175 results about "Mixed noise" patented technology

A FMCW-type radar device generates snapshot data from a beat signal that represents a received condition of the radar device every modulation period. Auto-correlation matrices generated by the snapshot data every modulation period are averaged every set of plural periods. The radar device calculates the target azimuth of a target object such as a preceding vehicle based on the averaged auto-correlation matrix based on MUSIC (MUltiple SIgnal Classification) method. This averaging is performed by weighting average based on an amount of mixed noise (or an interference amount) contained in the snapshot data in each modulation period. A weighting coefficient to be applied to the auto-correlation matrix in each modulation period is set to a value corresponding to the amount of mixed noise, namely, the interference amount of this modulation period. The weighting coefficient becomes large when the interference amount is small, and on the other hand, becomes small when it is large.

The embodiment of the invention provides a vocal print feature recognition method and a vocal print feature recognition system. The method comprises the following specific steps: after speech separation treatment based on hearing characteristics on pretreated noise-containing mixed noise, extracting a frequency cepstrum coefficient and a perceptual linear prediction coefficient of a signal, by utilizing the background distinction degree of the noise, analyzing the frequency cepstrum coefficient and the perceptual linear prediction coefficient under different noise environments so that the feature fusion is completed, and finally, in a pre-established vocal print feature template library, carrying out mode matching on the features after fusion by adopting a gaussian mixture model-universal background model (GMM-UBM). According to the vocal print feature recognition method, the human auditory system features and the traditional vocal print recognition method are combined, the problem that the vocal print recognition ratio under noise is low is solved from the point of bionics, and the vocal print feature recognition accuracy and system robustness under the noise environment are effectively promoted.

The invention discloses a method of filtering image mixed noise, it includes step 1: selecting input first pixel in original image which includes mixed noise; step 2: judging whether the pixel belongs to pulse noise, if it is, enter step 3. if else enter step 4; step 3: the pixel is median filter processed to filter the pulse noise, then enter step 6; step 4: ensuring the members of the un-pulse noise pixel; step 5: gauss noise is filtered by filter; step 6: checking whether the all pixels are processed, if it is not, the next pixel is fetched, then turn to step 2; if it is, new image whose mixed noise is filtered is output. The method can filter mixed noise in image, at the same time edge and detail information of image can be protected well, and calculation speed is high.

The invention discloses a two-channel beam forming speech enhancement method based on noise mixed coherence. Adaptive beam forming can effectively inhibit directional noise signals under the reverberation-free condition, but the inhibiting effect of the adaptive beam forming is greatly reduced in the presence of reverberation. Aiming at the problem, the invention provides the two-channel beam forming method based on noise mixed coherence. Considering that coherence and scattering noises exist simultaneously in a sound field, in the method provided by the invention, the hypothesis of replacing the traditional scattering sound field with a mixed noise field is provided, specifically, the method comprises the following steps: firstly, estimating the noise coherence in the mixed noise field, estimating a power spectrum of the noises by utilizing the noise coherence, and calculating a gain function of a frequency domain filtering by using the estimated result for the power spectrum of the noises; and carrying out frequency domain filtering processing on the noises and reverberant signals, and carrying out further processing on the residual noises by adopting a minimum variance distortionless response beam forming device. The experiment proves that the quality of the speech enhanced by adopting the method provided by the invention is obviously improved compared with that enhanced by adopting the traditional method.

The invention belongs to the technical field of digital image processing, provides a TV (total variation) image noise removal method based on noise priori constraint and can improve the noise detection accuracy and well protect image structural information. The technical scheme is that the TV image noise removal method based on the noise priori constraint comprises the following steps: step 1) inputting a noise-containing image I; step 2) enabling f to be an original sharp image; step 3) performing impulse noise influence on an image u in transmission and storage processes, and representing an image polluted by mixed noise with g; step 4) estimating noise positions with an ROAD (rank-ordered absolute difference) statistical method; step 5) constructing a TV-ROAD iteration noise removal model; step 6) solving an equation shown in the specification according to a noise removed image f obtained in the previous step to obtain a two-value matrix vector b. The method is mainly applied to digital image processing.

An echo processor includes an echo suppression amount decider which compares a state of an outgoing input signal to be sent including an echo with a state of an incoming input signal and calculates an echo suppression amount based on the comparison, a time-frequency transformer which transforms the outgoing input signal in the time domain into the frequency domain, thereby generating an amplitude spectrum and a phase spectrum, a noise-spectrum estimator which estimates a noise spectrum from the amplitude spectrum, a mixer which mixes an amount of the noise spectrum based on the echo suppression amount with the amplitude spectrum, and a frequency-time transformer which generates an outgoing output signal including a noise in the time domain on the basis of the amplitude spectrum, with which an amount of the noise spectrum is mixed by the mixer, and the phase spectrum, and outputs the outgoing output signal.

The invention relates to a sparse and low-rank matrix approximation-based hyperspectral image restoration method and belongs to the image processing field. The method includes the following steps that: a hyperspectral data sequence affected by mixed noises is acquired, or simulated mixed noises are artificially added into a clear hyperspectral image sequence, so that hyperspectral data to be processed can be obtained; the multi-band hyperspectral image data are segmented into a plurality of small data blocks, and each three-dimensional data block is pieced into one two-dimensional data matrix; a weighted Schatten p-normal type low-rank matrix approximation model is constructed for each two-dimensional data matrix; an extended Lagrange multiplier method is adopted to solve the model to obtain mixed noise-removed two-dimensional data matrixes; each two-dimensional data matrix is restored into three-dimensional hyperspectral data, so that a mixed noise-removed multi-band hyperspectral image can be obtained; and the above steps are repeated by using an iteration method to obtain a better restoration effect. The method can be effectively applied to fields such as remote sensing, geography, agriculture and military fields.

By using the adaptive filter, the reference input signal is processed so as to identify a pseudo-signal of a particular signal to be deleted. The pseudo-signal is subtracted from the mixture containing a target signal inputted from a microphone, the particular signal to be deleted, and a noise so as to obtain an error signal. A stationary noise is estimated to obtain a stationary noise estimated value. A non-stationary noise is estimated to obtain a non-stationary noise estimated value. The stationary noise estimated value is mixed with the non-stationary estimated value to obtain a mixed noise estimated value. An update amount is calculated according to a correlation value between the error signal and the reference input signal, and the mixed noise estimated value. According to the update amount, a coefficient of the adaptive filter is updated.

The invention relates to an image processing method for filtering mixed noise and belongs to the image processing technical field. The image processing method for filtering the mixed noise comprises enabling a filter window center and pixel points of coordinates in the image to achieve superposition by building the filter window; obtaining mid-values of gray values of all the image pixel points in the filter window and determining a range of the gray values by utilizing threshold values; obtaining mean values as pixel point gray values of the image coordinates according to the sum of the pixel gray values and the pixel point numbers within the obtained range of the gray values; obtaining all the pixel point gray values of all the coordinates of the entire image to achieve the entire image processing in the same manner. The image processing method for filtering the mixed noise has the advantages of being capable of effectively filtering the impulse noise by choosing the mid-values as the center of the gray level window; restraining the gaussian noise by setting the threshold values to determine the range of the gray values according to the actual conditions and performing average on the pixel point gray values which fall within the range; not only effectively filtering the mixed salt and pepper noise and the gaussian noise, but also being simple in algorithm and being small in calculated amount simultaneously and being beneficial to the real-time application of the engineering.

The invention provides a pipeline leak detection method and device. The method comprises the following steps of: performing time-frequency analysis on a pipeline mixed signal based on the short-time Fourier transform to obtain a time-frequency spectrum to which the mixed signal correspond; converting the time-frequency spectrum into a two-dimensional time-frequency image; and inputting the time-frequency image into a preset convolutional neural network model, and obtaining a leak type to which the mixed signal corresponds according to an output result of the convolutional neural network model.The time-frequency analysis of the pipeline mixed signal is performed by the short-time Fourier transform to obtain the time-frequency spectrum to which the mixed signal correspond without extractinga leak signal from the mixed signal, and thus, the interference of mixed noise is avoided. In addition, the time-frequency spectrum is converted into the two-dimensional time-frequency image; the two-dimensional time-frequency image is input to the preset convolutional neural network; the preset neural network model is obtained after training according to a mixed signal sample with the leak type,and can output the recognition result of the leak type; and therefore, the detection process is fast and accurate.

The invention provides a system and a method for testing the single noise quality in a multi-source noise environment, and relates to the field of acoustic measurement and sound quality analysis. An apparatus comprises a computer with a built-in signal processing module, a result output display module and a control module, an analog / digital conversion plate, a gain amplifier and a double-microphone array. The method includes: firstly, arranging the double-microphone array according to the position and the direction of a noise source; acquiring signals, calculating the sound intensity, and performing noise separation; and calculating the loudness and sharpness according to the sound intensity, and performing sound quality evaluation. According to the method, sound quality parameters are employed to evaluate the acoustic characteristics of a specific component in the mixed noise, the method is in accordance with the ear acoustic characteristic, the test is more targeted, and the noises can be more effectively controlled. According to the system, the realization is easy, the operation is convenient, the test precision is high, the blanks in the fields of acoustic tests and sound quality analysis are filled in, and the system can be widely applied to noise control engineering in multiple fields such as automobiles, spaceflight, and household electrical appliances etc.

The invention discloses a spatial-spectral weighted TV-based hyperspectral-image restoration method of non-convex low-rank relaxation. Firstly, gradient information of local spatial neighborhoods is utilized to establish weighted TV of spatial-spectral combination; then a gamma norm of a matrix is introduced to be used as non-convex relaxation of a matrix rank under a framework of low-rank restoration of a hyperspectral image, and spatial-spectral weighted TV is combined to establish a hyperspectral-image non-convex-low-rank-restoration model of spatial-spectral weighted TV; an ADMM (Alternating Direction Method of Multipliers) is utilized to decompose the model into multiple sub-problems, and a non-convex soft-threshold operator, split Bregman iteration, a soft-threshold shrinkage operator and the like are respectively adopted to solve the sub-problems after conversion; and a hyperspectral image after restoration is obtained. The method fully mines spectral and spatial information ofthe hyperspectral image, has very-good spatial structure retention performance and spectral fidelity, has good unbiasedness and robustness at the same time, and can quickly and effectively remove mixed noises to obtain the hyperspectral image with a good visual effect.

The invention discloses an infrared image dividing method and system for power system equipment based on wavelet analysis. Firstly, wavelet conversion, fuzzy entropy, a genetic algorithm and a mathematical morphology are used for carrying out image processing on an infrared image of the power system equipment. The method comprises the following steps: firstly, carrying out thermal failure detection on the power system equipment by an infrared thermal imager to obtain a thermal image; eliminating mixed noise of the thermal image by using the wavelet conversion to inhibit background interferences and enhance a target; carrying out combined optimization operation by applying the fuzzy entropy and the genetic algorithm to determine an optimal threshold value; extracting a target; solving a discontinuous boundary problem by using a waterline region dividing method of the mathematical morphology and dividing the image so as to find a largest communication region; and separating a target region. Finally, a position of a failure point of the power equipment and an element with a failure can be judged clearly according to the separated target region; an accident is prevented form occurring and overhauling under poweroff is not blindly carried out; the operation reliability of a power system is improved.

The invention discloses a communication machine room protection monitoring management system based on big data. The system comprises an environmental noise detection module, a sound comparison and separation module, a waveform recognition and analysis module, an environmental parameter storage database, a fault frequency detection module, an equipment association construction module, an equipmentoperation parameter detection module, a data adjustment and analysis module, a big data server and a prompt early warning terminal. According to the invention, the mixed noise in a machine room is analyzed; whether each device has a fault and the type of the fault are determined; the failure frequency of the device, the repair waiting time and the temperature change condition of the device are analyzed; the operation life evaluation coefficient corresponding to each device under the influence of the device and other devices is comprehensively analyzed by combining association tightness coefficients, fault repair time and the like of other devices and a device to be detected; the characteristic of high detection accuracy is achieved, protection early warning reminding is achieved, and protection monitoring management can be conveniently carried out on the devices in the machine room.

The present invention provides a method and a device for image processing based on k-means clustering and dictionary learning. The method comprises the steps of: initializing image parameters to perform partitioning processing of a noise image, utilizing a k-means algorithm to perform clustering of a given image block, utilizing an MOD-SVD dictionary training algorithm to each clustering, obtaining a training clustering dictionary {D1, D2, D3, ..., Dk}, aggregating each partitioning dictionary {D1, D2, D3, ..., Dk} to form an over-complete dictionary D to make D={D1, D2, D3, ..., Dk}, obtaining each partitioning dictionary, forming the over-complete dictionary D, utilizing an OMP (Orthogonal Matching Pursuit) algorithm to solve a corresponding sparse coefficient, setting certain iterations, updating the dictionary and the sparse coefficient corresponding to learning, and reconstructing a denoising image. The PSNR (a peak signal to noise ratio) of an image denoising effect of algorithmprovided by the invention is compared with the PSNR of the image denoising effect of a known spare coding algorithm and an improved sparse coding (ISC) algorithm in the conditions of different noisevariances [Delta] and mixed noise densities d, and it is shown that the denoising effect in the invention can reach a better effect.

The invention discloses a weighted sparse-based mixed denoising method. According to the method, a calculus of variations is added on the basis of a weighted sparse representation non-local training dictionary to match non-local similar blocks, and a dual method is used to solve a mixed denoised image, so that edge information of the image can be better stored. The method has a denoising effect superior to that of the existing algorithms, has a high peak signal to noise ratio and a high image feature similarity, has a good inhibiting effect for mixed noises, particularly can well store the image information of the image, and has certain improvement for the preservation of image features.

The invention relates to a DNN voice enhancement model based on MEE optimization criteria, and belongs to the field of voice enhancement of artificial intelligence. The model comprises an input layer,a hidden layer and an output layer, and the whole training model is divided into a training stage and an enhancement stage. In the training stage, and pure voice is added with different types of noises to establish mixed noise included voices in different signal to noise ratios; and features are extracted from the mixed voices and input to the DNN for training. In the enhancement stage, same features are extracted from the mixed voices to be tested, and input to the trained DNN for decoding, the network outputs estimation of the features of the pure voice, and waveform reconstruction is carried out to obtain a noise-reduced voice file. The model is highly universal for noise reduction of voices including non-smooth noises in practical problems.

The invention is suitable for the technical field of noise reduction earphones, and provides a filter design method and device for an active noise reduction earphone, and test equipment. According tothe invention, a feedback filter is designed in advance; according to the frequency response curve of the feedback filter, a transfer function from the external noise to the standard microphone in theear canal, a transfer function from the loudspeaker to a standard microphone in the ear canal, a transfer function from the external noise to a feed-forward microphone, a transfer function from the external noise to the feedback microphone and the transfer function from the loudspeaker to the feedback microphone, the frequency response curve of the feedback filter is calculated; the influence onthe ideal frequency response curve of the feed-forward filter is analyzed when the feedback filter is opened for noise reduction, and the ideal frequency response curve of the feed-forward filter is corrected, so as to design the feed-forward filter with the mixed noise reduction effect meeting the expectation according to the corrected ideal frequency response curve of the feed-forward filter.

The invention discloses a video de-noising method for Poisson, Gaussian and impulse mixed noises and belongs to the image processing field. The method includes the following steps that: each frame of image of a noise video is divided into image blocks, similar image blocks are searched in the frame sequence of the noise video for each image block through a matching method; Poisson-Gaussian mixed noise priors are constructed, a video de-noising model under a Poisson, Gaussian and impulse mixed noise environment is built; and the de-noising model is solved through using an optimization algorithm, and de-noised clear image blocks are obtained, and de-noised clear video images are generated based on the de-noised clear image blocks. A traditional video de-noising method is only used for a certain type of noises. The method of the invention can de-noise Poisson, Gaussian and impulse mixed noise videos, and therefore, with the method of the invention adopted, a video de-noising problem under a practical noise environment can be better solved. Compared with the traditional video de-noising method, the method of the invention can obtain higher-quality video de-noised images.

The invention provides an allocation method of a belt-shaped three-dimensional mixed noise reducing type urban forest. The method comprises the following steps: (1) selecting plants; (2) planting a row of first arbors in the outer side of a roadway along the roadway direction, and densely planting dwarf shrubs around the first arbors to form a first allocation area; (3) sequentially outwards planting rows of tall shrubs, second arbors and second herbaceous plants in the outer side of the first allocation area to form a second allocation area, wherein the second allocation area and the first allocation area are separated through a sidewalk; (4) enabling the width sum of the second allocation area and the first allocation area to be 30 to 35m, and length to be not less than that of a noise sensitive target along the roadway direction. According to the allocation method of the belt-shaped three-dimensional mixed noise reducing type urban forest, proper arbors, shrubs and herbaceous plants are selected and allocated to form a mixed forest zone with a certain width in the side of the roadway, and therefore, the noise can be repeatedly absorbed and reduced at a plurality of times during being transmitted in the space, and as a result, the purpose of noise reducing can be reached, the noise can be reduced by 28.3%.

The invention discloses a method for detecting weak pulse signals. The method includes the following steps that: one-dimensional multi-scale wavelet decomposition is performed on measured signals, namely, the measured signals are decomposed into N layers through one-dimensional wavelet transform, and the high-frequency portions of the signals are reconstructed through utilizing a wavelet decomposition coefficient, so that N high-frequency reconstruction signals dj and one low-frequency reconstruction signal a are obtained; sliding kurtosis processing is performed on the reconstruction signals dj, so that a corresponding sliding kurtosis time sequence cj can be obtained; and superimposing synthesis and normalization processing are performed on the sliding kurtosis time sequence cj, so that a final sliding kurtosis time sequence c can be obtained. After being subjected to the one-dimensional multi-scale wavelet decomposition preprocessing, the measured signals embody different time-frequency domain characteristics in different decomposition scales, and when the sliding kurtosis processing is performed on the signals, a detection effect of multi-slide window synchronization action can be realized, and therefore, differential analysis and processing on noises, interferences and pulses to be detected mixed in measured signals can be benefitted.

The invention relates to a transformer station mixed noise separation method. The method comprises: firstly obtaining noise characteristic frequency spectrum and a vocal print graph of a main noise source in a transformer station, after extracting 100 Hz and a series of harmonic frequency components in the frequency spectrum, through fourier inversion, obtaining a time-domain signal of body noise; through difference on frequency spectrum characteristics, realizing separation of body noise; then using characteristics of corona noise on time domain, that is, short-time characteristic of pulse; using a wavelet analysis method to identify occurrence of short-time pulse in a noise signal, separating to obtain the corona noise; and finally obtaining fan noise. The method provides theoretical foundation and evaluation method for noise inspection and acceptance and noise abatement under actual operating conditions in a transformer station.

A signal processing method includes: analyzing a noisy signal that is supplied as an input signal; generating mixed noise information by mixing a plurality of noise information about a noise to be suppressed based on the result of said analysis of the noisy signal; and suppressing the noise using the mixed noise information.

The invention provides an ultrasonic image mixed noise self-adaptive suppression method. According to the method, additive noise and multiplicative noise in an image are removed respectively; logarithmic transformation is carried out on the image after being subjected to the additive noise removing to enable the multiplicative noise to be converted into the additive noise; then, weighted average difference values of pixel points and neighbourhood points in the image as well as the variance of the difference values are calculated; noise judgment is carried out based on the difference values and the variance and the image is divided into a smooth area and a detail area; and after the different areas are respectively subjected to space-domain filtering and transform-domain filtering, the processed areas are added to obtain a de-noised image. Therefore, the advantages of the two filtering processing can be fully utilized to realize the self-adaptive processing that for different pixel points, different filtering methods are used. The method is simple and convenient; neighbourhood domain section is reasonable; de-noising effect is more obvious than that of single domain processing; and the method is high in practicality.

The invention discloses a mixed noise reduction method for underwater acoustic signals. VMD decomposition is conducted on the underwater acoustic signals containing noises, wherein the VMD decomposition quantity is determined by an EMD decomposition result; a correlation coefficient between each variation modal function decomposed by VMD and the original signal is calculated, and according to a threshold value of the correlation coefficient, noise variation modal functions are screened; summation is carried out on all the noise variation modal functions, and SVD differential spectrum noise reduction is carried out; signals are reconstructed, Savitzky-Golay filter is carried out, and the underwater acoustic signals after the noise reduction are obtained; correlation dimension and noise intensity of the underwater acoustic signals before and after the noise reduction are calculated, and timing sequence waveforms and two-dimensional phase diagrams before and after the noise reduction aregiven; The method can further eliminate noises, the phase diagrams of the underwater acoustic signals after the noise reduction have a more smooth rail, and attractors have a better regularity of geometric structures.

The invention relates to a novel window system (NWS) capable of naturally ventilating, lighting and reducing noise, and belongs to the technical fields of building, building technology, building physics, acoustics and noise control, lighting, ventilation and the like. A ventilating channel is formed by digital staggered glass, and micro-perforated sound absorption bodies are added into the glass to reduce the noise. The system comprises an active and passive mixed noise control mode, and treatments of a sound-attenuating louver, a sound-attenuating cover, a sound-attenuating bend and the like (see attached figures). The system considers the requirements of residents for current comfort in the daytime rather than minimum amount of air exchange at night. The system is transparent, so the system has little influence on natural light; and the system can be arranged on the vertical face of the existing building without appearance influence. A large amount of laboratory tests and numerical simulations in Cambridge University UK and Sheffield University show that the system can reach very good noise-reducing, ventilating and lighting effects. The system can be made of wood, steel, aluminum, plastic steel and the like, and is applicable to various buildings.

The invention belongs to the technical field of communication, and relates to an MSK signal demodulation method based on deep learning under mixed noises. The invention aims at solving the problems existing in a traditional wireless communication signal receiving strategy. Under a Gaussian noise and impulse noise mixed channel, the network structure provided by the invention has good error code performance when demodulating the MSK signal, the soft demodulation accuracy is effectively improved, modeling and parameter estimation do not need to be carried out on Gaussian and pulse mixed noise, and complex pulse parameter estimation in a traditional wireless communication signal receiving strategy is avoided.

Embodiments of the invention provide an image denoising method and device, and relate to the technical field of image processing. The method is applied to an electronic device. The method comprises the steps of acquiring a to-be-denoised image comprising mixed noises, and performing filtering processing on the to-be-denoised image to obtain an initial image; based on the initial image, constructing a plurality of structure groups and performing dictionary construction on each structure group in the plurality of the structure groups, thereby obtaining a learning dictionary corresponding to eachstructure group; building a mixed noise removal model based on the learning dictionary corresponding to each structure group; and performing cyclic iterative calculation on the mixed noise removal model until a preset number of iterations is satisfied, outputting the denoised image. The method is more accurate and the calculation efficiency is improved.

The embodiment of the invention provides a method and device for eliminating noise. The method comprises the following steps of: obtaining a parameter estimated value of a noise standard deviation function of a first signal with noise to be eliminated based on a mixed noise model, so that an estimated standard deviation function is obtained; carrying out variance stabilization transformation on the first signal according to the estimated noise standard deviation function to obtain a second signal with noise being signal-uncorrelated noise; denoising the second signal; carrying out inverse transformation of the variance stabilization transformation on the denoised second signal, so that the noise of the first signal is eliminated. The embodiment of the invention provides the method and device for eliminating noise, so that mixed noise containing both a signal-correlated noise component and a signal-uncorrelated noise component is effectively eliminated.