97 results about "Source separation" patented technology

The invention relates to the technical field of signal reconstruction, in particular to a complex electromagnetic environment-oriented millimeter wave signal blind source separation and reconstruction system, which comprises a frequency spectrum trend division module, a path fading construction module, an initial cluster label generation module, a multi-solution path screening module and a fusion reconstruction execution module. According to the method, power spectral density sequence processing is carried out on millimeter wave frequency domain data, a multi-dimensional feature group is formed according to a path loss factor, an angle of arrival and the like, and an initial feature cluster is screened through an Euclidean distance, so that the accuracy of signal source classification is effectively improved; after the frequency domain response and the phase contour are continuously subjected to point comparison, path screening is completed by integrating a mean square error residual value, the path misjudgment probability is reduced, time domain resampling and phase frequency offset standardization are executed in fusion reconstruction, the consistency and fidelity of signal reconstruction are enhanced, and the reconstruction precision is improved. The whole process improves the separation accuracy and reconstruction precision of mixed signals in a complex electromagnetic environment.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention relates to an array microphone noise reduction recording method based on cascade noise reduction and blind source separation, which belongs to the technical field of voice signal processing and recording, and comprises the following steps: configuring a multi-channel array microphone, ensuring that the amplitude and phase of a channel signal are consistent, and collecting an original multi-channel voice signal; a weighted kernel function blind source separation algorithm is adopted, signal-to-noise ratio distribution characteristics of signals are extracted, kernel function weights are given, and target voice and interference signal components are obtained through decoupling of an independent component analysis model; executing target-oriented adaptive cascade noise reduction, locking the voice of a keynote speaker through directional pickup, reducing noise, filtering out reverberation, and enhancing the voice of a far-field target by combining a voice mask neural network with a far-field pickup algorithm in sequence; and processing the target voice through voice feature perception lossless coding and storing the target voice. According to the invention, stable acquisition of multi-channel signals, accurate separation of mixed signals and layered suppression of noise reverberation are realized, the signal-to-noise ratio and definition of far-field voice are significantly improved, and the method is suitable for single-person speaking or multi-person dialogue scenes.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention relates to the technical field of electric power operation and maintenance, in particular to a transformer fault diagnosis method, device and equipment and a storage medium, and the method comprises the steps: collecting a transformer acoustic monitoring signal based on an acoustic microphone array, carrying out blind source separation and decoupling, and constructing an acoustic signal feature set; transformer operation condition parameters are extracted, mechanical state degradation evaluation is carried out according to the acoustic signal feature set, and a mechanical state health degree index is generated; and calculating an acoustic signal receiving timestamp of the acoustic microphone array, performing sound source distribution mapping, and constructing a sound source spatial distribution diagram. According to the method, blind source separation decoupling, mechanical state degradation evaluation, sound source distribution mapping and fault situation diagnosis are performed by acquiring the acoustic signals and the operation condition parameters, so that tiny mechanical faults in the transformer can be accurately identified, intelligent maintenance decision is realized, and the method has the advantages that the tiny mechanical faults in the transformer can be detected earlier; the real-time performance and reliability of fault diagnosis are improved, and the accident risk caused by diagnosis lag is reduced.

The invention relates to the technical field of power equipment fault monitoring, in particular to a distribution box fault monitoring method, and aims to solve the problem that a traditional monitoring method in the prior art depends on a single filtering or fixed threshold strategy and is difficult to effectively separate fault features. According to the method, mechanical vibration and electromagnetic interference signals can be effectively separated through an adaptive blind source separation algorithm, the problem of early fault signal identification in a mixed interference environment is solved by combining multi-scale feature fusion and a dynamic environment compensation mechanism, and the method has the advantages of improving monitoring accuracy and realizing accurate quantification of fault energy.

The invention discloses a leakage monitoring method based on an LNG fuel gas system. The leakage monitoring method comprises the steps that a self-adaptive frequency modulation continuous wave active acoustic scanning network is established; blind source separation is carried out on mixed signals in acoustic scanning network abnormal events by adopting self-adaptive kernel independent component analysis; constructing a leakage feature mapping model of the physical information neural network; leakage source accurate positioning and quantification based on acoustic tomography and Bayesian reasoning are carried out; multi-modal decision fusion is carried out based on the multi-dimensional data sources received in parallel, a false alarm suppression mechanism is set, and time continuity verification and space consistency verification are carried out; establishing a reinforcement learning model for autonomously optimizing a monitoring strategy according to environment change and system state, and realizing adaptive optimization; the strategy network after self-adaptive optimization is deployed at the cloud, actions are generated regularly according to the current state, the actions are issued to the regional gateway and the edge node for execution, and iterative updating is carried out, so that the monitoring accuracy in a complex environment is improved, and the false alarm rate is reduced.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

The invention discloses a music source separation method and system based on a hybrid expert self-attention network, and belongs to the technical field of audio signal processing and deep learning. The method comprises the following steps: receiving a time domain mixed audio signal, and obtaining a complex frequency spectrum through short-time Fourier transform; the method comprises the following steps: estimating a complex ideal proportion mask through an improved separator network MoEFormer, respectively modeling a time domain dependency relationship and a frequency domain dependency relationship in the separator network by adopting an axial attention mechanism, and replacing a feedforward network in a standard Transformer with a hybrid expert layer; the hybrid expert layer comprises an expert network specially designed for drum sound, Bass and human voice characteristics, and a self-adaptive weight distribution mechanism based on gating routing; and finally, reconstructing the separated time domain signal through inverse short-time Fourier transform. Through expert specialization and feature adaptive fusion, the problem of multi-sound-source feature confusion is effectively solved, and low calculation complexity is kept while the separation precision is improved.

The invention discloses a belt conveyor safety online monitoring method and device, a medium and a product, and relates to the field of belt conveyor safety monitoring, and the method comprises the steps: collecting a multi-vibration-source signal and a temperature signal of a belt conveyor through a vibration reduction and sensitization acoustic vibration optical cable device; the vibration reduction and sensitization acoustic vibration optical cable device is of a multi-core optical fiber spiral winding structure, and a gradient vibration reduction layer is integrated on the outer layer. Sequentially carrying out wavelet packet decomposition, blind source separation and deep learning optimization on the multi-vibration-source signal, and determining a single-vibration-source signal corresponding to each part; performing time synchronization on the single vibration source signal and the corresponding temperature signal by adopting a dual-optical fiber redundant link and an optical fiber gyroscope; and performing multi-source data fusion diagnosis by adopting a D-S evidence theory according to the synchronized single vibration source signal and the corresponding temperature signal in combination with a sound signal, and generating a fault early warning signal. The distributed optical fiber monitoring accuracy and stability of the belt conveyor can be improved.

A computing system including one or more processing devices configured to receive a first music sample and a second music sample. At a music source separation machine learning (ML) model, the one or more processing devices extract a first vocal component from the first music sample and a second vocal component from the second music sample. At a voice embedding ML model, the one or more processing devices extract one or more first embedding vectors from the first vocal component and one or more second embedding vectors from the second vocal component. The one or more processing devices compute a similarity value between the first and second embedding vectors and determine whether the similarity value is above a predefined similarity threshold. Based on the determination, the one or more processing devices output an indication of whether the first music sample has a same singer as the second music sample.

The invention discloses a graph signal blind source separation method in a white noise environment, and belongs to the technical field of signal processing. Aiming at the problem that the traditional blind source separation performance is reduced due to additive white noise, the invention provides a joint optimization scheme combining blind compression nonlinear noise suppression and image smoothing regularization. Firstly, a blind compression function is applied to a noisy observation image signal for preprocessing; secondly, carrying out mean value removal and whitening processing on the compressed signal; then, constructing a graph Laplacian matrix as a smoothing operator, establishing a joint diagonalization objective function fusing graph autocorrelation, a FastICA item and a graph smoothing regular item, and adopting a Givens rotation algorithm to iteratively optimize and solve a separation matrix; and finally, reconstructing a source signal through multiplication of the separation matrix and the whitening data. According to the method, noise interference is effectively suppressed through blind compression, signal structure consistency is maintained by using graph smoothing prior, and separation robustness, precision and convergence stability in a strong noise environment are remarkably improved.

The application discloses an end-to-end audio-visual source separation method based on dynamic gating fusion. The method uses a dynamically adjusted fusion mechanism to effectively fuse audio and visual features through a dynamic gating fusion module, and further separates the mixed audio signal through an audio-visual Transformer decoder. The method can accurately separate the target audio in a complex multi-source environment, significantly improving the quality of audio-visual source separation, especially in the presence of background noise and multiple sources. Experimental results show that the performance of the method on multiple benchmark datasets has been significantly improved, verifying its effectiveness and advantages in the audio-visual source separation task.

The application discloses a non-invasive load decomposition method based on CEEMDAN and FastICA, and belongs to the technical field of load monitoring. The method comprises the following steps: S1, collecting active power of total load and various single loads and preprocessing; S2, constructing a completely adaptive noise ensemble empirical mode decomposition (CEEMDAN) model, and decomposing total load power; S3, estimating the number of sources based on Bayesian information criterion; S4, performing dimension reduction by using maximum information coefficient (MIC); S5, performing load decomposition by using FastICA blind source separation; and S6, evaluating the approximation degree of decomposed signals and source signals. The application realizes load decomposition from the perspective of signal blind source separation, reduces the cumbersome load information feature extraction, and obtains complete load information through decomposition. Compared with deep learning, the model training time is greatly reduced.

This application provides a signal processing method, chip, electronic device, and storage medium. The method includes: acquiring an input signal, wherein the input signal is a speech signal received by multiple microphones; estimating the covariance matrix of the Nth frame of the input signal to obtain a target covariance matrix of the Nth frame; updating the demixing matrix based on the target covariance matrix of the Nth frame to obtain target elements in the demixing matrix of the Nth frame; performing amplitude demixing based on the target elements in the demixing matrix of the Nth frame to obtain a target demixing matrix of the Nth frame; and performing signal separation based on the target demixing matrix of the Nth frame and the input signal of the Nth frame to obtain an output signal of the Nth frame. The method provided in this application helps to balance the performance and robustness of blind source separation algorithms and improve the signal-to-noise ratio of speech signals.

This application discloses a music source separation method and wearable device, relating to the field of signal processing technology. It employs a UNet architecture neural network model for music source separation. The neural network model includes at least an encoder and a decoder. The method includes: acquiring the original signal of the music source to be separated; converting the original signal to the frequency domain to obtain a frequency domain signal; performing feature encoding processing on the frequency domain signal through the encoder to obtain encoded feature data, wherein the feature encoding processing includes feature extraction, and the encoder performs feature extraction processing at least by using causal convolutional TFC-TDF blocks; and performing feature decoding processing on the encoded feature data through the decoder to output the music source separation result. This application achieves low-latency music source separation in resource-constrained edge devices.

The invention discloses a streaming spatial audio separation method, streaming spatial audio separation equipment and a vehicle-mounted audio system, and relates to the technical field of audio processing. The streaming spatial audio separation method comprises the following steps: extracting a time-frequency feature based on a target sound source signal, performing multi-scale music feature extraction and frequency division compression on the time-frequency feature to obtain a compressed feature, and performing hierarchical long-time sequence dependence modeling on the compressed feature to obtain a multi-stage fusion feature, and performing frequency division decoding on the multi-level fusion features to obtain separated music elements. By adopting the scheme of the invention, high-fidelity, low-delay, multi-source separation and spatialization output of the music source under a resource-limited platform can be realized.

The application discloses an InSAR surface deformation multi-source vertical contribution analytical method based on deformation signal spectrum decomposition and belongs to the technical field of surface deformation monitoring. The steps are as follows: first, long-time SAR images are acquired, vertical deformation data and related rates and deformation variables are obtained through SBAS-InSAR technology processing; then, a plurality of auxiliary conditions are fused, regional spatial partitioning is realized through K-Means clustering; subsequently, CEEMDAN decomposition and mean threshold multi-scale reconstruction are performed on deformation signals of each partition, high-frequency items, low-frequency items and trend items are obtained; finally, ICA blind source separation and variance contribution rate analysis are performed, and shallow, medium and deep surface deformation information is inversed through directional reorganization; the K-Means clustering algorithm is adopted to perform spatial partitioning on the regional deformation field, the homogeneity and precision of subsequent signal processing are improved; and the CEEMDAN decomposition and multi-scale reconstruction method is introduced, adaptive and fine scale separation of deformation time series signals is realized; the blind source separation technology is applied to multi-scale component reorganization, and the physical attribution of the vertical position of the subsidence source is realized.

The invention provides a signal noise reduction method for acoustic monitoring of a tool wear state in a milling process, which relates to the technical field of intelligent manufacturing, and is characterized in that main shaft noise is eliminated through spectral characteristics of IMF components obtained through adaptive noise complete set empirical mode decomposition in combination with spectral characteristics of signals acquired by a multi-scene milling test; first-stage noise reduction is realized; performing fast independent component analysis on the residual IMF components after the first-stage noise reduction to realize blind source separation, and identifying and removing random noise components in combination with a fuzzy entropy threshold criterion to realize second-stage noise reduction; reconstructing a de-noised signal by using the residual signal components after the second-stage noise reduction, and screening out features conforming to a tool wear trend through a Kendall rank correlation coefficient; and inputting the screened features conforming to the tool wear trend into a convolutional neural network to realize high-precision intelligent monitoring of the tool wear state based on the sound signal. According to the invention, stable denoising performance is maintained under different working conditions.

The invention belongs to the technical field of signal processing, and relates to a mixed signal double-path blind source separation system and method, and a terminal. The blind source separation system comprises an encoder, a mask network and a decoder, the encoder receives a mixed signal, extracts features and then converts the features into high-dimensional feature representation, the mask network comprises a decomposition module and DPGCU blocks which are sequentially connected, each DPGCU block is provided with a dual-path LSTM structure and comprises a GCU block, and each GCU block comprises a GCU block; the decomposition module segments the high-dimensional features and inputs the segmented high-dimensional features to the first DPGCU block, a first path LSTM structure extracts local features of a plurality of segmented data and performs residual connection, and outputs local feature representation; the second path LSTM structure extracts global features represented by the local features and performs residual connection, and outputs global feature representation; the GCU block extracts global features of the global feature representation and outputs enhanced feature representation. And the output of each DPGCU block is used as the input of the next DPGCU block. The method has the advantages of strong signal separation capability, high separation precision and the like.

The invention relates to the technical field of coal cutter fault diagnosis, in particular to a coal cutter bearing early degradation detection method and system adopting a spiking neural network. The method comprises the following steps: collecting a bearing acoustic signal in real time and carrying out blind source separation and band-pass filtering preprocessing; converting the preprocessed signal into a logarithmic Mel time-frequency spectrogram, and coding the logarithmic Mel time-frequency spectrogram into a pulse event sequence through peak detection; inputting the pulse sequence into a pulse neural network model comprising a pulse convolutional layer and a pulse recurrent neural network layer, and extracting state features; and judging whether the bearing is degraded early based on the classification or deviation calculation. According to the method, by simulating biological auditory sense and a nerve processing mechanism, utilizing the characteristics of high sensitivity and low power consumption of the pulse neural network to time sequence signals and combining targeted noise reduction and coding, sensitive and accurate recognition of the weak characteristics of early degradation of the bearing in a strong noise environment is achieved, and the method is particularly suitable for intelligent edge monitoring of underground equipment.

The application relates to the technical field of hearing aids, in particular to a digital hearing aid automatic gain control method and system, which comprises the following steps: collecting a sound signal through a built-in microphone of a hearing aid; obtaining a mel spectrogram of the sound signal; dividing each frame of the mel spectrogram into mel frequency bands; determining the energy concentration degree of each mel frequency band; determining the noise saliency of each frame of the mel spectrogram; performing blind source separation on the collected sound signal by using a natural gradient algorithm; obtaining the separation difference degree of each iteration by combining the noise saliency of all frames of the mel spectrogram, the similarity degree between the target signal and the noise signal obtained after each iteration separation, and the similarity degree between the collected sound signal and the separated signal, so as to determine the step length of the next iteration; and performing automatic gain control on the target signal obtained through the blind source separation. Therefore, the hearing aid frequency interference can be reduced, and the user's auditory experience can be optimized.

The invention discloses a micro-impact signal reconstruction method and device based on blind source separation and compressed sensing, relates to the technical field of physical quantity measurement, can be applied to the fields of mine safety monitoring and geophysical exploration, and mainly aims to solve the problem that weak impact ground pressure signals are difficult to detect and extract under the existing strong background noise. The method mainly comprises the following steps: capturing a mixed acoustic emission signal generated in a to-be-monitored area under a mine well, and carrying out blind source separation on the mixed acoustic emission signal to obtain a plurality of independent initial signal components; screening the initial signal component based on an energy characteristic and a pulse characteristic to obtain an effective micro-seismic signal component; compressing the effective microseismic signal component according to a preset compression ratio to obtain a compressed measurement vector; and performing iterative sparse reconstruction on the compressed measurement vector through a pre-trained adaptive iterative sparse reconstruction model to obtain a reconstructed micro-impact signal of the to-be-monitored area. The method is mainly used for reconstructing mine underground micro-impact signals.

The invention discloses a prestressed electric pole centrifugal forming control method and system based on a material state, and particularly relates to the technical field of concrete electric pole centrifugal forming manufacturing. The method is used for solving the problem that it is difficult to effectively separate and identify key features truly reflecting the concrete compact state from sensor mixed signals under the strong mechanical disturbance working condition. Vibration and sound signals are synchronously collected in the centrifugal process to form a mixed signal, blind source separation is carried out on the mixed signal to extract a pure material state signal component, and multi-scale permutation entropy analysis is carried out on the component to extract characteristic parameters representing ordered evolution of an internal structure. And calculating a non-stationarity measurement index to evaluate the process balance, comprehensively evaluating the compact state of the material and the process balance based on the real-time change trend of the parameters, and dynamically adjusting the rotating speed of the centrifugal machine or the stage switching opportunity according to the compact state and the process balance. And the conversion from the dependence on a fixed empirical program to the self-adaptive closed-loop control based on the real-time feedback of the internal state of the material is realized.

The invention discloses a secret-related space multi-mode illegal behavior identification method based on a millimeter wave radar, relates to the technical field of intelligent security and protection, and aims to solve the problems of high missing and false alarm rate, privacy leakage and poor environmental adaptability of a traditional security and protection means. According to the method, hardware-level synchronization is realized through a millimeter-wave radar and a distributed microphone array, and a low-density point cloud sequence and multi-channel mixed audio in a monitoring area are acquired; reconstructing the low-density point cloud into a high-density point cloud capable of depicting fine human body postures by using a point cloud super-resolution model based on a reversible neural network, and extracting pure target voice through blind source separation and adaptive beam forming in combination with radar target azimuth information; then, respectively extracting point cloud behavior features and voice audio features, and performing deep fusion through a cross-modal collaborative attention mechanism to obtain joint features; and finally, inputting into a multi-layer perceptron classifier, outputting illegal behavior probability distribution through a Softmax function, and judging a result.

The invention relates to the technical field of transformer fault diagnosis, and discloses an exciting transformer fault diagnosis method, system and device and a medium, and the method comprises the steps: collecting an original voiceprint signal of an exciting transformer, optimizing the parameters of variational mode decomposition through employing a satin blue katingbird optimization algorithm, carrying out the mode decomposition of the original voiceprint signal, and carrying out the fault diagnosis of the exciting transformer; obtaining a plurality of intrinsic mode functions; constructing an input matrix, carrying out blind source separation by adopting a rapid independent component analysis method, and extracting a body voiceprint signal of the exciting transformer; performing feature extraction on the ontology voiceprint signal through a wavelet scattering network to obtain a wavelet scattering propagation vector, and inputting the wavelet scattering propagation vector into a pre-constructed liquid neural network model for training; and classifying fault types of the exciting transformer by using the trained liquid neural network model, and outputting a final fault diagnosis result. According to the invention, accurate identification and prediction of the fault of the excitation transformer in a complex electromagnetic environment and a multi-noise background are realized.

Embodiments of the present application disclose a voice signal processing method and device and electronic equipment. The device comprises a dereverberation module and a blind source separation module; wherein: the dereverberation module is configured to utilize a prediction coefficient matrix G(n) of the n th moment to perform dereverberation processing on a delay signal group Y(n-D) corresponding to an input signal y(n) of the n th moment, to obtain a dereverberation actual signal x(n) of the n th moment; the blind source separation module is configured to utilize a separation matrix W(n) of the n th moment to process the dereverberation actual signal x(n) of the n th moment, to obtain a separation actual signal z(n) of the n th moment; wherein the device further comprises a calculation module configured to calculate the prediction coefficient matrix G(n) of the n th moment by the following method, comprising: obtaining a separation matrix W(n-1) of the (n-1) th moment; obtaining the prediction coefficient matrix G(n) of the n th moment according to the separation matrix W(n-1) of the (n-1) th moment and a prediction coefficient matrix G(n-1) of the (n-1) th moment.

To provide an audio source separation processing pipeline system and method.SOLUTION: Systems and methods for audio source separation include receiving a single-track audio input sample having an unknown mixture of audio signals generated from a plurality of audio sources, and separating one or more of the audio sources from the single-track audio input sample using a sequential audio source separation model.SELECTED DRAWING: Figure 12

This invention belongs to the field of aeronautical acoustics and flight simulation technology, and relates to a method for cabin multi-source sound source evolution and sound field reconstruction under flight state constraints. It solves the problems of traditional blind source separation relying on statistical characteristics, sound field reconstruction relying on measured data, and low sound fidelity in flight simulators. This invention constructs a multi-source mixed acoustic signal-flight parameter synchronous dataset as input, constructs a flight state-sound source feature prior knowledge base as prior constraints, and constructs an objective function including reconstruction error, source independence regularization, and flight state constraint terms. The time-domain signals of each independent sound source are separated using the alternating direction multiplier method, and the sound source feature parameters are extracted as output. Flight state parameters are used as input, and an evolutionary mapping function is constructed based on the mean function. The target flight state is input into the evolutionary mapping function to obtain predicted sound source feature values, driving the equivalent source method to achieve sound field reconstruction, and outputting a three-dimensional sound field distribution. This invention achieves predictive, high-fidelity real-time reconstruction of the sound field within the entire flight envelope.

The application discloses a prestressed electric pole centrifugal forming control method and system based on material state, and particularly relates to the technical field of concrete electric pole centrifugal forming manufacturing, and is used for solving the problem that it is difficult to effectively separate and identify the key features which truly reflect the concrete compactness from the sensor mixed signals under strong mechanical disturbance working conditions; mixed signals are formed by synchronously collecting vibration and sound signals during the centrifugal process, the mixed signals are subjected to blind source separation to extract pure material state signal components, the components are subjected to multi-scale permutation entropy analysis to extract feature parameters representing the internal structure ordered evolution, and the non-stationarity measurement indexes are calculated to evaluate the process balance, the material compactness and the process balance are comprehensively evaluated based on the real-time change trends of the above parameters, and the centrifuge speed or the stage switching time is dynamically adjusted accordingly, so that the transformation from the fixed experience program to the adaptive closed-loop control based on the real-time feedback of the material internal state is realized.