49 results about "Spectral representation" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

A system and method are provided for monitoring a factory, comprising during a training period of time, training a machine learning model with training data including a plurality of known spectral representations each of which is designated in the training data as normal or abnormal; and during an operating period of time receiving a first audio sample from a first microphone in an array of microphones suspended above a plurality of mechanical devices, generating a first spectral representation from the first audio sample, providing the first spectral representation to the machine learning model, and determining that the first spectral representation is abnormal.

This application discloses a training method and apparatus for an audio codec model. The main technical solution includes: acquiring training data comprising multiple audio samples; training the audio codec model using the training data; wherein the training includes: extracting the spectral representation of the audio samples; extracting the acoustic feature representation of the audio samples using a first audio encoder; mapping the acoustic feature representation to a latent space using a second audio encoder to obtain the latent space feature representation of the audio samples; masking the latent space feature representation to obtain the masked latent space feature representation; performing decoding processing based on the latent space feature representation using an audio decoder to obtain the reconstructed spectrum; and updating the model parameters of the audio codec model using the reconstructed spectrum and the spectral representation. This approach can improve the semantic understanding capability of the audio codec model, thereby improving the performance of speech generation tasks.

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.

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.

A system and method for enhancing a call characterization of voice calls is disclosed. The method comprises: receiving, at the platform (1), the voice call (30) segmented in audio packets; generating a Call Detail Record "CDR" (12) comprising call parameters; processing, by the Digital Signal Processing "DSP" engine (300), the audio packets, wherein the processing step in turn comprises: computing audio spectral representations; and, accumulating a predetermined number of audio packets (30.1). The method further comprises: evaluating, by the Audio Segmentation "AS" engine (301), an audio type present in the accumulated audio packets; generating temporal marks (31), by the Audio Segmentation "AS" engine (301), indicating which audio segment correspond to each audio type; computing metrics (32), by the Metrics Computation "MC" engine (302), using the accumulated audio packets and the temporal marks; generating an enhanced Call Detail Record "eCDR" (33) by adding the metrics (32) to the "CDR".

Systems, methods, and non-transitory computer readable medium are disclosed for encoding tissue behavior. A bioreactor may be implemented, where the bioreactor comprises a device configured for growing tissue and a sensor assembly configured to detect functional response(s) of a tissue within the device. The systems and methods may also include a control unit communicatively coupled to the bioreactor and configured to obtain, from the bioreactor, waveform(s) comprising functional response(s) of the tissue within the device over a time frame. The control unit may also be configured to transform the one or more waveforms to spectral representation(s) that provide frequency-based characterizations of the functional response(s) of the tissue during the time frame thereby encoding a behavior of the tissue.

Method and system for electric vehicle charging The electric charging method is implemented by a system comprising a supervisory module, a plurality of charging stations and a compensation device adapted to inject a compensation current into the distribution network, each charging station being configured to communicate with the supervisory module via a communication link, the method comprising obtaining, from each charging station, a spectral representation of the current consumed by the station, a determination (72) of an overall spectral representation of the charging system from the spectral representations of the current consumed per charging station and per spectral rank, a calculation (75) of at least one parameter of at least one compensation current, as a function of the overall spectral representation and at least one predetermined power factor degradation threshold.The compensation current is injected into the electricity distribution network. See Figure 4 for the abbreviation.

The invention provides a self-supervised implicit hyperspectral mixed noise removal method coupled with low-rank prior, and relates to the field of hyperspectral remote sensing image denoising, the method comprises the following steps: realizing space and spectrum decoupling through low-rank characteristics, and designing an implicit space mapping network and an implicit spectrum representation network; respectively mapping input two-dimensional space coordinates and one-dimensional spectral coordinates to a high-dimensional feature space by using a coordinate position encoder; an image feature encoder excavates deep features of spatial and spectral dimensions of the hyperspectral image; after being spliced, the high-dimensional coordinate features and the image features are projected to a target signal space through a multi-layer perceptron decoder, and a restored low-rank subspace is multiplied by a spectrum substrate to obtain a denoised hyperspectral image. According to the method, a degraded image is taken as a supervision signal, an implicit neural representation self-supervision framework is constructed, total variation regularization constraint is introduced, violent fluctuation of local pixels is inhibited by using image continuity, multi-type high-intensity mixed noise is effectively removed, and image quality and availability are remarkably improved.

The invention discloses a building structure vulnerability assessment method based on a seismic oscillation parameter zoning map. The method comprises two parts of content: random artificial vibration generation based on the seismic oscillation parameter zoning map and building structure seismic vulnerability analysis based on a recurrence period. Obtaining target response spectrums of each street in five types of sites and four return periods; generating random man-made vibration based on a spectrum representation method, and matching a mean response spectrum and a target response spectrum of the random man-made vibration by adopting an optimization algorithm; and establishing a multi-degree-of-freedom shear type layer model based on the BWBN model. And determining parameter values of the multi-degree-of-freedom shear type layer model and maximum inter-layer displacement angle thresholds and loss ratios when different types of building structures are in different damage states. And establishing earthquake vulnerability curves when different types of building structures in different streets are located in different sites. According to the invention, the method is suitable for the unified assessment of the earthquake vulnerability of different regions and different types of building structures in China, and improves the applicability of the assessment method, the accuracy of the assessment result and the structural calculation efficiency.

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.

A machine may be configured to generate one or more audio fingerprints of one or more segments of audio data. The machine may access audio data to be fingerprinted and divide the audio data into segments. For any given segment, the machine may generate a spectral representation from the segment; generate a vector from the spectral representation; generate an ordered set of permutations of the vector; generate an ordered set of numbers from the permutations of the vector; and generate a fingerprint of the segment of the audio data, which may be considered a sub-fingerprint of the audio data. In addition, the machine or a separate device may be configured to determine a likelihood that candidate audio data matches reference audio data.

A method, computer program product, and computing system for enhancement of audio signals received from a plurality of microphones. A multichannel audio signal is received from a plurality of microphones and is processed with a short-time discrete cosine transform (STDCT) to generate a real-valued spectral representation of the multichannel signal encoding both magnitude and phase information. Magnitude- and phase-dependent weights are generated, and an enhanced single-channel signal is produced based upon, at least in part, the spectral representation of the multichannel signal and the magnitude- and phase-dependent weights.

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.

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.