61 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 discloses a motor abnormal sound detection method and system based on contact acquisition and small sample learning, and the method comprises the steps: directly coupling a motor housing through a contact vibration sensor, collecting an original vibration signal, and generating an anti-interference vibration signal; inputting the anti-interference vibration signal into a nonlinear resonance enhancement module to generate an enhanced sound signal; performing wavelet packet decomposition on the enhanced sound signal, extracting a multi-scale frequency band energy entropy, and generating a motor abnormal sound feature matrix by combining singular value decomposition dimension reduction; on the basis of a dynamic weight distribution element learning algorithm, a small number of normal samples and abnormal samples are utilized to construct an abnormal sound classification model; and inputting the motor abnormal sound characteristic matrix into an abnormal sound classification model, detecting transient abnormality through a sliding window time sequence matching algorithm, and outputting an abnormal sound judgment result. According to the embodiment of the invention, high-precision and low-false-alarm motor abnormal sound detection under complex working conditions can be realized.

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 relates to the technical field of voice communication, in particular to a voice signal processing method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: performing real-time noise classification on a received original voice signal based on a preset deep neural network noise recognition model; according to the noise classification result, selecting a corresponding combined noise reduction model to perform multi-stage noise reduction processing on the original voice signal to obtain a noise-reduced voice signal, the combined noise reduction model comprising one or more of a deep neural network-based noise reduction module, a Wiener filtering module and a spectral subtraction module; and on the basis of time domain feature extraction and frequency domain feature extraction, voice feature enhancement processing is performed on the noise-reduced voice signal to obtain the target voice signal, so that the transmission quality of the voice signal is improved.

The invention provides a factory boundary noise online monitoring traceability system and method, and belongs to the technical field of noise monitoring. The system comprises a data acquisition module; the data processing module is used for judging whether factory boundary noise processing is carried out or not; when the boundary noise processing is determined to be carried out, determining a direction corresponding to the directional microphone with the maximum sound level as a noise source direction; based on the noise source direction, a data acquisition instruction is generated and sent to the data acquisition module, and audio frequency information and video information, collected in real time and fed back by the data acquisition module, in the noise source direction are recycled; and taking the audio frequency information and the video information in the noise source direction as input parameters, and based on a pre-established abnormal sound source positioning model and a pre-established sound feature recognition model, obtaining a noise source visual sound field cloud picture and a noise classification recognition result. The purposes of noise comprehensive response, noise orientation definition and noise accurate traceability are achieved, evidence is provided for factory boundary noise source definition, and disputes are effectively avoided.

The invention particularly relates to a voice noise reduction method suitable for different noise environments, and relates to the technical field of voice signal processing. Analyzing a noise environment and characteristics; adaptive selection of a noise reduction algorithm; adaptive noise reduction processing is executed; and noise reduction effect evaluation and iterative optimization are carried out. According to the invention, refined noise classification is matched with the algorithm, so that the limitation of a traditional single algorithm in a complex environment is solved; the steady-state noise is dynamically counteracted by adopting an adaptive filter, the frequency domain gain of the unsteady-state noise is adjusted in real time through Wiener filtering, and the pulse noise is subjected to dynamic threshold processing by combining median filtering and wavelet transform; especially, the design of cooperation factors in wavelet transform can accurately distinguish signal details and noise: intensively suppress pulse noise, loosely retain details such as voices and consonants, and realize dynamic balance of noise reduction intensity and signal distortion.

The invention relates to the technical field of voice processing, in particular to an edge AI-based interphone audio processing method, system and device and a medium, and the method comprises the steps: collecting and analyzing an original audio through an audio collection assembly, and obtaining an audio feature set containing noise and scene features; inputting the image into an edge side audio analysis model to obtain a noise classification and scene recognition result; dynamically adjusting filter parameters, and generating a filtering parameter group adaptive to the current environment; extracting user voiceprint features to construct an exclusive voiceprint library, and optimizing the audio in combination with the filtering parameter group to generate a personalized processing audio; and training the model locally based on the personalized processing audio and the audio feature set, generating update parameters, and updating the audio analysis model for subsequent re-analysis. The technical problem that dynamic adaptation, personalized optimization and low-power-consumption and high-performance balance cannot be achieved at the same time in the prior art can be solved.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a switch cabinet discharge fault dynamic diagnosis method. The environment humidity, the electric field signal and the vibration signal of the switch cabinet are acquired in real time, the compensation parameter is dynamically generated based on the environment humidity change, and nonlinear compensation is performed on the electric field signal; frequency domain features of the compensated signals are extracted, a noise classification result is output, a discharge density threshold value is adjusted in a self-adaptive mode by combining environment state quantities and the noise classification result, and the environment state quantities comprise a real-time humidity value, an equipment load rate and a historical misjudgment rate; and calculating a mutual information value of a high-frequency energy ratio and an impact strength factor through hardware acceleration, triggering fault judgment when the mutual information value exceeds a dynamically adjusted discharge density threshold, and updating diagnosis model parameters according to a judgment result.

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 belongs to the technical field of electrocardiosignal classification, and discloses an electrocardiosignal quality automatic identification and classification method, which comprises the following steps: carrying out discrete wavelet decomposition on an electrocardiosignal to obtain a low-frequency sub-band, a reconstructed sub-band and a high-frequency sub-band, and carrying out time domain feature extraction on the low-frequency sub-band, the reconstructed sub-band and the high-frequency sub-band; according to the method, wavelet analysis, time domain feature extraction and a fault diagnosis technology are organically combined, and automatic identification and classification of electrocardiogram signal quality are achieved on the premise of not depending on event features such as QRS complex waves and RR intervals.

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.

The invention provides a method and a system for identifying fault data of a power transformer. The method comprises the following steps: acquiring online monitoring data of multi-dimensional operating parameters of the power transformer from a data input interface; effective monitoring data generated after the online monitoring data are preprocessed are input into a dynamic threshold value model for self-adaptive threshold value calculation, a threshold value calculation result is obtained, and the effective monitoring data with the threshold value calculation result not in a set dynamic threshold value range are marked as first-level abnormal data; performing physical constraint dual verification on the first-level abnormal data, and marking the first-level abnormal data which does not pass at least one verification as second-level abnormal data; and inputting a feature vector generated by feature extraction of the secondary abnormal data into a constructed noise classification model for noise type identification, and outputting a final identification result according to a confidence value and a noise type output by a noise classification module. According to the method and the system, redundant alarms are greatly reduced, and the fault positioning accuracy is improved.

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.

The present invention provides an environmental noise monitoring method based on multimodal data, which relates to the technical field of environmental monitoring and comprises the following steps: S1: synchronously collecting multimodal data in an environment, the multimodal data including original acoustic signals, meteorological data and video image data; S2: data processing and feature extraction; S3: dynamically adjusting the number of heads and weight calculation method of a self-attention mechanism based on the signal-to-noise ratio and time-domain variance of a spectrum graph, and then extracting a voiceprint feature vector based on the adjusted self-attention mechanism; S4: splicing the voiceprint feature vector, the meteorological feature vector and the visual feature vector into a multimodal feature matrix along the feature dimension, and performing weighted fusion on the multimodal feature matrix to generate a fused feature vector; S5: noise classification and early warning; the present invention solves the problem of limitations of single sensor data by fusing three heterogeneous modal information of acoustic signals, meteorological data and video images and combining a cross-modal self-attention mechanism to achieve feature weighted fusion.

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 environmental noise monitoring and control, and discloses an intelligent noise reduction method and system for audio equipment based on environmental noise, and the method comprises the steps: obtaining a real-time original audio signal, achieving the synchronous transmission of data, and obtaining a real-time noise data stream; performing audio signal spectrum analysis according to the real-time noise data stream to obtain frequency domain distribution characteristics; according to the frequency domain distribution characteristics, noise characteristic comparison is carried out, and a noise classification result is obtained; according to the noise classification result, corresponding suppression parameters are extracted, and a preliminary suppression scheme is obtained; performing preliminary noise reduction according to the real-time noise data stream and the preliminary suppression scheme, and analyzing the noise data stream subjected to preliminary noise reduction to obtain a noise intensity real-time fluctuation trend; and according to the noise intensity real-time fluctuation trend and the preliminary suppression scheme, dynamically adjusting suppression parameters, and generating a final noise reduction control instruction. According to the method, accurate classification and dynamic suppression of noise can be realized.

Implementations described herein relate to providing noise suppression for speech data with reduced power consumption. In some implementations, a computer-implemented method includes receiving a current time frame of speech data, e.g., after receiving a previous time frame associated with a previous noise suppression mask. The current time frame is transformed to a current frequency frame in the frequency domain. A noise classifier is used to determine whether to create a current noise suppression mask for the current frame. If it is determined to create the mask, the mask is created and multiplied by the current frequency frame to obtain a noise-suppressed frequency frame. If it is determined to not create the current mask, the previous noise suppression mask is multiplied with the current frequency frame to obtain the noise-suppressed frequency frame, without creating a mask. The noise-suppressed frequency frame is transformed to a time frame and output.

This application relates to the field of speech processing technology, and in particular to a method, system, device, and medium for audio processing of walkie-talkies based on edge AI. The method includes: acquiring and parsing raw audio through an audio acquisition component to obtain an audio feature set containing noise and scene features; inputting this feature set into an edge-side audio analysis model to obtain noise classification and scene recognition results; dynamically adjusting filter parameters accordingly to generate a filter parameter set adapted to the current environment; extracting user voiceprint features to construct a dedicated voiceprint library, and optimizing the audio using the filter parameter set to generate personalized processed audio; training a model locally based on the personalized processed audio and the audio feature set, generating updated parameters, and updating the audio analysis model for subsequent analysis. This application solves the technical problem that existing technologies cannot simultaneously achieve a balance between dynamic adaptation, personalized optimization, and low power consumption and high performance.

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.