327 results about "Time frequency spectrum" patented technology

The finding of the present invention is that the melody extraction or automatic transcription may be implemented clearly more stable and if applicable even less expensive when the assumption is considered sufficiently that the main melody is the portion of a piece of music which man perceives the loudest and the most precise. Regarding this, according to the present invention the time/spectral representation or the spectrogram of an interesting audio signal is scaled using the curves of equal volume reflecting human volume perception in order to determine the melody of the audio signal on the basis of the resulting perception-related time/spectral representation.

The invention provides an electric energy metering automation simulation laboratory which is used for experimenting, researching and producing an electric energy metering meter or evaluating the running capability of a traditional electric energy meter. A mode of simulating a true power utilization environment is adopted, an actual power utilization site environment is reproduced by using the modes of an interference signal source, a lightning stroke surge signal source, three-phase independent impedance simulation, the true access of an electric appliance and the like, the interference noiseof a power utilization network is recorded by a real-time frequency spectrum and waveform recording system, the running conditions of an electric energy metering device and a remote meter reading system under various power utilization environments are also observed, recorded and calculated, the obtained mass data are analyzed, and the relevant performance parameters of the electric energy metering device and a remote electric energy data collection system are summarized so as to obtain concrete quantitative data and provide a data support for constructing a relevant standard.

The invention discloses a vibration control method for a wind generator set tower. The method comprises the steps of collecting motor rotation speed data in real time and utilizing a cabin vibration sensor to collect vibration signals; converting the collected vibration signals into vibration signal peak-to-peak values, and when the vibration signal peak-to-peak values exceed a set alarm value, triggering vibration alarm and performing real-time frequency spectrum characteristic analysis; obtaining real-time frequency spectrum characteristics and a wind wheel rotating speed value at the time; comparing a complete machine vibration frequency point corresponding to the wind wheel rotating speed value in complete machine vibration frequency characteristics obtained in advance and energy concentration degree under the frequency in a real-time frequency spectrum characteristic spectrum, and analyzing vibration reasons and correspondingly performing vibration reduction processing according to the different reasons. According to the vibration control method, the reasons for causing complete machine vibration are distinguished through a spectral analysis algorithm, so that cabin vibration of a wind generator is controlled to guarantee complete machine safety, times of shutdown caused by vibration is reduced, and the wind generator set utilization rate is improved.

In order to implement the melody extraction or the automatic clearly more stable or to improve the transcription result, respectively, at the resulting segments or trajectories, respectively, of a melody line gained from a spectrogram of the audio signal a harmony mapping is performed such that a follower segment directly neighboring a reference segment in time direction is virtually shifted in the frequency direction by stages of octave, fifth and/or third in order to examine whether among the resulting lines of the octave, fifth and/or third there is one that fulfills a predetermined condition, like e.g. that the time/spectral representation along this line comprises a minimum which is larger by a certain factor than a minimum that it comprises along the reference segment line, and, if such a line exists, selects the same and actually performs the shifting of the follower segment. This way, errors in melody line determination may be corrected again. By a suitable selection of predetermined conditions for the lines of the octave, fifth and/or third, the direct neighborhood and further possible conditions, a faulty shifting may be prevented.

The invention relates to a seismic wave absorption and attenuation compensation method, and belongs to the geophysical exploration data processing technology. The method comprises the following steps: selecting one of VSP (vertical seismic profiling) initial data as reference data, carrying out generalized S-transform on the reference data, selecting sampling points as reference points, and recording the time and time-frequency spectrums of the sampling points; carrying out generalized S-transform on data of each sampling point, and dividing the time-frequency spectrum of corresponding frequency in the time-frequency spectrums of the reference points by the time-frequency spectrum of each sampling point so as to obtain an absorption and attenuation curve; determining a natural logarithm to figure out a phase compensation operator according to a formula of frequency difference of absorption and attenuation signals in a time frequency domain; and carrying out absorption and attenuation compensation and phase compensation in the time frequency domain to obtain a fine VSP wave field profile. The method has the advantages that noise can be eliminated during the process of fitting the absorption and attenuation curve; generalized S-inverse transform without energy loss is realized; signal-to-noise ratio and resolution of the data are improved after compensation; log resolution control is realized; the reference points are selected flexibly; and algorithm is simple and efficient.

The invention discloses a flutter online monitoring method for machining equipment. The method comprises the steps that a proper sampling window is selected; empirical mode decomposition is carried out on sampled vibration signals; decomposed eigen modalities are screened to obtain a characteristic eigen modality; Hilbert transformation is carried out on the characteristic eigen modality to obtain a time-frequency spectrum; statistical pattern analysis is carried out on the time-frequency spectrum to obtain characteristic parameters; the statistical characteristic parameters are compared with a set characteristic threshold value and the statistical characteristic parameter of a historical adjacent signal, and the vibration state of a system is judged. The flutter online monitoring method aims to solve the problems that a flutter detecting method is strong in sample dependency and poor in generalization ability, threshold value measurement is difficult, and judgment is not carried out in time, the method combining Hilbert-Huang transformation and statistical pattern recognition is provided, statistical modeling and clustering analysis are carried out on the time-frequency spectrum of the vibration signal based on the aggregation character of energy on frequency in the fluttering process, the characteristic parameters are utilized, the physical characteristic of cutting flutter is represented essentially, the cutting vibration state is effectively monitored in real time, and the judgment result is accurate and visual.

The embodiments of the invention provide a fault diagnosis method and apparatus for a rolling bearing. The fault diagnosis method for the rolling bearing comprises the following steps: acquiring vibration signals of the rolling bearing, and solving a time-frequency spectrum of the vibration signals; according to the time-frequency spectrum, determining an optimal order component of the vibration signals; according to the optimal order component, performing angle re-sampling on the vibration signals to obtain angle domain signals; and performing envelope analyzing on the angle domain signals, and determining fault positions of the rolling bearing, wherein determination of the optimal order component comprises: setting an equivalent frequency conversion scope and searching for all order components from the time-frequency spectrum; and calculating order energy of the order components and determining the optimal order component from the equivalent frequency conversion scope. The fault diagnosis method and apparatus for the rolling bearing realize automatic and rapid determination of the optimal order component so as to provide a basis for fault diagnosis of the rolling bearing, improve the fault diagnosis accuracy and avoid interference of manual factors.

The invention discloses a time-varying mixed-phase seismic wavelet extraction method based on time-frequency spectrum simulation. The method is characterized in that on the basis that improved generalized S transformation is carried out on a non-stationary seismic record, a time-frequency filter is introduced for the first time to filter a time-frequency spectrum of the seismic record, then a wavelet amplitude spectrum at each moment is estimated according to the spectrum simulation method, a mixed-phase spectrum of the wavelets is reconstructed in the seismic record with the time-varying wavelet amplitude spectrums removed according to the bispectrum method of high-order cumulants under the assumed condition that the phases of the wavelets are time-invariant, and the amplitude spectrums and the phase spectrum are combined to achieve extraction of the time-varying mixed-phase wavelets. Compared with a conventional stage extraction method, the time-varying wavelet extraction method has the advantages that the assumption that stages of the wavelets are stable is not needed, the time-varying wavelets can be accurately estimated even though the attributes of the wavelets at adjacent stages vary greatly, the mixed phase of the extracted wavelets is more consistent with the reality and seismic data can be analyzed and processed more finely and more accurately. The method has great application value in improving the resolution of the seismic data.

The invention relates to a non-stationery vibration signal instantaneous frequency estimation algorithm in a start and stop period of rotating machinery; firstly, an experiment table working model of a rotor of the rotating machinery is built, a non-stationery vibration signal sensor for measuring vibration signal, and a photo-electric sensor for measuring the rotating speed of a reference axis are arranged along the horizontal and vertical direction of the reference axis; after the obtained signal is analyzed by an order analysis software of an upper computer through a dynamic signal analyzer, STFT time-frequency spectrum containing multi-level component is obtained; the working frequency of the rotating machinery is used as an estimated starting frequency point, according to the requirements of sampling frequency and calculation accuracy, the frequency obtained by the STFT time-frequency analysis is equally divided into M groups, the start and stop period is equally divided into N time points for building grid meshes of N time points and M groups of frequency points; the route of minimum-deviation frequency point from the start point to the stop point is computed through Viterbi algorithm, after fitting is carried out, the instantaneous frequency estimation function value of the reference axis of the non-stationery vibration signal of the rotating machinery in the start and stop period is obtained.

The invention relates to the technical field of petroleum exploration, in particular to a time-frequency decomposition earthquake-fluid recognition method which includes establishing a time-frequency atom dictionary D according to Morlet wavelet function m(t)=exp[-betaXf2(t-tau)2]exp[i(2pif(t-tau)+phi)], and acquiring an initial matching atom of the Morlet wavelet function through calculation with a seismic trace and complex seismic trace method; performing matching decomposition on the seismic trace, performing iterative optimization with constraints of the time-frequency atom dictionary D in the neighborhood of the initial matching atom to acquire an optimal matching atom, stopping matching decomposition when preset conditions are achieved, and representing the initial seismic trace as a series of linear combinations of Morlet wavelet atoms; transforming the optimal matching Morlet atom into the time-frequency domain so as to acquire a time-frequency spectrum distribution of the initial seismic trace; extracting directly properties of earthquake fluid activity on a target stratum section on the time-frequency spectrum of earthquake materials; and predicting distribution range and space distribution of gas deposit according to the properties of the fluid activity. By the method, the distribution range and the space distribution of the gas deposit can be accurately predicted, so that a technical support is provided for favorable target optimization of natural gas exploration.

The invention belongs to the technical field of radio signal identification, in particular to a communication protocol signal identification method based on a depth residual network. The method comprises the following steps of: performing time-frequency analysis on a communication protocol signal in a sample library, and converting a time-frequency spectrum diagram of the signal into a gray-scaleimage; training The depth residual network model by gray-scale image. The trained depth residual network model is used to detect and identify the specific communication protocol signals trained in thetransmission process. As that depth residual network is applied to the field of communication signal identification, the invention overcomes the defect of high signal quality requirement and high prior information requirement of the traditional method, etc. At low SNR, multipath delay, When Doppler frequency offset and some features of signal are obscured by strong interference noise, Doppler frequency offset can still accurately identify the protocol type, and it does not depend on the prior information of received signal, so it can directly process the received IF signal, and has robust performance and high efficiency, which provides ideas for the subsequent research in this field, and has strong practical application value.

The invention discloses a time-frequency spectrum curve extracting method based on edge detection. The method includes the steps that firstly, a k-order origin moment of signal time-frequency distribution is calculated, then, a matrix containing time-frequency spectrum edge information is acquired according to an edge detection method, a time-frequency spectrum curve is acquired by judging the matrix and combining the edge information, parameter space is established according to an existing curve model so that parameters of the curve are estimated, and the time-frequency spectrum curve is extracted. Compared with a tradition detection method, the time-frequency spectrum curve extracting method has the advantages that the structural attributes of signal time-frequency distribution are fully used, the curve extraction accuracy is improved, and especially under the conditions that the signal to noise ratio is low and multiple time-frequency spectrum bands exist, the time-frequency spectrum curve can still be extracted through the method while the performance of a traditional extraction method is reduced and even the traditional extraction method fails. The performance of the method is better for time-frequency spectrum curve extraction.

The invention belongs to the field of digital signal processing, and particularly relates to a broadband digital receiver. The broadband digital receiver is used for solving the problem that an existing digital receiver is narrow in real-time frequency spectrum processing bandwidth and low in frequency spectrum resolution. The broadband digital receiver comprises an input end and an output end, and further comprises a first filter bank, an analog mixing module, a second filter bank, an analog-digital conversion module, a time-frequency transformation module and a frequency spectrum joint module, wherein the first filter bank is connected with the input end and the analog mixing module, the second filter bank is connected with the analog mixing module and the analog-digital conversion module, the time-frequency transformation module is connected with the analog-digital conversion module and the frequency spectrum joint module, and the output end is connected with the frequency spectrum joint module. The broadband digital receiver is suitable for the fields of electronic reconnaissance receiving, real-time frequency spectrum analysis, radars, communication and the like.

The invention discloses a ground object classification method based on robustness time frequency characteristics, which is mainly used for solving the problem that the prior art takes long time to perform classification on the vehicle objects and the human body objects under a low signal to noise ratio condition. The realization process comprises steps of performing normalization on recorded high signal-to-noise ratio signals to obtain training signals, 2 extracting a three-dimension time frequency characteristic and training a classifier from a time frequency spectrum of the training signals, 3 performing normalization on recorded low signal-to-noise ratio signal energy to obtain test signals, 4 extracting 3-dimension time frequency characteristics from the time frequency spectrum of the test signals, 5 transmitting the 3D time frequency characteristics of the test signal into the trained classifier to obtain an classification result. Under the condition that the signal-to-noise ratio is low and has no need of performing beforehand de-noise processing, the ground object classification method based on robustness time frequency characteristics can quickly and effectively realize the classification of the vehicle objects and the human body objects and can be used for radar object identification.

The invention relates to an early fault identification method of a rolling bearing under a variable rotating speed working condition. The method comprises the following steps that firstly, time-frequency analysis is carried out on a vibration signal of the rolling bearing to obtain a time-frequency spectrum; secondly, a peak value search method is adopted to extract an instantaneous frequency conversion in the time-frequency spectrum and data fitting is carried out; then high-pass filtering is carried out on an original vibration signal, and equal angle sampling is carried out on the originalvibration signal according to the instantaneous frequency conversion after data fitting to obtain an angle domain signal; finally, noise reduction processing is carried out on the original vibration signal and envelope spectrum analysis is carried out on the noise-reduced signal to identify faults of the rolling bearing. The method does not rely on a rotation speed sensor or human experience, andself-adaptively searches optimal parameters of the MCKD, moreover, the method is convenient and reliable, and is particularly suitable for vibration signal analysis of the rolling bearing with low signal-to-noise ratio under variable rotation speed working conditions.

The invention provides a high-precision time-frequency analysis method, and belongs to the field of digital signal processing. The method comprises the following steps of: (1)inputting original digital signals x(t), then carrying out empirical mode decomposition (EMD)to the original digital signals x(t), decomposing the original digital signals x(t) into sets of intrinsic mode function (IMF)components, namely ci(t) is the IMF component; (2) carrying out matching pursuit (MP)decomposition to each IMF component ci(t) respectively to obtain a time-frequency spectrum of each IMF component; and (3) carrying out mutual superposition to the time-frequency spectrum of each IMF component obtained in the step (2) to obtain the time-frequency spectrum of a high time-frequency resolution of the original digital signals x(t). The high-precision time-frequency analysis method disclosed by the invention avoids inaccuracy of time-frequency atoms when MP decomposition matching is implemented for composite signals directly, and MP decomposition is implemented for each IMF component, thereby being capable of obtaining the time-frequency spectrum with higher accuracy and time-frequency resolution and richer information.

The invention relates to a distribution network fault classification method based on a convolution depth confidence network. The method comprises the steps of firstly collecting the three-phase voltage, zero-sequence voltage and three-phase current of a low-voltage bus of a main transformer and a low-voltage side of the main transformer, and respectively interceptting the signal waveform data of one cycle wave before and after each fault condition as training samples; secondly, carrying out the time-frequency decomposition on the training sample data of step S1 by using the discrete wavelet packet transform, and obtaining the time-frequency matrix, then constructing the pixel matrix of the time-frequency spectrum map, and constructing the time-frequency spectrum map as the input of the subsequent CDBN model; then constructing the CDBN model to train two convolution-constrained Boltzmann machines in unsupervised learning mode, and adding the softmax classifier after the second CRBM to train the network model to effectively extract and automatically classify the fault features, and finally, using the trained model to realize the fault classification of distribution network. The method of the invention can realize accurate fault location.

Techniques are disclosed for sound source localization based on joint learning and evaluation of ITD and ILD representations that are measured in a complementary, correlation-based way using binaural time-frequency spectrums. According to one embodiment, from these measurements and learned representatives, which may, for example, be created by combinations of measurements from signals belonging to the same class, i.e., the same azimuthal location, probability distributions over frequency and class are computed. These probability distributions can be combined over cue and frequency using information-theoretic approaches to get a robust classification of the location and additionally a confidence measure for the quality of the classification result.

The invention discloses a rotary machinery fault feature extracting method based on local mean decomposition (LMD) and local time-frequency entropy. According to the technical scheme, the rotary machinery fault feature extracting method comprises the following steps of: (1) measuring rotary mechanical equipment by using an acceleration transducer, and acquiring vibration acceleration signals; (2) performing LMD on the vibration acceleration signals to obtain a plurality of pulse frequency (PF) components, and determining the instant amplitude and the instant frequency of each component; (3) making a time-frequency spectrum, dividing a time-frequency plane and calculating the local time-frequency entropy; and (4) extracting fault features by utilizing a local time-frequency entropy value as feature quantity and combining experiments. An analyzing process of a rotary machinery fault diagnosis system based on LMD is realized, difference of vibration signals of the equipment on time-frequency distribution and energy distribution in different states is researched, a local time-frequency entropy theory can be used for diagnosing fault of machinery, the local time-frequency entropy of the vibration signals in the different states is calculated after LMD conversion of the vibration signals, and the local time-frequency entropy value is used as the feature quantity to judge whether the equipment fails or not.

The invention provides a fault diagnosis method of a variable-speed rotation machine based on time-frequency spectrum segmentation. The method includes following steps: step 1, obtaining normalized time-frequency spectrums of signals through multi-resolution generalized S transform, and generating multi-resolution binarized time frequency spectrums; step 2, combining the binarized time-frequency spectrums with all resolutions, and obtaining an optimal binarized time-frequency spectrum; step 3, segmenting the optimal binarized time-frequency spectrum into a plurality of connected domains, and performing information annotation on each connected domain; step 4, extracting an optimal expression atom of each connected domain, forming an optimal atom set, and calculating the expression of a fault signal in the optimal atom set; and step 5, calculating the appearance time and amplitude of an impact theory, and realizing fault diagnosis of variable-sped mechanical equipment through informationcomparison. According to the method, most strong background noises can be filtered, and fault diagnosis of impact type faults including cracking, pitting corrosion or spalling etc. of the variable-speed rotation machine can be realized.

The invention discloses bowel sound detection method, device and system under noisy environment. The bowel sound detection method comprises steps of collecting a bowl sound mixed signal of a current user through a sensor, wherein the bowl sound mixed signal comprises a bowl sound signal and an environment interference signal; converting the bowl sound mixed signal to a digital signal; extracting a time-frequency spectrum characteristic of the digital signal; inputting the time-frequency spectrum characteristic of the digital signal into a trained convolution nerve network to process; and detecting a time point when the bowl sound happens so as to distinguish the bowl sound signal and the environment interference signal. The bowel sound detection method, device and system under the noisy environment utilize a difference of the time-frequency spectrum characteristics of the bowl sound signal and the environment interference signal, train a convolution nerve network to distinguish the bowl sound and the interference sound, can finish the detection on the bowl sound under the noisy environment, and can help to improve the accuracy of the bowl sound detection.