29 results about "Noise power spectrum" patented technology

The invention relates to a pipeline leakage detection method based on acoustic signal spectrum noise reduction, and belongs to the technical field of pipeline leakage monitoring. The method comprises the steps that pure background noise signals are collected through an acoustic sensor to serve as noise analysis samples, noise power spectrum estimation is conducted, and average noise power spectrum estimation is obtained; collecting an original acoustic signal containing potential leakage; performing spectrum analysis and phase reservation on the original acoustic signal after framing processing; a continuous noise reduction acoustic signal is output through spectral subtraction operation and signal reconstruction; performing feature extraction on the continuous noise reduction acoustic signal, performing anomaly detection on a current signal segment through a pipeline leakage detection model, and outputting a leakage signal segment; and leakage point positioning is carried out according to the time difference of the leakage signal segment reaching different sensors. Efficient and accurate detection of pipeline leakage is realized, and interference of background noise on a detection result is effectively overcome.

The invention discloses a voice interactive chart dynamic generation method and system based on a large language model, and relates to the technical field of chart generation, and the method comprises the steps: collecting a user voice signal, carrying out the preliminary framing, calculating the short-time energy, and analyzing a mute segment set signal; frequency domain signals are extracted from the set signals, gain is calculated through a filter, noise reduction is carried out on the frequency domain signals, the zero-crossing rate and noise reduction short-time energy are calculated, effective voice frames are screened, peak detection is carried out, the average voice speed is calculated, voice speed normalization is carried out, and signal values of sampling points in the frames are extracted. According to the method, the noise power spectrum density is extracted through FFT on the mute section, a frequency domain model of background noise is effectively established, a follow-up filter can accurately act on an actually existing frequency band interference area, weakening of the voice main signal spectrum is avoided, the Mel filter bank is accessed after the power spectrum is calculated through the FFT after frame windowing, and the noise power spectrum density is calculated through the FFT after frame windowing. Energy can be redistributed on the logarithmic Mel scale according to human ear perception characteristics.

The present application relates to the technical field of noise processing, and discloses a noise whitening method, device, equipment and storage medium, the method comprises the following steps: constructing an error function based on an equalization signal and a training sequence; calculating based on the error function to obtain gradient information; updating the tap coefficient of the noise whitening filter based on the gradient information to obtain a target tap coefficient; whitening the colored noise based on the target tap coefficient. The present application updates the tap coefficient of the noise whitening filter by formulating the error function and calculating the gradient, solves the problem that when the channel equalization technology is used to overcome the bandwidth limitation, the equalization enhances the colored noise, leads to uneven noise power spectrum, and thus reduces the IM-DD transmission rate, realizes the whitening of the colored noise, makes the power spectrum flat, and thus significantly improves the performance of the IM-DD system and increases the communication capacity.

The present invention relates to the field of noise filtering technology, specifically a communication line noise filtering system and method, the system comprising: a temperature coupling module, a voltage response module, a parameter joint adjustment module, and a link verification module. In the present invention, by collecting the temperature data of the inductor and capacitor nodes and inputting them into a piecewise linear regression model, the temperature deviation gradient of the resonant frequency is calculated, the noise suppression bandwidth threshold is dynamically matched in combination with the reference table, the frequency stability under temperature fluctuations is improved, the Kalman filter processes the voltage sampling to generate a fluctuation prediction value, the interval index is extracted based on the parameter mapping table, the least squares method is used to fit and generate the real-time inductor and capacitor parameters, and the voltage transient fluctuation is adaptively realized. The noise power spectrum density is calculated by fast Fourier transform, and the parameter rollback is triggered by comparing the 3dB attenuation point with the threshold difference, thereby forming a closed-loop control to ensure that the suppression bandwidth is continuously matched with the noise frequency band, reduce the bandwidth offset to within 1.2%, and optimize the response time to shorten it to 200ms.

The application discloses a laser phase noise measurement method based on delay self-heterodyne and digital demodulation, and relates to the technical field of laser measurement, and comprises the following contents: after being adjusted by a variable optical attenuator, to-be-measured laser is divided into a frequency shift arm and a delay arm, the frequency shift arm is frequency-shifted by an acousto-optic modulator, the delay arm is delayed by a delay optical fiber, and original electric beat frequency signals are obtained by a balanced photoelectric detector after the two arms are coupled; homologous and synchronous quadrature demodulation is carried out on the original electric beat frequency signals, envelope credibility judgment and abnormal sample repair are combined, and a continuous differential phase sequence is obtained; actual optical time delay is determined according to the beat frequency spectrum characteristics and the continuous differential phase sequence, and a differential phase power spectral density is formed; then, transmission relationship conversion is carried out according to the actual optical time delay, bounded spectrum compensation is implemented on a zero point neighborhood and a low credibility frequency point, and an intrinsic phase noise power spectral density is obtained. The application can improve the stability, continuity and result credibility of phase noise measurement under the condition of short delay.

This invention belongs to the field of noise floor reduction and proposes a frequency-band noise floor reduction method based on a spectrum analyzer. This invention can solve the problems of uneven noise power distribution and excessive noise power estimation. It includes the following steps: acquiring the noise floor signal of the spectrum analyzer, wherein the noise floor signal is a low-pass or band-limited white noise signal with a specified bandwidth; dividing the specified bandwidth into a first number of sub-segments and dividing the signal power before denoising into a first number of sub-segments; estimating the noise power under the specified bandwidth and dividing it into a first number of sub-segments; denoising each sub-segment and calculating the noise power spectral density of each sub-segment after denoising based on the bandwidth after segmentation, the signal power before denoising after segmentation, and the estimated noise power after segmentation; and ensuring that the noise power spectral density after denoising is not negative by using the compression factor method or the square root method.

The present invention discloses a relay station for digitally locking and real-time monitoring phase noise of an optical fiber link, belonging to the technical field of relay stations for optical fiber optical frequency transfer, which includes a main control circuit, a link locking circuit and a laser locking circuit; the link locking circuit is bidirectionally connected to the main control circuit, the link locking circuit sends a link locking error signal to the main control circuit, and the main control circuit sends a link locking instruction to the link locking circuit; the laser locking circuit is bidirectionally connected to the main control circuit, the laser locking circuit sends a laser locking error signal to the main control circuit, and the main control circuit sends a laser locking instruction to the laser locking circuit; the link locking circuit includes an optical frequency digital phase-locked loop, and the optical frequency digital phase-locked loop uses a DDS to drive an acousto-optic modulator to adjust the frequency and phase of an optical frequency signal. The optical frequency digital phase-locked loop is used to measure the phase noise power spectrum in real time, which can not only achieve link locking, but also determine whether the power spectrum of the signal after locking reaches the theoretical noise suppression limit.

The application provides a real-time sensing method for third-party construction damage events, and relates to the field of long-term monitoring of urban lifelines, which comprises the following steps: determining a cutoff frequency according to construction equipment information and an estimated noise power spectral density, and preprocessing an original vibration signal based on the cutoff frequency; calculating the product of the RMS values of P adjacent measuring points; combining D-S evidence theory to fuse the evidence of adjacent measuring points, eliminating abnormal interference signals by calculating a conflict coefficient K, and obtaining a processed signal; obtaining a dynamic threshold value, and determining the endpoints of the third-party construction damage event in the processed signal; and using a K nearest neighbor algorithm to perform feature matching and classification recognition on a to-be-identified sample, and identifying the event type of the third-party construction damage event. The application effectively improves the recognition accuracy and real-time performance, effectively eliminates abnormal data interference, enhances the robustness of feature extraction and classification, and significantly improves the intelligent level of the monitoring system.

The application discloses a ship radiated noise power spectrum measurement method based on frequency difference beam forming, comprising the following steps: obtaining a to-be-measured radiated noise signal received by a base array; constructing a difference frequency signal through the conjugate product of the to-be-measured radiated noise signal and a reference frequency signal; performing beam forming on the constructed difference frequency signal based on the difference frequency, and calculating the amplitude of the beam output signal of the difference frequency signal; correcting the amplitude of the beam output signal of the difference frequency signal by using the amplitude of the reference frequency signal to obtain the amplitude of the to-be-measured radiated noise signal, and calculating the power spectrum of the to-be-measured radiated noise signal. The application converts the signal outside the design frequency band of the base array to the inside of the design frequency band to perform beam forming, improves the array gain, better suppresses the environmental noise, improves the measurement performance of the base array on the radiated noise outside the design frequency band, greatly expands the frequency band width of the ship radiated noise measurement, and can realize the measurement of the radiated noise with a larger bandwidth by using a smaller aperture array.

The invention discloses a method for diagnosing a fault sound signal of a rotating machine, and the method comprises the following specific steps: collecting a sound signal of a waterwheel room, and sequentially carrying out the framing and windowing, and FFT processing, thereby obtaining the amplitude and phase of the signal; the method comprises the following steps: calculating a noise power spectrum estimated value, a signal-to-noise ratio, an over-reduction factor and a denoised signal power spectrum of a waterwheel room sound signal after FFT of a current frame, recovering the signal through IFFT, carrying out pre-emphasis processing on the recovered signal to obtain a denoised clean signal, decomposing the denoised clean signal into IMF components through a CEEMDAN method, calculating a residual component, making a data set, and obtaining the waterwheel room sound signal. And inputting the data set into the trained CNN-TCN neural network model for fault identification. The method is high in fault diagnosis accuracy.

The invention discloses a fish culture net cage noise tracking method based on optimal smoothness and minimum statistics, and relates to the field of aquaculture. According to the method, the smoothing parameter is automatically adjusted according to the frequency energy distribution characteristic of the input signal, the power spectrum is smoothed, the smoothing result is estimated by combining the minimum value deviation compensation, accurate tracking of the background noise signal is realized, the noise power spectrum component can be effectively extracted in a complex noise environment, and extra detection does not need to be carried out on a target signal. According to the method, a time-frequency adaptive smoothing parameter mechanism is introduced, the background noise power spectrum is tracked and estimated by fusing the minimum statistic, and the noise condition of the sampling signal can be effectively determined under the complex noise condition without training data; the problems that an existing noise separation method is poor in performance and needs a large amount of training data when separating non-stationary noise are solved.

The invention relates to the technical field of signal processing, in particular to a signal-to-noise ratio estimation algorithm based on an auto-encoder, which comprises the following steps of: firstly, selecting four groups of parameter signals with different code element rates and roll-off factors, each group containing 16 mainstream modulation type sub-signals, segmenting the sub-signals, adding Gaussian white noise to construct a data set, and performing data processing on the data set; dividing into a training set and a verification set according to the ratio of 7: 3, and calculating a noise adding signal and a power spectrum of corresponding noise by adopting a Welch method; secondly, constructing a stacked full-connection auto-encoder, taking a training set noise adding signal power spectrum as input, taking a noise power spectrum as a target, and introducing L2 regularization suppression over-fitting according to set batchsize and epoch training; and finally, inputting a test set noise adding signal into the trained model to obtain the noise average power, and calculating the signal average power and the signal-to-noise ratio by combining the noise adding signal power spectrum. The method can cover multiple signal parameters and modulation scenes, the low signal-to-noise ratio interval precision is better, the process is clear, engineering landing is easy, and the method is suitable for a multi-standard communication system.

The ultrahigh frequency photoelectric detector frequency response calibration device has the advantages of being high in calibration precision, easy and convenient to operate and wide in coverage frequency range, high-resolution scanning is achieved through constant-power control and high-purity beat frequency signals in the double-laser beat frequency method, and the ultrahigh frequency photoelectric detector frequency response calibration device can be expanded to the hundred GHz level without a high-speed modulation device. The system structure is simplified, extra calibration requirements are avoided, the ASE frequency sweep method eliminates dependence of light source frequency response calibration by using a broadband noise source and a filter amplification mechanism, the dynamic range and the signal-to-noise ratio are remarkably improved through noise power spectral density measurement, power fluctuation and system noise can be effectively suppressed by the two methods, and the system performance is improved. And the accuracy and reliability of calibration in an ultrahigh frequency scene are ensured.

The present invention provides a method, apparatus, medium, and device for speech keyword recognition, comprising: obtaining a frequency domain amplitude spectrum of a current frame signal; performing noise estimation to obtain a noise power spectrum of the current frame signal; estimating an initial a posteriori signal-to-noise ratio (SNR) of the current frame signal based on the noise power spectrum of the current frame signal and the noise power spectrum of the previous frame signal; estimating an initial a priori SNR of the current frame signal based on the initial a posteriori SNR of the current frame signal and the posterior SNR of the previous frame signal; calculating a probability of the presence of a target speech in the current frame signal based on the initial a posteriori SNR and the initial a priori SNR; and determining whether speech is present in the current frame signal based on the target speech probability. If the determination result is that speech is present in the current frame signal, inputting the current frame signal into a preset decoding graph for recognition to obtain a recognition result. This method is used to improve the problem of high speech keyword misrecognition rate in noisy environments.

The present invention relates to the field of signal detection technology, and more particularly to a noise floor estimation and threshold calculation method for a low signal-to-noise ratio (SNR) spectrally dense signal. The method determines the noise power spectrum density by statistically analyzing a histogram of the minimum to mean interval and finding the histogram maximum value. This method avoids the problem of misdetecting the power spectrum of a broadband signal as the noise floor when the signal width accounts for a large proportion of the entire spectrum width, addresses the shortcoming that most noise floor estimation algorithms cannot detect dense signals on the spectrum, and improves the accuracy of noise floor estimation.

The present invention relates to a method for characterizing traps in the gate oxide layer of MOSFETs, and the steps are as follows: 1) Measure the transfer characteristics to extract the threshold voltage, and test the noise power spectral density S of the device in the linear region id ; 2) Draw a double y-axis graph according to the measurement data to judge the noise fluctuation mechanism; 3) Calculate the trap energy position distribution in the gate oxide layer at a certain gate voltage V g ; 4) Repeat step 3 for all gate voltages to draw the energy position distribution diagram of the oxide layer traps. This method breaks through the limitation of only using uniform distribution or exponential distribution to characterize the trap distribution with position, and at the same time takes into account the non-uniformity of trap position and energy distribution, and by drawing a three-dimensional image, the distribution of oxide layer traps with energy position can be clearly observed.

The present invention discloses a speech enhancement method suitable for transient noise suppression, comprising the following steps: (1) performing a short-time Fourier transform on speech data in a clean speech data set to obtain a power spectrum; (2) using the power spectrum to train a VAE model weight; (3) performing a short-time Fourier transform on the noisy signal to be enhanced to obtain a time-frequency spectrum and a power spectrum; (4) performing a NMF on the power spectrum to calculate the time-domain index and frequency-domain index of each mode obtained by decomposition, estimating the components belonging to transient noise therein and filtering them out; (5) inputting the power spectrum after filtering out transient noise into the trained VAE model, combining the NMF with the estimated speech power spectrum and noise power spectrum; (6) subjecting the estimated speech power spectrum and noise power spectrum as well as the time-frequency spectrum of the noisy signal to Wiener filtering to obtain the time-frequency spectrum of the enhanced speech. The present invention can effectively suppress speech noise in a scenario where transient noise exists, while relatively completely preserving the speech components.

A system and method of harmonizing computed tomography (CT) images to reduce variability introduced by differing reconstruction kernels. The method includes acquiring a CT image of a plurality of regions of a patient. The method includes calculating, by a processing device, a noise power spectrum (NPS) of a uniform region among the plurality of regions. The method includes selecting, from a library of reconstruction kernels, a first reconstruction kernel based on the NPS of the uniform region of the plurality of regions. The method includes generating a harmonized CT image based on the first reconstruction kernel and the CT image.

The invention relates to the technical field of mechanical diagnosis, in particular to a fault diagnosis method for a wheel type carrying platform under high-concentration dust difference. Comprising the following steps: collecting temperature, vibration and sound information of feature parts and environment dust concentration information; preprocessing the collected information; and calculating a noise power spectrum corresponding to the environmental dust concentration information. Strengthening the sound information; and selecting a deep learning model according to the environmental dust concentration information, and outputting a diagnosis result. According to the method, the dust concentration-dependent noise power spectrum model is established, so that the interference of dust is effectively inhibited, and the characteristic signal-to-noise ratio is improved; and an environment parameter dynamic weight mechanism is constructed, and the dynamic weight mechanism adjusts signal weights in real time according to dust, so that the false alarm rate is reduced, and the fault diagnosis accuracy is improved. The limitation of a single signal is made up by adopting multi-modal data fusion, and the comprehensiveness of feature extraction is improved.

The invention discloses a power delay spectrum estimation method based on frequency domain noise whitening, which is applied to a power line communication system. According to the method, in order to solve the problems that power line channel frequency selective noise is high and a special silence period is lacked, the noise power spectral density is directly estimated from a received signal through frequency domain smoothing processing, a frequency domain whitening filter is designed according to the noise power spectral density to conduct whitening and normalization processing on channel response, the frequency selective noise is converted into flat noise, and the frequency selective noise is obtained. And a power delay spectrum with a substantially reduced noise floor is generated. On the basis, multipath components are accurately recognized through peak detection and parameter estimation, signals are reconstructed to separate and calculate signal and noise power, and finally high-precision signal-to-noise ratio estimation is achieved. According to the method, a silent period is not needed, frequency selective noise can be effectively resisted, and the accuracy of multipath parameter and signal-to-noise ratio estimation is remarkably improved.

The invention discloses a post-filtering method for an audio signal, and relates to the technical field of audio signal processing, and the technical scheme of the invention comprises the following steps: determining an initial first posterior signal-to-noise ratio based on a beam output signal and an estimated noise power spectral density value, and determining an initial first prior signal-to-noise ratio based on the first posterior signal-to-noise ratio; determining an initial first post-filtering weight according to the first prior signal-to-noise ratio, and performing preliminary noise suppression on the beam output signal by using the first post-filtering weight to obtain an intermediate processing signal; determining an optimized second posterior signal-to-noise ratio based on the intermediate processing signal and the noise power spectral density value, and determining an optimized second prior signal-to-noise ratio based on the second posterior signal-to-noise ratio; and determining an optimized second post-filtering weight according to the second prior signal-to-noise ratio, and performing post-filtering processing on the beam output signal based on the second post-filtering weight. According to the invention, residual noise can be suppressed more accurately and effectively.

The invention discloses an intelligent hearing aid noise reduction method combined with environmental noise evaluation, and relates to the technical field of hearing aid signal processing, comprising the following steps: collecting an audio signal and converting the audio signal to a frequency domain to obtain a frequency domain signal; extracting noise features from the frequency domain signal, calculating a noise time-varying index and spectrum flatness, and identifying an environmental noise type; estimating a noise power spectrum, determining a noise tracking parameter according to the environment noise type, and updating the noise power spectrum; calculating a signal-to-noise ratio parameter corresponding to the noise power spectrum, determining a noise reduction intensity factor according to the environmental noise type and the hearing characteristics of the user, and calculating a noise reduction gain based on the signal-to-noise ratio parameter and the noise reduction intensity factor; and performing noise reduction processing on the frequency domain signal by using the noise reduction gain, and converting the frequency domain signal to a time domain to output a processed audio signal. According to the invention, the noise tracking parameter is adaptively adjusted by identifying the environmental noise type, and the noise reduction intensity is determined in combination with the hearing characteristics of the user, so that the noise reduction effect and the user suitability of the hearing aid in a complex environment are improved.

The application discloses a noise estimation method and related device, the method comprises the following steps: obtaining the audio to be processed; processing the audio to be processed to obtain the power spectrum and the noise power spectrum of the audio to be processed; comparing the noise estimation value of each frame signal of the audio to be processed at each frequency point with the minimum power value of each frame signal at each frequency point to determine the first frequency point in each frame signal; wherein the noise estimation value of the first frequency point is less than the minimum power value; based on the number of the first frequency point at each frequency point sub-band of each frame signal, respectively determine whether noise mutation occurs at each frequency point sub-band of each frame signal; if it is determined that noise mutation occurs at the target frequency point sub-band of the target frame signal, the noise estimation value of the target frame signal at the target frequency point sub-band is updated to the power spectrum value of the target frame signal at the target frequency point sub-band. The application can accurately identify noise mutation and improve the updating efficiency of noise estimation.

This invention relates to the field of fan, speaker, and light design and control technology, and discloses a low-noise design method for a speaker fan based on fan noise. The method includes receiving the original audio signal, generating an audio power spectrum, and extracting low-frequency transient features; calculating the instantaneous masking threshold using a psychoacoustic model, and then inversely shaping the allowable noise power spectrum of the fan motor to generate a target fan spectrum; generating a fan drive signal based on the target spectrum using a random spread spectrum algorithm, and constructing an airflow velocity vector by combining the angular velocity fed back from the motor and the duct parameters; calculating the phase compensation amount based on the airflow velocity vector to generate a phase correction signal, and then combining the low-frequency transient features with an anti-phase aerodynamic impedance compensation component to synthesize a speaker signal; finally, driving the fan motor and speaker unit to operate synchronously. This invention utilizes the auditory masking effect to achieve invisible fan operation and cancels the interference of airflow on sound waves through flow field mapping and phase pre-distortion, achieving synergistic optimization of the sound field and airflow field.

A system and method of harmonizing computed tomography (CT) images to reduce variability introduced by differing reconstruction kernels. The method includes acquiring a CT image of a plurality of regions of a patient. The method includes calculating, by a processing device, a noise power spectrum (NPS) of a uniform region among the plurality of regions. The method includes selecting, from a library of reconstruction kernels, a first reconstruction kernel based on the NPS of the uniform region of the plurality of regions. The method includes generating a harmonized CT image based on the first reconstruction kernel and the CT image.

The application provides a noise spectrum detection method and system based on weak measurement, comprising the following steps: step 1, building a quantum weak measurement optical platform for noise spectrum detection, introducing noise in the weak coupling process to generate a time-varying phase parameter, step 2, determining the frequency position f to be detected, and adjusting two post-selection states according to the frequency position f, step 3, receiving the spectrum center after weak measurement through a spectrometer, and calculating the noise spectrum at the frequency f, step 4, repeatedly executing steps 2 and 3 to realize real-time detection of the noise power spectrum, and step 5, after the measurement is completed, performing data processing on the estimated results according to actual needs. The application makes up for the defects of small bandwidth and easy aliasing distortion of the existing noise detection technology, utilizes the weak value amplification technology, can achieve very high sensitivity and precision, and expands the application scenarios of a plurality of measurement systems based on weak measurement as a theory, and can be extended to the scene of noisy signal recovery.

The invention provides a real-time sensing method for a third-party construction damage event, and relates to the field of urban lifeline long-term monitoring, and the method comprises the steps: determining a cut-off frequency according to construction equipment information and an estimated noise power spectral density, and carrying out the preprocessing of an original vibration signal based on the cut-off frequency; calculating the product of the RMS values of the P adjacent measuring points; in combination with the D-S evidence theory, the evidences of the adjacent measuring points are fused, abnormal interference signals are eliminated by calculating a conflict coefficient K, and processed signals are obtained; obtaining a dynamic threshold value, and determining an endpoint of a third-party construction damage event in the processed signal; and carrying out feature matching and classification identification on the to-be-identified sample by adopting a K-nearest neighbor algorithm, and identifying the event type of the third-party construction damage event. According to the method, the recognition accuracy and real-time performance are effectively improved, abnormal data interference is effectively eliminated, the robustness of feature extraction and classification is enhanced, and the intelligent level of a monitoring system is remarkably improved.

The invention discloses a low-voltage cable fault fixed-point detection method and system. The low-voltage cable fault fixed-point detection method comprises the steps that a space sensing array composed of a main sensor, a plurality of reference sensors and a load current sensor is deployed on a low-voltage cable, and synchronous sampling is carried out; constructing a noise base matrix based on the output of the reference sensor, and inhibiting the environmental noise and electromagnetic interference in the main sensor signal through adaptive weight filtering; carrying out frequency domain analysis on the de-noising signal, constructing a gain function in combination with an estimated noise power spectrum, and carrying out selective enhancement on a fault characteristic frequency band; performing time-domain correlation analysis on the enhanced electromagnetic signal and the load current signal, extracting cross-correlation parameters, time-domain statistical characteristics and frequency-domain characteristics, and fusing the cross-correlation parameters, the time-domain statistical characteristics and the frequency-domain characteristics into a multi-dimensional characteristic vector; and finally inputting a pre-trained fault diagnosis model, and outputting a fault type and positioning information. The method effectively improves the extraction and identification capability of weak fault signals in a complex electromagnetic environment, and achieves the precise recognition and fixed-point detection of the fault type of the low-voltage cable.