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In this paper, we propose a new speech enhancement method using the common vector approach. Common vector approach is a subspace method used in recognition applications. In the proposed method, we separate the noisy speech data into magnitude and phase in frequency domain. And also magnitude data is separated into common and difference parts using common vector. It is considered that difference part contains the noise. Therefore, this part is cleaned using Linear Minimum Mean Square Error Estimation. After this process, the magnitude data is reconstructed by combining common part. The frequency domain speech data is rebuilt by sum of the reconstructed magnitude data and kept phase data and transform to time domain on each frame. The proposed method was evaluated under various noise conditions. The results are compared with several methods in well-known quality measures. [ABSTRACT FROM AUTHOR]

Fock-space (FS) multi-reference (MR) coupled-cluster (CC) method has emerged as compact tool to account for electronic structure of open-shell systems and molecules in low-lying excited states. Development of linear response (LR) has been one of the challenging problems in FSMRCC due to multiple-root nature of effective Hamiltonian. The recently developed constrained variational approach (CVA) has opened up a promising tool for efficient evaluation of analytic response properties. In this article, we present formulation of the method for excited state calculations. We discuss the decoupling of equations as a result of spin-adaptation and present some preliminary results for analytical dipole moments and polarizabilities of some molecules in low-lying triplet excited states. [ABSTRACT FROM AUTHOR]