16 results about "Stationary noise" patented technology

This application belongs to the field of coherent diffraction imaging technology, specifically disclosing an ultra-low dose coherent diffraction imaging method and apparatus. Based on a blind source separation strategy, this application uses principal component analysis to process the acquired diffraction signals, performing noise separation and updating in reciprocal space. This effectively separates mixed noise energy into noise components, thus avoiding crosstalk to the reconstruction process and significantly improving the convergence stability and robustness of coherent diffraction imaging when reconstructing diffraction signals with extremely low signal-to-noise ratios under ultra-low exposure doses. Simultaneously, this application constructs noise components separately for each scanning position and correlates noise components at different positions through low-dimensional spatial projection, achieving non-stationary noise separation. This enables more effective handling of random noise caused by the low quantum efficiency of ultra-short band detectors, thus maintaining extremely high noise robustness and reconstruction accuracy even under ultra-low exposure doses, achieving an effective improvement in resolution.

The application discloses a kind of deep development tomographic SAR imaging method based on Taylor linearization and classification prior feedback, obtains multiple preprocessed two-dimensional single view complex image data;Constructing city area-oriented tomographic SAR imaging model;ADMM solving framework and augmented Lagrangian function are constructed, and original variable, auxiliary variable and multiplier variable are iteratively updated;After iterative optimization, high-precision city three-dimensional point cloud imaging result is output, and three-dimensional reconstruction of city scene is realized.The application breaks through the limitation that single criterion is difficult to remove high-amplitude false target, effectively suppresses false target caused by non-stationary noise using spatial continuity prior, significantly improves the accuracy of obtaining target height information;By jointly optimizing the Taylor approximation of physical model, the prior guidance of semantic classification and the continuity constraint of spatial geometry, not only the super-resolution ability is improved, but also the smoothness and accuracy of the ground object elevation are ensured while preserving the details of complex structure.

This invention provides a method, device, electronic device, and storage medium for early warning of warp knitting machine faults, relating to the fields of intelligent textile manufacturing and predictive maintenance technology. Through a spindle phase triggering mechanism, heterogeneous data in multimodal observation data are forcibly aligned to the same physical coordinate system, achieving deep semantic alignment of heterogeneous data. A discrete sequence of fault primitives is obtained through a pre-trained feature encoding quantization model, filtering out inherent non-stationary noise during the efficient operation of the target warp knitting machine. Masked fault modeling is performed on the Transformer backbone network during joint training, enabling the pre-trained Transformer backbone network to grasp long-range structural priors in the production process. This allows the Transformer fault prediction model to achieve millisecond-level early warning based on only minor semantic deviations, without waiting for obvious damage to the fabric surface.

The application provides a high-dynamic-environment intercom voice enhancement method and system based on intelligent noise reduction. In response to a release event of a push-to-talk button, a two-state noise dictionary is constructed based on impulsive noise components and non-stationary noise components in background noise in a current high-dynamic environment. In response to a press event of the push-to-talk button, when it is detected that there is a transient region matching the impulsive noise components in the noisy intercom audio signal, online updating of the two-state noise dictionary is triggered to obtain an online updating dictionary. The noisy intercom audio signal is reconstructed based on the online updating dictionary to obtain an initial enhanced voice signal. Envelope reconstruction is performed on the voice segment with abnormal zero-crossing rate in the initial enhanced voice signal to generate a final enhanced voice signal. The technical scheme provided by the application can enhance conversation voice in a high-dynamic environment with non-stationary noise and impulsive noise.

The application discloses a single-frequency pulse signal detection method in a complex underwater acoustic environment and belongs to the field of target detection in a complex underwater acoustic environment. The application solves the problem that the existing detection method ignores the unique structural characteristics of target signals and background noise, resulting in low detection accuracy of target pulse signals in a complex marine environment noise background. The application adopts a data-driven method, does not require artificial experience parameters, and realizes linear skeleton feature extraction and structural enhancement through a linear skeleton structure feature extraction module and a graph attention skeleton feature connection enhancement module, effectively extracts the unique feature structure of target signals and background noise, solves the signal detection problem under the conditions of low input signal-to-interference noise ratio and coexistence of stationary noise components and non-stationary interference components in marine environment noise in a complex marine environment noise background, can stably extract and enhance the linear skeleton feature of the target signal in a complex underwater acoustic environment, and significantly improves the accuracy and robustness of target pulse signal detection. The application is mainly applied in a complex underwater acoustic environment.

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.

This invention discloses a speech signal recognition and separation method based on spatiotemporal clustering of robot voiceprints. It relates to the fields of robot voice interaction and digital signal processing technology. The method includes: a robot voice pickup module acquiring mixed speech signals in complex scenarios and performing preprocessing; extracting voiceprint feature vectors from the preprocessed mixed speech signals and marking human voice feature anchor points; constructing a feature matrix based on the voiceprint feature vectors and human voice feature anchor points, and selecting target human voice signal clusters through weight optimization and automatic clustering; using LSTM adaptive spectrum compensation to perform spectrum correction on the target human voice signal clusters to obtain a clean speech signal; inputting the clean speech signal into a robot speech recognition model and outputting the final recognition result. This invention can solve the problems of sound source number dependence, poor robustness to non-stationary noise, speech feature distortion, and long processing delay in existing technologies, improving the accuracy of robot speech recognition and real-time interaction capabilities in complex scenarios.

The application discloses a low signal-to-noise ratio direct spread signal detection method based on noise cancellation, and belongs to the field of communication spectrum sensing. The application adopts a minimum mean square error-based adaptive noise cancellation method, can track and eliminate non-stationary noise interference in real time, automatically optimizes filter parameters in an unknown channel environment, and enables a signal detection system to adapt to different background noise conditions. By using a cyclic spectrum analysis method, the cyclic stationary characteristics of the direct spread signal are extracted, and robust signal detection is realized in a low signal-to-noise ratio environment. The existence of the signal is determined through a characteristic spectrum peak on a non-zero cycle frequency, and the carrier frequency and pseudo code rate are further estimated, so that more accurate signal identification and parameter extraction are realized, the influence of noise uncertainty on the detection performance is avoided, and the robustness and reliability of detection are improved. Welch smoothing processing and short-time Fourier transform are combined, the variance of spectrum estimation is reduced when the cyclic spectrum is calculated, and the robustness of detection is improved.

The application discloses a fish culture net cage noise tracking method based on optimal smoothing and minimum statistics, and relates to the field of aquaculture. According to the frequency energy distribution characteristics of an input signal, the application automatically adjusts a smoothing parameter, smoothes a power spectrum, and combines minimum value deviation compensation to estimate a smoothing result, so that accurate tracking of a background noise signal is realized, noise power spectrum components can be effectively extracted in a complex noise environment, and additional detection for a target signal is not needed. The application introduces a time-frequency adaptive smoothing parameter mechanism, and fuses minimum statistics to track and estimate a background noise power spectrum. Without training data, the application can effectively determine the noise condition of a sampling signal in a complex noise condition, and solves the problems that existing noise separation methods have poor performance when separating non-stationary noise and need a large amount of training data.

This application, in the field of biomedical signal processing technology, specifically relates to a method and system for decoding EEG addiction, aiming to solve the problems of unstable non-stationary noise removal, insufficient long-term dependency capture ability, and difficulty in extracting deep spatial features in existing EEG analysis techniques. The method includes: acquiring multi-channel EEG signal data of the subject, preprocessing it, and constructing an input tensor; decomposing the input tensor using variational mode decomposition, with the parameters of the variational mode decomposition adaptively optimized using a particle swarm optimization algorithm; extracting deep spatial topological features using a residual graph convolutional network; inputting the deep spatial topological features into a Transformer encoder; and then processing them through an attention pooling layer before inputting them into a fully connected classification head to generate addiction risk classification results. The EEG addiction decoding method and system provided in this application enhance the ability and stability of addiction pathological feature recognition and can accurately assist in screening the genetic susceptibility of alcoholics.

The application provides a speech noise reduction method and device, electronic equipment and readable storage medium. After short-time Fourier transform and splicing processing are performed on a noisy speech signal to be processed, an input tensor is obtained. The input tensor is input into a feature extraction module of a pre-trained speech noise reduction model, so that the feature extraction module performs intra-frame spectral feature extraction and sub-band time sequence extraction on the input tensor combined with other features in the neighborhood, and outputs a target feature tensor. The target feature tensor is input into a full-band information processing module of the speech noise reduction model, so that the full-band information processing module performs full-band feature processing and global attention calculation on the target feature tensor aggregated with global context information, and outputs a target speech signal with noise removed. The speech noise reduction model is trained based on multiple objective functions. In this way, the noise reduction effect of the speech noise reduction model on the noisy speech signal and the robustness when suppressing non-stationary noise can be improved.

The application discloses a kind of based on tri-state classification voice activity detection's self-adapting wiener filtering noise reduction method and system and storage medium, for solving the technical problem that cochlear implant internal speech noise reduction algorithm is not good under non-stationary noise environment The effect of reducing noise is not good.The application first by improved voice activity detection audio frame is divided into speech frame, silence frame and noise frame three categories, and minimum value tracking in time-frequency domain is carried out to realize the power spectrum estimation of noise.The system further introduces noise environment change detection mechanism, dynamically assesses noise stability, and adjusts wiener filter parameter accordingly.Aiming at different types of frame and frequency point, the system dynamically optimizes filter parameter, including smoothing coefficient, gain lower limit and frequency band weight.Through this multi-level adaptive processing mechanism, the method can realize higher speech signal-to-noise ratio and clarity under complex noise environment, suitable for cochlear implant single microphone noise reduction.

This invention discloses a method for decoding electroencephalogram (EEG) signals based on the fusion of time-domain and frequency-domain signals, relating to the fields of brain-computer interfaces and EEG signal processing. The method includes: establishing a brain-computer interface data acquisition environment based on encoded modulation visual evoked potentials; acquiring the raw EEG observation signal set of the subject under multi-class encoded stimuli using a multi-channel EEG acquisition device; and adaptively filtering non-stationary noise, fully exploiting time-frequency complementary features, preserving high-order statistical information and fine-grained temporal structure of the signal, and significantly improving the accuracy and robustness of EEG signal decoding.

The application discloses a kind of OBS whale whistle signal detection identification method based on deep learning, belong to underwater signal identification classification technical field;Method includes: the original OBS record data is preprocessed;Periodic signal detection is carried out using STA / LTA-CV detection algorithm, the algorithm is combined with long short time average ratio and coefficient of variation constraint, effectively suppress non-stationary noise interference;A double-branch deep learning model is constructed, waveform branch uses CLSTM network to extract time domain feature, and time-frequency diagram branch uses ResNet50-CBAM network to extract time-frequency feature;Cross-modal feature fusion is realized by transformer encoder, and dynamic weight module is introduced to adaptively modal weighting;Finally, the whale signal class and confidence are output through classification head network;The method has the advantages of high detection efficiency, high recognition accuracy, and provides an effective technical means for marine bioacoustics research.

The application provides a signal noise reduction method, device, equipment, medium and product, and relates to the technical field of voice signal processing. The method generates a first noise estimation result focusing on music noise suppression and a second noise estimation result focusing on noise mutation tracking through a dual-path noise estimation unit, and dynamically regulates the update speed of the first noise estimation result based on the second noise estimation result, so that the smoothing process can be strengthened to deeply suppress music noise in a stationary noise scene, and the update speed of the first noise estimation result is accelerated to realize rapid tracking when noise mutates. In addition, the dual-path cooperative architecture breaks through the dependence of single-path noise estimation on fixed parameters, can adapt to the dynamic changes of different noise states, avoids noise residues or tracking delays caused by scene switching, finally realizes the improvement of the naturalness and clarity of the noise-reduced voice in all scenes, and optimizes the subjective listening experience of the user.