The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus...The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus phenomenon and the other is the motion blur due to the relative movement between the objectives and the camera. Compared with the recognition for the blurred image with only one blur model, the parameter estimation for the picture combining defocus and motion blur models is a more complicated mission. A method was proposed for computer to estimate the parameters of defocus blur and motion blur in cepstrum area simultaneously. According to characters of both blur models in the frequency domain, an adjustment approach was suggested in the frequency area and then convert to the cepstrum field to increase the accuracy of measurement.展开更多
The analysis and characteristic extraction of target echo characteristics are important in underwater target detection and recognition.Rigid acoustic scattering components are generally used as major echo contributors...The analysis and characteristic extraction of target echo characteristics are important in underwater target detection and recognition.Rigid acoustic scattering components are generally used as major echo contributors with relatively stable characteristic information.Previous studies focus on echo characteristics from a single angle,thereby limiting the amount of extracted characteristic information.This paper aims to establish a full-angle rigid echo components model and overcome the difficulty of the extraction of time delay characteristics of narrow-band acoustic scattering echoes.On the basis of the analysis of the target echo highlight model,the echo characteristics of rigid acoustic scattering components are extracted in the cepstrum domain,and a wavelet process is proposed to enhance the effect of time delay estimation.Experimental data indicate that the extracted time delay characteristics accord with the rigid echo characteristics of underwater target,thereby validating the effectiveness of the cepstrum method.展开更多
Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectr...Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.展开更多
As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current s...As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.展开更多
In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise,a method of fault...In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise,a method of fault feature extraction based on cepstrum pre-whitening(CPW)and a quantitative law of symplectic geometry mode decomposition(SGMD)is proposed.First,CPW is performed on the original signal to enhance the impact feature of bearing fault and remove the periodic frequency components from complex vibration signals.The pre-whitening signal contains only background noise and non-stationary shock caused by damage.Secondly,a quantitative law that the number of effective eigenvalues of the Hamilton matrix is twice the number of frequency components in the signal during SGMD is found,and the quantitative law is verified by simulation and theoretical derivation.Finally,the trajectory matrix of the pre-whitening signal is constructed and SGMD is performed.According to the quantitative law,the corresponding feature vector is selected to reconstruct the signal.The Hilbert envelope spectrum analysis is performed to extract fault features.Simulation analysis and application examples prove that the proposed method can clearly extract the fault feature of bearings.展开更多
This paper introduces the concept of cepstrum. By investigating the difference in source characteristics between earthquakes and explosions the paper infers the manifestation of source difference in various variable d...This paper introduces the concept of cepstrum. By investigating the difference in source characteristics between earthquakes and explosions the paper infers the manifestation of source difference in various variable domains, and seeks for effective means to express such source difference. Extending the approach of source discrimination from time and frequency domain to the cepstrum domain, the paper proposes a method of cepstrum analysis for recognizing the characteristics of seismic sources and establishes criteria for identifying the type of seismic sources. Cepstrum analysis on some recent earthquakes and explosions has been made, and the result shows that the method is quite effective in practice.展开更多
Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the poin...Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the point spread function(PSF)can be identified conveniently in the frequency field for peopleby manual means rather than for computer.This paper introduces a practical method for computer to esti-mate the defocus blur parameter in cepstrum area.Fourier transform plays an intermediate role in the pathto cepstrum domain.We suggest a weighted adjustment operation in the frequency domain and then con-vert it to the cepstrum field to increase the accuracy of recognition.展开更多
The dynamic load spectrum is one of the most important basis of design and dynamic characteristics analysis of machines. But it is difficult to measure it on many occasions, especially for mining machines, due to thei...The dynamic load spectrum is one of the most important basis of design and dynamic characteristics analysis of machines. But it is difficult to measure it on many occasions, especially for mining machines, due to their bad working circumstances and high cost of measurements. For such situation, the load spectrum has to be obtained by indirect determination methods. A new method to identify the load spectrum, cepstrum analysis method, was presented in this paper. This method can be used to eliminate the filtering influence of transfer function to the response signals so that the load spectrum can be determined indirectly. The experimental and engineering actual examples indicates that this method has the advantages that the calculation is simple and the measurement is easy.展开更多
Based on the deduction of the C parameter estimation, a new method to estimate C Parameters from the AR parameters of the ARMA model and auto-related function is proposed , The method reduces the computation complexit...Based on the deduction of the C parameter estimation, a new method to estimate C Parameters from the AR parameters of the ARMA model and auto-related function is proposed , The method reduces the computation complexity of the conventional interative algorithm in MA parameter estimation, thus make the algorithm of ARMA model spectral estimation simpler. On account of this method, a new algo- rithm of cepstrum analysis is put forward . Computer simulation indicates that the proposed cepstrum al- gorithm has the merits of few sidelobes and high resolution . And the algorithm is very useful to the ding- nosis of machinery failure .展开更多
For the poor adaptability of the original repeating pattern, an improved music separation method of multi-repeating structure of Mel cepstrum coefficient （MFCC） is proposed. Firstly, the MFCC coefficient matrix （39...For the poor adaptability of the original repeating pattern, an improved music separation method of multi-repeating structure of Mel cepstrum coefficient （MFCC） is proposed. Firstly, the MFCC coefficient matrix （39-dimensional data） of the music signal was extracted. Then the cosine characteristic was applied to the count of similarity matrix of MFCC, and the fragments with consistent similarity are putted together. Next different repeating patterns are built for different groups. Thereby the spectrums of the background music and vocal were separated combined with ideal binary masking （IBM）, and the corresponding time domain signals were obtained by inverse Fourier transform. Fnally, the improved method was tested on the music database of different types and length, and the separation results were compared with repeating method of Rafii and the non-negative matrix factorization based on flexible framework method of Ozerov. The experimental results showed that the separation performance of improved method was improved about 3 dB, and the performance of music with melody changed larger was significantly improved. Experiments verified that the improved method was an effective music separation algorithm and more stability.展开更多
A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech ar...A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech are extracted, and mapped to the eigenspace which is formed by eigenvectors of its scatter matrix, thereby the Structured Gaussian Mixture Model in the EigenSpace （SGMM-ES） is trained. The source and target speaker＇s SGMM-ES are matched based on Acoustic Universal Structure （AUS） principle to achieve spectrum transform function. Experimental results show the speaker identification rate of conversion speech achieves 95.25%, and the value of average cepstrum distortion is 1.25 which is 0.8% and 7.3% higher than the performance of SGMM method respectively. ABX and MOS evaluations indicate the conversion performance is quite close to the traditional method under the parallel corpora condition. The results show the eigenspace based structured Gaussian mixture model for voice conversion under the non-parallel corpora is effective.展开更多
The pulse condition is an important basis for diagnosis and treatment intraditional Chinese medicine. From ancient times, Chinese physicians have been ableto make out the pathological changes of the nine organs by pul...The pulse condition is an important basis for diagnosis and treatment intraditional Chinese medicine. From ancient times, Chinese physicians have been ableto make out the pathological changes of the nine organs by pulse-feeling andpalpation, based on the theory that the pathological mystique lies in the pulsecondition. Chinese physicians have obtained and identified all along the pulsecondition by their finger tips, so there have been inevitably many subjective factors展开更多
A cepstrum moving target detection （CEPMTD） algorithm based on cepstrum techniques is proposed for passive coherent location （PCL） radar systems. The primary cepstrum techniques are of great success in recognizing...A cepstrum moving target detection （CEPMTD） algorithm based on cepstrum techniques is proposed for passive coherent location （PCL） radar systems. The primary cepstrum techniques are of great success in recognizing the arrival times of static target echoes. To estimate the Doppler frequencies of moving targets, we divide the radar data into a large number of seg- ments, and reformat these segments into a detection matrix. Applying the cepstrum and the Fourier transform to the fast and slow time dimensions respectively, we can obtain the range information and Doppler information of the moving targets. Based on the CEPMTD outlined above, an improved CEPMTD algorithm is proposed to improve the detection performance. Theoretical analyses show that only the target＇s peak can be coherently added. The performance of the improved CEPMTD is initially vali- dated by simulations, and then by experiments. The simulation results show that the detection performance of the improved CEPMTD algorithm is 13.3 dB better than that of the CEPMTD algorithm and 6.4 dB better than that of the classical detection algorithm based on the radar cross ambiguity function （CAF）. The experiment results show that the detection performance of the improved CEPMTD algorithm is 1.63 dB better than that of the radar CAF.展开更多
In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by ...In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.展开更多
基金The National Natural Science Foundation of China (No 30570485)
文摘The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus phenomenon and the other is the motion blur due to the relative movement between the objectives and the camera. Compared with the recognition for the blurred image with only one blur model, the parameter estimation for the picture combining defocus and motion blur models is a more complicated mission. A method was proposed for computer to estimate the parameters of defocus blur and motion blur in cepstrum area simultaneously. According to characters of both blur models in the frequency domain, an adjustment approach was suggested in the frequency area and then convert to the cepstrum field to increase the accuracy of measurement.
基金Supported by the National Natural Science Foundation of China under Grant No.51279033,and National Science Foundation of Heilongjiang Province,China under Grant No.F201346
文摘The analysis and characteristic extraction of target echo characteristics are important in underwater target detection and recognition.Rigid acoustic scattering components are generally used as major echo contributors with relatively stable characteristic information.Previous studies focus on echo characteristics from a single angle,thereby limiting the amount of extracted characteristic information.This paper aims to establish a full-angle rigid echo components model and overcome the difficulty of the extraction of time delay characteristics of narrow-band acoustic scattering echoes.On the basis of the analysis of the target echo highlight model,the echo characteristics of rigid acoustic scattering components are extracted in the cepstrum domain,and a wavelet process is proposed to enhance the effect of time delay estimation.Experimental data indicate that the extracted time delay characteristics accord with the rigid echo characteristics of underwater target,thereby validating the effectiveness of the cepstrum method.
基金Project 59775004 supported by National Natural Science Foundation of China
文摘Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.
基金co-supported by the National Natural Science Foundation of China (Nos. U1933130,71731001,1433203,U1533119)the Research Project of Chinese Academy of Sciences (No. ZDRW-KT-2020-21-2)。
文摘As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.
基金The National Natural Science Foundation of China(No.52075095).
文摘In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise,a method of fault feature extraction based on cepstrum pre-whitening(CPW)and a quantitative law of symplectic geometry mode decomposition(SGMD)is proposed.First,CPW is performed on the original signal to enhance the impact feature of bearing fault and remove the periodic frequency components from complex vibration signals.The pre-whitening signal contains only background noise and non-stationary shock caused by damage.Secondly,a quantitative law that the number of effective eigenvalues of the Hamilton matrix is twice the number of frequency components in the signal during SGMD is found,and the quantitative law is verified by simulation and theoretical derivation.Finally,the trajectory matrix of the pre-whitening signal is constructed and SGMD is performed.According to the quantitative law,the corresponding feature vector is selected to reconstruct the signal.The Hilbert envelope spectrum analysis is performed to extract fault features.Simulation analysis and application examples prove that the proposed method can clearly extract the fault feature of bearings.
基金State Natural Science Foundation of China (40174011).
文摘This paper introduces the concept of cepstrum. By investigating the difference in source characteristics between earthquakes and explosions the paper infers the manifestation of source difference in various variable domains, and seeks for effective means to express such source difference. Extending the approach of source discrimination from time and frequency domain to the cepstrum domain, the paper proposes a method of cepstrum analysis for recognizing the characteristics of seismic sources and establishes criteria for identifying the type of seismic sources. Cepstrum analysis on some recent earthquakes and explosions has been made, and the result shows that the method is quite effective in practice.
基金the National Natural Science Foundation of China(No.30570485)
文摘Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the point spread function(PSF)can be identified conveniently in the frequency field for peopleby manual means rather than for computer.This paper introduces a practical method for computer to esti-mate the defocus blur parameter in cepstrum area.Fourier transform plays an intermediate role in the pathto cepstrum domain.We suggest a weighted adjustment operation in the frequency domain and then con-vert it to the cepstrum field to increase the accuracy of recognition.
文摘The dynamic load spectrum is one of the most important basis of design and dynamic characteristics analysis of machines. But it is difficult to measure it on many occasions, especially for mining machines, due to their bad working circumstances and high cost of measurements. For such situation, the load spectrum has to be obtained by indirect determination methods. A new method to identify the load spectrum, cepstrum analysis method, was presented in this paper. This method can be used to eliminate the filtering influence of transfer function to the response signals so that the load spectrum can be determined indirectly. The experimental and engineering actual examples indicates that this method has the advantages that the calculation is simple and the measurement is easy.
文摘Based on the deduction of the C parameter estimation, a new method to estimate C Parameters from the AR parameters of the ARMA model and auto-related function is proposed , The method reduces the computation complexity of the conventional interative algorithm in MA parameter estimation, thus make the algorithm of ARMA model spectral estimation simpler. On account of this method, a new algo- rithm of cepstrum analysis is put forward . Computer simulation indicates that the proposed cepstrum al- gorithm has the merits of few sidelobes and high resolution . And the algorithm is very useful to the ding- nosis of machinery failure .
基金supported by the National Natural Science Foundation of China(61371164,61275099,61102131)the Project of Key Laboratory of Signal and Information Processing of Chongqing(CSTC2009CA2003)+3 种基金the Chongqing Distinguished Youth Fundation(CSTC2011jjjq40002)the Natural Science Foundation of Chongqing(CSTC2012JJA40008)the Research Project of Chongqing Educational Commission(KJ120525,KJ130524)Graduate Research and Innovation Projects of Chongqing(CYS14140)
文摘For the poor adaptability of the original repeating pattern, an improved music separation method of multi-repeating structure of Mel cepstrum coefficient （MFCC） is proposed. Firstly, the MFCC coefficient matrix （39-dimensional data） of the music signal was extracted. Then the cosine characteristic was applied to the count of similarity matrix of MFCC, and the fragments with consistent similarity are putted together. Next different repeating patterns are built for different groups. Thereby the spectrums of the background music and vocal were separated combined with ideal binary masking （IBM）, and the corresponding time domain signals were obtained by inverse Fourier transform. Fnally, the improved method was tested on the music database of different types and length, and the separation results were compared with repeating method of Rafii and the non-negative matrix factorization based on flexible framework method of Ozerov. The experimental results showed that the separation performance of improved method was improved about 3 dB, and the performance of music with melody changed larger was significantly improved. Experiments verified that the improved method was an effective music separation algorithm and more stability.
基金supported by the Natural Science Foundation of China(61271360)the Application Fundamental Research Project of Suzhou(SYG201230)
文摘A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech are extracted, and mapped to the eigenspace which is formed by eigenvectors of its scatter matrix, thereby the Structured Gaussian Mixture Model in the EigenSpace （SGMM-ES） is trained. The source and target speaker＇s SGMM-ES are matched based on Acoustic Universal Structure （AUS） principle to achieve spectrum transform function. Experimental results show the speaker identification rate of conversion speech achieves 95.25%, and the value of average cepstrum distortion is 1.25 which is 0.8% and 7.3% higher than the performance of SGMM method respectively. ABX and MOS evaluations indicate the conversion performance is quite close to the traditional method under the parallel corpora condition. The results show the eigenspace based structured Gaussian mixture model for voice conversion under the non-parallel corpora is effective.
基金Project supported by the National Natural Science Foundation of China.
文摘The pulse condition is an important basis for diagnosis and treatment intraditional Chinese medicine. From ancient times, Chinese physicians have been ableto make out the pathological changes of the nine organs by pulse-feeling andpalpation, based on the theory that the pathological mystique lies in the pulsecondition. Chinese physicians have obtained and identified all along the pulsecondition by their finger tips, so there have been inevitably many subjective factors
文摘A cepstrum moving target detection （CEPMTD） algorithm based on cepstrum techniques is proposed for passive coherent location （PCL） radar systems. The primary cepstrum techniques are of great success in recognizing the arrival times of static target echoes. To estimate the Doppler frequencies of moving targets, we divide the radar data into a large number of seg- ments, and reformat these segments into a detection matrix. Applying the cepstrum and the Fourier transform to the fast and slow time dimensions respectively, we can obtain the range information and Doppler information of the moving targets. Based on the CEPMTD outlined above, an improved CEPMTD algorithm is proposed to improve the detection performance. Theoretical analyses show that only the target＇s peak can be coherently added. The performance of the improved CEPMTD is initially vali- dated by simulations, and then by experiments. The simulation results show that the detection performance of the improved CEPMTD algorithm is 13.3 dB better than that of the CEPMTD algorithm and 6.4 dB better than that of the classical detection algorithm based on the radar cross ambiguity function （CAF）. The experiment results show that the detection performance of the improved CEPMTD algorithm is 1.63 dB better than that of the radar CAF.
文摘In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.
