126 results about "Time–frequency representation" patented technology

The invention discloses a feature attention and bilinear gating fused speech emotion recognition method and device, and the method comprises the following steps: 1, collecting an audio file, obtaining corresponding label information, generating audio waveform and time frequency representation data through preprocessing, and constructing an audio waveform mask and a time frequency mask to mark an effective information region; 2, constructing a dual-path feature extraction module which comprises a time-frequency feature extraction module and a pre-training acoustic feature coding module; wherein the time-frequency feature extraction module models emotion correlation through local convolution and a multi-dimensional attention mechanism, and performs global time sequence modeling based on a bidirectional gated loop network; the pre-training acoustic feature coding module extracts high-level speech representation with high expression ability for emotion distinguishing by using a pre-training model; and step 3, constructing a feature fusion module and an emotion classification module, and combining with a dual-path feature extraction module to form a speech emotion recognition model.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention relates to the technical field of intelligent building heat load prediction and heat balance regulation and control, and discloses an intelligent building heat balance dynamic regulation and control method and system based on load prediction, and the method comprises the steps: constructing a time-frequency causal double-flow analysis network, mutual enhancement of the time-frequency characteristics and the causal relationship is realized; designing a hierarchical causal discovery algorithm, and mining a multilevel causal structure; developing a causal enhanced time-frequency representation learning method, and fusing time-frequency and causal information; establishing an intervention decision framework based on anti-fact analysis, and evaluating an intervention effect; an abnormal mode self-evolution recognition system is realized, and new abnormal modes are continuously learned; an interpretable abnormity diagnosis mechanism is developed, and a clear diagnosis report is provided; according to the method, the thermal load prediction accuracy is improved, the abnormal early warning capability is enhanced, the intervention efficiency is improved, the system interpretability is enhanced, and the optimization of the energy utilization efficiency is realized.

The invention provides a sound wave signal self-supervised learning enhanced robust noise reduction processing method and system, and relates to the technical field of signal processing, and the method comprises the steps: carrying out the feature enhancement of an initial time-frequency representation through a dynamic adaptive mask strategy, constructing a self-supervised reconstruction task based on the mask time-frequency representation, separating noise and signal subspaces in a semantic manifold space, and carrying out the self-supervised learning enhanced robust noise reduction. And establishing a probability propagation network in combination with time domain continuity characteristics to model a local dependency relationship, and finally generating a noise reduction weight and realizing semantic fidelity optimization. The method can effectively improve the noise reduction effect and semantic integrity of sound wave signals in a noise complex environment.

The present disclosure describes techniques for implementing improved audio source separation. A complex spectrum X is split into a plurality of K bands along a frequency axis by applying band-split operations on the complex spectrum X. The complex spectrum is a time-frequency representation of audio signals. Each of the plurality of K bands is denoted as Xk, k=1, . . . , K. Each band Xk comprises one or more frequency bins. Each individual multilayer perceptron is applied to each band Xk to extract latent representations and obtain outputs Hk0. A time-domain transformer and a frequency-domain transformer are applied on a stacked representation H0. Time-domain and frequency-domain transformers are repeatedly applying in an interleaved manner for L times to obtain HL output from the transformer blocks. The HL is input into a multi-band mask estimation sub-model. A complex ideal ratio mask is generated based on outputs from the multi-band mask estimation sub-model.

The invention provides a children practice situation evaluation method, medium and system, and belongs to the technical field of deep learning models.The children practice situation evaluation method includes the steps that children and standard playing audio signals are collected and preprocessed into time-frequency representation, a time window sequence is constructed to calculate window similarity, beat mark points are extracted to form a rhythm sequence, and the rhythm sequence is obtained; a rhythm perception analysis model is input to generate a rhythm feature representation vector, a multi-dimensional deviation vector is formed by applying multi-level time model analysis, a rhythm deviation tolerance range matrix is constructed, deviation similarity is calculated, and finally a rhythm performance score is output through a rhythm evaluation neural network model. According to the method, a convolutional neural network, a long-short term memory network and an attention mechanism are fused, and comprehensive quantitative evaluation of the playing rhythm performance of the children is realized through large-scale data set training and expert scoring verification.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is improved.

A method comprises: obtaining softmask values for frequency bins of time-frequency tiles representing an audio signal; reducing, or expanding and limiting, the softmask values; and applying the reduced, or expanded and limited, softmask values to the frequency bins to create a time-frequency representation of an estimated target source. An alternative method comprises, for each time-frequency tile: obtaining softmask values; applying the softmask values to the frequency bins to create a time-frequency domain representation of an estimated target source; obtaining a panning parameter and a source concentration estimates for the target source; determining, using the panning parameter estimate and the softmask values, a magnitude for the time-frequency representation of the estimated target source; determining, using the panning parameter estimate and the source phase concentration estimate, a phase for the time-frequency representation of the estimated target source; and combining the magnitude and the phase.

An audio processing system and method are provided, the audio processing system comprising: an input interface that receives an input audio mix and transforms it into a time-frequency representation defined by values of time-frequency units; a processor that maps values of the time-frequency units into a hyperbolic space by executing an embedded neural network trained to associate the respective time-frequency units to high-dimensional inserts and projecting the respective high-dimensional inserts into the hyperbolic space; and an output interface that accepts a selection of at least a portion of the hyperbolic space and renders a selected hyperbolic embedding that falls within the selected portion of the hyperbolic space.

The invention discloses an audio authenticity identification method and device based on a dual-path Transform, and the method comprises the following steps: preprocessing: converting an original audio signal into time-frequency representation, and extracting features through LFCC or STFT to obtain the time-domain features and frequency-domain features of the audio signal; the time-frequency representation feature = {B, F, T} containing time domain features and frequency domain features is obtained through preprocessing, B is the batch size, F is the number of frequency channels equal to the number of LFCC linear filters, and T is the number of time frames; the convolution encoder is used for encoding the input time-frequency representation through a plurality of 2D convolution layers and extracting local features; and respectively processing the frequency domain information and the time domain information through a double-path Transform structure to form semantic-acoustic information decoupling. Frequency domain information and time domain information are respectively processed through a double-path Transform structure, and efficient decoupling of semantic information and acoustic information is realized in combination with an attention mechanism, so that audio information of different levels is better extracted and utilized.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

An access node, user equipment, apparatuses, methods, and computer programs for a communication system. An apparatus for a wireless transmitter device includes a transmitter module for transmitting wireless transmissions and a processing module, which controls the transmitter module. The processing module generates one or more transmit symbols in a Delay-Doppler domain to obtain a Delay-Doppler representation; transforms the Delay-Doppler representation into a Time-Frequency domain to obtain a Time-Frequency representation, the Time-Frequency representation having a first bandwidth and a first duration; adds pilot symbols to the Time-Frequency representation to obtain a Time-Frequency representation with an extended second bandwidth or an extended second duration; transforms the Time-Frequency representation with the extended second bandwidth or the extended second duration to the time domain to obtain a time domain representation; and transmits the time domain representation to a wireless receiver device.

In accordance with implementations of the subject matter described herein, a solution for speech enhancement is proposed. In this solution, a target time-frequency representation at least indicating intensities of an input audio signal at different frequencies over time is obtained. The input audio signal comprises a speech component and a noise component. Frequency correlation information and time correlation information of the input audio signal is determined based on the target time-frequency representation. A target feature representation is generated based on the frequency correlation information, the time correlation information, and the target time-frequency representation. The target feature representation is for distinguishing the speech component and the noise component. An output audio signal is generated based on the target feature representation and the target time-frequency representation. The speech component is enhanced relative to the noise component in the output audio signal. In this way, the performance of speech enhancement can be improved.

The present invention discloses a rolling bearing fault diagnosis method based on a multi-scale graph transformer, and proposes a new graph node feature aggregation model, which aggregates the feature information of each neighborhood of the graph node into the feature representation of the central node, and uses the transformer to improve the utilization rate of important node information to enhance the feature representation. The implementation process is as follows: (1) performing wavelet transform on the collected original bearing fault data to obtain the fault time-frequency representation node, and using the first k fault time-frequency representation nodes of each fault time-frequency representation node to construct an adjacency matrix; (2) aggregating the multi-scale feature information of the fault time-frequency representation node through a multi-scale feature aggregation module, and then constructing the fault time-frequency representation node structure encoding to save the structural information of the fault time-frequency representation node, and then inputting it into the transformer module for feature extraction; (3) using the SoftMax classifier to classify the extracted fault features. Experimental results show that the method of the present invention is an effective fault diagnosis method.

The invention discloses a uterus electromyographic signal generation method based on a deep generative model, which comprises the following steps: 1, acquiring uterus electromyographic signals of the abdomen of a pregnant woman through a multi-channel surface electrode, and carrying out filtering, noise reduction and normalization preprocessing; 2, performing short-time Fourier transform on the preprocessed signal to obtain time-frequency representation, and decomposing the time-frequency representation into low-frequency and high-frequency components; 3, respectively establishing reconstruction processes of the low-frequency component and the high-frequency component, and obtaining discrete potential representation through training; 4, on the basis of an encoder and a decoder which are subjected to reconstruction training, modeling is conducted on the low-frequency discrete sequence and the high-frequency discrete sequence through a bidirectional Transform prior model, and potential space distribution of the low-frequency discrete sequence and the high-frequency discrete sequence is learned; and 5, in a generation stage, sampling through a priori model to obtain a token sequence, and generating a high-quality uterine myoelectricity signal after decoding and inverse short-time Fourier transform. The signal generated by the method has high authenticity and stability while maintaining the consistency of the time sequence characteristics and the frequency spectrum, and can be applied to uterine contraction monitoring and premature delivery risk prediction.

The invention relates to the technical field of data processing, and discloses a speech enhancement method, device and equipment based on stream matching and a medium, which can be applied to the financial and medical fields, and the method comprises the following steps: obtaining noise speech data, and carrying out time-frequency conversion processing and standardization processing to obtain standardized time-frequency representation data; performing stream matching input construction processing based on the standardized time-frequency representation data, and inputting the data to a stream matching model for processing to obtain enhanced time-frequency representation data; performing anti-standardization processing on the enhanced time-frequency representation data to obtain anti-standardized time-frequency representation data; and performing time domain reconstruction processing on the destandardized time-frequency representation data to obtain enhanced voice waveform data. In the invention, aiming at the ubiquitous problems of poor fidelity and large reasoning time delay caused by discrete quantization in the existing speech enhancement method, enhanced speech waveform data can be obtained by introducing a stream matching modeling and time frequency-time domain consistent processing chain, so that the speech detail fidelity is improved and the time delay is reduced.

This invention provides a quantum-derived Newton-Raphson optimal fractional-order spectrogram generation method, comprising the following steps: Step 1: Acquire the original audio signal and construct a fractional-order spectrogram based on fractional Fourier transform (FRFT); Step 2: Perform nonlinear scaling compression on the fractional-order spectrogram using a Mel filter bank to generate a fractional-order Mel spectrogram; Step 3: Construct an adaptive optimization framework with information entropy minimization as the objective function to measure the information fidelity between the spectrogram and the original signal; Step 4: Use the quantum-derived Newton-Raphson optimization algorithm (QNRBO) to globally optimize the fractional-order order, frame length, and frame shift hyperparameters to generate the optimal fractional-order spectrogram; Step 5: Input the optimal fractional-order spectrogram into a downstream speech recognition model. This technical solution aims to systematically solve core problems such as insufficient traditional time-frequency representation capabilities, rigid hyperparameter configuration, limited optimization algorithm performance, and feature-task disconnect.

The application discloses a kind of CNN-based rotating machinery local fault signal sparse time-frequency representation method, comprising the following steps: step S1, utilize rotating machinery local fault signal response model and signal preprocessing to construct time-frequency spectrum data set for network model training;Step S2, construct a convolutional neural network model and train the model;Step S3, the rotating speed signal of acquisition equipment end is collected and the fault characteristic time-frequency ridge of each order when fault occurs in different positions is calculated, simultaneously the vibration signal of acquisition equipment end is collected and the time-frequency spectrum sample that can be used for the input of convolutional neural network model is obtained by signal preprocessing;Step S4, the trained convolutional neural network model is used to extract sparse feature to time-frequency spectrum sample, and the sparse time-frequency representation of the sample is obtained;Step S5, extract fault characteristic time-frequency ridge and compare each order fault characteristic time-frequency ridge one by one, further determine the fault occurrence position, to complete the high-precision fault diagnosis of rotating machinery.

This disclosure describes techniques for achieving improved audio source separation. The complex spectrum X is divided into K frequency bands along a frequency axis by applying a frequency band division operation to the complex spectrum X. The complex spectrum is a time-frequency representation of the audio signal. Each of the K frequency bands is represented as Xk, k = 1,..., K. Each frequency band Xk comprises one or more frequency intervals. Each individual multi-layer perceptron is applied to each frequency band Xk to extract potential representations and obtain an output Hk0. A time domain transformer and a frequency domain transformer are applied to the stacked representation H0. The time domain transformer and the frequency domain transformer are repeatedly applied L times in an interleaved manner to obtain an HL output from the transformer block. The HL is input into a multi-band mask estimation sub-model. The complex ideal ratio mask is generated based on the output from the multiband mask estimation sub-model.

A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm.

The invention discloses an eye movement artifact noise reduction method and device based on multilayer decomposition and fractal analysis, and belongs to the technical field of electroencephalogram signal processing.The method comprises the steps that empirical mode decomposition is conducted on original electroencephalogram signals to obtain a plurality of intrinsic mode functions, and the intrinsic mode functions are summed to obtain an eye movement artifact template; performing continuous wavelet transform on the eye movement artifact template to obtain a time-frequency representation matrix, and extracting a column vector of the time-frequency representation matrix as a feature vector; clustering the feature vectors based on frequency to obtain a plurality of different clusters, wherein each cluster comprises a plurality of column vectors under the same frequency classification; the fractal dimension of each cluster is calculated, the cluster with the minimum fractal dimension value is selected for time domain signal reduction, eye movement artifact signals are obtained and removed from the original electroencephalogram signals, and the electroencephalogram signals obtained after noise reduction are obtained. Under the condition that main components of the electroencephalogram signals are not affected, eye movement artifacts can be effectively removed, the accuracy and integrity of the electroencephalogram signals are guaranteed, and the method is suitable for various electroencephalogram signal processing and analysis tasks.

The invention discloses a Jiangsan silk bamboo flute technique identification method. The method comprises the following steps: receiving an audio signal; generating a time-frequency representation; extracting features from the time-frequency representation; inputting the extracted features into a pre-trained convolutional neural network; inputting the extracted features into a pre-trained recurrent neural network; inputting the output of the convolutional neural network and the recurrent neural network into a pre-trained hidden Markov model; and integrating the outputs of the convolutional neural network, the recurrent neural network and the hidden Markov model to generate a final technique recognition result. According to the method, the influence of different playing styles and environmental noise can be better adapted by using short-time Fourier transform and Mel-frequency cepstrum coefficients, and the adaptability and robustness of the model are enhanced by capturing the subtle changes of the bamboo flute technique, including the frequency fluctuation of tremolo, the pitch track of gliding and the strength change of beating sound, so that the method is more suitable for popularization and application. And the fineness and the accuracy of feature extraction are further improved.

The present disclosure provides a physical information neural network-based pose estimation method, device and equipment, relating to the technical field of computer, the method comprising: collecting an original sensor sequence and preprocessing the original sensor sequence to obtain a time-frequency representation; determining a multi-scale feature and a residual attention weight based on the time-frequency representation; inputting the multi-scale feature and the residual attention weight into a physical information neural network model to obtain an angular velocity estimation and a pose quaternion update term; calculating a physical residual loss according to the angular velocity estimation and the pose quaternion update term and a physical kinematics constraint, and constructing a total loss function based on the physical residual loss and a measurement consistency loss; optimizing network parameters of the physical information neural network model based on the total loss function to obtain a trained pose estimation model; compressing and deploying the pose estimation model, and generating a pose estimation result based on real-time collected sensor data through the pose estimation model. Low-cost and high-precision pose estimation can be achieved.

The invention relates to the technical field of artificial intelligence and machine learning, and discloses a method and a system for realizing broadband noise point identification of an automobile audio based on time-frequency analysis, and the method comprises the steps: adding a variable-length window function of an audio analysis fragment, obtaining a windowed audio analysis fragment, analyzing an initial time-frequency spectrum of the windowed audio analysis fragment, and obtaining an initial time-frequency spectrum of the windowed audio analysis fragment; generating an instantaneous frequency estimation matrix of the windowed audio analysis segment; combining the instantaneous frequency estimation matrix with the initial time-frequency spectrum to obtain enhanced time-frequency representation of the windowed audio analysis fragment, analyzing noise time-frequency units of the enhanced time-frequency representation, and communicating the noise time-frequency units to obtain a broadband noise point candidate region of the windowed audio analysis fragment; and analyzing the real noise point probability of the broadband noise point candidate region, and determining broadband real noise points of the broadband noise point candidate region. According to the invention, the efficiency and accuracy of automobile audio broadband noise point identification can be improved.

The application relates to an intelligent production line rotating machinery health evaluation method based on a hypergraph convolution fuzzy network, which comprises the following steps: collecting time domain signals generated by various faults of a rotating machinery system during operation to obtain corresponding time-frequency representations; dividing the time domain signals and the time-frequency representations according to fault types, adding labels, and constructing vibration data; using a hypergraph model to construct hypergraphs with the time domain signals and the time-frequency representations as graph nodes to obtain two hypergraphs under each fault type; using a weight fuzzy module to fuse the nodes of the two hypergraphs under each fault type; inputting the fused hypergraph features into a multilayer perception hybrid module; calculating the probabilities of all fault classes through an output full connection layer and a classification function to realize training, optimization and updating of the hypergraph convolution fuzzy network. The application increases the interpretability of the model and can obtain more robust rotating machinery system health evaluation results.

The invention discloses a keyless phase-change rotating speed bearing fault diagnosis method and device, and belongs to the technical field of bearing fault diagnosis. The method comprises the following steps: acquiring a variable rotating speed bearing fault vibration signal; a pre-constructed TFRPET algorithm is applied to the vibration signals, and high-resolution time-frequency representation TFR is obtained; the TFRPET algorithm compresses the width of a TF ridge through a peak value extraction operator PEO, and the energy aggregation of the TF ridge is improved; detecting a TF ridge from a time-frequency representation (TFR) according to a ridge detection technique and selecting a reference instantaneous frequency (IF) from the TF ridge; and carrying out angular domain resampling and order spectrum diagnosis on the vibration signal by using the reference instantaneous frequency IF to obtain a fault diagnosis result of the keyless phase change rotating speed bearing. The method does not need to depend on a key phase device, can adapt to a variable rotating speed working condition, achieves high-resolution TFR through the TFRPET algorithm, and solves the problem that in the prior art, the TFR resolution is insufficient, and the bearing fault judgment accuracy is affected.

The application discloses a kind of water turbine cavitation degree detection methods based on cavitation perception multi-path deep learning network model, comprising: obtaining acoustic signal sequence collected from water turbine equipment and normalizing, construct including one cavitation feature extraction path, one periodicity feature extraction path, one multi-scale mel spectrum extraction path of extracting time-frequency representation feature, feature fusion module, main classifier and auxiliary classifier in the cavitation perception multi-path deep learning network model, feature fusion module is adaptively fused to the cavitation feature and periodicity feature extracted, and learns fusion weight;Again, the physical feature vector after fusion is input into auxiliary classifier and classified, and multi-scale mel spectrum feature is input into main classifier and classified;And based on including main classification loss, auxiliary classification loss and cavitation perception alignment loss in the target cost function, the model is trained.The application can provide interpretable physical feature representation while maintaining high classification accuracy.

The invention relates to the technical field of signal processing, in particular to a time-frequency analysis method for a non-stationary signal under coexistence of harmonic waves and pulses, which comprises the following steps: acquiring a linear frequency modulation signal in which a harmonic component and a pulse component exist at the same time, and solving a threshold value compared with a chirp rate; calculating the chirp rate of each time frequency point of the linear frequency modulation signal based on a chirp rate estimator; when the chirp rate of a certain time-frequency point is less than or equal to a threshold value, considering that the time-frequency point tends to the harmonic characteristic, and redistributing the time-frequency point in the frequency direction; when the chirp rate of a certain time-frequency point is greater than a threshold value, considering that the time-frequency point tends to pulse characteristics, and redistributing the time-frequency point in the time direction; and adding the two redistribution results to obtain the time-frequency representation of the linear frequency modulation signal. According to the method, the time-frequency representation with concentrated energy can be generated for the harmonic and pulse components in the signal at the same time.

According to embodiments of this disclosure, a method, apparatus, device, and storage medium for text-to-speech conversion are provided. The method includes generating a predicted speech representation of the target text read by a first speaker, based on a target text to be converted and a first timbre of a first speaker. The predicted speech representation indicates speech features that vary over time. The method further includes generating a predicted time-frequency representation of the target text read by a second speaker, based on the predicted speech representation and a second timbre of a second speaker. The predicted time-frequency representation indicates the speech signal strength that varies over time at different frequencies. The method further includes converting the predicted time-frequency representation into audio of the target text read by the second speaker. This reduces the difficulty of prediction and improves the sound quality of the generated audio.

The present invention relates to a deep learning time-frequency representation method for radar micro-motion features, and belongs to the field of radar signal processing technology. The present invention first pre-processes radar echo data to obtain target information data containing a distance dimension; then designs a deep learning network model that represents the micro-motion features of radar targets, and the model includes four parts: a basis function selection module, a feature aggregation module, an energy concentration module, and an output module. A data set is constructed to train and verify the model; finally, the pre-processed measured target data is input to complete the time-frequency representation of the target micro-motion features based on a deep learning method. The present invention can achieve high-resolution micro-motion feature representation of radar targets. Compared with traditional time-frequency analysis methods, it not only shows the advantage of a high degree of energy aggregation, but also shows the details of micro-motion features that traditional time-frequency analysis methods fail to show, thereby improving the representation capability of the target's micro-motion features.