527 results about "Wiener filter" patented technology

A noise suppression device receives data representative of a noise-corrupted signal which contains a speech signal and a noise signal, divides the received data into data frames, and then passes the data frames through a pre-filter to remove a dc-component and the minimum phase aspect of the noise-corrupted signal. The noise suppression device appends adjacent data frames to eliminate boundary discontinuities, and applies fast Fourier transform to the appended data frames. A voice activity detector of the noise suppression device determines if the noise-corrupted signal contains the speech signal based on components in the time domain and the frequency domain. A smoothed Wiener filter of the noise suppression device filters the data frames in the frequency domain using different sizes of a window based on the existence of the speech signal. Filter coefficients used for Wiener filter are smoothed before filtering. The noise suppression device modifies magnitude of the time domain data based on the voicing information outputted from the voice activity detector.

The invention discloses a voice signal enhancement system and a voice signal enhancement method. The method comprises the following steps of: filtering an input signal by an adaptive filter to obtain a voice reference signal and performing noise extraction processing on the input signal by using a block matrix to obtain a noise reference signal; filtering the noise reference signal and the voice reference signal through multi-channel Wiener filtering to obtain a denoised voice signal; performing voice enhancement processing on the denoised voice signal according to the noise reference signal to obtain an enhanced voice signal and a voice existence probability signal; and updating a filter coefficient of the adaptive matching filter, the block matrix and a filter coefficient and a learning step length of a multi-channel Wiener filter according to the voice existence probability signal. By the voice signal enhancement system and the voice signal enhancement method, under the condition that a microphone array is relatively simple, the problems that convergence cannot be guaranteed, the speed is low and the real-time enhancement distortion is high during real-time tracking of a target source in a practical application environment can be solved.

The disclosed method uses the Wiener frequency filtering to suppress noise in noisy sound signals (u(t)). This method includes a preliminary step in which the sound signals (u(t)) to be noise-suppressed are digitized by sampling and subdivided into frames. The method then includes a first series of steps including the creation of a noise model on N frames, the estimating of the spectral density of the noise and of the energy of the noise model and the computing of a coefficient that reflects the statistical dispersion of the noise. It also includes a second series of steps including the computation of the spectral density of the signals to be noise-suppressed fore each frame. The coefficients of the Wiener filter are modified for each successively processed frame, by the parameters determined at the end of the two series of steps, so as to introduce an energy compensation and an adaptive overestimation of the noise.

Techniques are described that can be used to determine parameters of an adaptive Wiener filter to apply to a video region. The following parameters of the Wiener filter may be adjusted: coefficients, coefficient quantization, filter type, filter size, prediction mode, entropy encoding, and number of filter tables. The parameters associated with the lowest rate distortion cost of the encoder are selected for transmission with the encoded video. If not using adaptive Wiener filtering results in a lowest rate distortion cost, then adaptive Wiener filtering is not used for the video region. If using adaptive Wiener filtering results in a lowest rate distortion cost, then the parameters applied by the adaptive Wiener filtering that result in the lowest rate distortion cost are communicated with the filtered video region.

The present invention relates to an apparatus and method of estimating a channel based on a channel delay spread in a mobile communication system. Pilot subcarriers are inserted between data subcarriers at predetermined intervals to estimate a channel at locations of the pilot subcarriers. An approximate channel delay spread value is estimated by using an autocorrelation value of a pilot signal. Then, a channel for data subcarriers between the pilot subcarriers is estimated by using a Wiener finite impulse response (FIR) filter that has a separate coefficient according to the estimated delay spread value. Accordingly, by changing the Wiener FIR filter coefficient according to the delay spread value of the channel, it is possible to estimate the channel so as to be adaptive for a change of the channel over time. Since the filter coefficients, which are calculated in advance, are used, it is possible to reduce the amount of calculation required when calculating the filter coefficients. As a result, an apparatus for estimating a channel can be easily achieved.

The invention provides a video real-time de-noising method based on a structure tensor and Kalman filtering. The method includes the following steps that an image frame to be processed at the current moment is acquired, and n frames of images which are before the current frame and have been stored and de-noised; the image frame to be processed at the current moment is pre-filtered with an average filter; based on the structure tensor, movement of the current image frame is estimated by fully utilizing close time and space relations between the image frame to be processed at the current moment and adjacent image frames before the image frame to be processed at the current moment; based on the movement estimation result, de-noising is performed in a time domain with a Kalman filtering method; de-noising is performed in a space domain with a Wiener filtering method; two de-noised images are synthesized, and the final de-noised image is acquired through weighting. With the method, high-noise video can be de-noised, and a good de-noising effect is achieved; besides, because complicated interactive calculation does not exist, achievement of hardware such as FPGAs is facilitated, and real-time the high-noise video can be de-noised.

The invention provides a multi-target positioning method of a bistatic multi-input multi-output radar, comprising the following steps of: (1) transmitting mutually orthogonal phase-coded signals by M transmitting array elements, and receiving the phase-coded signals by N receiving array elements, wherein the distances of the M transmitting array elements and the N receiving array elements are all of half wavelengths; (2) carrying out matched filtering on the received phase-coded signals by a matched filter of a receiver of each receiving array element; (3) carrying out multistage Wiener filtering on a matched signal data covariance matrix space, and carrying out forward recursion to obtain a signal subspace; (4) carrying out high-resolution DOA (Direction of Arrival) estimation by using an ESPRIT algorithm, wherein a pairing algorithm is used for carrying out the automatic pairing on two-dimensional parameters; and (5) realizing multi-target positioning according to cross points at two angles so as to obtain the positions of space targets. The multi-target positioning method provided by the invention has the advantages of low computation complexity, high computation speed, high estimation accuracy and can be used for positioning the sea-surface or low-altitude targets during tracking and guidance.

An audio filter consists of two substantially identical stages with different purposes. The first stage (301) whitens detected noise, while preserving speech or other audible information in an undistorted manner. The second stage (303) effectively eliminates the residual white noise. Each stage, in one embodiment, includes a Mel domain based error minimization stage (108). A two stage Mel-frequency domain Wiener filter (300) is designed for each speech time frame in the Mel-frequency domain, instead of linear frequency domain. Each Mel domain based error minimization stage (108) minimizes the perceptual distortion and reduces the computation requirement to provide suitably filtered audible information.

The invention discloses a method for scanning ultrasonic microscope and measuring thickness, sound velocity, density and attenuation of a thin material simultaneously. The method includes: 1) the thin material is placed on the surface of a base material, an ultrasonic probe is located right above the base material and the thin material, ultrasonic echo signals s1 (t) and s2 (t) of the base material and the thin material are obtained; 2) deconvolution based on the wiener filtering and autoregressive spectral extrapolation technique is performed on the s2 (t) to obtain a signal h1 (t), and an initial value of acoustic transition time is selected; 3) initial values of other variables are selected, reflection coefficient frequency spectrum of the thin material is matched to obtain optimal values of acoustic impedance, acoustic transition time and acoustic attenuation coefficient of the thin material; 4) deconvolution based on the wiener filtering and autoregressive spectral extrapolation technique is performed on s1 (t)+s2 (t); and 5) the thickness, the sound velocity, the density and the attenuation of the thin material are calculated. Four-variable high-accuracy simultaneous measurement of the thin material can be achieved, and the problem of convergence domain is solved when the frequency spectrum is matched.

The invention discloses a noised voice end point robustness detection method. The method comprises the following steps of constructing an estimation method of a noise power spectrum of each frame of acoustical signals in filtering and providing a time-varying updating mechanism of a noise spectrum; firstly, carrying out iterative wiener filtering on a frequency spectrum of each frame of voices; then, dividing into several sub-band and calculating a frequency spectrum entropy of each sub-band; and then making successive several frames of sub-band frequency spectrum entropies pass through one group of median filters so as to acquire each frame of the frequency spectrum entropies; according to values of the frequency spectrum entropies, classifying input voices. By using the algorithm, the voices and noises, and a voice state and a voiceless state can be effectively distinguished. Under different noise environment conditions, robustness is possessed. The algorithm has low calculating cost, is simple, is easy to realize and is suitable for application of real-time voice signal processing system of various kinds of systems needing voice end point detection. The method is a real-time voice end points detection algorithm which adapts to a complex environment, and voice end point detection and voice filtering enhancement are completed together in a one-time state.

A method of improving picture quality in a composite video burst signal includes dividing the composite video burst signal into a plurality of frequency bands using a low pass filter and a high pass filter, performing wavelet packet filtering of frequency bands including a chrominance signal having energy higher than a specified threshold among the plurality of frequency bands, and performing Wiener filtering of frequency bands including a chrominance signal having energy lower than a specified threshold.

The invention provides an image deblurring method based on camera motion detection. According to the image deblurring method based on the camera motion detection, motion parameters of a camera during exposure are firstly obtained through an inertial navigation device, and a rough movement locus of the camera is estimated according to the motion parameters; then a blurring core of an image during the exposure is obtained through the multi-look geometry according to the movement locus; finally, a sharp image which is deblurred is obtained according to the Wiener filtering algorithm. According to the image deblurring method based on the camera motion detection, the moving condition of the camera during the exposure can be accurately detected, the blurring core can be generated fast and accurately according to the movement locus, and finally the relatively sharp image can be obtained though inverse filtering.

The invention discloses an adaptive high-precision phase estimation method for an interferometric SAR, comprising the following steps of: structuring optimum weight vectors in combination with a Wiener filter theory, performing an eigen decomposition on an optimum covariance matrix composed of the optimum weight vectors to obtain a signal subspace and a noise subspace, adequately utilizing a corresponding pixel pair and the coherent information of the neighboring pixels thereof to structure a space spectrum function according to the orthogonality of the signal subspace and the noise subspace in a MUSIC (multiple signal classification) algorithm, and precisely estimating the interferometric phase between the corresponding pixels via a spectral peak searching. The optimum weight is obtained by only a Wiener filter without the need to determine a registration error and the direction thereof, thereby solving the problem of large computational burden in the traditional InSAR (interferometric synthetic aperture radar) interferometric phase estimation. The adaptive high-precision phase estimation method for the interferometric SAR disclosed by the invention is adaptive to the field of accurate surface parameter inversion of InSAR complex scene and the like.

The invention provides a motion blurring and defocusing composite blurring image restoration method. By the method, parameter estimation and image restoration can be performed on a motion blurring and defocusing image, and the method comprises the following steps of: (1) establishing a gauss white noise template, and convoluting a degraded image and the white noise template to fulfill the aim of removing noise; (2) estimating a main blurring direction and a secondary blurring direction of the image through an image energy spectrum; (3) calculating a main directional derivative matrix and a secondary directional derivative matrix of the image; (4) performing self-correlation operation and directional accumulation operation on the main directional derivative matrix and the secondary directional derivative matrix respectively; (5) estimating a main direction blurring length and a secondary direction blurring length according to the self-correlated accumulation curve of a main directional derivative and the self-correlated accumulation curve of a secondary directional derivative; (6) establishing a composite blurring model according to the obtained main direction blurring length and secondary direction blurring length; and (7) restoring the degraded image by using wiener filtering.

The invention discloses an image denoising method based on self-adaptive EPLL algorithm. The method comprises the following steps: selecting training sample at random, carrying out overlapped blocking on each training image successively, and training a Gaussian mixture model by the maximum likelihood estimation method; respectively calculating the maximum likelihood probability of each image block relative to the Gaussian mixture model after overlapped blocking of images to be processed, and classifying the image blocks to obtain a corresponding image block set of each Gaussian submodel; in allusion to each image block, according to the classification result and with the combination of the well-trained Gaussian mixture model, denoising the image block by a Wiener filtering method; carrying out sparse representation on each denoised image block and then reconstructing images to be processed; and carrying out parameter updating on the Gaussian mixture model according to a classification result of the denoised image blocks. By the method, images can be denoised more accurately and a better image denoising effect can be obtained. The method can be widely applied in the field of image denoising.

The invention discloses a voice denoising method and system based on L1/2 sparse constraint convolution non-negative matrix decomposition. In single-channel voice enhancement, it is assumed that noised voice signals v(i) are additively relevant to noise signals n(i) and voice signals s(i), i.e., v(i)=n(i)+s(i), and noise-base information is obtained by training specific noise by use of a CNMF method; and then by taking a noise base as prior information, a voice base is obtained by decomposing noised voice by use of a CNMF_L1/2 method, and finally, voice after denoising is synthesized. According to the method, correlation of voice between frames can be better described; and strong-sparse constraining is performed on a voice-base coefficient matrix by use of L1/2 regular item, and the voice after separation comprises less residual noise. Compared to conventional methods such as a spectral subtraction method, a wiener filtering method and a minimum mean square deviation logarithm domain spectrum estimation method and the like, the voice after enhancement can be understood more easily.

The invention provides a residual echo inhibition method and device. The method comprises steps of: acquiring the energy of an error signal in an echo filtering process, the cross-power spectrum density of the error signal and a near-end microphone acquisition signal, the self-power spectrum density of the error signal, the energy of the near-end microphone acquisition signal, the reverberation power spectrum density of a reference signal, and the sound attenuation coefficient of an echo; computing a filter coefficient of frequency-domain Wiener filtering according to the energy of the near-end microphone acquisition signal and the energy of the error signal; computing a filter coefficient of similar Wiener filtering according to the cross-power spectrum density of the error signal and the near-end microphone acquisition signal, the self-power spectrum density of the error signal, the reverberation power spectrum density of the reference signal, and the sound attenuation coefficient of the echo; and filtering the residual echo according to the filter coefficient of frequency-domain Wiener filtering and the filter coefficient of similar Wiener filtering. The method and the device can be suitable for varied application environments and are capable of accurately filtering the residual echo and guaranteeing good voice quality.

The invention discloses an implement method and a device of channel estimation. The method comprises the following steps: converting the extracted channel estimation result of the reference signal to time domain to obtain the time domain initial channel estimation sequence of the reference signal; weighing the time domain initial channel estimation sequence to obtain the time domain channel shock response sequence of the reference signal; obtaining the power delay spectrum of the reference signal according to the time domain channel shock response sequence, and obtaining the relevant function of the frequency domain of the reference signal according to the power delay spectrum; and carrying out normalization treatment on the relevant function of the frequency domain, and carrying out Wiener filtering calculation according to the normalized relevant function of frequency domain to obtain the final channel estimation results. In the invention, corresponding Wiener filtering coefficient can be obtained according to the real situation of RS, thus ensuring that the channel estimation process can be better carried out based on RS, avoiding the problem that the channel estimation process does not fit for the change of the actual environment due to the adoption of fixed Wiener filtering coefficient, and being capable of effectively improving the performance and accuracy of channel estimation.