50 results about "Noiselet" patented technology

The invention relates to a seismic phase pickup method based on time-frequency analysis and deep learning, and belongs to the field of seismology, a deep neural network comprising an empirical mode decomposition module, a residual shrinkage module, an LSTM layer, a transformer layer, an up-sampling layer and a convolutional layer is established, a linear rectification function is adopted as an activation function, an Adam adaptive optimization function is adopted for back propagation, and a seismic phase is obtained. And taking a binary cross entropy loss function as a loss function, and taking a mean absolute error function, an error standard deviation, an accuracy rate, a recall rate and an F1 score as evaluation indexes to obtain a deep neural network model. When the frequencies of the noise, the P wave and the S wave are different, empirical mode decomposition can effectively separate the noise, the P wave and the S wave, so that the network is helped to better identify the P wave and the S wave. Even for broadband noise, the noise can be decomposed into different intrinsic mode functions, so that the noise content in the intrinsic mode functions where P waves and S waves are located is reduced. The time-frequency analysis method can effectively improve the anti-noise capability of the network.

The invention provides a passive source surface wave data screening and imaging method based on noise wave field feature analysis, and the method comprises the steps: carrying out the preprocessing of an original passive source noise record, and carrying out the segmentation of the processed original data; performing statistics on the RMS of the segmented original record according to rows, and calculating a variable coefficient of each data segment about an RMS curve; setting a threshold value according to an RMS curve variable coefficient, and screening data segments; setting a threshold value to distinguish a data segment beneficial to imaging from a data segment interfering imaging by using the difference of the variable coefficients; and performing interference superposition processing on the screened data segments to obtain a high-quality surface wave signal. According to the method, effective surface wave signals can be extracted from short noise records through analysis and statistics of wave field features, the noise data duration requirement is reduced, the acquisition cost is reduced, and the noise data processing efficiency is improved. According to the method, noise data types can be effectively screened, surface wave signals with higher quality are extracted, and the signal-to-noise ratio of an imaging result is improved.

The invention relates to the technical field of indoor and outdoor positioning, in particular to a campus indoor and outdoor seamless positioning system and method based on multi-source data fusion, and the system is provided with a data acquisition module, a data pre-analysis module, a feature recognition module and a positioning signal regulation and control module. A noise intensity curve of a mobile terminal is drawn in a feature extraction time window through a data pre-analysis module, a noise fluctuation parameter and a noise period trend parameter are determined through a feature recognition module to screen feature mobile terminals, and a feature period fluctuation curve is fitted through a positioning signal regulation and control module. And determining a signal sampling frequency adjustment mode of the positioning signal acquired by the characteristic mobile terminal. According to the invention, the mobile terminals located in the indoor and outdoor junction area are screened according to the positioning signals received by the mobile terminals and the environmental noise intensity, the signal sampling frequency of the positioning signals is adaptively adjusted according to the actual moving trend of the mobile terminals, and the reliability of the campus indoor and outdoor seamless positioning system is improved.

The invention discloses a metallized film capacitor health state assessment method and system based on audible noise signal analysis, a medium and equipment, and the method comprises the steps: building a discharge noise monitoring experiment loop; the metalized film capacitors in different health states are detached and connected to the discharge noise monitoring experiment loop; an alternating current and direct current superposition voltage experiment is carried out in the discharge noise monitoring experiment loop, the temperature of the metallized film capacitor in different health states is kept at 25 DEG C constantly in the experiment process, after pressurization is completed, noise waveforms are collected for 10 s to obtain noise waveform data, and voltage reduction and experiment stopping are carried out on the metallized film capacitor; according to the noise waveform data, respectively calculating sound pressure level levels of the metallized film capacitor in different health states, and simultaneously carrying out FFT analysis to obtain frequency domain characteristics of the metallized film capacitor; and acquiring a reference value of the sound pressure level at the initial operation moment, and judging the health state of the metallized film capacitor according to the sound pressure level and the frequency domain characteristics to obtain health or failure classification.

The invention relates to a cable line partial discharge waveform classification method and device, and the method comprises the following steps: collecting the partial discharge waveform data of a cable line, extracting the fat degree of the waveform, the maximum slope of the waveform leading edge, and the maximum signal-to-noise ratio, and inputting the data into a pre-trained partial discharge waveform classification model, classifying the partial discharge waveform and the noise waveform to obtain a partial discharge waveform classification result; the training process of the partial discharge waveform classification model comprises the steps of obtaining training data, inputting the training data into an SVM classification model for training, performing parameter adjustment on the SVM classification model through a global optimal parameter algorithm model to obtain optimal parameters for training of the SVM classification model, and finally obtaining the trained SVM classification model as the partial discharge waveform classification model. Compared with the prior art, the method has the advantages that the training time of the whole partial discharge waveform classification model is greatly shortened, and meanwhile, the partial discharge waveform classification model is better in performance and higher in accuracy.

The invention discloses an edge computing gateway platform architecture interaction method and device and a medium, and relates to the technical field of acoustic man-machine interaction, and the method comprises the steps: receiving an original sound wave signal, carrying out the human voice activation detection and wake-up word confirmation of the original sound wave signal through a hierarchical wake-up detection mechanism, and generating an effective sound wave segment; performing fast Fourier transform on the effective sound wave segments to generate frequency domain energy features; extracting energy distribution of the frequency domain energy features, and generating a dominant frequency set according to a preset energy threshold value; constructing a sound wave-instruction rule base, and inputting the dominant frequency set into the sound wave-instruction rule base to generate a primary acoustic instruction; converting the primary acoustic instruction into a text instruction, and generating an instruction data set through an Agent decision agent; and collecting space coordinates and network packet loss rate parameters of the edge equipment, and generating a transmission path scheme through a space field intensity gradient algorithm in combination with the instruction data set. According to the method, the instruction recognition accuracy in the noise environment is improved through anti-noise wave analysis.

A noise intrusion position estimation device (100) comprises: a measuring unit (10) having a pair of detection units (11a, 11b) for simultaneously measuring the time variation of a noise waveform on a transmission cable (40) at two observation points separated from each other on the transmission cable (40); and a computing unit (30) for receiving the pair of noise waveforms measured simultaneously from the measuring unit (10), time-reversing the received pair of noise waveforms, using the two observation points as positions of a signal source of the time-reversed noise in a transmission line model (40S) reflecting the electrical characteristics of the transmission cable (40), performing a transmission path analysis using the time-reversed waveform as an excitation waveform, and outputting the position of a peak value obtained based on the result of the transmission path analysis as a position of noise intrusion into the transmission cable (40).

The invention discloses a high-frequency electrotome negative plate impedance detection method and system and a high-frequency electrotome, and the detection method comprises the steps: an impedance detection signal preprocessing step: determining an acquisition time interval of an impedance detection signal according to a current working mode and a corresponding optimal acquisition time interval; only the impedance detection signals in the acquisition time interval are collected; the acquisition time interval is one or more carrier periods in which the signal-to-noise ratio of the impedance detection signal is higher than the average value of the signal-to-noise ratio in a unit modulation period T under the current working mode; and the optimized impedance detection signal is filtered and amplified and then is input into an impedance calculation module to calculate and obtain a real-time impedance value of the negative plate of the high-frequency electrotome. Interference is filtered out by monitoring information such as power and impedance intervals when the high-frequency electrotome works and combining noise waveform characteristics of different working modes.

The application discloses a kind of Φ-OTDR system coherence fading inhibition method and device based on spatial resolution internal redundant data, specifically: first, simulate random one-dimensional continuous signal and noise waveform, and carry out channel replication expansion, add different random noise to the waveform of each channel, obtain multi-channel signal and noise data set;Then set the structure, parameter, activation function, loss function and optimizer of time sequence neural network model, data set is sent into model and is trained;Then judge whether the model error meets the requirements, if it does not meet the requirements, adjust the structure, parameter, activation function, loss function and optimizer of model, retrain the model;If it meets the requirements, the actual data detected by Φ-OTDR system is processed and packed, and is sent into the trained model for prediction, to obtain the signal characteristics distributed along the whole optical fiber.The application has low complexity, reduces the influence of coherence fading noise on Φ-OTDR system, and improves the transmission ability of the system.

The invention discloses a self-adaptive computing power optimization in-vehicle noise active control system and method. Order optimization is carried out on a secondary path filter of the secondary path model, so that the weight coefficient of the secondary path filter meets the convergence requirement and the order of the secondary path filter meets the minimization requirement, and the secondary path filter after order optimization is used for carrying out pre-filtering on the noise reduction reference signal; the active noise reduction control filter is subjected to order optimization, so that the weight coefficient of the active noise reduction control filter meets the convergence requirement, the order of the active noise reduction control filter meets the minimum requirement, the active noise reduction control filter after order optimization is used for generating noise reduction sound waves, noise reduction is carried out on in-vehicle noise corresponding to the noise reduction reference signal, and noise reduction is carried out on the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction processing result meets the set requirement, it is judged that the order optimization of the active noise reduction control filter is completed, and the active noise reduction control filter after the order optimization is completed is used for conducting noise reduction processing on the in-vehicle noise corresponding to the noise reduction reference signal.

This invention discloses a method and system for real-time tracing of live stream hotlinking based on multi-source heterogeneous signaling fusion. The method includes multi-source heterogeneous signaling acquisition, multi-source heterogeneous signaling feature extraction, construction of a hotlinking adaptation loss function, hotlinking tracing model design, parameter dynamic optimization, and real-time hotlinking determination of live streams. This invention belongs to the field of data processing, specifically referring to a method and system for real-time tracing of live stream hotlinking based on multi-source heterogeneous signaling fusion. This solution constructs a hotlinking adaptation loss function based on the arc-tangent power-law fallback characteristic to adapt to multi-signaling modal noise fluctuations; embeds a risk probability site inversion penalty term to effectively improve the reliability of real-time hotlinking tracing of live streams; and constructs a full-process prevention and control system: designs a parameter dynamic optimization strategy driven by inversion rate to dynamically adapt to data fluctuations, and establishes a structure-gradient dual-dimensional inversion control mechanism to avoid inversion risks throughout the entire link from model structure design to parameter gradient optimization, significantly improving the hotlinking tracing effect.

The invention relates to a frequency spectrum occupancy rate calculation method based on a dynamic double-threshold level, and belongs to the field of radio monitoring. The receiver monitors the power of a radio signal of a tested frequency band at a tested place for a long time; continuously acquiring environmental noise samples through a noise sampling channel, and continuously updating background noise features by adopting a sliding window real-time sampling technology; based on a noise baseline, introducing a hysteresis comparison principle, generating a high threshold and a low threshold, and calculating a dynamic double-threshold value; a hysteresis comparison thought is introduced, and a high-low double-threshold judgment mechanism is established; when the signal is between the double thresholds, secondary verification is started, a secondary verification program comprises time domain feature analysis on the signal so as to effectively distinguish weak signals from noise fluctuation, and the misjudgment probability is reduced; and performing statistics on energy of the target frequency band signal within a certain time, and performing signal identification and occupancy rate calculation based on the signal energy and double thresholds. According to the invention, the frequency spectrum occupancy rate measurement accuracy can be effectively improved.

The present application relates to a kind of well seismic signal-noise superposition wave field high-dimensional multi-component feature intelligent separation method, belong to geophysical oil and gas resource exploration field.Establish time-domain multi-component decomposition result serves different dimension differentiation characteristic property expression;Six-dimensional characteristic attribute space based on multi-component signal-noise component is established, and the mathematical characteristics of complex signal wave and multiple types of noise in DAS record, such as direct wave and reflected wave, are revealed;Dual-stage highly integrated framework with tree model as base learner is set up to complete the feature point category determination separation task in high-dimensional attribute space, so as to accurately and efficiently realize low-damage preservation of signal wave field and maximum suppression of noise wave field.The method is flexible, accurately preserves the amplitude characteristics and frequency energy components of DAS two-dimensional exploration record signal field in well, the mathematical basis is reliable and reliable, and the feature is strongly interpretable, which provides an important basis for thin and fine layer detection, oil and gas reservoir exploitation and other practical applications.

An oscilloscope includes one or more ports to connect to a device under test (DUT) and receive a signal, one or more analog-to-digital converter (ADC) to produce a waveform of digital samples of the signal, and one or more processors to: acquire and determine a measure of a noise waveform, acquire a waveform of a repeating pattern from the ADCs and determine its frequency spectrum, identify a spectral impulse portion of the frequency spectrum, determine a measure of a flat portion of the frequency spectrum, use the measure of the flat portion and the measure of the noise waveform to produce a noise compensation ratio, scale the flat portion with the noise compensation ratio and combine it with the spectral impulse portion of the frequency spectrum to produce a noise compensated frequency spectrum, convert the noise compensated frequency spectrum to a time domain waveform to measure performance of the DUT.

The application discloses a metamorphic concrete and roller compacted concrete joint quality identification method, system and equipment based on a ground penetrating radar. One-dimensional original waveform data of a metamorphic concrete and roller compacted concrete joint position is obtained by using a ground penetrating radar. After data preprocessing, noise waveform data is obtained. The noise waveform data and the original waveform data are integrated into a data set after being uniformly labeled. The data set is divided into a training set, a validation set and a test set. Based on the training set and the validation set, a DNFN model is used to construct and train a concrete joint quality identification model. The trained concrete joint quality identification model is used to infer and predict the data in the test set to determine whether the metamorphic concrete and roller compacted concrete joint position quality is qualified or unqualified. The application can quickly, accurately and non-destructively identify whether the joint position quality is qualified or unqualified, thereby providing reliable technical support for engineering quality control.

This invention provides a method, apparatus, and electronic device for identifying mechanical vibration noise from a controllable vibration source. The method includes: acquiring raw noise waveform data of the controllable vibration source under at least one mechanical vibration state using a preset detector array; acquiring raw working waveform data of the controllable vibration source under an excitation state and a preset scan length using the detector array; preprocessing the raw noise waveform data and raw working waveform data to obtain noise waveform data and working waveform data, respectively; identifying noise characteristics corresponding to the mechanical vibration state based on the noise waveform data; and identifying the resonant interference wave characteristics of the controllable vibration source under an excitation state based on the working waveform data. This improves the accuracy of mechanical vibration noise identification from a controllable vibration source and provides a basis for improving noise suppression in the black triangle region of a controllable vibration source.

The invention relates to a power supply fault diagnosis method and device, power supply equipment and a computer readable medium. The method comprises the steps of collecting a power supply signal of a target power supply; wavelet packet decomposition is carried out on the power supply signal to obtain a time sequence coefficient sequence of a plurality of wavelet sub-band signals of the power supply signal, and the time sequence coefficient sequence is used for representing the noise change of each wavelet sub-band signal in the time dimension; determining a signal characteristic of the power supply signal based on the time sequence coefficient sequence of each wavelet sub-band signal, and determining a diagnosis threshold value of the current diagnosis window based on each time sequence coefficient sequence; and inputting the signal characteristics and the diagnosis threshold value into a fault diagnosis model, analyzing the signal characteristics by using the fault diagnosis model, and judging whether the target power supply fails or not according to the diagnosis threshold value. According to the method, the diagnosis threshold is adaptively adjusted along with the fluctuation of real-time noise, the conditions of fault false alarm and fault missing alarm are effectively avoided, and the technical problem that a traditional power supply fault diagnosis method is low in fault diagnosis accuracy is solved.

The invention relates to the technical field of integrated circuits, and discloses a crosstalk delay determination method and device and computer equipment. The method comprises determining a first input waveform of a first unit in a disturbed network under an ideal interconnection condition and a noise waveform caused by an attack network at a first input end of the first unit; performing linear superposition on the first input waveform and the noise waveform to obtain a superposed waveform at the first input end; and based on the superposed waveform, carrying out output waveform comparison at the first output end of the first unit to obtain a target crosstalk delay caused by the attack network in the disturbed network. A first input waveform of a disturbed network under ideal interconnection and a pure noise waveform introduced by an attack network are separated and calculated, linear superposition is carried out on the first input waveform and the pure noise waveform, and finally output waveform comparison analysis is carried out on the superposed waveform, so that crosstalk delay is quantized, and the crosstalk performance is improved. The method which is high in calculation efficiency and can accurately quantify the crosstalk delay of the circuit is realized.

The invention discloses a control method and system of an acoustic early education machine, particularly relates to the field of intelligent speech recognition, and aims to solve the problem of adaptive adjustment of speech recognition sensitivity in a complex environment. A sound source environment is accurately captured, sound source characteristic vectors including information such as distance, direction and frequency are generated, and an accurate basis is provided for noise evaluation and sensitivity adjustment. Through feature clustering and time-frequency domain energy analysis, in combination with adaptive dynamic mapping of noise change rates, noise fluctuation and change trends are evaluated in real time, noise reference levels adapted to different environments are generated, and accuracy and stability of speech recognition are ensured. Through the combination of the noise reference level and the change rate, the voice detection window duration and the pickup range are adaptively optimized, the responsiveness and the environmental adaptability of voice recognition are improved, it is ensured that equipment efficiently works in a complex family environment, and stable and accurate voice control is provided.

The invention relates to the technical field of multi-microphone noise reduction, in particular to a multi-mic-array audio noise reduction system which comprises the following steps: a synchronous calibration module, an information source separation module, a weight configuration module, a noise discrimination module and a speech segment scoring module. According to the method, accurate separation of information sources is realized through time domain cross point analysis and dynamic slope sequence screening based on an energy change curve, response offset of multiple channels is corrected in real time, superposition energy trend comparison and a co-action relationship classification mechanism, and weights are cooperatively adjusted in combination with frequency distribution characteristics and time response parameters; the consistency discrimination capability of the channel to different frequency ranges and directions is enhanced, the sensitivity of noise recognition is improved by adopting an extreme value sequence and energy fluctuation characteristic analysis, and the speech segment integrity is dynamically scored by fusing the periodic overlap ratio and the splicing smoothness; the adaptive capturing capability of details such as complex sound sources, frequency band energy, time delay drift and noise fluctuation is improved, and the noise suppression effect and the signal separation accuracy are optimized.

The application relates to an OCT image processing method and system based on adaptive weighted bilateral filtering deconvolution, which comprises the following steps: preprocessing an original OCT signal, including removing background noise, wave number calibration, spectrum shaping and dispersion compensation, to obtain an OCT reconstructed image; adjusting the spatial weight and intensity weight of the OCT stretched image through histogram stretching to enhance contrast, and adaptively denoising the image; further optimizing the image by using a blind deconvolution algorithm, iteratively updating the estimated image and a point spread function (PSF), and obtaining a high-definition OCT reconstructed image. By using the adaptive weighted bilateral filtering and blind deconvolution technology, the application realizes efficient denoising and detail reservation of the OCT image, thereby obtaining a high-definition reconstructed image.

A drum audio processing method and apparatus and an electronic drum. The method comprises: acquiring a digital audio signal and, on the basis of the digital audio signal, generating an oscillatory wave and a noise wave (102); modulating the oscillatory wave to obtain oscillatory wave parameters for controlling the pitch and the volume (104); modulating the noise wave to obtain noise wave parameters for controlling the timbre (106); and, on the basis of the oscillatory wave parameters and the noise wave parameters, performing sound effect processing in real time, and performing sound mixing output (108). Using the method and apparatus can perform sound effect processing and sound mixing output in real time, and accurately and dynamically output timbres that are very close to true timbres, and the tone quality does not depend on storage, and has a low delay.

The invention belongs to the technical field of seismic exploration, and provides a deep underground excavation noise source wave field extraction method and system, and the method comprises the steps: carrying out the preprocessing of data collected by a pilot sensor and a detection sensor, and carrying out the weighted mutual dry interference of the processed data of the pilot sensor and the detection sensor, according to the method, wave field feature recovery of tunneling noise is realized, effective signal quality is improved, a data basis is provided for subsequent unfavorable geological inversion imaging, wave field feature recovery can be realized, noise wave fields such as multiples and the like can be effectively suppressed, and the method has the advantages of being simple in structure, low in cost and high in reliability. The rock breaking seismic source wave field of the heading machine is recovered; according to the method, the segmentation number and superposition times of the data with the limited sampling length are increased, the utilization rate of related data in a short time is enhanced, random interference can be filtered out, the signal-to-noise ratio is improved, and a basis is provided for later data processing.

The present invention provides a timekeeping device and method for automatic switching of multiple master clocks. Relying on seamless switching equipment, the reference of the physical signal output by the timekeeping system can be selected between multiple atomic clocks, which expands the number of candidate master clocks while saving the use of phase trimmers. At the same time, a master clock switching strategy is provided to reduce the impact of random noise fluctuations on the system time signal of a single atomic clock. The performance of the master clock of the present invention is no longer determined by a single atomic clock, and it can be flexibly selected from multiple atomic clock frequency signals; the master clock switching process is different from the previous switching method. The switching is performed before the master clock signal undergoes frequency control. It is a switch on the frequency source of the timekeeping system, which can reduce the number of phase trimmers used; at the same time, the master clock selection strategy provided by the method also ensures that the system time will not have a large phase deviation due to the noise fluctuation of a single clock.

The invention relates to a noise reduction treatment method for a range hood and the range hood. And a position tracking device, a physical reference sensor, a physical error microphone and a loudspeaker are physically arranged at the smoke exhaust ventilator. The noise reduction processing method comprises the following steps: acquiring ear positions of a person in front of the range hood by using the position tracking device; matching a pre-trained virtual error microphone according to the ear position; noise reduction processing is carried out by using an adaptive active noise reduction algorithm, a noise related signal collected by a physical reference sensor is used as a reference signal x (n), an adaptive filter coefficient W (n) is obtained based on a first secondary path estimation matrix # imgabs0 #, a second secondary path estimation matrix # imgabs1 # and a transfer function estimation matrix # imgabs2 #, and a noise reduction sound wave control signal is generated; and the loudspeaker is controlled to play the noise reduction sound waves to realize active noise reduction. The range hood (100) is configured and subjected to noise reduction treatment according to the noise reduction treatment method.

The present application relates to a seismic phase picking method based on time-frequency analysis and deep learning, belonging to the field of seismology, a deep neural network comprising an empirical mode decomposition module, a residual shrinkage module, an LSTM layer, a transformer layer, an up-sampling layer and a convolution layer is established, a linear rectifier function is used as an activation function, an Adam adaptive optimization function is used for back propagation, a binary cross-entropy loss function is used as a loss function, and the average absolute error function, error standard deviation, precision, recall and F1 score are used as evaluation indexes to obtain a deep neural network model. When there are differences in the frequencies of noise, P waves and S waves, empirical mode decomposition can effectively separate the three, thereby helping the network to better identify P waves and S waves. Even for wideband noise, the noise will be decomposed into different intrinsic mode functions, thereby reducing the noise content in the intrinsic mode function where the P wave and S wave are located. This time-frequency analysis method can effectively improve the noise immunity of the network.

The application discloses a C+L transmission system noise wave automatic filling method and device, relates to the optical communication technical field, and the C+L transmission system noise wave automatic filling method comprises the following steps: if the power of the optical amplifier of the downstream station of the C+L transmission system is abnormal, then starting optical channel monitoring OCM scanning, acquiring the frequency and power information of the missing channel; based on the frequency and power information of the missing channel, starting the automatic filling of the noise wave.The application can maintain the stability of the service channel of the C+L transmission system in the dynamic process.

A kind of metalized film capacitor health state evaluation method, system, medium and equipment based on audible noise signal analysis, in the method, discharge noise monitoring experimental loop is built;Different health states of metalized film capacitor are disassembled and connected to the discharge noise monitoring experimental loop;AC superimposed voltage experiment is carried out in the discharge noise monitoring experimental loop, the temperature of metalized film capacitor in different health states is kept constant at 25 DEG C during the experiment, after pressurization is completed, 10s noise waveform is collected to obtain noise waveform data, metalized film capacitor is depressurized, and the experiment is stopped;The sound pressure level of metalized film capacitor in different health states is calculated according to the noise waveform data respectively, and FFT analysis is carried out simultaneously to obtain its frequency domain characteristics;The reference value of sound pressure level at the initial running time is obtained, and the classification of health or failure is obtained according to the judgment of the sound pressure level, frequency domain characteristics of metalized film capacitor health state.

The invention relates to the technical field of data processing, and discloses a noise waveform calculation method and device, and computer equipment, and the method comprises the steps: carrying out the estimation of an initial noise waveform for a to-be-tested network based on a noise-free waveform obtained through the simulation of the to-be-tested network, and the chip information of a chip to which the to-be-tested network belongs; performing multiple translation transformation on the initial noise waveform to obtain a transformed noise waveform after each translation transformation; for any transformation noise waveform, superposing the transformation noise waveform into the noiseless waveform so as to generate a noisy waveform corresponding to the transformation noise waveform; comparing the noisy waveform with the noiseless waveform to obtain an influence coefficient of each transformed noise waveform; and based on the influence coefficient, selecting and determining a target noise waveform from the transformed noise waveforms. The method has the beneficial effect that the estimation accuracy of the noise waveform is improved.