a method for edge data cleaning of industrial equipment integrated into a Manufacturing Execution System (MES). The method includes: acquiring the MES production configuration database through an edge computing unit to construct a dynamic data object context dictionary, and encapsulating the original sensor data objects into a standardized data stream; extracting statistical moment features and trend indicators within a time-series sliding window, and identifying noise types by combining dictionary logic constraint thresholds and classification models; dynamically matching and repairing algorithms to perform reconstruction based on the identification results, generating high-fidelity data; mapping the data to the overall equipment efficiency calculation logic of the MES, and iterating the noise classification weight parameters based on feedback deviation closed-loop iteration. This invention, through a business-driven dynamic dictionary and multi-dimensional feature classification and identification logic, achieves self-evolution of the cleaning strategy, significantly improving the processing accuracy and real-time performance of industrial data under complex operating conditions.

A method and apparatus for improving image quality using noise classification are disclosed. According to one aspect of the present disclosure, a method for improving image quality is provided, comprising: a step of generating a noise class map corresponding to the noise intensity of an input frame; a step of merging the input frame and the noise class map to generate a merged frame; and a step of inputting the merged frame into a pre-trained image quality improvement network to generate an output frame with improved image quality compared to the input frame.

The invention relates to a smart classroom speech recognition method, system and device based on a neural network, and a medium. The method comprises the following steps: acquiring noisy voice data of a classroom environment and performing feature extraction to obtain a voice feature set; identifying a dominant classroom noise type through a noise classifier to obtain a noise type identifier; processing the voice feature set through convolution kernel configuration corresponding to the noise type identifier to obtain local precise features, and processing the local precise features through a multi-head self-attention mechanism to obtain key focusing features; based on a fusion weight corresponding to the noise type identifier, carrying out weighted summation on the local precise feature and the key focusing feature to generate a fusion feature; performing channel-level attention weight calculation on the fusion features and performing feature data screening to obtain lightweight optimization features; and inputting the lightweight optimization features into a classification decoder to obtain a speech recognition text result. The method can be adapted to a complex noise environment in a classroom, and the accuracy of speech recognition is improved.

The present disclosure relates to the technical field of computers, comprising an object classification method and device based on deep reinforcement learning under complex noise conditions. By obtaining data to be classified; based on the noise classification model obtained by deep reinforcement learning, the noise classification of the data to be classified is performed to obtain the noise classification corresponding to the data to be classified; the noise reduction algorithm corresponding to the noise classification is called to perform noise reduction processing on the data to be classified to obtain the data after noise reduction; based on the object classification model obtained by deep reinforcement learning, the data after noise reduction is subjected to object classification to obtain the object classification result; the problem of poor noise removal effect of the traditional denoising algorithm can be solved; in the case of many noise types, the denoising algorithm can be adaptively switched for noise reduction processing, thereby improving the noise reduction effect of the data to be classified under complex noise conditions.

The application discloses a kind of based on noise classification and deep learning's multi-source electromagnetic noise suppression method, belong to geophysical electromagnetic detection technical field.The method includes: original electromagnetic observation sequence is segmented and preprocessed;Low-frequency noise is suppressed using the improved U-Net network of encoder embedding Mamba time series modeling module;The strong noise type in data segment is identified by ROCKET classifier;According to the classification result, the second improved U-Net network trained for the corresponding noise type is called to carry out class-oriented denoising;Finally, complete high-quality data is obtained by splicing data segments.The present application uses the processing framework of "low-frequency pre-suppression-noise classification-class denoising", combines the strong time series modeling capability of Mamba module and the high-efficiency classification performance of ROCKET, significantly improves the suppression accuracy and signal fidelity of complex, multi-source electromagnetic noise, especially suitable for ground and air electromagnetic exploration data processing.