In this paper an improved multi_scale estimation method of signal is gaven.The detail signals on each scale are further more decomposed with wavelet transform.The subdetail signals and subsmoothing signals are process...In this paper an improved multi_scale estimation method of signal is gaven.The detail signals on each scale are further more decomposed with wavelet transform.The subdetail signals and subsmoothing signals are processed with different threshold respectively. The smoothing signals on the coarsest scale are processed with Kalman fitering and reconstructed with the detail signal on each scale.At last the signal estimate value on the original scale is obtained.展开更多
Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT...Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.展开更多
As wavelet basis in wavelet analysis is neither arbitrary nor unique,the same signal dealing with different wavelet bases will generate different results.Therefore,how to construct a wavelet basis suitable for the cha...As wavelet basis in wavelet analysis is neither arbitrary nor unique,the same signal dealing with different wavelet bases will generate different results.Therefore,how to construct a wavelet basis suitable for the characteristics of the analyzed signal and solve its algorithm and realization is a fundamental problem which perplexed many researchers.To solve these problems,in accordance with the basic features of the measured millisecond blast vibration signal,a new wavelet basis construction method based on the separation blast vibration signal is proposed,and the feasibility of this method is verified by comparing the practical effect of the newly constructed wavelet with other known wavelets in signal processing.展开更多
Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to colle...Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis （PCA）, the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.展开更多
文摘In this paper an improved multi_scale estimation method of signal is gaven.The detail signals on each scale are further more decomposed with wavelet transform.The subdetail signals and subsmoothing signals are processed with different threshold respectively. The smoothing signals on the coarsest scale are processed with Kalman fitering and reconstructed with the detail signal on each scale.At last the signal estimate value on the original scale is obtained.
基金Supported by Tianjin Municipal Science and Technology Commission (No.09JCYBJC02200)
文摘Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.
基金Projects(51078043,51278071,51308072)supported by the National Natural Science Foundation of China
文摘As wavelet basis in wavelet analysis is neither arbitrary nor unique,the same signal dealing with different wavelet bases will generate different results.Therefore,how to construct a wavelet basis suitable for the characteristics of the analyzed signal and solve its algorithm and realization is a fundamental problem which perplexed many researchers.To solve these problems,in accordance with the basic features of the measured millisecond blast vibration signal,a new wavelet basis construction method based on the separation blast vibration signal is proposed,and the feasibility of this method is verified by comparing the practical effect of the newly constructed wavelet with other known wavelets in signal processing.
基金supported by National Natural Science Foundation(No.60875061)
文摘Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis （PCA）, the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.
