提出并设计了一种基于正交光相干接收的光载波上下边带信号分离技术,可用在布里渊散射分布式光纤传感系统中,实现对斯托克斯光和反斯托克斯光的无损分离。该技术利用正交光相干接收技术,保留了光场的相位信息,使光载波的上下边带在不同...提出并设计了一种基于正交光相干接收的光载波上下边带信号分离技术,可用在布里渊散射分布式光纤传感系统中,实现对斯托克斯光和反斯托克斯光的无损分离。该技术利用正交光相干接收技术,保留了光场的相位信息,使光载波的上下边带在不同的输出端口处于相干叠加或相干抵消状态,从而实现上下边带的分离。结果表明,该技术可使光载波的上下边带分别从两个端口输出,且输出与输入信号功率线性相关,具有很好的线性度。分离之后的上边带光信号和下边带光信号之间的串扰小于-20 d B。与常用的光学滤波器方法相比,该技术无温度敏感器件,具有很好的稳定性和可靠性。展开更多
The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of...The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources.The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique(DUET) which assumes the number of sources a priori.In the proposed algorithm,the Short-Time Fourier Transform(STFT) is used to obtain the sparse rep-resentations,a clustering method called Unsupervised Robust C-Prototypes(URCP) which can ac-curately identify multiple clusters regardless of the number of them is adopted to replace the histo-gram-based technique in DUET,and the binary time-frequency masks are constructed to separate the mixtures.Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio(SIR),and maintains good speech quality in the separation results.展开更多
This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) metho...This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint diagonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.展开更多
文摘提出并设计了一种基于正交光相干接收的光载波上下边带信号分离技术,可用在布里渊散射分布式光纤传感系统中,实现对斯托克斯光和反斯托克斯光的无损分离。该技术利用正交光相干接收技术,保留了光场的相位信息,使光载波的上下边带在不同的输出端口处于相干叠加或相干抵消状态,从而实现上下边带的分离。结果表明,该技术可使光载波的上下边带分别从两个端口输出,且输出与输入信号功率线性相关,具有很好的线性度。分离之后的上边带光信号和下边带光信号之间的串扰小于-20 d B。与常用的光学滤波器方法相比,该技术无温度敏感器件,具有很好的稳定性和可靠性。
文摘The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources.The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique(DUET) which assumes the number of sources a priori.In the proposed algorithm,the Short-Time Fourier Transform(STFT) is used to obtain the sparse rep-resentations,a clustering method called Unsupervised Robust C-Prototypes(URCP) which can ac-curately identify multiple clusters regardless of the number of them is adopted to replace the histo-gram-based technique in DUET,and the binary time-frequency masks are constructed to separate the mixtures.Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio(SIR),and maintains good speech quality in the separation results.
基金Supported by the National Natural Science Foundation of China (60801052)Aeronautical Science Foundation of China (2009ZC52036)
文摘This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint diagonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.
