36 results about "Spectral coefficient" patented technology

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

The invention relates to the field of artificial intelligence deep learning model optimization, and discloses an ultra-low parameter efficient fine tuning method based on sparse frequency domain projection. Comprising the steps of setting an upper projection matrix which is frozen after random initialization in an attention mechanism layer or a feedforward mapping layer of a pre-training model for mapping a low-dimensional reconstruction result to an original weight dimension; constructing a sparse spectrum matrix in a frequency domain, generating a non-zero index set according to a frequency priority and a random sparse mixed strategy, and keeping the whole training process fixed; performing inverse transformation on the sparse spectrum matrix to obtain a time domain reconstruction matrix, and generating a weight increment through linear mapping of an upper projection matrix; only updating the non-zero spectrum coefficient under the limited parameter budget, and keeping the pre-training weight and the upper projection matrix frozen; optionally, spectrum regularization, energy constraint and gradient clipping are applied in the training stage, and quantification and distillation joint optimization is performed in the reasoning stage.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a gearbox fault diagnosis method and system based on graph structure signal processing, and the method comprises the following steps: calling a vibration data sequence, detecting slope change and marking nodes, constructing edge connection and edge weight to obtain a graph structure, calculating a spectral coefficient to recognize a high-density frequency interval, and carrying out the fault diagnosis. Extracting a spectrum boundary, obtaining a gradient difference value of a spectrum edge graph, positioning a node sequence, extracting a peak value, judging a fault type, and outputting a gearbox fault diagnosis result. According to the method, graph structure reconstruction is carried out through a node slope hopping trend, an edge connection direction and a weight reference quantity are constructed through inter-node amplitudes and time intervals, the sensitivity of a graph structure to weak impact characteristics is enhanced, disturbance frequency band screening is carried out based on spectrum coefficient slope inversion frequency, and the node response degree is mapped through spectrum edge amplitude gradient differences. And establishing a node amplitude direction sequence, matching the node amplitude direction sequence with a standard direction template, judging a fault boundary and a graph structure breakpoint position, and realizing accurate identification of an early fault of the gearbox.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

Embodiments of the present application provide an audio encoding and decoding method, device, equipment and storage medium. The method comprises: determining a category of an audio frame to be encoded as a target category; wherein the category of the audio frame is divided according to a numerical range of quantized spectral coefficients; selecting an encoding table corresponding to the target category from a plurality of generated encoding tables corresponding to categories as a first target encoding table; and encoding the quantized spectral coefficients of the audio frame to be encoded by using the first target encoding table. It can be seen that in the present scheme, different encoding tables are used to encode audio frames of different categories, and the category of the audio frame is divided according to the numerical range of the quantized spectral coefficients, that is, the present scheme takes into account the different characteristics of different quantized spectral coefficients, thereby improving the encoding efficiency.

The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values. Said method comprises processing the preceding spectral coefficients, using the processed preceding spectral coefficients for determining a context class being one of at least two different context classes, using the determined context class and a mapping from the at least two different context classes to at least two different probability density functions for determining the probability density function, and arithmetic encoding the current spectral coefficient based on the determined probability density function wherein processing the preceding spectral coefficients comprises non-uniformly quantizing absolutes of the preceding spectral coefficients for use in determining of the context class.

The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values.Said method comprises processing the preceding spectral coefficients, using the processed preceding spectral coefficients for determining a context class being one of at least two different context classes, using the determined context class and a mapping from the at least two different context classes to at least two different probability density functions for determining the probability density function, and arithmetic encoding the current spectral coefficient based on the determined probability density function wherein processing the preceding spectral coefficients comprises non-uniformly quantizing absolutes of the preceding spectral coefficients for use in determining of the context class.

The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values.Said method comprises processing the preceding spectral coefficients, using the processed preceding spectral coefficients for determining a context class being one of at least two different context classes, using the determined context class and a mapping from the at least two different context classes to at least two different probability density functions for determining the probability density function, and arithmetic encoding the current spectral coefficient based on the determined probability density function wherein processing the preceding spectral coefficients comprises non-uniformly quantizing absolutes of the preceding spectral coefficients for use in determining of the context class.

An encoder for encoding a current frame of an audio signal depending on one or more previous frames of the audio signal is provided. The previous frames precede the current frame, each of the current frame and the one or more previous frames having one or more harmonic components of the audio signal, each of the current frame and the one or more previous frames having a plurality of spectral coefficients in a frequency domain or in a transform domain. To generate an encoding of the current frame, the encoder is to determine an estimation of two harmonic parameters for each of the harmonic components of a most previous frame of the previous frames. Moreover, the encoder is to determine the estimation of the two harmonic parameters for each of the harmonic components of the most previous frame using a first group of three or more of the plurality of spectral coefficients of each of the previous frames of the audio signal.

An audio signal decoder (200) for providing a decoded representation (212) of an audio content on the basis of an encoded representation (310) of the audio content comprises a transform domain path (230, 240, 242, 250, 260) configured to obtain a time-domain representation (212) of a portion of the audio content encoded in a transform-domain mode on the basis of a first set (220) of spectral coefficients, a representation (224) of an aliasing-cancellation stimulus signal and a plurality of linear-prediction-domain parameters (222). The transform domain path comprises a spectrum processor (230) configured to apply a spectrum shaping to the first set of spectral coefficients in dependence on at least a subset of the linear-prediction-domain parameters, to obtain a spectrally-shaped version (232) of the first set of spectral coefficients. The transform domain path comprises a first frequency-domain-to-time-domain converter (240) configured to obtain a time-domain representation of the audio content on the basis of the spectrally-shaped version of the first set of spectral coefficients. The transform domain path comprises an aliasing-cancellation stimulus filter configured to filter (250) the aliasing-cancellation stimulus signal (324) in dependence on at least a subset of the linear-prediction-domain parameters (222), to derive an aliasing-cancellation synthesis signal (252) from the aliasing-cancellation stimulus signal. The transform domain path also comprises a combiner (260) configured to combine the time-domain representation (242) of the audio content with the aliasing-cancellation synthesis signal (252), or a post-processed version thereof, to obtain an aliasing reduced time-domain signal.

Method for applying a P-dimensional discrete Fourier transform (DFT) to a sequence of N samples of a sensor signal, with P, N > 2, wherein the method comprises the following step, performed by a processor circuit (20): receiving one of the samples after the other and whenever a new sample or a group of successive new samples, comprising a predefined number J of samples, is received, with 1 <J<N, empfangen wird: • Provide x for each new sample value n , a sequence index n indicating which of the 1 to N samples has been received, where n is in 1,..., N; and • Select or generate a corresponding DFT vector for each new sample value. d → ( n ) depending on the order index n of the sampled value, where the DFT vector d → ( n ) P comprises pointer values ​​consisting of frequency points f p The P-dimensional DFT can be derived by S * exp(-i 2 π f p t n ), where p in 1,...,P is the index of the pointer value in the DFT vector d → ( n ) is and S is a scaling factor and t n the sampling time of the new sample value x n is and i is the imaginary unit with i 2 = -1 is; • Applying the respective new sample value x n as a multiplication factor on its corresponding DFT vector d → ( n ) to generate a respective spectral contribution vector Δ y → (n) = d → (n) xn; • Adding the respective spectral contribution vectors Δy → (n) to an accumulation vector Δ y → (n) = Δ y → (n) + y → (n − 1); and when, for all N samples, their corresponding resulting contribution vector has been added to the accumulation vector, providing the accumulation vector y → ( N ) as DFT spectral coefficients of the N samples, characterized in that a) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • Providing a rasterized time pattern by defining a minimum time step size t̂0 and • Generating the respective DFT vector d → ( n ) for the respective sample value of the sequence index n using a P-dimensional basis DFT vector d → ^ ( 0 ) and the DFT vector d → ( n − 1 ) by applying element-wise multiplication, where d → ^ ( 0 ) Pointer elements exp(-i 2 π f pt̂0) with p in 1,...,P includes a time difference t n - t n-1 the sampling time of the last sample value x n and the preceding sample value x n-1 is expressed as the integer multiple, or the next larger integer multiple, or the next smaller integer multiple μ = [tn − tn − 1t^0] of t̂0, where [·] is the rounding operator, such that d → (n) = diag { d → ^ (0)} μ d → (n − 1), or b) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • Retain storage of M > 1 pre-calculated vectors diag { d → ^ ( m )} for different time values ​​{t̂1,...,t̂ M} in the memory as d → ^ ( m ) , , m = 1 ... M, with phasor elements exp(-i 2 π f p t̂ m ) , p = 1,...,P, and for a given sample value x n , at the sampling time t n , the vector for index m = arg min|{t n - t n-1 - t̂ m} applied to the preceding DFT vector with the nearest time difference by element-wise multiplication to provide the DFT vector d → (n) = diag { d → ^ (m)} d → (n − 1) for n > 1, or c) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • Provide, for M > 1, a set of possible time step sizes {t̂1,..., t̂ M} and for the sample value x n The time step t̂ m taken, the one closest to the time difference t n - t n-1 of two consecutive samples d → ( n ) = diag { d → ^ ( m )} d → ( n − 1 ) for rn > 1 and m = 1,..., M with d → (m) = (exp (−i 2 π f 1 t ^ m) exp (− i 2 π f 2 t ^ m) ⋮ exp (− i 2 π f P t ^ m)) and m = argmin | { tn − tn − 1 − t ^ m} | , or d) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • successive approach to the respective time difference t n - t n-1 of two consecutive samples x n-1 and x n , where for a maximum time difference max{t n - t n-1} = Δt̂ for the respective order index n and a time difference resolution of M bits, a minimum time step to Δt 2 M will be and for a given time difference t n - t n-1≤ Δt̂ a combination of predefined time steps is obtained by taking the time difference between the current and the previous sample value Δt n = t n - t n-1 is and Δt n is quantized with M bits, where m = 1 is the most significant bit and m = M is the least significant bit, and • the approach with a time difference of Δτ1 = Δt n starts • the degree of quantization is defined by T m = Δ t ^ 2 m • for m = 1 T 1 = Δ t ^ 2 will and • the m-th bit is derived from wm = ⌊ Δ τ m T m ⌋ results, with Δ τ m = Δ τ m − 1 − wm − 1 T m − 1 for rm > 1 Δτm = Δtnfu ¨rm = 1 as a quantized time difference, ⌊ ⋅ ⌋ denotes the rounding operator and for m = 1, w 1 = ⌊ Δ tn T 1 ⌋ , • when all bits have been calculated, the binary word is represented by the vector w → = ( ​​w 1 w 2 ⋯ w M ) T with w m ∈ {0,1} for m = 1, ...,M • and for a set of possible step sizes resulting from T m results { t ^ 1 ,..., t ^ M} = { T 1 , T 2 ,..., TM} The quantized time difference is calculated as Δ τ = (T 1 T 2 ⋯ TM) w → = ∑ m = 1 M wm T m, which is mapped to the subset of predefined DFT vectors (37) that are used to obtain the DFT vector d → ( n ) for the current time t n required: d → ( n ) = ∏ wm diag { wmd → ^ ( m )} d → ( n − 1 ) for rwm = 1 and m = 1,..., M , where only those predefined vectors d → ^ ( m ) be selected for the w m = 1 applies.

A method for decoding an encoded audio bitstream in an audio processing system is disclosed. The method includes extracting from the encoded audio bitstream a first waveform-coded signal comprising spectral coefficients corresponding to frequencies up to a first cross-over frequency for a time frame and performing parametric decoding at a second cross-over frequency for the time frame to generate a reconstructed signal. The second cross-over frequency is above the first cross-over frequency and the parametric decoding uses reconstruction parameters derived from the encoded audio bitstream to generate the reconstructed signal. The method also includes extracting from the encoded audio bitstream a second waveform-coded signal comprising spectral coefficients corresponding to a subset of frequencies above the first cross-over frequency for the time frame and interleaving the second waveform-coded signal with the reconstructed signal to produce an interleaved signal for the time frame.

The present application relates to a voice processing method, apparatus, electronic device, and readable medium. The method includes: obtaining spectral coefficients of a voice frame to be processed; calculating a glottal gain based on the spectral coefficients of the voice frame to be processed to obtain a first gain, where the first gain corresponds to the glottal characteristics of the voice frame to be processed; calculating an excitation gain based on the spectral coefficients of the voice frame to be processed to obtain a second gain, where the second gain corresponds to the excitation signal of the voice frame to be processed; performing compensation prediction based on the spectral coefficients of the voice frame to be processed to obtain a control coefficient, where the control coefficient is determined according to the energy of the spectral coefficients of the audio frame to be processed; and performing gain control on the voice frame to be processed according to the first gain, the second gain, and the control coefficient to obtain a target voice frame. This method can process the human voice part during noise reduction without the need to train for various types of noises. Therefore, it reduces the impact on the completeness of training data and improves the noise reduction effect.

The invention discloses a voice noise reduction method and device in combination with non-negative matrix factorization and time-frequency masking and a storage medium, and belongs to the technical field of Bluetooth audios, and the method comprises the steps: inputting monaural voice PCM data, and executing low-delay improved discrete cosine transform to obtain an MDCT spectral coefficient; calculating sub-band energy according to the MDCT spectrum coefficient, and constructing a noisy voice amplitude spectrum observation matrix; performing non-negative matrix factorization on the noisy voice amplitude spectrum observation matrix, and calculating an NMF amplitude spectrum gain; performing feature extraction according to the sub-band energy, and calculating an NN amplitude spectrum gain through a pre-trained deep neural network; fusing the NMF amplitude spectrum gain and the NN amplitude spectrum gain, and outputting a noise reduction gain; obtaining a noise reduction spectral coefficient according to the noise reduction gain and the MDCT spectral coefficient; and according to the noise reduction spectrum coefficient, continuing to execute the coding process, and outputting a noise reduction voice code stream. According to the invention, based on the MDCT spectral coefficient, voice noise reduction is realized by combining non-negative matrix factorization and time-frequency masking, and the tone quality is ensured.

The invention relates to an audio signal identification method and device, a kitchen range system and computer equipment. The method comprises the following steps: acquiring a current audio signal; performing feature extraction processing on the current audio signal to obtain at least two current spectrum coefficients, and performing coding processing on the current spectrum coefficients to obtain current coding sequence data corresponding to the current spectrum coefficients; and inputting all the coding sequence data into the completely trained audio signal identification network, activating pulse coding neurons in the audio signal identification network based on the coding sequence data, and obtaining an identification result corresponding to the current audio signal based on the activated pulse coding neurons. By means of the method, the problems that in the prior art, the calculation amount is too large, and the calculation efficiency is low can be solved.

The invention discloses a bone conduction speech enhancement method and device based on cepstrum and deep learning, a storage medium and equipment, and belongs to the technical field of Bluetooth audio coding and decoding, and the method comprises the steps: inputting PCM audio data, executing low-delay improved discrete cosine transform, and outputting a bone conduction speech spectrum coefficient; obtaining an enhanced bone conduction speech spectrum coefficient by combining cepstrum and deep learning according to the bone conduction speech spectrum coefficient; and according to the enhanced bone conduction speech spectrum coefficient, continuing to execute a standard LC3 coding process, and outputting an enhanced speech code stream. According to the invention, through combination of cepstrum and deep learning, LC3 coding is carried out on the bone conduction voice, the tone quality is enhanced, the user experience is improved, the feature extraction steps of coding and deep learning are reused, and the storage space and the computing power demand are saved.

The present application provides a voice processing method, apparatus, electronic device and readable medium. The method includes: obtaining spectral coefficients of a voice frame to be processed; performing feature calculation according to the spectral coefficients of the voice frame to be processed to obtain an audio feature vector of the voice frame to be processed; performing glottal gain calculation according to the audio feature vector to obtain a first gain, the first gain corresponding to the glottal feature of the voice frame to be processed; performing excitation gain calculation according to the audio feature vector to obtain a second gain, the second gain corresponding to the excitation signal of the voice frame to be processed; performing compensation prediction according to the audio feature vector to obtain a control coefficient, the control coefficient being determined according to the energy of the spectral coefficients of the voice frame to be processed; and performing gain control on the voice frame to be processed according to the first gain, the second gain and the control coefficient to obtain a target voice frame. This method can effectively process noise types and noise environments not included in the training data and improve the noise reduction effect.

This invention provides a seismic image processing method and apparatus, relating to the field of image processing technology. The method includes: decomposing a seismic image based on its sub-bands and pixel count to obtain multiple image block matrices; performing an orthogonal transformation on each image block matrix to obtain a transformed image block; extracting spectral coefficients from each transformed image block; performing an arc-orthogonal transformation on each set of spectral coefficients to obtain an approximate matrix corresponding to each image block matrix; subtracting each image block matrix from each approximate matrix to obtain a difference image matrix; and decomposing the difference image matrix based on sub-band update values ​​and pixel count update values ​​until all sub-bands and pixels are traversed to obtain all transformed image blocks. The apparatus executes the above method. The method and apparatus provided by this invention can accurately and efficiently perform seismic image processing.

The present invention is applicable to the technical field of speech processing, and provides a speech recognition method and device based on a microcomputer. The speech recognition method based on the microcomputer includes: acquiring a speech signal to be processed, and extracting hierarchical time-domain features in the speech signal to be processed; extracting hierarchical frequency-domain features in the speech signal to be processed; when a first phoneme sequence is the same as a second phoneme sequence, inputting the first phoneme sequence or the second phoneme sequence into a language model to obtain a speech recognition result output by the language model; when the first phoneme sequence is different from the second phoneme sequence, processing the speech signal to be processed by using a multi-stage cascade filter to obtain spectral coefficients; and recognizing the speech recognition result according to the spectral coefficients. By combining hierarchical feature extraction with a multi-stage cascade filter, the present invention significantly improves the performance of the speech recognition system under low signal-to-noise ratio and high-noise backgrounds, and effectively solves the limitations of traditional speech recognition technologies in complex environments.

Fig. 1 illustrates an encoder (100) according to an embodiment. The encoder (100) is configured for encoding a current frame of an audio signal depending on one or more previous frames of the audio signal. The one or more previous frames precede the current frame. Each of the current frame and the one or more previous frames comprises one or more harmonic components of the audio signal. Moreover, each of the current frame and the one or more previous frames comprises a plurality of spectral coefficients in a frequency domain or in a transform domain, The encoder (100) is configured to determine a change of a pitch or an estimation of a change of the pitch between a most previous frame of the one or more previous frames and a current frame. Furthermore, the encoder (100) is configured to generate an encoding of the current frame depending on an estimation of two harmonic parameters for each of one or more harmonic components of the most previous frame, and depending on the change of the pitch or the estimation of the change of the pitch. Moreover, Fig. 1 illustrates a decoder (200) according to an embodiment. The decoder (200) is configured for reconstructing a current frame of an audio signal. One or more previous frames of the audio signal precede the current frame. Each of the current frame and the one or more previous frames comprises one or more harmonic components of the audio signal. Moreover, each of the current frame and the one or more previous frames comprises a plurality of spectral coefficients in a frequency domain or in a transform domain. The decoder (200) is to receive an encoding of the current frame. Moreover, the decoder (200) is to determine an estimation of two harmonic parameters for each of the one or more harmonic components of a most previous frame of the one or more previous frames. Furthermore, the decoder (200) is to reconstruct the current frame depending on the encoding of the current frame. The decoder (200) is to reconstruct the current frame depending on the estimation of the two harmonic parameters for each of the one or more harmonic components of the most previous frame. Moreover, the decoder (200) is to reconstruct the current frame depending on a change of a pitch or an estimation of a change of the pitch between a most previous frame of the one or more previous frames and a current frame.

A method comprises adjusting, based on an adjustment factor, a quantized energy envelope of a subband of a frame of an audio signal to obtain an adjusted quantized energy envelope of the subband. Bit allocation for a plurality of subbands of the frame is performed based on the adjusted quantized energy envelope, wherein at least one subband in the plurality of subbands has at least one allocated bit. At least a part of spectral coefficients included in the at least one subband is quantized, based on the at least one allocated bit, to obtain quantized spectral coefficients. A bitstream is obtained that includes the quantized spectral coefficients.

An encoder (100) for encoding a current frame of an audio signal from one or more previous frames of the audio signal is provided according to embodiments. The one or more previous frames precede the current frame, wherein each of the current frame and the one or more previous frames comprises one or more harmonic components of the audio signal, wherein each of the current frame and the one or more previous frames comprises a plurality of spectral coefficients in a frequency domain or in a transform domain. To generate an encoding of the current frame, the encoder (100) will determine an estimate of two harmonic parameters for each of the one or more harmonic components of a most previous frame of the one or more previous frames. Further, the encoder (100) will determine the estimate of the two harmonic parameters for each of the one or more harmonic components of the most previous frame using a first group of spectral coefficients constituted by three or more of the plurality of spectral coefficients of each of the one or more previous frames of the audio signal.

This invention discloses a hyperspectral image stitching method and system based on spectral fitting. The method includes extracting feature points from each hyperspectral image, matching feature points from multiple hyperspectral images, transforming feature points obtained from the feature points, constructing a spectral fitting function for the overlapping region based on the set of feature point pairs, obtaining the corresponding spectral coefficients by minimizing the spectral fitting function, calculating the new spectral values ​​of pixels in the overlapping region, keeping the non-overlapping region unchanged, and then fusing them to obtain the final panoramic image, achieving a better stitching effect.

An encoder for encoding an audio signal. The encoder is configured to encode the audio signal in a transform domain or a filter bank domain, wherein the encoder is configured to determine spectral coefficients of the audio signal for a current frame and at least one previous frame, wherein the encoder is configured to selectively apply a predictive encoding to individual spectral coefficients or groups of spectral coefficients separated by at least one spectral coefficient.

This application discloses a Bluetooth audio pitch shifting method, apparatus, medium, and Bluetooth device, belonging to the field of audio encoding and decoding technology. It mainly includes: calculating the amplitude pseudo-spectrum of each spectral coefficient using each spectral coefficient in the current frame audio spectral coefficients obtained through discrete cosine transform, and calculating the fundamental frequency of the current frame audio signal corresponding to each spectral coefficient using a predetermined number of adjacent spectral coefficients before and after each spectral coefficient; calculating the fundamental frequency of the current frame audio signal corresponding to the current frame audio spectral coefficient using the amplitude pseudo-spectrum; processing the phase and frequency of the current frame audio fundamental frequency to obtain the pitch-shifted current frame audio fundamental frequency; generating the pitch-shifted current frame audio spectral coefficients using the pitch-shifted current frame audio fundamental frequency, and completing the remaining Bluetooth audio encoding and / or decoding steps. This application can reduce system complexity, reduce computational load, reduce system latency, improve user experience, and flexibly set pitch shifting parameters to meet the needs of different users.

The present application relates to a kind of network converter broadband oscillation polynomial base function prediction method, belong to the field of power system automation and power electronics control technology, a kind of network converter broadband oscillation polynomial base function prediction method, comprising the following steps: S1: establishing network converter system state space model and extracting time domain analytical solution;S2: construct polynomial frequency domain base function, establish frequency domain analytical characterization framework;S3: derive time-frequency analytical mapping operator matrix;S4: online acquisition system initial value, calculate time domain power series expansion coefficient;S5: execute analytical mapping, solve the frequency domain spectral coefficient of current step;S6: recursive update state, realize the rolling prediction of broadband oscillation frequency spectrum;S7: output frequency spectrum prediction result (frequency, amplitude) and be used for upper layer damping control, the present application, establish a kind of direct mapping mechanism from "time domain analytical solution" to "frequency domain spectral coefficient", realize the unified characterization and closed-form solution of spectral feature in system state evolution process.

The invention relates to the technical field of hydrological simulation, and particularly discloses an extra-small watershed layered three-dimensional hydrological-hydrodynamic full-coupling simulation method, which comprises the following steps: collecting multi-source data to construct a three-dimensional space discrete grid and dividing initial time units; the control equation set is converted into an algebraic system based on a Legendre polynomial and a Gaussian-Loabar node; adaptively adjusting a time unit length and a spectral basis function order according to the local time gradient change rate; a convergence spectral coefficient is obtained through spectral delay correction iteration solution; and finally, forcibly executing mass conservation constraint, controlling step recalculation according to a local time error, and gradually outputting a flow hydrograph and water depth and infiltration rate distribution according to time unit endpoints. According to the method, the dual self-adaption of the time step length and the polynomial order is realized, the numerical oscillation of the infiltration rate caused by the sudden change of the ponding depth can be effectively inhibited, the mass conservation is ensured, and the stability and the efficiency of the rainstorm flood simulation of the extra-small watershed are improved.

A method for decoding an encoded audio bitstream in an audio processing system is disclosed. The method includes extracting from the encoded audio bitstream a first waveform-coded signal comprising spectral coefficients corresponding to frequencies up to a first cross-over frequency for a time frame and performing parametric decoding at a second cross-over frequency for the time frame to generate a reconstructed signal. The second cross-over frequency is above the first cross-over frequency and the parametric decoding uses reconstruction parameters derived from the encoded audio bitstream to generate the reconstructed signal. The method also includes extracting from the encoded audio bitstream a second waveform-coded signal comprising spectral coefficients corresponding to a subset of frequencies above the first cross-over frequency for the time frame and interleaving the second waveform-coded signal with the reconstructed signal to produce an interleaved signal for the time frame.

A signal processing method and device includes obtaining spectral coefficients of a current frame of an audio signal, in which N sub-bands of the current frame comprises at least one of the spectral coefficients. A total energy of M successive sub-bands of the N sub-bands, a total energy of K successive sub-bands of the N sub-bands, and an energy of a first sub-band are obtained to determine whether to modify original envelope values of the M sub-bands. When the original envelope values of the M sub-bands are modified, encoding bits are allocated to each of the N sub-bands according to the modified envelope values of the M sub-bands.

The invention relates to a multi-mode satellite signal simulation method, which is used for a user verification test and comprises the following steps of: constructing a multi-mode satellite signal; carrying out discrete sampling on the multimode satellite signal, calculating original discrete energy of each mode, and generating a coupling coefficient of each mode based on an energy ratio; performing normalization processing on the sampled multimode satellite signal to obtain a standardized signal; extracting the frequency domain spectral coefficients of each mode in the standardized signal, and performing weighted synthesis on the frequency domain spectral coefficients of each mode by taking the coupling coefficient as a weight to generate an overall spectrum signal; and converting the total spectrum signal to a time domain to obtain a discrete simulation satellite signal. The method can improve the customization capability, energy balance, feature richness and safety of multimode satellite signal simulation, and is suitable for scenes of high-precision navigation test, anti-interference verification and the like.