365 results about "Filter bank" patented technology

The invention discloses a ship equipment vibration test method and system based on frequency domain kurtosis mapping and non-Gaussian signal generation. The ship equipment vibration test method comprises the following steps: collecting an actual ship impact vibration signal and extracting a target power spectral density curve; the method comprises the following steps: identifying an impact energy concentration frequency band based on spectrum energy distribution, carrying out frequency band division by adopting an elliptical filter bank with a second-order section structure, and calculating an envelope kurtosis value of each frequency band to establish a kurtosis-frequency mapping model; after the initial Gaussian driving signal is generated, a non-Gaussian driving signal is generated through a phase randomization and amplitude dynamic scaling algorithm based on a mapping model; a multi-target optimization controller based on Pareto leading edge search is adopted, a power spectral density error and a kurtosis error are taken as optimization targets, synchronous optimization is carried out through fast non-dominated sorting and an adaptive hybrid variation strategy, and a driving signal is output. According to the invention, the broadband and non-Gaussian impact vibration environment of the ship equipment in the actual severe sea condition can be reproduced in a high-fidelity manner, and the authenticity of the test and the fault mode coverage capability are remarkably improved.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention discloses an acousto-optic combined detection and fusion positioning method and device for underground gas leakage, and belongs to the technical field of industrial safety monitoring. In order to solve the problems of high false alarm rate, insufficient positioning precision, poor dynamic environment adaptability and the like of a single sensor in the prior art, the invention provides a multi-modal signal space-time collaborative fusion technical scheme which specifically comprises an acousto-optic heterogeneous sensor, an integrated MEMS ultrasonic array and a tunable laser spectrum module. Microsecond-level synchronous acquisition of acousto-optic signals is realized through a GPS taming clock, and a cross-correlation time delay compensation model is designed to eliminate space-time mismatch errors. And designing a three-stage dynamic fusion framework: improving LMS adaptive filtering to suppress industrial noise, establishing a light intensity attenuation model in combination with dust environment concentration, carrying out multi-modal feature extraction of acousto-optic signals, carrying out signal preliminary fusion, and judging a gas leakage condition. And constructing an acousto-optic Bayesian joint probability model, and calculating the leakage posterior probability. And optimizing positioning parameters based on a double-Kalman filter group, predicting sound wave time delay by forward filtering, and correcting a light diffusion coefficient by backward filtering to realize three-dimensional coordinate solution. Temperature, humidity and dust concentration parameters are fused in real time, a sound velocity calculation model and a light intensity attenuation factor are corrected, and the robustness of complex working conditions is improved. According to the invention, the advantages of acousto-optic signals are combined, errors of various complex environmental factors are further fused and compensated, the overall error is greatly reduced, and the method has the characteristic of dynamic adaptation, and is very suitable for gas leakage detection and positioning in complex environments such as underground environments and the like.

The invention relates to a time domain broadband adaptive beam forming method and system based on Chebyshev weighting, and belongs to the field of array signal processing. Aiming at the technical problems of poor real-time performance and high sidelobe of the existing broadband beam forming, the method comprises the following steps of: dividing a received broadband signal into a plurality of sub-bands, obtaining an optimal weight vector for each sub-band by adopting a minimum linear constraint iterative algorithm after down-conversion, constructing an expected response of an FIR filter according to each weight, obtaining a filter coefficient through L2 norm convex optimization, and finally obtaining a high sidelobe beam according to the expected response of the FIR filter. And performing time domain weighting on each array element signal by using a filter bank, and outputting a directional beam. According to the method, the calculated amount is low, a main lobe and interference null can be formed in real time in a broadband, and a certain side lobe height is kept.

The invention belongs to the technical field of speech recognition and acoustic modeling, and particularly relates to a high-fidelity end-to-end acoustic modeling method combining speech intelligibility reconstruction and autoregressive feedback optimization, which comprises the following steps of: firstly, establishing an intelligibility loss function based on a human speech intelligibility model; subjective definition features of the voice are reconstructed through a frequency spectrum reconstruction network, intelligibility related features are extracted and embedded in combination with a perceptual weighted filter bank, and the intelligibility related features and Mel frequency spectrum features are input into an acoustic encoder in parallel; based on this, by introducing a voice intelligibility reconstruction and autoregressive feedback mechanism, while the conciseness of an end-to-end voice modeling framework is maintained, systematic improvement of voice sharpness, naturalness and stability is realized, and the method can be widely applied to scenes such as intelligent voice assistants, voice transcription, virtual anchors, voice restoration, cross-language voice generation and the like. And the method has extremely high practical application value and popularization prospect.

The invention discloses a wind driven generator large-torque transmission chain resistance increasing control method and system, and relates to the technical field of wind driven generator resistance increasing control. The method comprises the steps that operation parameters of a wind driven generator are collected in real time and preprocessed; calculating a rotation fluctuation frequency set and a corresponding amplitude spectrum of the current time window, and tracking and locking the dominant vibration frequency; constructing a fan state space prediction model containing the torsional vibration state of the transmission chain, outputting the predicted variable quantity of the torque of the transmission chain in the next control period, constructing a filter bank based on the dominant vibration frequency, performing filtering compensation on the predicted variable quantity, and generating an electromagnetic torque given value; and performing phase lead compensation on the electromagnetic torque given value, generating a control instruction, sending the control instruction to the wind power converter, and controlling the wind power generator to output the corresponding electromagnetic torque by using the wind power converter. Through systematic multi-link cooperative processing, the vibration suppression effect and the operation stability of the transmission chain can be comprehensively improved.

The invention provides a ship radiation noise identification method and system based on a double-low-rank adjustment network. A ship radiation noise identification method based on a double-low-rank adjustment network comprises the following steps: performing Mel filter bank transformation on a ship radiation noise signal to obtain a Mel time-frequency spectrogram of the noise signal; inputting the Mel time-frequency spectrogram into a trained noise recognition model, and outputting a target category of the ship radiation noise; the noise recognition model is generated based on a pre-trained reference model containing a plurality of residual blocks, low-rank adaptation is performed on a weight space through a weight low-rank adaptive module, and low-rank adaptation is performed on a feature space through a feature low-rank adaptive module. The method has the advantages that the classification performance of ShipsEar and DeepShip data sets can be remarkably improved under the condition that only 0.55% of parameters are increased, and the method has low overhead and high effectiveness.

The invention provides an inter-channel crosstalk suppression method in a multi-channel optical module, which aims at the problems of inter-channel interference and signal overlapping caused by wavelength shift in the multi-channel optical module, calculates the wavelength shift by adopting a proportional integral differential control method through collecting the reading of a temperature sensor and the output wavelength data of a laser device in real time, and finally suppresses the crosstalk in the multi-channel optical module. The threshold value is dynamically adjusted in combination with the environment temperature change, and the interference risk index is quantified; the wavelength drift trend is extracted through time sequence analysis, the channel interval adjustment logic is optimized, the filter bank is dynamically configured to improve the signal purity, meanwhile, the wavelength parameter is corrected through the offset compensation value, and the stability is ensured. If the transmission quality requirement is met, configuration parameters are locked, a correction model is matched and optimized through historical data, potential risks are predicted in combination with state estimation filtering, and the logic adaptability is enhanced and adjusted. According to the invention, efficient correction of wavelength drift and interference minimization are finally realized, and the transmission quality and operation stability of the multi-channel optical module are improved.

The invention relates to the technical field of defect detection, in particular to a corrugated paper defect detection method and system based on machine vision. The method comprises the following steps: acquiring a corrugated paper image and converting the corrugated paper image into a frequency domain; identifying a periodic texture peak point based on amplitude spectrum angle energy distribution, generating a texture suppression filter, and combining with a multi-scale Gaussian high-pass filter to obtain a composite filter bank; the filter bank is used for filtering the frequency domain signals and performing energy weighted fusion; performing inverse transformation on the fused signal to obtain an enhanced image; and segmenting the enhanced image to obtain candidate defects, and verifying by combining the gradient information of the original image to determine a final defect. According to the scheme, the texture background can be adaptively inhibited, the multi-scale defect is enhanced, the false alarm rate is effectively reduced in combination with gradient check, and the detection accuracy and robustness are improved.

The invention discloses a brain-computer interface multi-task fine coding and decoding method and system based on electrical stimulation to induce SSSEP, and the method comprises the steps: in a divergent resting somatosensory stimulation test and a somatosensory attention orientation task, applying different frequencies of electrical stimulation and LED stroboflash to the five fingers of a single hand at the same side by using a multi-mode five-finger stimulator, collecting at least six-lead electroencephalogram signals such as C3, Cz, C4, O1, Oz and O2, and carrying out the detection of the electroencephalogram signals; sSVEP features of O1, Oz and O2 leads are analyzed and extracted through a filter bank and are subjected to weighted fusion, and SSVEP classification probability vectors are obtained; the method comprises the following steps: extracting SSSEP classification feature vectors of C3, Cz and C4 leads through a common spatial mode, performing classification to obtain SSSEP probability vectors, finally performing linear weighted fusion on the two vectors, and taking a maximum element to judge a decoding category. According to the method, multi-cortex areas are activated through somatosensory and visual bimodal neural coding, the decoding precision and response speed are improved, the training period is shortened, and rehabilitation medicine is adapted.

The invention relates to the technical field of broadband signal synthesis and high-speed digital-to-analog conversion, in particular to an oversampling-based real-time frequency interleaving digital-to-analog conversion system aliasing suppression method and system, and aims to solve the problem of spectrum aliasing caused by non-ideal characteristics of a digital sub-band decomposition filter and an analog filter under a real-time architecture of the system. In the invention, the sub-band decomposition is completed by adopting the linear phase complementary filter bank, so that distortion introduced by a transition band of a digital filter is avoided; by restraining system parameters such as passband and stopband frequencies and local oscillation frequency of a sub-band decomposition filter, a protection band is set by an oversampling mechanism, a frequency margin is reserved for a transition band of the filter, and aliasing components are suppressed. According to the method, extraction aliasing of a digital domain and mirror image aliasing of an analog domain are avoided at the same time from the system architecture, the frequency spectrum purity of output signals is improved, the method is suitable for a real-time system for processing continuous data streams, and technical support is provided for achieving a high-performance broadband signal source.

The invention discloses a dynamic reconfigurable signal processing platform based on a ZYNQ architecture, and belongs to the technical field of digital signal processing, and the dynamic reconfigurable signal processing platform comprises a channel preprocessing unit, a high-speed AD sampling unit, a ZYNQ-PL logic unit and a ZYNQ-PS algorithm processing unit. A radio frequency signal is fed in from an antenna end and then enters the channel preprocessing unit, a balun circuit and a filter bank are adopted to carry out preselection filtering, amplification and conditioning on the radio frequency signal, and the signal is extracted and sent to the high-speed AD sampling unit; the high-speed AD acquisition unit converts a received analog signal into a digital signal and transmits the digital signal to the ZYNQ-PL logic unit, and the ZYNQ-PL logic unit is connected with the ZYNQ-PS algorithm processing unit through an AXI bus; wherein the ZYNQ-PL logic unit is composed of a static region and three dynamic regions, and is mainly used for realizing a logic part of a functional waveform and completing reconstruction and loading of the functional waveform; and the ZYNQ-PS algorithm processing unit completes algorithm processing, realizes modulation and demodulation of a functional waveform and realizes a functional algorithm.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

Aspects of the subject disclosure may include, for example, a digital timing skew compensator, including: a first polyphase adaptive filter bank having a first input and a first output; a first input matrix coupled to the first input of the first polyphase adaptive filter bank; a first subtractor coupled to the first output of the first polyphase adaptive filter bank, wherein the first subtractor removes a first signal generated by the first polyphase adaptive filter bank from digital outputs of sub analog-to-digital converters (ADCs) of a time-interleaved ADC to create a desired signal, wherein the desired signal is coupled to the first input matrix; a modulation matrix that modulates the digital outputs of the sub-ADCs to create a modulated signal vector; a second polyphase adaptive filter bank having a second input and a second output; a second input matrix coupled to the second input of the second polyphase adaptive filter bank; and a second subtractor coupled to the second output of the second polyphase adaptive filter bank, wherein the second subtractor removes a second signal generated by the second polyphase adaptive filter bank from the modulated signal vector to create an error signal, wherein the error signal is coupled to the second input matrix. Other embodiments are disclosed.

The utility model relates to the technical field of microwave communication, in particular to a cavity filter bank integrated structure, which comprises a main body with a cavity, a top opening of the main body is communicated with the cavity, a cover plate covers the top opening of the main body, and the cover plate is detachably connected with the main body through a plurality of sunk screws. Two tuning modules are integrated in the cavity, the bottom of the main body is provided with an output interface which is inserted with a 0.9 mm signal needle of an external system, the output interface is welded and fixed on the cavity through a customized coaxial connector, and the cover plate is provided with an input interface. According to the utility model, by optimizing the cavity layout and the interface design, high-performance signal filtering under a compact size is realized, and special plug-in requirements are met.

The invention discloses a bearing fault diagnosis method and system based on a multi-source weighted frequency spectrum reconstruction model, and the method comprises the steps: carrying out the Fourier transform of a bearing vibration signal, and obtaining a frequency spectrum; performing weighting processing on the frequency spectrum to generate a weighted entropy spectrum; dividing initial frequency boundary points; iteratively adjusting the initial frequency boundary point to obtain an optimized frequency boundary point; constructing a coupling transfer function; performing iterative calculation to obtain a unified frequency boundary point; constructing a filter bank based on the unified frequency boundary points, and performing sub-band segmentation on each weighted entropy spectrum to obtain a plurality of frequency spectrum sub-bands; overlapping and fusing the corresponding frequency spectrum sub-bands which belong to different weighted entropy spectrums and are in the same frequency range to obtain a reconstructed frequency domain signal; performing inverse Fourier transform on the reconstructed frequency domain signal to obtain a reconstructed time domain signal; performing envelope spectrum demodulation on the reconstructed time domain signal to obtain an envelope spectrum; and determining a bearing fault based on a preset bearing fault type table and the fault characteristic frequency in the envelope spectrum. The method is used for improving the accuracy of bearing fault diagnosis.

The invention belongs to the technical field of extrusion process monitoring, and particularly relates to a cable extrusion process state online monitoring method and system based on acoustic sensing, so as to solve the technical problem of inaccurate cable extrusion process state monitoring in the prior art. The monitoring method comprises the following steps: S1, when a transient impact factor is greater than a preset threshold value, windowing a signal frame by adopting an asymmetric window function with a main lobe in front and a side lobe in back; s2, performing fast Fourier transform on each windowed signal frame to obtain a frequency spectrum of each windowed signal frame; filtering the frequency spectrum of each signal frame by using a filter bank and calculating logarithmic energy of each filtering channel; s3, selecting cepstrum coefficients of which the ratio is greater than a selection threshold to form a process state feature vector; and inputting the process state feature vector into a state classifier to obtain a current cable extrusion process state. According to the method, redundancy and noise interference are eliminated, so that the accuracy of process state monitoring is improved.

The invention discloses a signal reading system and method for superconducting microwave multiplexing device detection, and belongs to the field of superconducting microwave multiplexing device detection, and the system comprises a server which can issue a detection configuration instruction to a signal board, receive and return detection data, operate detection software, execute spectrum analysis and data analysis, and generate a detection result; the signal board is provided with a circuit containing a programmable logic device, and the circuit can output accurate intermediate frequency signals and excitation current signals according to instructions of the server, processes output signals of the detected superconducting microwave multiplexing devices mixed by the frequency mixing board through a critical sampling filter bank algorithm to obtain detection data, and transmits the detection data back to the server; and the frequency mixing plate can perform up-conversion on the intermediate frequency signal generated by the signal plate to radio frequency to form a radio frequency detection signal, output the radio frequency detection signal to each detected superconducting microwave multiplexing device, perform down-conversion on the radio frequency response signal returned from each superconducting microwave multiplexing device to intermediate frequency, and output the radio frequency response signal to the signal plate. The system can detect the quality of the device with low computing resources.

The invention discloses a motor imagery electroencephalogram decoding method based on multi-view space-time convolution and an attention mechanism, which comprises the following steps of: firstly, constructing multi-view data by utilizing a filter bank, and enhancing training data by adopting a frequency band sensing fragment splicing strategy so as to expand sample distribution; secondly, respectively and accurately capturing specific time sequence features and spatial features of different frequency bands through a frequency adaptive convolution kernel and depth separable convolution; meanwhile, in combination with a channel attention mechanism and a time sequence statistics module, feature channel weights are effectively utilized, and a mean value and a logarithmic variance in a time window are calculated, so that the modeling capability of the model for key spatio-temporal information is enhanced. According to the method, the problem of insufficient spatio-temporal feature mining in the motor imagery task is effectively solved, and a competitive result is obtained.

The invention discloses a transformer fault diagnosis method fusing phase positioning and a self-adaptive voiceprint map, and relates to the technical field of power equipment state detection. The method comprises the following steps: collecting multichannel sound wave signals through a sound wave sensor, and estimating the spatial position of a fault source; performing weighted fusion on the multi-channel sound wave signal by using the positioning information to generate a main signal; performing adaptive window long and short time Fourier transform and trainable Mel filter bank processing on the main signal to generate a two-dimensional voiceprint map; according to the method, the map is processed in combination with spatial position information, the enhanced voiceprint map is input into a probability distribution mapping module, deep fusion and mutual verification of fault type identification and spatial positioning are realized, and the system performance is continuously improved through a closed-loop optimization mechanism. The transformer fault diagnosis method effectively solves the problems that in a traditional transformer fault diagnosis method, signal fusion is insufficient, feature extraction self-adaptive capacity is weak, diagnosis and positioning are separated and the like, and the accuracy, reliability and self-adaptive capacity of transformer fault diagnosis are improved.

Method for model-based prediction in critically sampled filterbanks, audio signal processing device and computer-readable storage medium SOLUTION: A method in an audio source coding system determines a first subband signal of an audio signal using an analysis filterbank (612) comprising a plurality of analysis filters providing a plurality of subband signals in a plurality of subbands, determines model parameters (613) of a signal model, and determines prediction coefficients to be applied to previous samples (614) of a first decoded subband signal derived from the first subband signal based on the signal model, based on the model parameters and based on the analysis filterbank. The time slot of the previous sample is before the time slot of the first sample 615. The method also determines an estimate of the first sample by applying the prediction coefficient to a previous sample.SELECTED DRAWING: Figure 6b

The application discloses a multi-octave wideband frequency conversion assembly, which comprises a frequency conversion circuit, an amplitude control circuit and a switch filter set, wherein the frequency conversion circuit expands the input radio frequency to an intermediate frequency signal through two local oscillator frequency points for the input radio frequency signal and outputs the intermediate frequency signal to the amplitude control circuit; the amplitude control circuit adjusts the amplitude of the intermediate frequency signal output by the frequency conversion circuit; the switch filter set filters the signal output by the amplitude control circuit through a multi-path filter set, filters out harmonics and stray signals, and performs amplitude correction to obtain a multi-octave intermediate frequency output signal. The 3-4GHz signal is expanded to a ten-octave signal of 0.2-2GHz which can be directly processed by a digital circuit through the two local oscillator frequency points of 4 / 5GHz, the output power reaches 10dBm, the harmonic suppression reaches more than 48dBc, the power flatness reaches ±1dB, the dynamic range reaches more than 60dB, and the step is only 0.25dB.

This invention provides a quantum-derived Newton-Raphson optimal fractional-order spectrogram generation method, comprising the following steps: Step 1: Acquire the original audio signal and construct a fractional-order spectrogram based on fractional Fourier transform (FRFT); Step 2: Perform nonlinear scaling compression on the fractional-order spectrogram using a Mel filter bank to generate a fractional-order Mel spectrogram; Step 3: Construct an adaptive optimization framework with information entropy minimization as the objective function to measure the information fidelity between the spectrogram and the original signal; Step 4: Use the quantum-derived Newton-Raphson optimization algorithm (QNRBO) to globally optimize the fractional-order order, frame length, and frame shift hyperparameters to generate the optimal fractional-order spectrogram; Step 5: Input the optimal fractional-order spectrogram into a downstream speech recognition model. This technical solution aims to systematically solve core problems such as insufficient traditional time-frequency representation capabilities, rigid hyperparameter configuration, limited optimization algorithm performance, and feature-task disconnect.

The invention discloses a voiceprint detection-based upender fault detection method and system. The method comprises the following steps of: acquiring a sound signal in an operation process of an upender and preprocessing the sound signal; performing framing and fast Fourier transform to obtain frequency domain spectrum data; obtaining a Mel spectrum through a Mel filter bank, performing logarithmic operation on the Mel spectrum to obtain a logarithmic Mel spectrum, and extracting voiceprint features; performing normalization processing and splicing to obtain a voiceprint feature sequence to be detected; establishing a normal voiceprint model and a fault voiceprint model to form a voiceprint model library; using the improved RealNVP model to output a matching result with the voiceprint model library, and obtaining a running state category result; according to the invention, the operation state monitoring and fault identification of the upender are realized, and the stability and accuracy of fault detection are improved.

This application relates to a feature extraction apparatus, device, and storage medium for time series data, belonging to the field of artificial intelligence technology. The method includes: acquiring a time-domain signal corresponding to a user's time-series data, wherein the time-series data represents the user's economic behavior data; performing N-level frequency domain decomposition on the time-domain signal using an N-level filter bank to obtain multiple time-series sub-band signals of different frequency bands, wherein N is a positive integer greater than or equal to 1; and performing feature extraction on each of the time-series sub-band signals to obtain the target features of the time-series data. This application can acquire the performance of time-series data in different frequency bands and can extract more features from the time-series data.

A reactive filter assembly comprising a reactive filter acoustically coupled to a transducer operable to generate an audio frequency by air nonlinearity demodulation of an ultrasonic frequency, wherein the reactive filter has a number of acoustic passes set to provide the transducer with a radiation impedance to improve the transducer efficiency.

A neural network system is provided, implementing a generative model for autoregressively generating a distribution for a plurality of current filter-bank samples of an audio signal, wherein the current samples correspond to a current time slot, and each current sample corresponds to a channel of the filter-bank. The system includes a hierarchy of a plurality of neural network processing tiers ordered from a top to a bottom tier, each tier trained to generate conditioning information based on previous filter-bank samples and, for at least each tier but the top tier, also on the conditioning information from a tier higher up in the hierarchy, and an output stage trained to generate the probability distribution based on previous samples for one or more previous time slots and the conditioning information from the lowest processing tier.

A digital-to-analogue converter (DAC) device includes an input terminal for receiving an input signal that includes m MSBs and n least significant bits LSBs. The device includes a coarse DAC that generates a first voltage from the m MSBs, utilizing resistors and switches coupled to a reference voltage. Coupled with a clock signal of a predetermined frequency, a pulse generator produces pulse signals, including a first pulse signal with a first average voltage and a second pulse signal with a second voltage approximately half of the first average voltage. A fine DAC generates a second voltage using then LSBs to select among the pulse signals. An output filter combines the first and second voltages to produce the final output signal. Integrated circuits containing DAC devices and packages containing the integrated circuits are also described.

The invention discloses a two-channel biorthogonal filter bank FPGA (Field Programmable Gate Array) implementation method based on matrix decomposition. The method comprises the following steps: for a two-channel biorthogonal filter bank, firstly determining an initial coefficient of a filter, then decomposing the initial coefficient into a coefficient retention sequence and a coefficient matrix, and further decomposing the coefficient matrix into a common matrix, a low-pass branch matrix and a high-pass branch matrix through common-base N-dimensional intercepted LU decomposition; the FPGA implementation scheme is designed by combining the structural characteristics of the two-channel biorthogonal filter bank and adopting the time division multiplexing technology, the number of multipliers is effectively reduced through efficient multiplexing of a multi-phase structure and the multipliers, and the hardware resource overhead in FPGA implementation is remarkably reduced. On the premise that orthogonality and reconstruction precision of the filter bank are guaranteed, operation efficiency and data throughput capacity of the system are improved, and a feasible and efficient solution is provided for FPGA implementation of a high-performance and real-time signal processing system.

To provide a method and an audio processing unit for decoding an encoded audio bitstream.SOLUTION: A method for decoding an audio bitstream includes receiving an encoded audio bitstream, decoding audio data to generate a decoded low-band audio signal, extracting high frequency reconstruction metadata and filtering the decoded low-band audio signal with an analysis filterbank to generate a filtered low-band audio signal, and extracting a flag indicating whether spectral transformation or harmonic transposition is to be performed on the audio data and regenerating a high-band portion of the audio signal using the high frequency reconstruction metadata and the filtered low-band audio signal according to the flag.SELECTED DRAWING: Figure 1