75 results about "Spectral representation" patented technology

The invention discloses a high-order random wave representation method for a non-Gaussian wind field, and the method comprises the following steps: S1, deducing a frequency-wave number spectrum of a random wave based on a target power spectrum density and a theoretical coherence function model gamma (xi, omega); s2, defining frequency-wave number three spectrums according to the frequency-wave number spectrums, and obtaining a biased three-coherence function of high-order random waves; s3, calculating a pure frequency-wave number spectrum through a partial three-coherence function; s4, performing Fourier-Stetius integral expansion on the random waves, decomposing an orthogonal increment process, and establishing a new orthogonal increment process according to a pure frequency-wave number spectrum and a pure frequency-wave number three-spectrum; and S5, substituting the established orthogonal increment into the Fourier-Stetius integral expansion of the random wave in the step S4 to obtain the high-order spectrum representation of the non-Gaussian random wave. The invention further discloses a non-Gaussian wind field simulation method and system and a storage medium.

A method of augmenting a training dataset of voice samples is provided. An audio processing system obtains voice samples and groups the voice samples into classes of spectral representations. The system obtains warp distributions associated with the classes of spectral representations and determines spectral change ratios based on a comparison of the warp distributions. The system determines transformations based at least in part on the spectral change ratios and applies the transformations to the voice samples grouped into the classes of spectral representations to generate a set of augmented voice samples. The system compiles the training dataset using at least the set of augmented voice samples. A recognition model is trained using the training dataset.

The invention discloses a hyperspectral super-resolution reconstruction method based on subspace tensor decomposition, and the method specifically comprises the following steps: firstly, employing a singular value decomposition technology, extracting a spectrum subspace from a low-resolution hyperspectral image, and obtaining a hyperspectral subspace; and through a continuous difference regularization method, spectral representation which is closer to a real spectrum and has stronger continuity and higher smoothness is obtained. Then, learning is carried out on the multispectral image with the high spatial resolution, so that a tensor coefficient is obtained; specifically, block clustering is carried out on a multispectral image, coefficients in the same class group are integrated into a three-dimensional tensor, and low-rank tensor prior and row sparsity constraint are utilized to estimate the tensor coefficients. And finally, generating a super-resolution reconstruction result based on the obtained spectrum subspace dictionary and the subspace coefficient.

The invention discloses a multi-point non-stationary non-Gaussian random process efficient simulation method based on a spectral representation method, and belongs to the technical field of random process simulation. Aiming at the problem of low efficiency of existing potential Gaussian random process power spectrum matrix solving and matrix decomposition, the method comprises the following steps: determining a power spectrum matrix and non-Gaussian features of a target process, establishing a transfer equation by using a Mehler formula, calculating time and frequency interpolation points based on the amplitude of the power spectrum matrix, and performing matrix decomposition and orthogonal decoupling at the interpolation points. And generating a potential Gaussian process sample through interpolation and fast Fourier transform, and finally converting to obtain a target process sample. According to the method, time-frequency domain resource allocation is optimized through a self-adaptive interpolation strategy, so that interpolation points are concentrated in an area with high energy, redundancy decomposition is avoided, and the calculation efficiency is remarkably improved on the premise of ensuring the simulation precision.

An apparatus for decoding an encoded multichannel signal includes: a base channel decoder for decoding an encoded base channel to acquire a decoded base channel; a decorrelation filter for filtering at least a portion of the decoded base channel to acquire a filling signal; and a multichannel processor for performing a multichannel processing using a spectral representation of the decoded base channel and a spectral representation of the filling signal, wherein the decorrelation filter is a broad band filter and the multichannel processor is configured to apply a narrow band processing to the spectral representation of the decoded base channel and the spectral representation of the filling signal.

The invention provides a frequency estimation method and device, equipment and a medium, and relates to a signal processing technology. The method comprises the following steps: for a to-be-estimated signal sequence, when the amplitude difference between a first large spectral line and a second large spectral line is smaller than a preset threshold value, performing frequency shift operation on the signal sequence to obtain a frequency-shifted target signal sequence; the first-time large spectral line and the second-time large spectral line are located on the two sides of the maximum spectral line respectively and are determined according to spectral representation corresponding to the signal sequence; and determining the frequency of the signal sequence according to the target spectrum representation corresponding to the target signal sequence. According to the invention, the accuracy of frequency estimation is improved.

Disclosed herein are systems and methods for eardrum acoustic pressure estimation for hearing device applications. A receiver signal is produced through a receiver of a hearing device, or a physical extension of the receiver, placed inside an ear canal of a user. A microphone signal is sensed, in response to the receiver signal, using a microphone placed outside the ear canal. Based on the receiver signal and the microphone signal, a reliability signal of the microphone signal and a feedback-path signal are computed. A spectral representation signal of the feedback-path signal is computed, and a real-ear response signal is computed using a mathematical combination of the spectral-representation signal and the reliability signal. The computed real-ear response signal is used to modify an operational characteristic of the hearing device, or to make a recommendation for modification of an operational characteristic or a physical characteristic of the hearing device.

The invention discloses an intelligent identification method and system for a dishwashing detergent volatile matter spectrogram, and relates to the technical field of spectrogram characterization learning and intelligent identification, and the method comprises the steps: setting a temperature time sampling grid, and collecting and constructing a dishwashing detergent volatile matter spectrogram tensor. A spectrogram view is generated based on the physical reasonable enhancement operator set, and spectrogram tensor self-supervised contrast learning is carried out to obtain a spectrogram representation vector. And fitting the volatilization kinetic parameters according to the spectrogram representation vector, fusing the volatilization kinetic parameters and outputting an identification result. Static spectrogram recognition of the tableware detergent volatile matter is upgraded to temperature-time dynamic modeling, temperature-time tensor data capable of being aligned is provided, robust characterization is obtained through self-supervised learning under physical consistency enhancement constraint, dynamic fingerprints are extracted and subjected to fusion recognition with characterization, and a recheck closed loop is provided. Therefore, an enforceable and reviewable identification process is still kept under the conditions of drifting, noise, missing spectrum segments and small samples.

An audio device is disclosed, comprising multiple microphones and an audio module. The multiple microphones generate multiple audio signals. The audio module coupled to the multiple microphones comprises a processor, a storage media and a post-processing circuit. The storage media includes instructions operable to be executed by the processor to perform operations comprising: producing multiple instantaneous relative transfer functions (IRTFs) using a known adaptive algorithm according to multiple spectral representations for multiple first sample values in current frames of the multiple audio signals; and, performing distractor suppression over the multiple spectral representations and the multiple IRTFs using an end-to-end neural network to generate a compensation mask. The post-processing circuit generates an audio output signal according to the compensation mask. Each IRTF represents a difference in sound propagation between each predefined microphone and a reference microphone of the microphones relative to sound sources.

Methods, systems and apparatuses, including computer programs encoded on a computer storage medium, for training a neural network to perform speech synthesis, one of which includes the steps of: training a first neural network to obtain a training data set for processing spectral representations of audio samples and generating predictions of the audio samples, where after training, the first neural network obtains the spectral representations of the audio samples from a second neural network and generates, for a plurality of audio samples in the training data set, ground-truth spectral representations of the audio samples, processing the ground-truth spectral representations with a third neural network to generate updated spectral representations of the audio samples, and training the first neural network with the updated spectral representations, where the third neural network is configured to generate updated spectral representations similar to the spectral representations generated by the second neural network.

An audio device is disclosed, comprising multiple microphones and an audio module. The multiple microphones generate multiple audio signals. The audio module coupled to the multiple microphones comprises a processor, a storage media and a post-processing circuit. The storage media includes instructions operable to be executed by the processor to perform operations comprising: producing multiple instantaneous relative transfer functions (IRTFs) using a known adaptive algorithm according to multiple spectral representations for multiple first sample values in current frames of the multiple audio signals; and, performing distractor suppression over the multiple spectral representations and the multiple IRTFs using an end-to-end neural network to generate a compensation mask. The post-processing circuit generates an audio output signal according to the compensation mask. Each IRTF represents a difference in sound propagation between each predefined microphone and a reference microphone of the microphones relative to sound sources.

Controlling a concealment method for a lost audio frame associated with a received audio signal is provided. At least one bin vector of a spectral representation for at least one tone is obtained, wherein the at least one bin vector includes three consecutive bin values for the at least one tone. Whether each of the three consecutive bin values has a complex value or a real value is determined. Responsive to the determination, the three consecutive bin values are processed to estimate a frequency of the at least one tone based on whether each bin value has a complex value or a real value.

Systems, methods, and non-transitory computer readable media for encoding tissue behavior are disclosed. A bioreactor may be implemented, wherein the bioreactor includes a device configured to grow tissue and a sensor assembly configured to detect a functional response of tissue within the device. The system and method may also include a control unit communicatively coupled to the bioreactor and configured to obtain a waveform from the bioreactor including a functional response of the tissue within the device over a time range. The control unit may also be configured to transform one or more waveforms into a spectral representation that provides a frequency-based characterization of the functional response of the tissue during the time range to encode a behavior of the tissue.

A spectral representation of an audio signal having consecutive audio frames can be derived more efficiently, when a common time warp is estimated for any two neighbouring frames, such that a following block transform can additionally use the warp information. Thus, window functions required for successful application of an overlap and add procedure during reconstruction can be derived and applied, the window functions already anticipating the re-sampling of the signal due to the time warping. Therefore, the increased efficiency of block-based transform coding of time-warped signals can be used without introducing audible discontinuities.

The invention relates to a method and system for reconstructing a hyperspectral image based on an RGB image, and the method comprises the steps: S1, selecting an RGB image in a space; s2, processing the RGB image to obtain end member information of the RGB image; s3, generating end member spectral representation based on the end member information of the RGB image; s4, encoding, decoding and attention modeling are carried out on the RGB image, and abundance information of the RGB image is obtained; and S5, constructing a rough hyperspectral image based on the end member information of the RGB image and the abundance information of the RGB image, performing feature enhancement on the rough hyperspectral image and the RGB image, and performing fusion to obtain a final hyperspectral image. According to the invention, the hyperspectral image can be effectively constructed based on the RGB image.

A device and method for manipulating an audio signal includes a windower for generating a plurality of consecutive blocks of audio samples, the plurality of consecutive blocks including at least one padded block of audio samples, the padded block having padded values and audio signal values, a first converter for converting the padded block into a spectral representation having spectral values, a phase modifier for modifying phases of the spectral values to obtain a modified spectral representation and a second converter for converting the modified spectral representation into a modified time domain audio signal.

A device and method for manipulating an audio signal includes a windower for generating a plurality of consecutive blocks of audio samples, the plurality of consecutive blocks including at least one padded block of audio samples, the padded block having padded values and audio signal values, a first converter for converting the padded block into a spectral representation having spectral values, a phase modifier for modifying phases of the spectral values to obtain a modified spectral representation and a second converter for converting the modified spectral representation into a modified time domain audio signal.

The application discloses a hyperspectral soil component inversion method and system for heterogeneous small samples, and the method comprises the following steps: S1, a unified data interface covering satellite, unmanned aerial vehicle and ground laboratory observation modes is established by taking a geographical space position as an index to obtain original spectral data, and the original spectral data is mapped to a public band space and subjected to standardization processing and normalization processing to obtain a standardized data set; and S2, a pre-training model based on a double-layer mask mechanism is constructed according to the standardized data set to obtain a spectral representation backbone network, wherein the pre-training model has a special encoder architecture and a multi-dimensional reconstruction loss function.

The invention discloses a response spectrum matching space change multi-point seismic oscillation generation method and system based on S transformation and a terminal. The method comprises the following steps: obtaining first intermediate seismic oscillation after seed seismic oscillation response spectrum matching of each station by adopting a single-point response spectrum matching seismic oscillation algorithm; by combining the time-frequency power spectral density of the first intermediate seismic oscillation and the spatial coherence between the two sites, performing simulation by adopting a spectral representation method to obtain coherent seismic oscillation of each site; adopting a single-point response spectrum matching seismic oscillation algorithm to obtain second intermediate seismic oscillation of each site after coherent seismic oscillation response spectrum matching; performing S transformation on the coherent seismic oscillation and the second intermediate seismic oscillation to obtain an S transformation coefficient, and performing phase updating on the S transformation coefficient of the second intermediate seismic oscillation by using a synchronous phase shuffling factor obtained by further calculation; and obtaining the target seismic oscillation of each station according to the S transformation coefficient after the phase update. According to the invention, high-precision multi-point response spectrum matching seismic oscillation generation capable of keeping seismic oscillation coherence is realized.

This invention proposes a method and system for ECG clustering based on multi-lead semantic consistency, belonging to the field of ECG signal processing technology. The method includes: constructing a similarity correlation matrix for an ECG signal set using adaptive graph learning; learning the spectral representation of each lead of the ECG signal using spectral clustering on the similarity correlation matrix, whereby spectral clustering utilizes multi-lead shared spectral embedding to measure the correlation between ECG signals, obtained through an optimized minimum edge weight objective function; wherein the optimized minimum edge weight objective function includes the multi-lead shared semantic similarity matrix; after obtaining the spectral embedding matrix, constructing a spectral rotation objective function by introducing an orthogonal rotation factor matrix; merging the minimum edge weight objective function and the spectral rotation objective function into a total objective function, and optimizing the total objective function using the alternating direction multiplier method to obtain the ECG clustering result. This invention significantly improves the clustering quality through the learning of semantically consistent graphs.

The present invention discloses a two-dimensional shape decomposition method and system based on salient branch detection. First, salient points on the shape contour are extracted based on the shape salient characteristic function; secondly, multiple salient branches on the shape are extracted through multi-label graph segmentation; thirdly, the segmentation endpoint candidate set and the accompanying point set of the salient points are determined by the salient branches; finally, the decomposition result of the shape is obtained through a recursive spectral clustering process in combination with the visibility of the points on the shape and the salient branch information of the shape. The present invention can efficiently decompose complex two-dimensional shapes into multiple approximately convex parts through the shape salient characteristic function, and can especially handle shapes with multiple slender curved branches. The present invention can avoid specifying parameters such as the number of salient branches and the number of shape decomposition parts. At the same time, the entire decomposition process is carried out in the spectral representation space, which can produce semantically clearer decomposition results and improve the robustness of the algorithm parameters.

Techniques described here introduce a processing pipeline for range estimation in phase-based ranging (PBR) based on scene types. The processing pipeline includes a scene identifier to identify a type of channel environment in PBR. The processing pipeline includes parallel moving average spectrums for oversampling data based on the identified scene for range estimation and faster Kalman Filter estimate convergence. The processing pipeline identifies a scene to characterize a channel environment between an initiator and a reflector based on data measured during PBR. The processing pipeline generates multiple spectral representations of the data based on the scene identified to estimate the range between the initiator and the reflector. The scene is identified based on statistical properties of the data and thresholds associated with different scenes. The processing pipeline may apply MVDR beamforming technique to generate the parallel moving average spectrums using filtering parameters adaptive to the scene followed by Kalman filtering.

Methods and apparatus to analyze recordings in leak detection are disclosed. An example apparatus includes a leak detection sensor to record a plurality of recordings and a memory. The example apparatus also includes a processor to convert one or more of the plurality of recordings to a corresponding one or more spectral representations, calculate a spectral average based, at least in part, on the one or more spectral representations, store the spectral average to the memory, and generate a data packet based, at least in part, on the spectral average. The example apparatus also includes a transceiver to transmit the data packet to another device.

The invention discloses a spectral band selection method based on fractional domain transformation and differentiable information imbalance. The spectral band selection method is used for obtaining a band subset with diagnosis information. The method comprises the following steps: S1, collecting a medical hyperspectral image, and dividing the medical hyperspectral image into a training-verification set and a test set; s2, performing fractional Fourier transform on the hyperspectral data of the training-verification set to obtain transform domain spectral representations under different orders; s3, evaluating the retention degree of the candidate wave band subset on the transform domain spectral characteristics under different orders based on the differentiable information imbalance, and carrying out wave band selection; and S4, determining an optimal transformation order, performing fractional Fourier transform and waveband selection processes of the test set data, and generating an optimal waveband subset. According to the method, information most related to lesion judgment can be effectively reserved while the data dimension is reduced, the recognition accuracy and robustness of the lesion area in the medical hyperspectral image are effectively improved, and the method has important clinical application value.

The application discloses a kind of progressive space-time spectrum characterization learning cultivated crop type monitoring systems, comprising;Remote sensing image acquisition module for obtaining the multi-temporal multispectral remote sensing image of target area on covering cultivated crop complete growing season;NDVI extraction module for calculating NDVI according to each phase ground surface real reflectivity time series image data;Spectrum perception module for reconstructing the time series spectral features of cultivated crop;Time series perception module for obtaining the time series aggregation features of cultivated crop based on the phenology curve of cultivated crop to the reconstructed time series spectral features of cultivated crop;Spatial modeling module for introducing position coding on the time series aggregation features of cultivated crop based on the field location of cultivated crop, spatial modeling is carried out, and the spatial features of cultivated crop are obtained;Classification module for generating the type distribution map of cultivated crop according to cultivated crop spatial features.

An apparatus for generating a synthesis audio signal using a patching control signal has a first converter, a spectral domain patch generator, a high frequency reconstruction manipulator and a combiner. The first converter is configured for converting a time portion of an audio signal into a spectral representation. The spectral domain patch generator is configured for performing a plurality of different spectral domain patching algorithms, wherein each patching algorithm generates a modified spectral representation having spectral components in an upper frequency band derived from corresponding spectral components in a core frequency band of the audio signal. The spectral domain patch generator is furthermore configured to select a first spectral domain patching algorithm from the plurality of patching algorithms for a first time portion and a second spectral domain patching algorithm from the plurality of patching algorithm for a second different time portion in accordance with the patching control signal to obtain the modified spectral representation.

The invention discloses a texture direction detection method based on texture detection technology fusion, and the method comprises the steps: S1, carrying out the image conversion and denoising processing of a color image, and generating a gray image; s2, counting the number of pixels of different gray levels in the gray image to obtain a gray distribution diagram, and obtaining a first texture direction according to the gray distribution diagram; s3, extracting edge information in the image to obtain a gradient magnitude image, and generating a clear edge image according to the gradient magnitude image; s4, extracting a second texture direction of the edge image according to Hough transform; s5, performing spectral analysis on the image to obtain spectral representation of the image, and calculating an amplitude spectrum of a frequency spectrum to obtain a third texture direction; and S6, fusing the first texture direction, the second texture direction and the third texture direction to obtain a final texture direction. By fusing multiple detection technologies, the defects of a single method in processing different texture types, noise levels and complexity are overcome, and a more comprehensive and robust texture direction detection scheme is provided.

A computer-implemented method for generating a spectral model representation of an electromagnetic element for determining an electromagnetic response to electromagnetic radiation interacting with the electromagnetic element is presented. The spectral representation contains a two-dimensional array of elements corresponding to respective wave vectors, each of which is defined by (different) respective pairs of wave numbers in two directions transverse to each other. The electromagnetic elements are geometrically described as a plurality of slices stacked transversely to these directions, and each slice contains one or more polygons. The method includes determining points along each of the polygon edges, a calculation coefficient being calculated based on a range of the corresponding polygon edge in a lateral direction. By performing a fast Fourier transform (FFT) algorithm using the coefficients, Fourier components are generated in each of the lateral directions for each of the wave vectors.

The invention relates to a corn kernel quality evaluation method, a machine readable storage medium and equipment, and belongs to corn kernel quality evaluation in the field of agricultural products, the method comprises the following steps: firstly obtaining the absorbance spectrum and the protein content of each sample in a sample set, then preprocessing the absorbance spectrum, converting a spectrum view into a graph structure in a graph neural network, constructing a quality evaluation model, sequentially training and evaluating the model, and finally evaluating the quality of the corn kernels by using the established quality evaluation model. According to the method, explicit modeling and feature fusion are carried out on potential relationships among all views, and adaptive enhancement of key spectral information and suppression of redundant features are realized; by combining hierarchical feature extraction and a multi-scale fusion strategy, the obtained spectral representation has both a global trend and local details, and high prediction precision and good robustness can still be kept under the condition of large sample difference or limited data scale.

The invention provides an ocean flow field reconstruction method based on multi-modal hybrid filtering and latent code modulation, and relates to the technical field of ocean dynamics analysis, and the method comprises the steps: carrying out the data preprocessing, constructing a space-time sample index, and obtaining a space sampling point coordinate; carrying out context embedding, carrying out mixed frequency coding, then carrying out multiplicative interaction and hierarchical condition modulation to form frequency selective feature representation, and carrying out deep residual stabilization to obtain implicit features of a current residual layer; the operation of the multiple residual layers is repeated until final implicit features are obtained; and generating a reconstructed flow field through coordinate-by-coordinate linear projection based on the final implicit features. According to the invention, tunable multi-scale spectrum expression is formed through mixed frequency coding. And large-scale flow state characteristics and environment background information are represented through hierarchical condition modulation based on context embedding. A multiplicative conditional modulation mechanism is constructed to strengthen high-frequency response, and layer normalization, learnable layer scaling and a random depth regularization mechanism are introduced in a deep feature propagation stage to carry out deep residual stabilization.