Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based ...Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.展开更多
To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were ...To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were the most,followed by Chlorophyta(10),Cyanophyta(1),Chrysophyta(1),Pyrrophyta(1). The phytoplankton abundance was(15. 6-810) × 104 ind·L^(-1); the biomass was(0. 07-2. 876) mg·L-1; Shannon-wiener index was 1. 05-3. 24; Pielou evenness index was 0. 27-0. 96. Using Shannon-wiener index and Pielou index,the water quality of Genhe River was assessed,and the results showed that the water quality was the best at 5# sampling point,the water quality was good in 3#,4#,7#,8#,9# sampling points,and there was a state of pollution at other sampling points. Canonical correspondence analysis and Pearson correlation analysis showed that iron ion,transparency,p H value,water depth and water temperature were important environmental factors that affect the distribution of phytoplankton,and copper ion,nitrite ion and COD also significantly affected the distribution of phytoplankton.展开更多
A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based o...A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.展开更多
基金supported by the National Natural Science Foundation of China(No.51279033).
文摘Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.
基金Supported by Supported by the United Nations Environment Program(DXAL-2014-002)
文摘To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were the most,followed by Chlorophyta(10),Cyanophyta(1),Chrysophyta(1),Pyrrophyta(1). The phytoplankton abundance was(15. 6-810) × 104 ind·L^(-1); the biomass was(0. 07-2. 876) mg·L-1; Shannon-wiener index was 1. 05-3. 24; Pielou evenness index was 0. 27-0. 96. Using Shannon-wiener index and Pielou index,the water quality of Genhe River was assessed,and the results showed that the water quality was the best at 5# sampling point,the water quality was good in 3#,4#,7#,8#,9# sampling points,and there was a state of pollution at other sampling points. Canonical correspondence analysis and Pearson correlation analysis showed that iron ion,transparency,p H value,water depth and water temperature were important environmental factors that affect the distribution of phytoplankton,and copper ion,nitrite ion and COD also significantly affected the distribution of phytoplankton.
文摘A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.
