2442 results about "Noise suppression" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

The invention provides an unmanned aerial vehicle multi-dimensional information fusion method and system based on acousto-optic-electric composite detection. According to the method, acoustic frequency band signals, visible light image sequences and electromagnetic field intensity change data are collected, acoustic characteristic flow, optical characteristic flow and electrical characteristic flow are generated, and based on the time deviation between a low-frequency vibration mode and transient electromagnetic pulses, multi-channel dynamic calibration is carried out on an image distortion area in the optical characteristic flow. The method comprises the following steps: acquiring an acoustic feature flow, an optical feature flow and an electrical feature flow, correcting space-time coordinate parameters of the acoustic feature flow, the optical feature flow and the electrical feature flow, generating a calibration feature flow, carrying out anti-interference fusion processing on the calibration feature flow, extracting multi-dimensional coupling features, inputting the multi-dimensional coupling features into an unmanned aerial vehicle flight path planning model, and generating a three-dimensional space situation model; according to the invention, the physical space-time consistency alignment and noise suppression of the multi-modal data are realized, and the obstacle positioning precision and the dynamic trajectory prediction real-time performance are significantly improved.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention discloses a gait rehabilitation body-equipped robot time series data modeling optimization method based on frequency domain learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing, calculating gait parameters, and generating a patient state vector; enhanced features are obtained by applying multi-scale convolution and frequency domain processing; performing block processing and position coding to obtain a coding enhancement feature, and performing noise suppression processing to obtain a residual fusion feature; a final fusion feature is obtained by applying a self-attention mechanism and frequency domain alignment fusion processing; performing frequency domain decomposition and multi-scale memory filtering to obtain space-time fusion features; evaluating the rehabilitation progress, generating a gait correction vector and a correction time sequence curve, and outputting a smooth safety control sequence. According to the method, the influence of high-frequency noise is reduced, so that the robustness and accuracy of the model are improved; stable characterization of key gait features is realized, and the precision and consistency of the model in processing complex time sequence data are enhanced; and the control performance and the simulation precision of the gait rehabilitation robot are improved.

The invention relates to the technical field of audio noise reduction, and discloses a Bluetooth headset environment noise adaptive adjustment method and system, and the method comprises the steps: collecting multi-band sound data of headset environment noise through a preset acoustic sensor array, generating a noise spectrogram, extracting a noise masking threshold through time-frequency joint analysis, and collecting a headset audio signal; next, noise suppression is carried out on the audio signal by using a multi-order digital filter bank to obtain a primary noise reduction signal, the primary noise reduction signal is optimized into an optimized noise reduction signal based on a signal-to-noise ratio, and the frequency response matching degree of the optimized noise reduction signal and the original audio signal is calculated; then, performing phase correction and amplitude-frequency equalization processing according to the frequency response matching degree to obtain an audio equalization parameter; and finally, monitoring residual noise energy based on the parameter, generating a noise regulation and control instruction, driving adaptive sampling, and generating an adaptive noise reduction scheme. According to the invention, the audio noise reduction effect of the Bluetooth earphone can be improved.

The invention provides an optical fiber communication equipment monitoring method and system based on big data and a medium, and belongs to the technical field of optical fiber communication and big data. The method comprises the following steps: synchronously acquiring operating parameters and environmental parameters of optical fiber equipment through a multi-source sensor network, and generating a standard parameter information matrix through space-time alignment and noise suppression; further extracting characteristics such as an optical signal stability index and vibration energy anomaly through time-frequency domain conjoint analysis, and constructing an equipment health state matrix; and predicting a degradation process based on a bidirectional LSTM model of an attention mechanism, and outputting a visual diagnosis result in combination with a dynamic threshold grading alarm mechanism. According to the scheme, the problems of high false alarm rate and insufficient prediction capability of a traditional monitoring method are solved, the false alarm rate is reduced, and the fault prediction accuracy is improved.

The invention relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention discloses a multi-modal self-adaptive child voice interaction method for an educational service robot, relates to the technical field of voice signal processing, and provides a multi-modal self-adaptive child voice interaction method for an educational service robot through multi-array microphone dynamic acquisition and beam forming, self-adaptive noise suppression, multi-speaker separation and speaker identification, and child exclusive model training and online fine tuning. Technical means such as context semantic correction and multi-modal fusion are adopted, so that the target voice focusing and recognition accuracy is greatly improved; multi-person voice tracks are distinguished through an end-to-end network, and rapid distinguishing of a multi-source voice scene is realized in combination with model optimization of child range features; meanwhile, correct vocabularies and pronunciation information are traced back to a speaker library and a child sound model by using real-time semantic correction and interactive feedback, and multi-mode cooperation of a camera and a positioning sensor is used for assisting, so that beams are quickly adjusted and interference is suppressed when a child moves at will and environmental noise suddenly increases, and the sound quality of the child is improved. And natural and smooth voice interaction experience is provided in class and family scenes.

The invention belongs to the technical field of artificial intelligence and intelligent sensing, particularly relates to a multi-source sensor fusion sensing system based on adaptive noise suppression, and aims to solve the problems of noise interference, modal mismatch and insufficient robustness in multi-source sensor fusion in a complex dynamic environment. The system comprises a front-end preprocessing module, an adaptive noise suppression engine, a multi-modal feature alignment unit, a credibility-driven fusion reasoning core and a closed-loop feedback optimization mechanism. Through real-time noise modeling and dynamic weight adjustment, high-precision alignment and fusion of multi-source signals are realized, and the sensing stability and real-time performance in an extreme scene are significantly improved.

The invention discloses an improved underwater image enhancement method. The method comprises the following steps of lightweight downsampling and feature extraction; performing multi-stage attention enhancement and feature optimization; multi-path feature reconstruction is carried out; and performing multi-stage output fusion and image restoration. According to the method, a UNet + + model is introduced into the field of underwater image enhancement for the first time, standard convolution of a down-sampling layer is replaced by lightweight convolution, a three-stage attention mechanism of local noise suppression, space-channel collaborative optimization and global color cast compensation is cascaded after each layer of lightweight convolution, and a multi-level enhancement result is generated. And finally fusing and outputting through a weighted average strategy. And through lightweight convolution replacement, the model parameter quantity and the calculation complexity are remarkably reduced, and the calculation efficiency is improved. Through a three-stage attention mechanism, typical degradation problems of suspended particle noise, edge blur, color distortion and the like of the underwater image are accurately repaired. According to the method, the common artifact problems of light spots and the like and the excessive smoothness problem in the existing method are solved.

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

The invention relates to the related technical field of vibration noise control, in particular to an intelligent vibration noise control method and system using frequency spectrum characteristics, and the method comprises the steps: collecting rail transit vibration noise signals, extracting time-frequency domain mixed characteristics, carrying out multi-source separation, generating a noise source contribution degree matrix, configuring a strategy set, and monitoring changes to generate feedback. And the active damper is adjusted in real time for noise reduction. The technical problems that the noise suppression effect is limited and the noise reduction requirement of a complex time-varying scene cannot be accurately matched due to the fact that vibration noise control is difficult to adapt to a dynamically changing vibration noise environment are solved, time-frequency domain mixed features are extracted, overlapped noise sources are identified and separated more accurately, complex characteristics of vibration and noise are comprehensively considered, and the noise reduction effect is improved. And the active damper is dynamically adjusted so as to adapt to changes of vibration and noise, and the accuracy and effectiveness of dynamic adjustment are improved.

The invention relates to multi-mode signal processing, and provides a signal processing method of a computer system. The method comprises the following steps: acquiring different modal signal data by using a plurality of sensors and performing timestamp synchronization; a depth time sequence alignment network and a multi-layer dynamic time warping algorithm are utilized to ensure signal time consistency; multi-modal signal features are extracted and fused through a multi-stream depth feature extraction network and a cross-modal attention mechanism; the system adopts a dynamic convolution kernel generation and meta-learning technology to carry out adaptive signal processing; noise suppression and signal optimization are carried out by using a multi-mode noise reduction auto-encoder and a generative adversarial network, and parameters are optimized and processed through closed-loop feedback control and reinforcement learning strategies. According to the method, synchronization, fusion, adaptive processing, noise suppression and real-time feedback control of multi-mode signals are realized, and the method has the advantages of high precision, high robustness and real-time performance.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention relates to the technical field of speech processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a speech enhancement method based on multi-scale feature learning, which comprises the following steps: framing an input audio signal, extracting a Mel-frequency spectrum feature, extracting a frequency domain feature by using a multi-scale convolutional neural network, and carrying out multi-scale feature learning on the frequency domain feature; carrying out coding dimension reduction on the image; noise is suppressed through a deep residual network, enhanced audio features are generated, a non-autoregression generative model is adopted for feature conversion, and finally a generative adversarial network is used for reconstructing a target voice waveform. Voice frequency domain features are extracted through the multi-scale convolutional neural network, and the feature expression ability of different frequency bands is improved; noise suppression is carried out through a deep residual network, and the purity of the voice signals is enhanced; feature conversion is optimized through a non-autoregression generation model, and the modeling efficiency of speech enhancement is improved; the target voice waveform is reconstructed through the generative adversarial network, and the naturalness and definition of the generated voice are improved.

The invention relates to the technical field of civil engineering, and discloses a foundation pit slope protection pile concrete strength evaluation and intelligent reinforcement integrated method which comprises the following steps: S1, data acquisition and transmission: acquiring a slope protection pile structure state and environmental parameters in real time through a multi-source sensor network, the sensor network comprises a mechanical sensor, an environment sensor and a hydrological sensor which are deployed in a spatial distribution manner; s2, data preprocessing and feature extraction: performing space-time fusion processing, including noise suppression, space-time reference unification and multi-dimensional feature extraction, on the acquired multi-source sensing data, and generating a structured feature matrix; according to the technical scheme, the multi-source sensor network and the deep learning model are combined, the strength of the foundation pit slope protection pile concrete is monitored and evaluated in real time, the accurate and lossless strength prediction effect is achieved, the problems of destructive testing and precision limitation are avoided, and the test efficiency is improved. The defects of low precision and poor real-time performance under complex geological conditions in the prior art are overcome.

The invention provides a body armor composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and a storage medium, and relates to the technical field of nondestructive testing. Ultrasonic guided wave, pulsed eddy current and infrared thermal wave excitation signals are synchronously applied to a body armor composite interlayer, and multi-modal response signals of all positions are collected; and generating an original multi-modal response signal set. And inputting the signal into a pre-trained deep convolutional neural network, determining a target frequency band parameter and a target noise suppression parameter of each modal response signal, and outputting an adaptive modulation parameter group. Performing frequency domain filtering and noise suppression processing on an original signal set to generate an optimized signal set, inputting spatial frequency features into a cross-modal feature fusion model, adaptively associating defect sensitive features of different modals through an attention mechanism in the model, and generating a three-dimensional defect distribution diagram of the composite interlayer, thereby realizing millimeter-level precision defect positioning. The precision and efficiency of body armor composite interlayer defect positioning can be improved.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention relates to the technical field of voice processing, in particular to an intelligent conference content real-time translation method based on voice recognition, which comprises the following steps of: acquiring an original voice signal, generating an enhanced voice stream by adopting a sudden change noise suppression algorithm, and synchronously extracting a voiceprint fingerprint spectrum; inputting the enhanced voice stream into a hierarchical separation network, performing multi-speaker voice decoupling based on a voiceprint fingerprint spectrum, outputting a voice segment with an identity tag, and triggering updating of an incremental term knowledge base; generating a cross-language semantic consistency vector, and constructing a dynamically updated context memory pool at the same time; and converting the semantic consistency vector into a target language stream. According to the method, the spatial accuracy and semantic independence of voice decoupling are improved, a structured input basis is provided for subsequent semantic modeling and translation, and the method is particularly suitable for conference scenes with cross speaking and frequent overlapping speech streams.

The invention relates to the technical field of audio noise reduction, in particular to a multi-scene adaptive noise reduction method and system for audio terminal equipment. The method comprises the following steps: identifying a real-time environmental sound signal; environment noise source identification is carried out on the real-time environment sound signal, noise distribution perception modeling is carried out, and an environment noise distribution field is constructed; performing current scene noise change analysis on the environment noise distribution field, performing dynamic noise distribution rendering, and constructing a dynamic noise scene model; performing hedging suppression parameter calculation based on the dynamic noise scene model, performing adaptive noise suppression processing, and constructing an adaptive noise suppression strategy; acquiring an audio input signal of a user; and performing local audio gain processing on an audio input signal of a user, and performing multi-band decomposition to generate an audio frequency parameter of each band. The stability and the accuracy of the noise reduction effect are improved, so that the practicability and the adaptability of the audio terminal equipment are enhanced.

The invention relates to the technical field of narrow-band filters, and discloses an ultra-low power consumption narrow-band filter implementation method based on a digital phased array system, and the method comprises the following steps: S1, collecting input signal data, carrying out the adaptive preprocessing, and generating preprocessed signal data; s2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data; s3, performing narrowband filtering parameter matching processing according to the digital beam forming data to generate narrowband filtering parameter data; in the narrowband filtering processing process, disturbance of environmental electromagnetic interference to a target signal is identified and eliminated in real time through adaptive noise suppression and a multi-dimensional signal preprocessing mechanism, so that the system can guarantee the pointing precision of digital beam forming, the pointing offset problem in the space signal capturing process is avoided, the accuracy of narrowband signal extraction is improved, and the accuracy of narrowband signal extraction is improved. And the reliability of filtering output and the signal fidelity are enhanced.

The invention relates to the technical field of voice recognition, in particular to a multi-channel voice conversion and synchronous transmission system applied to simultaneous interpretation. Multi-language voice signals are collected through a multi-microphone array, background noise is dynamically eliminated by adopting a noise suppression technology combining spectral subtraction and deep learning, and a dynamic information source verification symbol is generated to ensure data synchronization integrity; a blind source separation technology is combined with time-frequency analysis and time delay estimation to realize multi-language signal separation and synchronization, and a time sequence is dynamically adjusted through voice activity detection; an end-to-end ASR-NMT-TTS model is constructed to realize voice real-time translation and synthesis, and low-delay transmission is carried out based on a 5G network; the real-time monitoring module is adopted to dynamically adjust the output delay and the signal-to-noise ratio, and the translation delay and the synchronization precision are optimized in combination with user feedback. According to the method, dynamic noise reduction, multi-source synchronization and a self-adaptive feedback mechanism are integrated, the problems of distortion and delay of multi-language simultaneous transmission in a complex noise environment are solved, and the obvious technical synergistic effect and practicability are achieved.

The invention relates to the technical field of vibration and noise control, discloses a new energy automobile motor high-frequency vibration and noise active suppression system and method, and aims to solve the problems of poor high-frequency vibration and noise suppression effect, real-time performance, adaptive capacity and system cost complexity of an existing new energy automobile motor. The method comprises the following steps: acquiring and preprocessing motor operation state data, extracting high-frequency harmonic characteristics, and generating predicted harmonic characteristics in combination with working condition prediction; and superposing the self-adaptive compensation quantity to a motor voltage instruction, and driving an inverter through pulse width modulation to realize active suppression of high-frequency vibration and noise. The system comprises a data acquisition preprocessing module, a harmonic feature extraction module, a working condition sensing prediction module, a self-adaptive compensation quantity generation module and a pulse width modulation correction module. By the adoption of the technical scheme, efficient, real-time and self-adaptive suppression of high-frequency vibration and noise of the motor can be achieved, the level of the high-frequency vibration and noise is remarkably reduced, the driving comfort is improved, and the complexity and cost of the system are optimized.

The invention relates to the field of signal processing, and discloses an FPGA-based dynamic multi-modal signal fusion algorithm and an adaptive noise suppression system, and the algorithm comprises a signal collection module which is used for collecting signals of different modals through a multi-modal sensor, adding a timestamp and a sensor identifier to each modal signal, and obtaining an original signal; the space-time calibration module is used for performing time synchronization and space external parameter calibration on the original signal and outputting a space-time aligned single-mode signal; and the noise suppression module is used for mapping high-dimensional signals to low-dimensional Riemannian manifolds through a manifold embedding algorithm for each single-mode signal aligned in time and space, and suppressing non-Gaussian noise by using a manifold thermonuclear filtering algorithm. Space-time labels are given to multi-mode signals by means of a signal acquisition module, time deviation and space position difference between sensors are eliminated through a space-time calibration module, and non-Gaussian noise is filtered out through a noise suppression module based on manifold learning and intrinsic characteristics of the signals are reserved.