36 results about "Feature compensation" patented technology

A noise suppression method using multiple digital images performs a de-noising process with the multiple digital images. First, a feature weighting procedure and an image feature compensation of a target pixel are performed on each digital image, and then a cross reference is performed on the multiple continuous or similar digital images to suppress noises for the target pixel.

The invention discloses a method for noise suppression by using multiple digital pictures, which is used for denoising by virtue of multiple digital pictures, wherein the digital pictures are firstly subject to a feature weighting processing program and the picture feature compensation of target pixels, and then noise suppression is carried out on the target pixels by carrying out interactive reference on multiple continuous or similar digital pictures. The method for noise suppression by using multiple digital pictures, which is provided by the invention, comprises the following steps: acquiring multiple digital pictures; carrying out a first pixel compensation program on each digital picture; and carrying out a second pixel compensation program on a comparison picture in the digital pictures. In the noise suppression method disclosed by the invention, pixels of which the picture features are similar and the degree of similarity is high are used to repair, thus not destroying the digital pictures, so as to obtain better picture quality.

The invention discloses a feature compensation method based on rapid noise estimation in a speech recognition system. The method is characterized in that noise parameter estimation in the feature compensation is separated from pure speech estimation, and noise estimation and pure speech estimation are achieved through different Gaussian mixture models (GMMs). A GMM containing less Gaussian units is used for extracting noise parameters from a noisy tested speech; another GMM containing more Gaussian units is used for being combined with an estimated single Gaussian noise model to obtain a noisy GMM matched with the current test environment; and finally the noisy GMM is used for calculating the posterior probability of the noisy tested speech and the pure speech feature vector is estimated from the noisy tested speech through the minimum mean square error method. According to the method, estimation accuracy of the pure speech can be guaranteed while the calculated amount is reduced.

The embodiment of the present invention discloses an audio processing method, related equipment, a storage medium and a program product, wherein the method includes: acquiring the target audio data to be processed and the spectrum information of the target audio data, and the target audio data contains audio The missing segment, the spectral information includes the spectral features of the context audio segment of the audio missing segment; according to the spectral features of the context audio segment, perform feature compensation processing on the spectral information of the target audio data to obtain the target audio The compensated spectrum information corresponding to the data; audio prediction is performed by using the compensated spectrum information to obtain predicted audio data; the audio missing segment in the target audio data is compensated according to the predicted audio data, and the compensation of the target audio data is obtained Audio data that improves the accuracy of predictive compensation for packet loss audio.

The invention discloses a robust speech recognition method based on multi-band characteristic compensation, comprising: decomposing MFCC (Mel frequency cepstrum coefficient) extracted in noise environment directly in a cepstrum domain to obtain sub-MFCCs of four bands; then, using a pre-trained Gaussian hybrid model on each band to perform characteristic compensation on the sub-MFCC of the present band to obtain pure sub-characteristic parameters; finally, subjecting the estimated sub-MFCCs of the four bands to characteristic combination to obtain complete MFCC of pure speech. The method can provide improved recognition performance for a speech recognition system in a limited noise environment, and improved noise robustness for the system.

The present application discloses an accuracy compensation method for feature mapping scaling, which includes the following steps: obtaining mapping data of original features and target features, wherein the mapping data at least includes bit width, number of channels, number of feature horizontal pixels and feature vertical pixels Number; according to the original feature map and the number of feature horizontal pixels and the number of feature vertical pixels of the target feature, calculate the target feature in the horizontal direction and the number of feature vertical pixels based on the bit width on each channel indicated by the channel number. Interpolation coordinates in the vertical direction; according to the respective interpolation coordinates of the original feature map and the target feature, calculate the interpolation of the original feature at each interpolation coordinate of the target feature based on the bit width on each channel indicated by the channel number Weight; determine the target feature map according to the pixel value of the original feature at each position and the interpolation weight at each interpolation coordinate. The application also discloses the corresponding computer system and storage medium.

A servo control system includes a position command generator, a position detector for a feed axis, a positional deviation obtainer for calculating a positional deviation, a position control loop, a band limiting filter for attenuating a high frequency component of the positional deviation, a dynamic characteristic compensation element for advancing a phase, a learning controller including the band limiting filter and the dynamic characteristic compensation element, a sine wave sweep input unit for applying a sine wave sweep to the position control loop, a frequency characteristic calculator for estimating the gain and phase of position control loop input and output signals, and a learning control characteristic evaluation function calculator for calculating an evaluation function, which indicates a position control characteristic with the learning controller based on a frequency characteristic by actual measurement and a frequency characteristic of the learning controller.